A LATENT PROFILE PERSPECTIVE ON EMOTIONAL DEMANDS MANAGEMENT VIA EMOTION REGULATION AND JOB CRAFTING BEHAVIORS By Sergio Miguel Marquez A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Psychology—Doctor of Philosophy 2025 ABSTRACT This study sought to integrate emotion regulation theory and job crafting theory to argue these phenomena function as parallel processes to manage emotional demands in the workplace. A latent profile analysis (LPA) was conducted using indicators that included every emotion regulation strategy from Gross’s (1998; 2008) process model of emotion regulation and approach-avoidance oriented styles of relationship, skill, task, and cognitive job crafting to form profiles. Four profiles were hypothesized to emerge along a 2 (proactive vs. reactive) x 2 (approach-oriented vs. avoidance oriented) framework of distinct emotional demands management styles. Two studies were conducted each using a sample of paid participants from an online participant pool. Study 1 (N = 469) supported the four-profile hypothesis but not in the framework as expected. However, profiles’ emotion regulation behaviors conformed to their relative proactivity and temperament scores. Study 2 (N = 370) is a two-time point study that replicated a four-profile structure across time using participant-matched data separated by 2 weeks. Similar to study 1, the framework did not emerge as expected but profiles’ behaviors conformed to relative proactive and temperament scores. Moreover, profiles showed distinct patterns of emotion regulation behaviors and differences in well-being, disposition, demands and resource values that correspond to the JDR model of burnout (Demerouti & Bakker, 2001). The implication of these profiles’ emergence on further integrating emotion regulation theory and job crafting theory and the application of LPA for synthesizing phenomena are discussed. ACKNOWLEDGEMENTS I’d like to express my gratitude to my committee for pushing and supporting me throughout this work. I’m especially grateful for my chair, mentor, and good friend, Dr. Daisy Chang. Thank you for setting me up for success and never allowing me to do anything less than my best. You’ve been the source of my confidence and self-esteem throughout my graduate career and beyond. There aren’t words to express how much I appreciate everything you’ve done for me. I’ll never forget our great conversations, your incredible restaurant recommendations, or the several pep talks that got me through my never-ending life circumstances. To my all my friends and colleagues — across Florida, Michigan, and beyond — thank you for keeping me sane and loved throughout this journey. All the unforgettable nights out, Magic cards slinging, and late-night conversations over video games formed the backbone to my time in graduate school. Thank you for making this experience so much more worth it. Lastly, I have my mom to thank for all my endless dedication, motivation, and perseverance. Thank you for your unconditional love and support for anything and everything I do. Seeing your pride and admiration throughout all the hardship is what made me never consider quitting. Without your sacrifice of moving to this country and always making ends meet, I would not have been the first in our family to earn a PhD. You are my everything, Mami. Love you. iii TABLE OF CONTENTS Introduction ..................................................................................................................................... 1 Emotion Regulation ...................................................................................................................... 11 The Emotion Regulation Oversight Within Organizational Sciences .......................................... 16 Job Crafting ................................................................................................................................... 22 The Emotion Regulation and Job Crafting Overlap ..................................................................... 27 Latent Profile Analysis ................................................................................................................. 48 Expected Profile Organization ...................................................................................................... 54 Study 1 .......................................................................................................................................... 89 Method – Study 1 .......................................................................................................................... 90 Results – Study 1 .......................................................................................................................... 98 Discussion – Study 1 ................................................................................................................... 108 Study 2 ........................................................................................................................................ 111 Method – Study 2 ........................................................................................................................ 119 Results – Study 2 ........................................................................................................................ 125 Discussion – Study 2 ................................................................................................................... 142 General Discussion ..................................................................................................................... 149 REFERENCES ........................................................................................................................... 160 APPENDIX A: JOB CRAFTING SCALE (BINDL ET AL., 2019) .......................................... 183 APPENDIX B: EMOTION REGULATION QUESTIONNAIRE (GROSS & JOHN, 2003) ... 184 APPENDIX C: SITUATION SELECTION SCALE (WEBB ET AL., 2017) ........................... 185 APPENDIX D: EMOTION REGULATION SURVEY (DIEFENDORFF ET AL., 2008) ...... 186 APPENDIX E: EMOTION REGULATION QUESTIONNAIRE (SCHUTTE, 2009) ............. 187 APPENDIX F: PROACTIVE PERSONALITY (BATEMAN & CRANT, 1993) .................... 188 APPENDIX G: APPROACH-AVOID TEMPERAMENT (ELLIOT & THRASH, 2010) ....... 189 iv APPENDIX H: BAS/BIS (CARVER & WHITE, 19944) .......................................................... 190 APPENDIX I: EMOTION DISPLAY EXPECTATIONS AT WORK (DIEFENDORFF ET AL., 2003) ........................................................................................................................................... 191 APPENDIX J: EMOTIONAL DEMANDS (STRAZDINS, 2000) ............................................ 192 APPENDIX K: EMOTIONAL DEMANDS (XANTHAPOLOU ET AL., 2012) ..................... 193 APPENDIX L: SOCIAL SUPPORT (ROUSSEAU & AUBE, 2010) ....................................... 194 APPENDIX M: WORK AUTONOMY (BREAUGH, 1985) .................................................... 195 APPENDIX N: MASLACH BURNOUT INVENTORY – EMOTIONAL EXHAUSTION SUB- SCALE (MASLACH & JACKON, 1986) .................................................................................. 196 APPENDIX O: WORK ENGAGEMENT SCALE (SCHAUFELI ET AL., 2002) ................... 197 APPENDIX P: ROTTERDAM EMOTIONAL INTELLIGENCE SCALE (REIS) (PEEKAR ET AL., 2017) ................................................................................................................................... 198 APPENDIX Q: PANAS – WATSON ET AL. (1988) ................................................................ 199 APPENDIX R: COPING FLEXIBILITY SCALE (KATO, 2012) ............................................ 200 APPENDIX S: CONTEXT SENSITIVITY INDEX (CSI) (BONANNO ET AL., 2020) ......... 201 APPENDIX T: CFC-14 SCALE (JOIREMAN ET AL., 2016) ................................................. 202 APPENDIX U: LIST OF TABLES ............................................................................................ 203 APPENDIX V: LIST OF FIGURES .......................................................................................... 226 v Introduction Emotion regulation is the process of automatically or consciously managing one’s emotions by altering the initiation, maintenance, or modification of emotions and their intensity, occurrence, or duration (Gross & Thompson, 2007; Webb et al., 2012). The most prominent model of emotion regulation is the process model of emotion regulation posited by Gross (1998). This model outlines five strategies individuals can employ before and/or after experiencing an emotion to alter the emotion’s meaning, intensity, and expression as it unfolds. Organizational sciences have grown considerable interest in emotions as research continues to show that they are critical for several significant outcomes of interest, such as job performance (Weiss & Cropanzano, 1996), job engagement (Rich et al., 2010), well-being (Balducci et al., 2011), motivation (Erez & Isen, 2002), leadership (Joseph et al., 2015), and more. The majority of emotion regulation literature within the organizational sciences is linked to Grandey’s (2000) emotional labor as emotion regulation theory, where she mapped deep acting and surface acting — the two primary emotional labor strategies — to Gross’s antecedent- focused and response-focused strategies, respectively. In Gross’s (2007) emotion regulation process model, antecedent-focused strategies refer to several strategies that can be used prior to the occurrence of emotion and response-focused emotion regulation strategies refer to methods for altering physiological or behavioral reactions following an emotion (e.g., suppressing the internal feeling of frustration by smiling instead). The synthesis of Gross’s (2006; 1998) emotion regulation process model and contemporary emotional labor research extended the former’s literature significantly. This is because Gross’s Emotion Regulation Process Model had a rich history of theoretical development and research compared to the newer topic of emotional labor. Importantly, this melding opened a door which allowed for multi-level, longitudinal, and even 1 momentary perspectives into the emotional labor process and the emotions driving it (Gabriel & Diefendorff, 2015; Grandey, 2000; Grandey & Melloy, 2017). Despite the explosion of research on emotion regulation stemming from these advances, investigation of work-related emotion regulation is far from complete. The remaining three strategies from Gross’s process model (i.e., situation selection, situation modification, and attentional deployment) are sorely under-investigated, particularly in the organizational sciences. Broadly, situation selection refers to approaching or avoiding specific situations depending on anticipated emotional reactions, situation modification refers to altering one’s external environment and context, and attentional deployment refers to shifting one’s attention away or toward specific stimuli (Gross, 2007). Because the jobs and contexts most relevant to emotional labor center around service-related interpersonal interactions and adhering to a job’s explicit display rules (Grandey & Gabriel, 2015; Grandey & Melloy, 2017), it makes sense that cognitive change and response-focused strategies are so often featured in this research. The three remaining strategies are nevertheless relevant at work. For instance, we may consider how an employee may choose to approach a mildly-annoyed customer (situation selection) before they grow too upset and difficult to manage (situation modification), start this interaction by immediately asking the customer to explain the problem, and shift focus and effort to addressing the customer’s problem as opposed to the customer’s emotional displays (attentional deployment). In this case, employees can engage several different strategies to manage their emotions before even engaging in cognitive change and response-focused strategies. Overlooking situation selection, situation modification, and attentional deployment is further problematic considering some research suggests that situation selection, situation 2 modification, and attentional deployment strategies are adopted commonly, and even employed more often, than some cognitive change and response-focused strategies (Diefendorff et al., 2008). Diefendorff et al.’s work further showed that some strategies were employed more often than others dependent on the different target, such as customers versus coworkers. These results suggest that emotion regulation is occurring far before individuals initiate an interpersonal interaction and that different contexts and situations may affect which strategy is used. This is inconsistent with emotional labor’s investigation, which is predominantly centered around cognitive change and response-focused strategies. Another important note about Diefendorff et al.’s research is that their data was collected from individuals outside of an emotional labor context (i.e., an interpersonal interaction with a perceived display rule). This begs the question of how and why individuals employ various emotion regulation strategies during work outside the typical emotional labor context. To investigate this question further, the context of strategy use must extend beyond the typical emotional labor context. Incorporating situation modification and situation selection into emotion regulation models within organizational psychology could help researchers consider the specific motivations behind the decision to alter a specific work context or choose to approach it. Moreover, these strategies encourage researchers to consider how various forms of employee-led work changes shape outcomes like well-being or satisfaction whenever employees modify their work context to meet emotional goals and reach desired emotion states. Gross (2007; 2011) posits that situation selection and modification help shape the impact of strategies more proximal to the emotion experience – such as attentional deployment, cognitive change, and response-focused strategies. Emotion regulation models considering solely of cognitive change and response-focused strategies are thus incomplete because they do not 3 consider the impact of more proactive, distal strategies on subsequent, more proximal strategies. Situation modification and selection strategies acknowledge how individuals anticipate emotional reactions to future circumstances, consider these circumstances’ emotional consequences, weigh these consequences relative to desired affective states and other goals, and make their decision to behave accordingly (Gross, 2007). Without these missing strategies, emotion regulation researchers are only looking into the end of the emotion regulation sequence. Current emotion regulation models are not wholly equipped to investigate this process within organizations. The bulk of emotion regulation research is rooted in cognitive psychology and is most interested in theoretically mapping the process of emotion regulation itself, its strategies, and exploring its underlying mechanisms (Gross, 2015; Koole, 2009). On the other hand, organizational psychologists are interested in emotion regulation within a specific context – an organization. Because of cognitive psychology’s context agnosticism, theory and measurement of emotion regulation strategies may be too vague or broad to link with organizational concepts such as leadership, motivation, or job performance. It is organizational theory’s role to color-in what is unaddressed by emotion regulation theory to understand its implications in the workplace (Gross 1998; Grandey & Melloy, 2017). When considering the application of situation modification to the organizational context, it is also necessary to understand the phenomena (e.g., abilities, motives, work-related demands) that drive how and why employees would choose some situations over others (Gross, 1998; Grandey & Melloy, 2017). Because organizational scientists have not considered how these emotion regulation strategies (i.e., situation modification, situation selection, and attentional deployment) may relate to different resources and individual factors, the neglected strategies (i.e., situation selection, 4 situation modification, and attentional deployment) are left both empirically under-featured and theoretically underdeveloped compared to cognitive change and response-focused strategies. However, incorporating emotionally distal strategies like situation selection requires there to be a solid foundation for explicating how individuals could react to and manipulate the work context and features before an emotion even occurs (Gross & Thompson, 2007). Job crafting is a construct that could offer this insight. Job crafting is defined as “the changes to a job that workers made with the intention of improving the job for themselves” (Bruning & Campion, 2018, p. 499). In their meta-synthesis of job crafting, Lazazzara et al. (2020) found several examples of job crafting behaviors that function like emotion regulation to manage emotional demands and experiences. Examples include employees seeking to “make one’s work less emotionally intense” (Renkema et al., 2018) or nurses’ “entertaining patients” to make them easier to manage and to also enjoy their job (Fuller & Uwin, 2017). Because the motivation of these behaviors is to manage one’s emotional experiences at work, job crafting can be considered under the theoretical purview of emotion regulation. Job crafting theory also provides an outline of behaviors that individuals perform to modify their job’s relational and physical work environment and manage their emotional demands (Brunning & Campion, 2018; Zhang & Parker, 2019). Job crafting can take several forms, such as relational, structural, or cognitive (Brunning & Campion; Zhang & Parker, 2019). This means that job crafting behaviors can alter one’s work context and circumstances across relational, structural, and cognitive domains and modify the likelihood of experiencing different emotional demands (e.g., increasing the chance of encountering a difficult customer or running into a friendly coworker). In this case, those individuals who manage emotional demands by restructuring their physical and relational work environments and changing the probability of 5 encountering specific situations are arguably also performing emotion regulation, specifically in the forms of situation selection, modification, and attentional deployment. Job crafting behaviors such as ‘entertaining patients’ and ‘making work less emotionally intense’ also show this overlap because they simultaneously demonstrate how individuals both make their job easier to handle (i.e., job crafting) and modify their emotions (i.e., emotion regulation). Theoretically, job crafting behaviors can be mapped onto situation selection, modification, or attentional deployment strategies since they are in parallel managing experienced emotions and demands by shaping the events experienced and their features. Thus, organizational scientists seeking to expand the role of contextual and relational factors in emotion regulation models might consider job crafting a promising tool. This is because job crafting theory suggests relational and contextual factors are fluid and that individuals can change them (Bruning & Campion, 2018; Zhang & Parker, 2019). Given these conceptual overlaps, these semi-permanent changes enacted through job crafting function like situation selection, selection modification, and attentional deployment strategies. Applying a job crafting lens to emotion regulation offers insight into the role of the physical and social work context on the emotion regulation process that employees proactively enact. Unpacking processes like job crafting and emotion regulation is difficult and complex. Moreover, temporality must be considered because these behaviors are employed in different times and sequences. Improper consideration can unintentionally result in an inconsistent understanding of how these processes impact employees (Grandey & Gabriels, 2015; Grandey & Melloy, 2017). Some emotion regulation and job crafting researchers have begun addressing this issue through profile-centered models to better understand the idiosyncrasies of the emotion regulation and job crafting processes (Gabriel et al., 2015; Makigangas, 2018; Nguyen & 6 Stinglhamber, 2019). Rather than investigating strategies independently, researchers have investigated emergent behavioral combinations that describe how employees utilize cognitive change and response-focused emotion regulation strategies together (Gabriel et al., 2015). Profile-centered perspectives purport to extend variable-centered approaches, which predominantly seek to connect independent constructs to specific antecedents and outcomes (Craig & Smith, 2000). Profile centered approaches, on the other hand, try to consider how unique combinations of these constructs exist within individuals (Oberski, 2016). A profile- centered approach, for example, helps researchers understand emotion regulation by considering how strategies work together (Gabriel et al., 2015; Grandey & Melloy, 2017). For example, in Gabriel et al.’s (2015) study on U.S service workers, they found five unique profiles of emotional labor. These profiles showed how emotion regulation behaviors impacts well-being by organizing employees according to their reliance solely on antecedent-focused strategies, solely response-focused strategies, a high degree of both, a moderate degree of both, and low levels of both. This profile structure was also recently replicated by Nguyen and Stinglhamber (2019), who further linked these unique profiles to different mistreatment outcomes. This application of a person-centered approach underscored how traditional variable- centered approaches can hide how employees realistically behave (Craig & Smith, 2000; Oberski, 2016). There are several reasons to apply person-centered approaches to all strategies together. First, a profile-centered approach can uncover unique patterns that describe how employees approach the task of emotion regulation altogether. In the case of Gabriel et al. (2015), considering deep acting and surface acting (i.e., cognitive change and response-focused emotion regulation) together expanded the understanding of the relationship between these strategies, which are commonly considered as opposite from one another. In a similar vein, 7 considering all five of Gross’s (1998; 2007) emotion regulation strategies employed by an individual may resolve inconsistencies within the emotional labor and emotion regulation literature, such as the unexpected increase of deep acting following pleasant customer interactions (e.g., Totterdell & Holmann, 2001) or deep acting occurring after surface acting (Gabriel & Diefendorff, 2015). Further, this person-centered perspective could help explain if and how distal strategies (e.g., situation selection and modification) work in conjunction with proximal strategies, and their relationship to work-related context and circumstances (Troy et al., 2013). Second, cognitive change and response-focused emotion regulation are the most investigated strategies and most proximal to the emotion (Gross et al., 2007). Focusing on these strategies excludes the possibility that employees can anticipate events and proactively regulate. In other words, the notion that employees are active agents in the workplace is absent. A profile- centered approach can organize the different behaviors used together to manage their emotional demands. This grouping can reveal strategy configurations that distinguish proactive individuals (i.e., individuals who rely on distal strategies such as situation selection and situation modification) from reactive individuals (i.e., those who rely on strategies closer to the emotion experience). Moreover, these profiles can be further linked to individual differences and outcomes to tease apart whether these profiles differ on important outcomes. Altogether a profile-centered perspective organizes the idiosyncratic nature of emotion regulation across jobs and workplaces by grouping individuals based on their approach, allowing researchers to dissect the shared factors linking these groups and compare their outcomes. Lastly, a profile-centered approach can reveal patterns of job crafting behaviors and emotion regulation behaviors that underscore their several theoretical parallels. Because the 8 goals of job crafting and distal forms of emotion regulation are overlapping, a profile-centered approach can identify unique patterns of behaviors where specific types of job crafting and emotion regulation co-occur. Categorizing these behaviors into discrete profiles then allows for a holistic evaluation of several theoretically-linked behaviors together rather than trying to interpret how they work using several interactions. This gives researchers an efficient method to identify groups of behaviors that co-occur together as expected or observe unexpected patterns of behaviors overlooked by variable-centered approaches. Moreover, in the case where the profile indicators share a substantial theoretical foundation (e.g., shared antecedents, shared outcomes, and mechanisms) but have not been investigated together, a profile-centered approach is one way to synthesize these phenomena by comparing across these variables of interest. Further analyses may also investigate new relationships if an outcome that has been theoretically established for some indicators but not others differ across profiles. In this study, this could open the window to uncovering other new contextual and relational variables critical to work-related emotion regulation. Thus, a profile-centered approach can serve as a theoretical foundation for further investigation of an emotional labor process by considering contextual and relational factors as mutable, dynamic factors, much like current job crafting models. The purpose of this study is to expand the list of emotion regulation strategies investigated in the workplace. By utilizing a profile-based analysis, this study hopes to uncover subpopulations of individuals based on the patterns emotion regulation strategies employed. Lastly, this study intends to substantiate the specific behaviors and mechanisms used to regulate one’s emotions at work by incorporating job crafting literature. This study can complement recent theoretical and practical emotion regulation research by bridging the momentary and distal 9 perspectives of emotion regulation to form insight into how individuals manage their emotions at work. 10 Emotion Regulation Affect regulation is understood as the process of consciously or automatically managing one’s affective state (Gross 1998; 2007). Gross (2015) delineates affect as an umbrella term for the affective states altered via affect regulation. Specifically, the three specific states are: stress- responses, mood, and emotions. Stress-responses occur following significant events and involve full body reactions characterized by a negative affective state emerging from inability to manage situational demands (Lazarus, 1993). Moods are longer term states that engender broad tendencies to approach or avoid situations (Lang, 1995). Lastly, emotions are loosely coupled changes which alter individual subjective experiences, physiology, and behavioral responses over time (Gross, 2015; Gross & Thompson, 2007). Emotions are similar to stress-responses, but emotions include both positive and negative states and primarily center around subjective experience (Gross, 2015; Lazarus, 1993). Purposeful, goal-oriented changes to one’s affective state is known as affect regulation and includes three broad categories: emotion regulation, coping, and mood regulation (Gross, 2015). Emotion regulation is a group of regulation strategies seeking to alter which emotions are experienced, when they are experienced, how they are experienced, and how they are expressed to influence an emotion’s trajectory (Gross et al., 1998; Gross et al., 2011). Emotion regulation is distinguished from coping because coping’s purpose is to alleviate stress responses and can occur over longer time periods (e.g., coping with bereavement; Gross, 2015). Mood regulation is characterized based on its focus to alter predominantly subjective feeling states. However, because moods, emotions, and stress- responses share some characteristics, affect regulation behaviors belonging to one category of affect regulation (e.g., coping) can also impact other affective states (e.g., mood). Thus, it is 11 helpful to keep in mind how affect regulation behaviors largely overlap despite these distinctions. The emotion regulation process functions to modify the intensity, occurrence, or duration of an emotion (Gross, 1998). Emotion regulation can further occur both at the conscious and unconscious levels (e.g., like an individual reflexively and unconsciously turning away from a disgusting stimulus or choosing to focus on the positive aspects of a situation rather than the negative; Gross, 1998). Therefore, individuals can seemingly alter their emotions in an infinite number of ways. However, there are five broad categories of strategies: situation selection, selection modification, attentional deployment, cognitive chance, and response modulation. These five strategies organize emotion regulation behaviors according to when they alter the emotion along its unfolding, starting from before its inception to immediately thereafter. These strategies are thought to vary in effectiveness. These strategies also help organize the different contextual and individual characteristics that influence an emotion’s impact on an individual. Situation selection is the most distal emotion regulation strategy (Gross & Thompson, 2007; Gross, 2008). This strategy is characterized by the anticipation of emotion eliciting situations and events followed by the choice to approach or avoid specific objects, people, or situations based on their anticipated emotional reaction. Situation selection requires significant proactivity and good affective forecasting (i.e., the ability to accurately predict the likelihood and impact of positive or negative emotions from a situation). Situation selection is often featured in cognitive-behavioral therapy as an intervention to help increase exposure to beneficial situations (Jacobsen et al., 2001) or decrease exposure to harmful situations (Kober & Bolling, 2014). Situation modification refers to actions that modify a situation for the purpose of altering its subsequent emotional impact (Gross, 1998; Gross; 2015). Situation modification specifically 12 includes the modification of external, physical environments, as opposed to one’s cognitions about a situation. Situation modification is investigated commonly from a lifespan development perspective and is viewed as an adaptive response to life changes (Heckhausen et al., 2010). Situation modification and selection can sometimes be difficult to differentiate because changes to a situation can engender a novel situation. Attentional deployment refers to shifts of attention away or toward an emotion eliciting stimuli through distraction or concentration (Gross, 2008). Attentional deployment is the first strategy to occur in an “internal” environment (Gross & Thompson, 2006). This is because attentional deployment pertains to shifting gaze toward or away specific stimuli or eliciting specific memories to evoke a desired emotional response (Rothbart, Ziaie, & O’Boyle, 1992). Attentional deployment is employed throughout one’s lifetime (Isaacowitz et al., 2009; Rothbart et al., 1992) and is particularly effective for downregulating negative emotions (Bennett et al., 2007). Cognitive change encompasses strategies which change the meaning of the situation or emotion experience (Gross, 1998; Gross, 2008). Cognitive change can be applied to external (e.g., “I should not be anxious about performing my solo because this is an opportunity to show my skill”) or internal situations (“My heart is racing because I’m excited, not nervous!”). The most studied form of cognitive change is reappraisal, which involves changing the meaning of a situation through the meaning of the emotion itself or the self-relevance of the emotion eliciting event (Gross, 2008). Cognitive change, however, can also refer to altering how one views their capabilities and abilities to navigate a scenario (e.g., “I know I can handle this situation, no matter what!”). Cognitive change has been shown to be one of the most effective emotion 13 regulation strategies (Webb et al., 2012). Cognitive change is the last of the antecedent-focused strategies, because it is the closes to the emotion (Gross, 1998; 2008). Response modulation strategies alter physiological, behavioral, and experiential responses following an emotion (Gross, 1998; Gross, 2008). Response modulation is the only response-focused strategy. Response-focused strategies are varied, ranging from using substances (e.g., alcohol, cigarettes, food), physical exercise, or expression suppression to manage one’s affective state (Gross, 1998). The previous four emotion regulation strategies have been linked to effective management of affective states and other well-being outcomes; in contrast, response modulation strategies are typically linked to outcomes such as worse memory (Johns et al., 2008), high exhaustion (Hulsheger et al., 2010), and lower liking during interpersonal interactions (Ben-Naim et al., 2013). Altogether, the process model of emotion regulation describes how individuals manage their emotions using different strategies. These strategies are organized based on when and how the emotion is altered. These regulation strategies have been empirically tied to various outcomes across several fields of psychology and beyond, on the basis of their effectiveness, impact on well-being, or other area-specific outcomes like life satisfaction or therapy effectiveness (Webb et al., 2012; Gross, 2015). Research continues to show an emotion’s impact depends not only on whether emotion regulation is employed, but which strategies are employed, personal factors, and the interpersonal and contextual circumstances surrounding the strategy (Dore et al., 2016). Accordingly, the process model of emotion regulation continually expands across different fields and research topics. Within the organizational sciences, emotion regulation’s most common application is the emotional labor space. This has led to several insights on how emotion regulation impacts workers’ functioning. Current emotional labor research has developed 14 comprehensive models built on a staggeringly wide range of methods and levels of analysis. However, the limited application of emotion regulation theory has led to several oversights in this space. 15 The Emotion Regulation Oversight Within Organizational Sciences The investigation of emotion regulation through the lens of emotional labor has resulted in a somewhat limited range of employee samples and methods represented. Grandey and Melloy’s (2015) review on emotional labor brings to light the sample bias present in emotional labor research. Retail, customer service, and call center employees are disproportionally featured within emotional labor studies while upper management and white-collar samples are uncommonly sampled. This is not totally surprising –emotional labor originates from Hochschild’s (1995) sociological work on retail workers. Early qualitative research on emotion work, the basis for emotional labor work, focuses on these workers almost exclusively (Rafaelli, 1988; 1989; Rafaelli & Sutton, 1989). Moreover, the nature of customer service work, with its frequent customer interactions and expected maintenance of positive emotional displays, creates a space ripe for the investigation of emotional labor. Hochschild’s influence is further noted in the content of emotional labor research questions. Emotional labor was posited as harmful to workers, which led to most research questions centering around the impact of emotional labor on employee health and well-being. Grandey and Melloy (2017) underscore how outcomes rarely venture beyond employee well-being (i.e., exhaustion and engagement) or performance. Grandey and Melloy (2017) are keen to address these oversights and propose items in the future research agenda. First, they note how little is known about emotion regulation within jobs absent of display rules. This is a significant oversight considering that motives to regulate emotions span beyond display rules and that individuals regulate their emotions not only in reaction to customers, but also to navigate social interaction with colleagues or complete work tasks (Diefendorff et al., 2008; Tamir, 2009). Emotional labor research has also been slow to incorporate theories that highlight the role of emotions across processes such as job performance 16 (Affective Events Theory; Weiss & Cropanzano, 1996), leadership (Emotions as Social Information Model; Van Cleef, 2009), motivation (Opponent Process Theory; Solomon & Corbit, 1974), and training (Lindsay et al., 2018). Each of these theories pose emotions as a critical mechanism and indirectly suggests emotion regulation is necessary to effectively accomplish these processes. The omittance of jobs outside of retail or customer service (i.e., those that require following display rules) leaves emotion regulation process theory potentially under-developed within the organizational sciences. If organizational scientists wish to apply emotion regulation theory to understand broader employee functioning at work, the link between work goals and emotion goals must be expanded beyond display rules (Grandey & Gabriel, 2015; Grandey & Melloy, 2017). Otherwise, the emotion regulation literature will be limited to understanding emotional labor goals. Another significant oversight by organizational scientists is the limited investigation of emotion regulation strategies. Specifically, situation selection, situation modification, and attentional deployment emotion regulation strategies have not been featured. This may stem partly from the previously mentioned sample bias given the scenarios that elicit emotional labor largely occur in discrete episodes (e.g., a customer service interaction or abusive supervision). Cognitive change and response-focused strategies are appropriate for this because they can be employed flexibly as dynamic ‘reactions’ to an unfolding situation (Diefendorff et al., 2019; Gabriel & Diefendorff, 2014). This also aligns with the typical retail environment, where employees may not have sufficient autonomy to choose which customers they interact with and how they interact with them. However, limiting the investigated strategies to those most proximal to the emotion fails to acknowledge that employees can still employ other emotion regulation strategies prior to cognitive change or response modulation. Whether individuals 17 employ more proactive strategies in anticipation of customer demands, such as situation selection or situation modification, remains yet to be investigated. In other words, models of emotional labor only describe discrete interpersonal episodes through two proximal strategies but do not consider whether other distal strategies are employed prior to the occurrence of the episodes. Ultimately, emotional labor research characterizes employees as passive agents by neglecting how emotion regulation goals and their regulation could occur proactively. Lastly, emotional labor methodologies reflect the limited perspective of how emotion regulation occurs. Recent trends have helped uncover underlying processes of emotional labor through methodologies such as daily diary studies and event sampling methods. Gabriel and Diefendorff (2015) mapped the order of antecedent-focused strategies, response-focused strategies, and emotions during a customer service episode using continuous momentary assessments in the scale of seconds. This is an exciting direction in the literature. Moving beyond cross-sectional designs towards dynamic measurement across several time scales has painted a nuanced picture of the emotional labor process. However, one criticism is that these methodologies slightly favor micro perspectives over macro perspectives. Consider how failing to consider emotion regulation beyond an event can help explain conflicting results. Recall Diefendorff’s (2015) momentary assessment of deep acting, surface acting, and emotion throughout an instance of customer incivility. Their results did not align with Gross’s (1998) emotion regulation model because surface acting (i.e., a post-emotion, response-focused strategy) occurred before deep acting (i.e., a pre-emotion, antecedent-focused strategy). But they only recorded one instance of customer incivility. What happens in-between real customer interaction episodes? Did participants employ any other strategies prior to the experiment? Now consider the role of other strategies. One plausible explanation is that attentional deployment 18 helped participants shift focus toward or away from specific stimuli, impacting the overall emotion regulation trajectory. In this case, attentional deployment may help explain why ‘deep acting’ was performed at unexpected times. For instance, because attentional deployment occurs before deep acting but was not considered during assessment, any impact of attentional deployment may have been attributed to deep acting instead. When no strategies beyond cognitive change and response-focused strategies are considered, the effectiveness of cognitive change and response-focused methods can become muddled and unknown. This episodic perspective begs the question of what happens in the gap between episodes. This leaves the consideration of attentional deployment, situation modification, and situation selection strategies stifled because these strategies revolve around the interplay between the environment and the worker outside of interpersonal interaction episodes. Researchers should acknowledge this these periods likely contain important emotion regulation behaviors. An individual who experiences supervisor abuse may afterwards rush into a room alone to calm themselves down as a form of situation selection to manage their current and future affective states. However, this regulation occurs outside a period of interpersonal interaction and has implications for future emotion regulation. Unfortunately, current research on antecedent- or response-focused emotion regulation overlooks the effects of situation selection, situation modification, and even attentional deployment strategies by focusing on the strategies occurring during an interpersonal event. There is one paper featuring situation selection, selection modification, and attentional deployment emotion regulation strategies alongside cognitive change and response-focused strategies. Diefendorff et al. (2008) showed that these strategies are heavily utilized – even more so than some cognitive change and response-focused strategies. In their study, 75% of 19 participants reported “seek[ing] out an individual that makes me feel good,” 73% reported “keep[ing] myself busy with something else,” and 75% reported “do[ing] something enjoyable to improve my mood.” These examples exemplify situational selection, attentional deployment, and situation modification strategies, respectively. In comparison, the most frequently reported cognitive change and response-focused strategies were reported being used by 72% and 64% of the sample, respectively. They also found that the emotion regulation strategy adopted varied by the target, context, and anticipated emotion. For example, participants were more likely to use cognitive change and response-focused strategies (e.g., “change how I think about a situation” or “hide how I feel”) with customers and use situation selection more often with coworkers (e.g., “avoid a situation I know will make me feel bad”). The attentional deployment strategy “keeping myself busy” was reported most often for low workloads while “do something that is enjoyable” was reported most often for managing personal and physical problems at work. Response- focused strategies were most prevalent when managing fatigue (i.e., “pretend I’m in a good mood”) while frustration was often managed via situation modification (i.e., “removing myself from the situation”). Altogether, these findings suggest that individuals considered both current and desired emotion states, work context, and interpersonal context when choosing specific emotion regulation strategies. Therefore, individuals seemingly manage their emotions in a dynamic, proactive manner by considering the emotional demands they anticipate facing. However, there is another issue remaining even if situation selection, situation modification, and attentional deployment strategies are included in more studies. Measuring how often these strategies are employed does not provide information on the characteristics of the surrounding context. There are several different aspects to a context that motivate someone toward or away from that environment, and there are similarly many different aspects of a 20 situation someone can alter or shift their attention toward to or away. Diefendorff & Greguras (2009) found that perceptions of power can lead to different emotion regulation strategies depending on whether the target is a customer, manager, or coworker. That is, employees select their emotion regulation behaviors to fit the target and context. There is little research that focuses on how the content of contextual characteristics (e.g., power, display rule, familiarity with target, etc.) shift from interaction to interaction, and how these shifts alter the effectiveness and occurrence of emotion regulation behaviors across context. In order to better understand the effectiveness of every emotion regulation behavior, there ought to be a good theoretical grounding of the context around these behaviors to better understand why are effective. However, other theories can help address this problem. One promising area is job crafting. Job crafting theory explicates the relationship between employees’ management of their work demands and the purposeful changes they make to their interpersonal and structural context (Bruning & Campion, 2018; Zhang & Parker, 2019). Job crafting encompasses several different types of demands (Zhang & Parker, 2019), but special attention should be given to job crafting aimed at managing emotional demands. As discussed below, job crafting theory may help explicate the specific structural and relational changes that individuals enact at work to manage their emotions and emotional demands – paralleling emotion regulation. 21 Job Crafting Job crafting is defined as the changes to a job that workers make with the intent of improving the job for themselves (Bruning & Campion, 2018). Job crafting behaviors underscore how and why employees alter their work context to create a better job for themselves and best manage their work demands. The primary conceptualization of job crafting draws on the Job Demands-Resources model based on Tims, Bakker, and Dekkers (2012). They posit job crafting is the process by which employees manage their resource acquisition, resource conservation, and job demands through job crafting behaviors. Job resources are job characteristics that help employees meet their work goals, reduce job demands, and/or promote personal growth or development (Bakker & Demerouti, 2007). These job characteristics span several different realms, such as social (e.g., relational intimacy, power), contextual (i.e., autonomy, role clarity), or personal (e.g., proactivity, affect). Resources can also vary largely across occupations, roles, and tasks (Bakker & Demerouti, 2001). Job demands are job characteristics that require sustained effort and are costly in terms of mental or physical energy. Job demands can vary depending on how they impact employees, for example either by hindering optimal functioning or promoting growth and development. So, the nature of demands, not just their amount, partly help explain whether its impact on an employee’s well-being and functioning is either detrimental or beneficial. Altogether, the resource crafting model of job crafting relies on the JD- R model to explicate how job crafting facilitates employee performance and well-being at work. In this model, job crafting refers to behaviors used by employees to enact desired work changes by increasing challenge demands, reducing hindrance demands, or increasing structural and social resources (Tim et al., 2012). 22 There is a second camp of job crafting theory referred to as role crafting. Role crafting stems from the original conceptualization of job crafting by Wrzesniewski and Dutton (2001), who sought to explain the change employees make to their job to meet needs-based motives. The role crafting perspective asserts that role crafting modifies work role boundaries and tasks through three types of crafting: structural, social, and cognitive crafting (Bruning & Campion, 2018). Structural job crafting (sometimes referred to as task crafting) refers to the changes individuals make to their work tasks and physical work environment (Bruning & Campion, 2018; Zhang & Parker, 2019). Examples of task crafting can be offloading a task to someone else or leaving the office door closed to better focus on an assignment. Relational crafting (also referred to as social crafting) is the process of changing the relationships between an employee and others at work. One example of relational crafting can be skipping extra-work events like happy hours to establish a strict boundary between life and work. Lastly, cognitive crafting refers to altering the meaning of work circumstances and the work itself. An example of cognitive crafting may be that a janitor who re-appraises their work as fundamental to the organization’s success to add greater meaning to their job. The role crafting perspective is helpful for understanding the role of job crafting in helping employees shape their jobs to better fit their values (Zhang & Parker, 2019). However, these job crafting distinctions are adopted independent of the role crafting perspective because research shows these job crafting types help explain distinct work outcomes (Bindl et al., 2019). This taxonomy further provides specific description of job crafting behaviors apart of their consequences – like increasing resources or reducing demands. This specificity is helpful to theoretically consider the content, nature, and motives for job crafting behaviors, which are arguably vague when viewed from the JD-R perspective. 23 This study adopts these role crafting distinctions for several reasons. First, theorizing the link between job crafting and emotion regulation based on their impact on emotional demands is contingent on explaining the specific social and structural characteristics that job crafting alters (Bakker & Demerouti, 2017; Duarte et al., 2020). Some research has already considered job crafting behaviors as proactive means to manage emotional demands at work that function as effective, distal forms of emotion regulation (Lazazzara et al., 2020). Distinguishing between structure, social, and cognitive job crafting help explain how emotional demands are managed by identifying methods according to their changes, like altering the physical work environment, changing what someone is paying attention to, or who they ignore. This specificity further helps connect job crafting behaviors to specific emotion regulation strategies when testing any interdependence or whether synergy between behaviors emerge. The resource crafting model, on the other hand, is useful for hypothesizing differences in outcomes and observed differences in resources not otherwise illuminated through role crafting taxonomy. The mixed role-resource job crafting perspective is supported by prior research that show role-oriented job crafting and JDR- oriented job crafting show comparable relationships across various outcomes and antecedents (Bruning & Campion, 2018; Zhang & Parker, 2019). The most significant framework beyond resource and role crafting within job crafting is the approach-avoidance goal motivation distinction for job crafting (Bruning & Campion, 2018; Zhang & Parker, 2019). These approach and avoidance motivations have helped explain contradictory findings that job crafting can lead to increased and decreased job engagement, job satisfaction, exhaustion, and other outcomes (Rudolph et al., 2017). Approach crafting is job crafting behavior that is active, effortful, motivated, and problem-focused (Bruning & Campion, 2018) and avoidance crafting behavior is characterized by evading, eliminating, or reducing parts 24 of one’s work and job. Bruning and Campion (2018) melds the approach-avoidance framework with role and resource crafting to fully integrate these job crafting streams that otherwise developed in parallel. They propose approach-oriented job crafting, encompassing resource crafting behavior such as metacognition and work organization, or approach-oriented role crafting behaviors like social and job role expansion. Avoidance crafting, on the other hand, encompasses work-role reduction and withdrawal crafting - which refers to reduction of oneself physically or mentally from work situations, events, or people (Bruning & Campion, 2018). Their paper outlines the most notable differences between these job crafting motives in terms of their outcomes. Approach-oriented crafting was related to greater positive outcomes such as physical and cognitive work engagement and meaning while avoidance-crafting featured negative outcomes such as work withdrawal, boredom, and turnover intentions (Bruning & Campion, 2018). Job crafting behaviors should thus not always be assumed to benefit the employee and may instead lead to outcomes incongruent with how theory characterizes job crafting (Rudolph et al., 2017). These results further suggest the distinction between role and resource crafting may be less important for understanding the impact of job crafting on employees compared to the employees’ motivation for job crafting. Thus, approach and avoidance motives are a necessary distinction to understand how job crafting impact employees and why job crafting sometimes may harm employee functioning. The final job crafting framework worth discussing is Zhang and Parker’s (2019) job crafting hierarchy based on their review of extant literature. This hierarchy organizes behaviors by their approach-avoidance motivations, whether they target resources or demands, and if it occurs behaviorally or cognitively. Zhang and Parker’s (2019) hierarchy largely relies on the JD- R model to explicate the job crafting process and its outcomes, foregoing the characterization of 25 behaviors according to their effect on work roles. Zhang and Parker’s (2019) delineation between behavioral and cognitive job crafting is interesting. The debate on whether cognitive job crafting is a form of job crafting or not arose due to differing perspectives between role and resource crafting (Bakker, Tims, and Derks, 2012). This is because cognitive crafting’s role in managing job roles versus work demands and resources vastly differ. The role crafting perspective views cognitive crafting as a crucial strategy for managing job roles because employees alter the meaning of their job and work identity to improve work-value fit (Berg, Dutton, & Wrzesniewski, 2013). However, the JD-R crafting perspective argues that cognitive crafting does not alter job content, a criterion in the original definition of job crafting. Instead, some researchers argue cognitive crafting is better understood as a form of work adaptation (Bakker et al., 2012). Zhang and Parker (2019) argue that cognitive crafting should be considered based on evidence that cognitive crafting influences work meaning, work identity, and emotions, and also improves employee attitudes (Berg et al., 2013; Weseler & Niessen, 2016). Zhang and Parker’s review also brought attention to the lack of empirical work not only on cognitive crafting, but job crafting in general. Altogether, the state of job crafting research is that recent theoretical burgeoning is awaiting empirical work. This is in part because validated measures for each combination of approach-avoid, cognitive-behavioral, and resource-demands job crafting distinction proposed by Zhang and Parker (2019) are unavailable. On the bright side, this introduces the opportunity for other literatures to test new ideas. For this paper, the distinctions between approach-avoidance motivations, role-resource, and behavior-cognitive job crafting are helpful for comparing the nature and consequences of job crafting to emotion regulation. Accordingly, the following segment is dedicated to expanding the overlap between emotion regulation and job crafting based on these distinctions. 26 The Emotion Regulation and Job Crafting Overlap The parallels between emotion regulation and job crafting are apparent. In converging these parallels, one may note how forms of behavioral job crafting could map onto specific emotion regulation strategies or function as a unique form of emotion regulation itself. Job crafting can fit under several different emotion regulation strategies based on its content and target of crafting. However, this dissertation proposes that situation selection and situation modification mirror what researchers typically consider as job crafting. But parallels between cognitive crafting with cognitive change and attentional deployment strategies are also plausible considering the significance of changing the “internal” environment to manage emotional demands and resources. However, establishing this overlap is not only contingent on describing the similarities between these processes and their behaviors. To best explain why job crafting and emotion regulation behaviors synergize also depends on whether similar outcomes, resources, and motives are shared between these processes. Thus, the following sections are dedicated to this shared ground. First, the nature of the demands they manage are discussed and followed by discussion on the resources relevant to both emotion regulation and job crafting. Finally, the lens and framework for organizing these behaviors based on their similarities is proposed. Job Demands If job crafting and emotion regulation are the primary means to manage and cope with these demands, what then constitutes an emotional demand? To start, job demands refer to physical, mental, social, or organizational job aspects that require sustained effort and incur physical or psychological costs to an individual (Bakker & Demrouti, 2017; Demerouti, 2001). The origins of demands can be traced to three sources: working with people (e.g., teachers, 27 nurses), things (e.g., construction workers, production line workers), or information (e.g., air traffic controller, data analysists). This is because employees must expend mental or physical effort when completing any form of these tasks (Bakker & Demrouti, 2017). For instance, an air traffic controller’s tasks demand significant attention to a constantly changing environment which leads to significant cognitive effort. A construction worker’s effort, on the other hand, may originate from exerting themselves lifting and carrying materials across their site. Despite radically different work tasks, these examples showcase why work can leave employees feeling exhausted or tired. Accordingly, job demands have been shown to impact employees’ well-being and motivation negatively through outcomes such as burnout, exhaustion, absence duration, and disengagement (Bakker et al., 2004; Demerouti et al., 2001; Lee & Ashforth, 1996; Bakker & Demerouti, 2007). Over time, demands can wear down employees’ physical and mental energy to the point where employees enter a state of prolonged exhaustion if not provided sufficient resources to meet demands (Demerouti, 2001). Research findings also show that certain demands instead lead to increased engagement, vigor, and dedication (Bakker et al., 2003; Ven den Broeck et al., 2008; Bakker et al., 2005). Van den Broek et al. (2010) proposed that this is a function of qualitatively different appraisals across demands. Job demands featuring greater cognitive demands, time pressure, and workload are thought to spark growth and development because they offer opportunities to express curiosity, competency, and thoroughness (Van den Broeck et al., 2010; Cavanaugh et al., 2000; McCauley et al., 1994). This led to the conclusion there are two types of demands - job hindrances and job challenges (LePine, Podsakoff, & LePine, 2005; Podsakoff et al., 2007). Job hindrances align with original conceptualized of demands as negative stressors because they undermine work goal achievement and engender negative feelings such as frustration and anger (Bakker & Sanz- 28 Vergel, 2013; Lazarus & Folkman, 1984; Tims et al., 2012). Hindrances are thus typically considered threatening and energy depleting. Job challenges encompass job demands that are both energy depleting and stimulating, alike the notion of eustress (Selye, 1956). Challenge demands promote individual growth and are reacted to positively from employees (Bakker & Sanz-Vergel, 2013; Lazarus & Folkman, 1984; Tims et al., 2012). Job challenges have thus been linked to increased performance, motivation, and job satisfaction vigor, dedication, and engagement (Bakker et al., 2006; Halberg et al., 2007; LePine et al., 2004; Van den Broeck et al., 2008). Altogether, what constitutes a demand comes down to whether the job characteristic is one that incurs energy expenditure – physical or mental – on an employee. The effects of a demand can further vary depending on its nature and the individual’s appraisal. That is, whether the demand is one that presents as an obstacle to effective work performance or stimulating, engaging, and motivating to an individual. This distinction is important to keep in mind when considering how job crafting and emotion regulation behaviors are enacted across different types of demands. Specifically, the nature of demands may be helpful for showing why the effectiveness of job crafting or emotion regulation behaviors may vary, or for linking the motives of these behaviors to how demands are managed. This study, however, focuses explicitly on emotional demands at work. The exact definition of an emotional demand is difficult to trace. That is because emotional demands can take several different forms, such as having to feel or express specific emotions for work or manage one’s emotional reactions to frustrating, challenging, or unpleasant work circumstances (Adams, 2006; Hochschild, 1983; Rafaeli & Sutton, 1987). Emotional demands are featured predominantly in studies on nursing and service occupations because explicit display rules inform how employees must interact with clients as part of their job performance, thus creating 29 emotional demands (Brotheridge & Grandey, 2002; Diefendorff et al., 2006). However, it is important to note that emotional demands can and often do arise from jobs without display rules too, through tasks involving interpersonal interaction, conflict, or employee management (Basche & Fischer, 1998; Brief & Weiss, 2002). Early work from Steinberg (1999) dissects the emotional content of work using qualitative and quantitative data from employees across nursing, policing, and managerial jobs. Analyses suggest the drivers of emotional demands can be categorized into 1. face-to-face (or voice-to-voice) contact and reading the emotions of others, 2. managing the emotions of others, and 3. employees’ management of their emotions, emotional displays, and acting. Face-to-face emotional demands are driven by the need to recognize verbal and non-verbal cues to discern the intent of those who do not clearly communicate their feelings (like an unruly patient; Steinberg, 1999). Qualitative responses featured police officers and nurses who recalled the need to quick judge whether someone is under the influence of drugs or alcohol when trying to understand their feelings based on little to no other information. Office workers reported similar, but far less extreme, experiences of when they had to quickly recognize whether a client is angry or not. Demands associated with managing others’ emotions are driven by the effort to change others’ behavior and mindset (Hochschild, 1983; Steinberg, 1999). This type of demand spans occupations beyond nursing, policing, and management, and is present in occupations like customer service, retail, sales, and more (Steinberg, 1999). Participants’ experiences ranged from a nurse who described the emotional demands associated with convincing elderly patients to move out of their homes due to safety concerns, to one manager reporting the need to constantly motivate employees by boosting morale, to a non-profit coordinator who must manage positive communication to colleagues and agencies to promote following a reduce-reuse-recycle paradigm (Steinberg, 1999). The final category of demands 30 originates from managing one’s own emotions and affective displays. These demands are mostly investigated by emotional labor researchers given that they stem from the effort of controlling one’s emotions and affect (Steinberg, 1999). Similar to physical and cognitive demands, this form of emotional demands has shown to be effortful, difficult, and also incur mental and physical costs (Grandey & Melloy, 2017). For instance, Steinberg (1999) provided quotes from police sergeants who expressed the difficulty of ‘turning their emotions and display on and off’ and reported criticism from colleagues when failing to do so. AIDs/STD educators also reported the necessity of seeming ‘unjudgmental’ to establish trust with their clients in addition to always ‘listening actively and appearing interested’ is emotionally demanding (Steinberg, 1999). The content of emotional demands can thus be understood according to their purpose, ranging from employees’ self-reactions to their emotions, managing how their emotions are displayed, to managing and understanding the emotions of others. Notably, this perspective assumes that emotional demands originate solely from interpersonal interactions. Therefore, any changes made to an employees’ social context can also impact emotional demands indirectly. Moreover, each form of emotional demand discussed by Steinberg (1999) evoke strong emotions from employees facing these demands, suggesting each form of emotional demands and emotion regulation are closely related. Emotional demands, thus, are faced in every job, not just public facing jobs, as employees must navigating the process of managing their emotions in response to these demands. Emotional demands can be elicited by work events and tasks too. For instance, Pekrun and Frese (1992) proposed that work-related emotions may originate from three sources: work- related, task-related, and non-task related emotions. Work-related emotions stem from job characteristics, task-related emotions stem from a specific task, and non-task related emotions 31 stem from the social work context. They also proposed that specific emotions are linked to these origins. For example, task-related emotions are linked to emotions, such as boredom, enjoyment, pride, or shame; while social-related emotions are linked to emotions such as gratitude, anger, and jealousy (Pekrun & Frese, 1992). While the linkage of these specific emotions to task or social specific job characteristics has not been empirically shown, this paper helps expand emotional demands beyond interpersonal interactions into work tasks and events. Along similar lines, other studies have linked life and work events to specific emotional reactions. O’conner et al. (1994) tied situational constraints (e.g., low work autonomy) to strong, negative affective reactions in a sample of Air Force personnel. Wallbott and Scherer (1989) sampled university students around the world and found different discrete emotions are connected to specific experiences. Joy, for instance, was triggered most often by interpersonal interactions and task completion, anger by injustice, and fear by encountering real or imagining dangerous situations. Despite their student sample, Wallbott’s and Scherer’s study can be extended to the work context because each mentioned event also occurs at work (e.g., a nurse managing an angry, violent patient). The existing research supports the argument that emotional demands at work can be elicited by events beyond interpersonal interactions, which is important to note given that job crafting refers to how employees manage desired and undesired aspects about their work. Like relational crafting, task crafting can thus also serve emotion regulation purposes when considering the motives underlying which tasks employees choose to complete more or less frequently. Altogether, this study takes emotional demands to mean specific work demands that incur physical and mental costs through the effort required to understand and manage one’s emotions, understanding and managing the emotions of others, and managing the personal emotional 32 impact of work affective events. These demands are core to linking job crafting and emotion regulation behaviors because both behaviors act as strategies to manage and cope with emotional demands – whether it is by changing how an event is encountered or how the meaning of an emotion or event is constructed. Moreover, the content of emotional demands helps further understand which resources are necessary for the management of these demands and why these resources function as motivational sources to help employees meet emotional demands. Job Resources and Outcomes A central tenet of the JD-R model is that job demands lead to burnout when employees do not have sufficient resources to cope with their demands (Demerouti, 2001). Job resources are theoretically proposed as necessary to manage emotional demands and to maximize employee performance and well-being. Job resources have been linked to increased employee engagement and job performance, and decreased levels of emotional exhaustion (Borst et al., 2020; Halbesleben, 2010; Mazzetti et al., 2021; Nielsen et al., 2017). Job resources have also shown to buffer the relationship between workload and exhaustion, supporting the assertion that negative well-being outcomes occur when employees’ demands overwhelm their available resources (Demerouti, 2001; Demerouti & Bakker, 2010). The following resources and outcomes featured are variables previously shown as theoretically relevant to both emotion regulation and job crafting behaviors. These resources also have additional relevance to emotional demands specifically. The outcomes investigated were similarly chosen based on their relationship to emotional demands based on prior research. This is done to facilitate integrating emotional regulation and job crafting processes by showing they function synergistically to manage emotional demands. One way to accomplish this is by noting their shared relationships on variables and outcomes (i.e., resources and outcomes). 33 Resources can be understood via several different dimensions and levels, such as organizational, situational, interpersonal, task, and personal (Bakker & Demerouti, 2006; Demerouti & Bakker, 2010; Halbesleben et al., 2014). However, this study will consider only structural resources. The term structural resources comes from ten Brummelhuis and Bakker’s (2010) work-home resources model. Their paper proposes different types of resources based on their transience. Structural resources are those that are durable, last a long time, and can be used more than once to deal with stressful situations. The rationale for focusing on structural resources is twofold. First, they are simple to capture, compared to resources like attention or physical energy, thanks to their temporal stability (ten Brummelhuis and Bakker, 2010). Second, these resources are malleable, unlike resources like proactive personality and emotional intelligence, which is crucial because differences across these resources theoretically reflect the impact of emotion regulation and job crafting on employees’ work environment and situations (ten Brummelhuis and Bakker, 2010). In other words, these resources can be the target of job crafting and emotion regulation behaviors themselves. Comparing these resources is one way to substantiate the average effects of employees’ motivated work behaviors to manage their emotional demands. Job Autonomy Job autonomy is characterized by a work environment that allows individuals to exercise control over their actions and be the causal agent in their actions (Crant, 1995; Deci & Ryan, 1985). Job autonomy is a crucial job characteristic linked to important outcomes across work and life domains, like increased job satisfaction, job commitment, job performance (Cerasoli, 2016; Deci & Ryan, 2006; Spector, 1985), and well-being (i.e., lower burnout, anxiety, and higher life satisfaction; Dittmar et al., 2014; Fischer & Boer, 2011). Job autonomy has been a critical 34 resource from the inception of the JD-R framework because it is a critical component of motivational processes, such as goal setting and pursuit (e.g., Hackman & Oldman, 1980; Hobfoll, 2001; Humphrey, 2007; Karasek, 1979). Job autonomy also helps fulfill one’s psychological need for autonomy and lead to work goal attainment via higher work engagement and well-being (Deci & Ryan, 2008; Ryan & Deci, 1985; 2000; Van den Broeck et al., 2008). Thus, job autonomy provides the ability to enact successful goal pursuit, work motivation, and work demands management to meet one’s desired work environment. Job crafting research often features job autonomy as a central resource. Intuitively, job autonomy provides necessary leverage for employees to enact job crafting because redesign work characteristics requires the opportunity and ability for employees to enact job changes (Wang et al., 2016). Job autonomy is positively linked to proactive behavior because it allows individuals to reflect about one’s job and enact their desired changes (Hornung & Rosseau, 2007). There is ample empirical evidence suggesting greater job autonomy is linked to job crafting (Kim & Lee, 2018; Leana et al., 2009; Lyons, 2008; Wang et al., 2016). For instance, Rudolph et al.’s (2017) meta-analysis (k = 122) found a moderate, positive relationship between job autonomy and job crafting (r = .28). Interestingly, the job autonomy and job crafting relationship may vary over time. Petrou et al.’s (2018) daily diary study showed days marked by high a level of job autonomy was associated with job crafting behaviors (i.e., higher resource seeking and lower demand reduction). Niessen et al. (2016), on the other hand, found that job autonomy did not significantly predict job crafting (overall vs. task, relational, or cognitive job crafting) two weeks later. So, while research suggests that job autonomy is a critical resource for job crafting, this relationship may be bound by time. It may be the case that the time window to job craft effectively is small, and missing this window leads to less effective crafting. 35 Job autonomy has also been investigated in the context of emotion regulation strategies and their effectiveness. In the broader emotion regulation literature, studies show the controllability of an event leads to more adaptable emotion regulation, and effective management of subsequent stress (Bonanno et al., 2004; Troy et al., 2013). The ability to autonomously enact situation selection emotional regulation has revealed interesting, unique benefits to managing emotions via physiological responses and image reactions. Experimental studies have shown that participants who were provided the autonomy to choose when a negative image is viewed, they report decreased negative reaction, skin conductance, and respiratory reactivity toward the image compared to those who were not offered autonomy (Thulliard & Dan-Glauser, 2017). This autonomy effect persists for physiological responses (i.e., skin conductance and respiratory reactivity) even when the autonomous decision is not respected (Thulliard & Dan-Glauser, 2020) or when offered a non-meaningful choice (i.e., selecting a bogus word that is not related to the following image; Thulliard & Dan-Glauser, 2021). However, it is important to note that participants did not report different negative reaction toward a negative image compared to controls in these latter cases. Nevertheless, autonomy seemingly buffers the impact of negative emotions – presumably because autonomy provides a sense of control that alleviates physiological stressors. In Benita et al.’s (2019) experimental study, participants were instructed to pursue specific emotion goals before shown two fear-eliciting film clips. Participants were then offered the choice to continue pursuing these goals between the first clip and the second. Those in the autonomy-supportive condition (i.e., instructions featuring non-controlling language, rationale, acknowledging difficulties) were more likely than the controlling condition (i.e., instructions featuring language like “you must” and “should”) to independently pursue their emotion goals following the first film. So, an environment that supports autonomy is also critical 36 for ensuring employees feel supported to enact emotion regulation efforts to buffer the effects of emotional demands. Altogether, autonomy is critical for the successful pursuit of one’s emotional goals and for partly buffering the negative impact of negative stimuli. Autonomy supportive environments provide necessary resources for employees to manage their emotional demands effectively (Bruning & Campion; Wang et al., 2016; Zhang & Parker, 2019). This is in part because autonomy provides individuals the decision latitude to change their context and enact emotion regulation behaviors freely and because autonomous behavior is rewarding (Bruning & Campion; Wang et al., 2016; Zhang & Parker, 2019). Within organizations, autonomy also emerges as a critical resource for effective emotion regulation. Grandey and Melloy’s (2017) multi-level emotional labor model positions autonomy as a significant contextual resource that moderates the relationship between emotional labor and outcomes. In support of this idea, both Wharton (1993) and Erickson (1991) showed that the positive relationship between emotional labor and well-being (e.g., emotional exhaustion, depression, self-esteem) was weaker for those with higher levels of job autonomy, suggesting that autonomy helps mitigate the detrimental effects of emotion regulation. Abraham (1996) similarly showed that emotional dissonance—the conflict between expressed and experienced emotions—was inversely related to autonomy. Lastly, Grandey et al. (2005) found that surface acting was less distressing for employees with higher autonomy compared to lower autonomy. In total, job autonomy provides the necessary bandwidth for employees to manage emotional demands via job crafting and emotion regulation. Social Support Social support is defined as “psychological or material resources provided to a focal individual by partners in some sort of social relationship.” (Jolly et al., 2021). Social support is a 37 key factor in employee performance and well-being at work. For this reason, social support is often featured within JD-R models of burnout and engagement (Halbesleben, 2006; Jolly et al., 2021). The significance of social support is partly explained by Daniels et al.’s (2013) qualitative study on how support helps employees. They uncovered four themes: social support allows employees to 1) solve work problems, 2) exercise job control, 3) regulate affect, and 4) social support interacts with job control to solve work problems. Meta-analytical results overwhelmingly support this assertion, as social support has been shown to buffer the stressor- strain relationship, mitigate perceived stressors, improve sleep, and increase positive job attitudes (Holland et al., 2017; Kent de Grey et al., 2018; Viswesvaran et al., 1999). Other studies further show that social support is critical for employees’ functioning at work because it mediates the relationship between stress and well-being (Rehman et al., 2020), promotes training and transfer (Reinhold et al., 2018), and mitigates exhaustion (Rigg, Day, & Addler, 2013). Moreover, interventions aimed at increasing social support at work have been shown to reduce burnout and absenteeism up to one-year later (Dierendonk et al., 1998). Altogether, social support is necessary for employees to manage their job demands and achieve well-being at work. Social support has been linked to effective emotion regulation and job crafting. Models of proactive behavior have shown social support helps predict proactive behavior (Bakker et al., 2015; Parker et al., 2010; Crant, 2000). This is because social support supports autonomy through access to greater assistance, feedback, and unique perspectives (Seibert et al., 2001; Shin et al., 2018). Several studies have found a strong positive relationship between social support and job crafting (Bakker et al., 2014; Rofcanin et al., 2019; Wang et al. 2020). For instance, Demerouti et al. (2018) showed that increased social support from coworkers led to greater promotion-focused job crafting. Research has also showed that social support is related to 38 employee performance and work engagement through job crafting (Kersieck et al., 2019; Rofcanin et al., 2019; Wang et al., 2020). On the other hand, job crafting – specifically relational crafting – is also linked to increased social support, presumably because job crafting functions to increase job resources. Tims et al. (2013) showed that social resources (i.e., social support) mediated the relationship between relational crafting and increased work engagement, job satisfaction, and reduced burnout. This aligns with the notion social support is critical for employees to both enact of and benefit from job crafting and in accordance with the idea that social support is crucial for helping employees manage problems at work (Daniels et al., 2013). The link between emotion regulation and social support has been theorized from the start of early emotion regulation work. Riperre (1977) asked participants “what’s the thing to do when you are depressed?” and found that “seeing people” was one of the most common coping strategies mentioned. “Social support” also consistently emerges thematically amongst other mood regulation studies (Aspinwall & Taylor, 1997; Gallup & Castelli, 1989; Morris & Reilly, 1987; Thayer et al., 1994), especially amongst women (Amirkhan, 1990; Flaherty & Richman, 1989). Contemporary regulation theories also underscore the significance of social support in emotion regulation. For instance, references to social support are often provided indirectly in examples of emotion regulation (specifically situation selection and modification) in behaviors like “seeking out a friend with whom can have a good cry” (Gross, 1998, p. 283) and “sharing great news with close friends” (Gross, 2014, p. 9). Zaki and Williams’s (2013) interpersonal emotion regulation framework was developed to acknowledge how emotion regulation efforts often include others. They suggest a move beyond emotion regulation work emphasizing attentional deployment, reappraisal, and response-modulation regulation strategies – which all rely on one individual alone to regulate their emotions. Rather, their interpersonal emotion 39 regulation is likened to situation selection and situation modification and draws on prior work showcasing the important emotion regulation behaviors, such as sharing positive or negative emotional states (Gable & Reis, 2010; Rime, 2007), seeking advice (Lazarus & Folkman, 1984; Nils & Rime, 2012), or being around others when anticipating stressors (Shacter, 1959; Taylor et al., 2000). Intuitively, effective interpersonal emotion regulation and emotion goal striving cannot occur without adequate social support. The relationship between emotion regulation and social support has also been empirically shown in qualitative and quantitative research. Daniels et al.’s (2013) qualitative study showed that employees leverage social support to regulate their emotions using behaviors like expressing shared affect (e.g., sharing feelings of frustration with another coworker) or calling on someone to help reappraise a situation (e.g., a leader acknowledging their team’s anxiety and encouragingly telling their team they excel under pressure). Brotheridge and Lee (2002) found that social support (operationalized as ‘rewarding relationships at work’) mitigated the effects of surface acting on depersonalization and emotional exhaustion through authenticity. Social support has further shown to be negatively related to surface acting and positively related to deep acting (Mesmer-Magnus et al., 2012), and to buffer the relationship between surface acting and job dissatisfaction (Duke et al., 2009). Interestingly, one study found that receiving social support did not impact emotional labor distress, but providing support to others did (Uy et al., 2016). However, this relationship may not be consistently positive. Other studies have found greater social support counterintuitively strengthened the relationship between deep acting and exhaustion (Hwa et al., 2012) and attenuated the relationship between emotional labor (i.e., both deep and surface acting) and job satisfaction (Johnson, 2004). This may be because the reciprocal nature of social support serves to increase emotional demands as individuals are 40 expected to provide emotional support to their coworkers in addition to typical emotional demands. Alternatively, findings could suggest a differential relationship between social support and outcomes based on the types of emotion regulation strategies adopted (i.e., proximal strategies like surface/deep acting versus situation selection and modification). Altogether, it is undeniable that social support is critical for effective emotion regulation and job crafting. Social support allows individuals to enact emotion regulation behaviors by increasing individual’s social network. This social network expansions provides the opportunity for individuals to enact emotion regulation or job crafting behaviors, like increasing the opportunity for individuals to vent, express themselves authentically, or decrease the effort necessary to manage emotional demands. Moving onto the outcomes of emotion regulation and job crafting, it is important to note that these outcomes are idiosyncratic to these processes. The outcomes included in a study ought to be selected to match the content of emotion regulation and job crafting behaviors. Moreover, outcomes should theoretically reflect the consequences of effective or ineffective management of emotional demands. Accordingly, these outcomes were selected based on their conceptual and empirical similarities across emotion regulation and job crafting research. Each following segment discusses the conceptual basis of the respective outcome and provides an overview of findings across job crafting and emotion regulation research. Work Engagement Work engagement is an active, positive state that is composed of three factors: vigor, dedication, and absorption (Tims et al., 2013). Vigor is characterized by high levels of energy, willingness to put effort, and perseverance. Dedication implies enthusiasm and willingness to meet challenges at work. Absorption is the quality of being fully concentrated and focused at 41 work (Tims et al., 2013). Work engagement is one of the most investigated indicators of employee well-being (Blanchflower & Oswald, 1999; Saks & Gruman, 2014) and is considered the antipode of burnout (Demerouti et al., 2001; Maslach et al., 2001; Montgomery et al., 2003; Salanova et al., 2001; Schaufeli & Bakker, 2004; Schaufelli, Bakker, & Salanova, 2013). Accordingly, high levels of engagement have been linked to desired outcomes at both the organizational and individual levels. Increased employee voice, job satisfaction, commitment, and turnover intentions have all been linked to self-reported engagement levels (Gruman & Saks, 2011; Halbesleben et al., 2010; Harter et al., 2002; Harter et al., 2009), justifying its significant theoretical and practical roles. Work engagement is a central outcome in the job crafting literature. Job crafting is posited to engender work engagement since job crafting is a form of job design that helps employees meet their psychological needs and foster their well-being (Bruning & Campion, 2018; Demerouti & Bakker, 2014; Zhang & Parker, 2019). Several meta-analyses have shown a consistent, positive relationship between job crafting and work engagement. Rudolph et al. (2017) found a relationship between overall job crafting (i.e., increasing structural resources, social resources, and challenge demands) and work engagement of rc = .45. Interestingly, including the “decrease hindrance demands” factor attenuates the relationship between overall job crafting and work engagement (down to rc = .36). Eliminating hindrance demands may inhibit work engagement, despite being a form of job crafting. However, this is corroborated by Lichtenthaler and Fischbach’s (2019) meta-analysis on promotion- and prevention-oriented job crafting. Their study also compared effects to observe the stability of this relationship over time. Their cross-sectional meta-analytic SEM model found that promotion- oriented job crafting (i.e., increasing job resources and challenging job demands; expansion- oriented task, relational, and cognitive crafting) was positively related to work engagement (β = 42 .26). Prevention-oriented job crafting (decreasing hindering job demands; contraction-oriented task and relational crafting) was negatively related (β = -.14) to engagement. Their longitudinal model showed these relationships hold over time as well, but with notably weaker effect sizes (promotion-oriented β = .11; prevention-oriented β = -.03). Petrou et al.’s (2012) longitudinal study also showed that daily levels of work engagement are positively related to daily-level seeking challenges and negatively related to daily-level reducing demands. However, this relationship did not emerge for ‘seeking resources’ job crafting behaviors. So, there is some evidence that the job crafting and work engagement relationship may be partly contingent on the type of crafting used. Most importantly, however, is that approach-oriented job crafting, characterized by increases in demands and resources, has consistently shown to predict increased levels of work engagement. This is likely because individuals increase the resources necessary to meet work demands, which promote personal development across cognitive, skill, and relational domains and also help meet psychological needs. On the other hand, prevention-oriented job crafting, characterized by decreasing job demands and contracting away from resources, does not help employees meet work demands or buffer their detrimental effects, in addition to leaving psychological needs unmet. The link between emotion regulation and work engagement is predominantly couched within the emotional labor literature and centers around surface acting and deep acting comparisons. The groundwork of this work is the observation that poor management of emotional demands leads to little to no work engagement. For instance, Hueven et al. (2006) showed that emotionally-charged customer interactions led to decreased work engagement through emotional dissonance. Diestal et al. (2014) similarly found that emotional dissonance at noon positively predicted end of day work exhaustion. However, other studies suggest that 43 emotional demands may increase work engagement (E.g., Liu & Cho, 2017; Xanthapolou et al., 2013). These findings are not contradictory from the JD-R perspective. Rather, this suggests that poor management of emotional demands undermines work engagement while effective management, via behaviors like emotion regulation, may lead to increased work engagement. This idea is supported by several studies showing that deep acting is positively related and surface acting is negatively related to work engagement (Han et al., 2017; Mroz, 2016; Pelosi, 2015; Yoo, 2016; Yoo & Arnold, 2014; Yoo & Jeong, 2017). Other studies indirectly examined the relationship between emotional labor and work engagement by including emotion regulation as a moderator or mediator. For instance, emotion regulation has been shown to alter the relationship between work engagement and variables such as emotional intelligence (Yuan et al., 2019), self-oriented emotion recognition (Bechtoldt et al., 2011), positive and negative affect (Castellano et al., 2019), competency and relatedness (Pelosi, 2015), and organizational commitment (Sezen-Gultekin et al., 2021). Although the relationship between emotion regulation and work engagement are not directly tested, they nevertheless show effective management of emotions through emotion regulation is critical for the development of work engagement. Emotional Exhaustion Emotional exhaustion is a sub-component of burnout and is characterized by a lack of energy, negative affect, and the sense that one’s emotional resources have depleted (Leiter & Maslach, 2004; Maslach & Jackson, 1986). The negative impact of emotional exhaustion on employees cannot be understated. From a performance perspective, emotional exhaustion is harmful to job performance (Janssen et al., 2010; Wright & Cropanzano, 1998) and OCBs (Cropanzano et al., 2003). Emotional exhaustion is further linked to increased levels of 44 absenteeism (Borritz et al., 2006), turnover intentions (Cropanzano et al., 2003), and turnover itself (Wright & Cropanzano, 1998). Emotional exhaustion is also known to negatively impact well-being, as shown by prior studies linking emotional exhaustion to depression (Hart & Cooper, 2001), cardiovascular disease (Toppinen-Tanner et al., 2006), fatigue (Michielson et al., 2004), and low life satisfaction (Boekhorst et al., 2016). High emotional exhaustion is a state of poor employee well-being that undermines performance and prevents employee flourishing. For this reason, investigation of the predictors of emotional exhaustion is necessary for identifying who is most likely to experience emotional exhaustion, mitigating or eliminating the negative effects of emotional exhaustion, and designing work interventions or training to combat emotional exhaustion. The following segments will center on emotional exhaustion. Job crafting is imperative for reducing levels of emotional exhaustion. This is because job crafting is critical for managing employee demands and resources. JD-R perspectives of job crafting stress the importance of these behaviors for differentiating between employees who thrive and employees who languish (Bruning & Campion, 2018). Accordingly, the relationship between job crafting and emotional exhaustion is that insufficient job crafting precipitates a lack of resources, which leads to greater emotional exhaustion and burnout from being unable to meet demands (Ruysseveld et al., 2011; Schaufelli & Bakker, 2014). In effort to show job crafting’s impact on emotional exhaustion, Van den Ven et al. (2013) found that emotional demands and emotional exhaustion were related across two time points one year apart, moderated by emotional support seeking, a behavior akin to relational job crafting. Lichtenthaler and Fischbach’s (2019) cross-sectional meta-analysis on promotion- and prevention-oriented job crafting found that these behaviors were negatively (r = -.11) and positively (r = .20) related to the emotional exhaustion component of burnout (k = 13; N = 3,428), respectively. Rudolph et 45 al.’s (2017) meta-analysis similarly found that job strain, a synthetic construct grouping of emotional exhaustion and burnout created to meet the k ≥ 3 sample criterion, was negatively correlated to structural resources-increasing job crafting (k = 9; N = 3,342; rc = -.157) and positively correlated to hinderance demands-reducing job crafting (k = 16; N = 5,631; rc = .15). Other studies have examined the link between job crafting and emotional exhaustion over time and across different time scales. Petrou et al. (2015) sampled police officers across two time periods that were 6 months apart. Officers who sought challenges and crafted resources exhibited lower levels of exhaustion. In contrast, those who reduced demands also showed greater exhaustion. At the daily level, Demerouti et al. (2015) found that daily levels of seeking resources negatively predicted exhaustion. Job crafting is a critical, flexible tool that employees can employ to avoid emotional exhaustion across a range of several demands and job strains (e.g., workload, incivility, abusive supervision, etc.). Emotion regulation studies also suggest that emotional exhaustion follows poorly managed emotional demands. Original conceptualizations of emotional labor (i.e., Hochschild, 1983) posited that the commodification of one’s emotions is emotionally exhausting and always harmful to individuals. Emotional labor is effortful and costly, regardless of strategy, because controlling one’s feelings, reappraising situations and emotions, and suppressing authentic feelings require cognitive and physical resources (Gross & John, 2003; Richards & Gross, 2000). These assertions have been supported for surface acting, given moderate to strong positive relationships with emotional exhaustion and strain emerge consistently (Brotheridge & Lee, 2002; Hulsheger & Schewe, 2011; Peng et al., 2009). Results for deep acting, however, are less consistent. Different studies have revealed a weak positive relationship between deep acting and emotional exhaustion (Hulsheger & Schewe, 2011), no relationship (Brotheridge & Lee, 2002; 46 Huppertz et al., 2020; Wagner et al., 2014), or a negative relationship (Hwa, 2012; Peng et al., 2009; Wrobel, 2013). Similar to work engagement, the relationship between emotion regulation and emotional exhaustion may be contingent on a given strategy’s effectiveness in mitigating the negative effects of emotional demands. This may be because the primary drivers of emotional exhaustion are emotional dissonance and emotional demands, so this relationship varies based on how effective employees are to cope with and manage these demands. For example, Van Dijk and Brown (2006) found the relationship between surface acting and emotional exhaustion is partially mediated by emotional dissonance. Bohlman et al. (2020) found the relationship between proactive work behaviors and emotional work fatigue was moderated by emotion regulation skills. In particular, proactive work behaviors were positively linked to fatigue for participants with lower emotion regulation skills. Accordingly, the relationship between emotion regulation, job crafting, and emotional exhaustion is well established; however, theory and research suggest these behaviors are not the origin of emotional exhaustion. Rather, job crafting and emotion regulation can be thought of as tools that shape how and whether demands lead to emotional exhaustion. Just like all tools, the question is which tool is helpful for what problem. 47 Latent Profile Analysis The challenge of drawing from JD-R model to investigate this question, however, is that job demands and resources are idiosyncratic, such that every employee has different work goals and different access to resources. Moreover, proposing and testing a comprehensive model of emotion regulation and job crafting is impossible. However, shifting the focus on individual behavior instead offers a perspective which can help uncover patterns of emotion regulation, job crafting behaviors, and their synergy critical for understanding differences in emotional demands management (Williams & Kibowski, 2016). There are several reasons why shifting towards behaviors themselves is promising in this paper. Organizing behaviors themselves partially circumvents the need to consider an overwhelming number of resources and demands in a single model. That is because comparisons across occupations and contexts can be captured through shared behavioral patterns independent of context. This allows researchers to draw conclusions between emotion regulation and job crafting behaviors more clearly, observe the emergent behavioral patterns of how employees manage their emotional demands, and investigate some broader resources and outcomes across behavioral patterns before hypothesizing more specific variables. One promising tool for applying this perspective is latent profile analysis (LPA). LPA is a statistical analysis that uncovers hidden groups or categories within data based on shared variance between observed indicators (Oberski, 2016). LPA is partly an exploratory process due its nature of variable and data reduction, but can also serve as a powerful tool to test a-priori assumptions of expected profiles based on theory (Williams & Kibowski, 2016). Moreover, LPA calculates the likelihood of an individual belonging to a specific emergent group, which researchers can use to then understand the characteristics or compare variables across profiles 48 (Willains & Kibowski, 2016; Oberski, 2016). LPA contrasts typical variable-centered data analyses which emphasize variable relationships by controlling for all other factors. Variable- centered approaches can unintentionally obscure relationships because it assumes variables are related equally across the population (Masyn, 2013). A person-centered approach instead assumes that relationships between variables are heterogeneous within the population to identify similarities and differences across individuals based on the relationship of interest (Larsen & Hoff, 2006; Masyn, 2013). A person-centered perspective acknowledges that several, related behaviors can synergize (Willains & Kibowski, 2016; Oberski, 2016). For instance, two profiles can share high values on a shared indicator and vary substantially on outcomes based on other indicators. LPA thus supplements variable-centered approaches, which obfuscate the role of other variables in explaining the relationship between two variables. Person-centered approaches are one way to acknowledge the role other variables in explaining a relationship without unintentionally washing away important information captured by other relevant variables. This study seeks to advance the emotion regulation literature that has taken a variable- centered approach. The application of LPA will help investigate whether emotion regulation strategies and job crafting behaviors synergy can be used to describe unique approaches to emotional demands management beyond the typically investigated strategies (i.e., cognitive change and response-focused strategies). This approach contrasts with typical job crafting and emotion regulation studies that investigate behaviors independently. Of course, LPA alone will not revolutionize how organizational sciences theorize or investigate emotion regulation. But, a person-centered approach offers a perspective that aligns closer to how employees realistically behave at work. Consider how person-centered emotional labor research has already uncovered unique patterns of deep acting and surface acting and exhaustion (Gabriel et al., 2015). One hope 49 is the inclusion of all emotion regulation strategies alongside job crafting behaviors reveals unique synergies that complement emotion regulation and job crafting research, whom both seek to understand the process of how employees manage emotional demands. This way, the idiosyncrasies of emotion regulation and job crafting strategy selection are respected and hopefully closer aligned with how individuals manage everyday emotional demands. LPA has been previously applied to emotion regulation or job crafting. As noted earlier, Gabriels et al. (2015) applied LPA to emotional labor and found five unique profiles with unique patterns of emotional labor behaviors, antecedents, and work and well-being related outcomes. In this case, LPA uncovered a specific combination of emotional labor behaviors that otherwise have been overlooked or considered dysfunctional at work. This study also highlighted the role of personal factors for explaining differences between employees’ need to engage in emotional labor. Nguyen and Stinglhamber (2020) replicated this profile structure and further linked profile membership to work mistreatment (e.g., customer incivility, organizational dehumanization, abuse supervision) and other work-related outcomes (e.g., job satisfaction, emotional exhaustion, and turnover intentions). The replication of the same number of profiles and characteristics as Gabriel and colleague’s study is both exciting and surprising. These five profiles emerged over two studies and three different samples: a U.S. sample, a Singapore sample, and a U.K. sample. The consistency of the profile structure over varied cultural contexts suggests it may be more important to consider emotional labor strategies as patterns of behaviors – rather as two different strategies. For instance, variable-centered approaches have previously found differences in emotional labor strategies and outcomes across culture (Allen et al., 2014). Rather, LPA findings suggest that patterns of emotional labor emerge can emerge across culture, and these patterns help explain employee well-being and performance. Importantly, these labor profiles papers 50 underscore how idiosyncratic the emotional labor process may be and that grouped behaviors help address this barrier. These profiles also disrupt the ‘good’ versus ‘bad’ dichotomy prevalent within emotional labor literature. To date, one study has applied LPA to understand the job crafting process. Makigangas (2018) investigated the relationship between job crafting profiles and work engagement. She hypothesized two job crafting profiles according to approach and avoidance crafting motivations – an ‘active’ profile characterized by individuals who increase job challenges and resources, and another ‘passive’ profile of individuals who reduce hindrance demands. Ultimately, results supported the hypothesized 2-profile structure of ‘active’ and ‘passive’ crafters. ‘Active’ crafters were characterized by greater levels of work engagement across two time points one week apart, further suggesting that individuals hold their job crafting profile over time. The ‘active’ profile made up 94% of the sample while the ‘passive’ profile represented the remaining 6%. The significance of this paper is in showing how workers tend to employ different job crafting behaviors compared to disengaged workers, who ignore their work context. Moreover, this study suggests job crafting is a commonly occurring behavior while disengaged workers are the exception. Like the previous emotional labor studies, Makigangas (2018) showed how a phenomenon and outcomes can be better understood by considering how several behaviors operate in tandem. This paper hopes to contribute to literature in several ways. First, establishing profiles of emotion regulation and job crafting behaviors addresses the qualitative similarities between these two processes who have not been previously considered similar. Moreover, this perspective advances emotion regulation research by conceptually expanding what functions as a strategy. Because emotion regulation and job crafting theories posit that respective behaviors can function 51 to manage emotional demands at work, this suggests job crafting may be considered distal emotion regulation strategy akin to situation selection and modification. Emergent profiles containing both emotion regulation and job crafting behaviors suggest that both literatures will benefit from applying theoretical perspectives and drawing on findings from the other to advance their understanding of each phenomenon. This study also answers to calls by emotion regulation scholars to investigate beyond cognitive change and response-focused strategies. As shown before, emotion regulation strategies are employed differentially depending on specific contextual and interpersonal factors (Diefendorff et al., 2008). Introducing situation selection, selection modification, and attentional deployment strategies extends prior LPA studies by considering synergy amongst more emotion regulation behaviors. The addition of these strategies offers the opportunity to observe whether new profiles emerge and if how the previously established profile structure changes when considering new behaviors. For instance, how does attentional deployment fit within the previous profiles, and what does its inclusion suggest for outcomes? How do distal strategies fit with more proximal strategies? Will there be a group of individuals who only engage in emotion regulation or job crafting? Consideration of distal strategies across emotion regulation and job crafting processes will thus supplement prior studies’ focus on proximal behaviors (i.e., cognitive change, response-focused strategies) by possible uncovering novel profiles formed around employees’ strategy preferences across time. Lastly, LPA can offer a taxonomy through profiles to understand emotion regulation behaviors across different occupations and samples. Focusing on behaviors themselves can create a shared understanding by reducing all the possible combinations of behaviors into discrete groups that are easier to digest. Moreover, this avoids the complexity of wrangling the 52 infinite number of resources and demands across occupations because profiles reflect strategies to manage emotional demands, and not the effects of the resources or demands themselves. Instead, interpretations can center around the implication of profile membership across and within occupations. 53 Expected Profile Organization Profiles should be organized using available theory and findings to avoid inaccurate or misguided post-hoc interpretation of findings, otherwise LPA can be seen as dust-bowl empiricism (Williams & Kibowski, 2016). Furthermore, patterns emerging from LPA may not be interpretable or meaningful without proper theoretical grounding on the behaviors forming profiles. In this study, the expected emergence of profiles are organized based on concepts shared across job crafting and emotion regulation literatures. Specifically, profiles are organized around employees’ dispositional motivations and methods for enacting emotion regulation and job crafting. Both emotion regulation and job crafting at work are motivated, goal-driven behaviors (Niessen et al., 2016; Tamir & Millgram, 2017; Tamir et al., 2020), suggesting employees rely on these processes to meet work-related goals, which is to manage emotional demands and resources at work. Furthermore, these profile distinctions are made between approach-avoid and proactive-reactive motivations because they guide the underlying motives and approaches of demands and resource management. These distinctions are also based on prior theories and research which emphasizes the importance of a person-situation interaction for understanding the effects of emotion regulation and job crafting behaviors on employees (Troy et al., 2013). That is, job crafting and emotion regulation strategies cannot be simply organized into ‘good’ and ‘bad’ strategies; rather, employees’ specific personality, goals, and the circumstances they employ these strategies dictate whether a strategy is effective or ineffective (Paul et al., 2023). In other words, these behaviors should not be considered ‘good’ or ‘bad’ but neutral instead, because their effectiveness is a function of the strategy, the situation, and the person (Dore et al., 2016; English, 2017). The goal of this study is then to show organize these behaviors into unique profiles based on their motivations (approach-avoid) and method 54 (proactive-reactive). Moreover, this study hopes to also show that emergent profiles can uncover specific behavioral patterns that help compare significant resource and outcome differences across employees. Approach and Avoidance Motivations The first distinction to be made across profiles is between approach and avoid motivations. Approach and avoid motivations are personality dimensions that describe general sensitivities to affective stimuli (i.e., positive or negative) and the behavioral disposition to react to the present or imagined affective stimuli (Elliot & Thrash, 2010). Approach motivations are characterized by “the energization of behavior by, or the direction of behavior toward, positive stimuli (objects, events, possibilities)” while avoidance motivations are defined instead by behavior moving away from negative stimuli (Elliot & Thrash, 2006, p. 112). Energization refers to the initiation of behavior toward a specific direction, psychological or physical (Elliot & Thrash, 2006). That is, individuals can physically move toward or away from stimuli or psychologically distance stimuli away from them; whereby stimuli can represent an almost infinite number of concrete or internally generated objects, events, possibilities (Elliot, 2006). The valence of the stimuli is the central evaluative dimension of these motivations, and thus are the central drivers of approach-avoid behaviors (Elliot, 2006; 1999). These motivations are incredibly valuable for understanding goal setting and pursuit, because they guide a goal’s content and the behaviors employed to reach goals (Elliot & Thrash, 2002). Accordingly, approach-avoid motivations are frequency featured within the field of organizational psychology and beyond, and have helped explain a wide range of phenomena ranging from job performance to job attitudes to employee well-being (see Lanaj et al., 2012 and Gorman et al., 2012 for relevant reviews). 55 Approach-avoid motivations are not just fundamental to the goal setting and striving processes itself but goal content too (Elliot & Thrash, 2002). Approach-avoid motivations lead to general sensitivities to specific stimuli based on valence, which manifest into desired end states (i.e., goals) organized around these sensitivities. These valence sensitivities are so powerful they are considered a central determinant of goal adoption and commitment (Lewin, 1935; Locke & Latham, 1990). Generally, goals are understood to follow the hedonic principle of seeking pleasure and avoiding pain, and around the desires to reach positive (e.g., skill mastery, reach positive affect) or avoid negative (e.g., disappointment, frustration) outcomes (Lewin, 1935; Locke & Latham, 1990). This distinction in goal content leads to differences in goal pursuit behaviors, which lead further to differences in successful pursuit of these goals themselves. For instance, Yeo et al. (2009) showed that approach-avoidance motivations help explain performance, such that approach orientations are positively related to intra- and inter-individual performance while avoidance to lower interindividual performance. Cellar et al.’s (2011) meta- analysis similarly showed that trait approach-avoidance motives helped distinguish between high and low performers, such that approach-oriented individuals performed better compared to those who were avoidance-oriented. They also found the relationship between trait goal orientation was stronger for self-regulation behaviors (i.e., self-monitoring, self-evaluation, self-efficacy, and self-reactions) compared to task performance. Theoretically, these performance differences across motives may be partly explained by affect. Approach goals are generally linked with greater positive affect and avoidance goals with negative affect (Barclay & Kiefer, 2012). As participants pursue approach goals, they also experience greater levels of positive affect, which is energizing and promotive of further goal-related behaviors, such as self-efficacy, persistence, or goal setting. Alternatively, negative affect is argued to inhibit goal pursuit because it 56 negatively impacts resources and may make individuals more inclined to lower goal standards, experiencing discouragement and anxiety after failing to meet a goal (Higgins & Spiegel, 2004). For these reasons, it is reasonable to expect that approach and avoidance motivations could provide an important distinction between emergent profiles. In other words, the clustering of emotion regulation and job crafting profiles – and associated differences between resources and outcomes – are distinguishable between approach or avoid motives. Expected differences in behaviors should thus align with the methods employees use (e.g., emotion regulation or job crafting) according by approach-avoidance motivations. The following segments build this argument by reviewing research exploring approach-avoidance themes across job crafting and emotion regulation research. Job crafting and Approach-Avoidance Motivations Job crafting theory has distinguished crafting based on approach-avoid motivations to better understand the various consequences of job crafting. As discussed earlier, Approach- oriented job crafting expands employees’ job roles, add job challenges, and develops employees’ skills and abilities (Zhang & Parker, 2019). Avoidance-oriented job crafting reduces the number of tasks, responsibilities, requirements, and effort expenditure from employees (Zhang & Parker, 2019). Initial support for this distinction is found within Tims et al. (2013), who showed job crafting behaviors which increased social resources, structural resources, and challenge demands led to greater job engagement, job satisfaction, and lower burnout. Job crafting targeted at reducing job hindrances, however, did not impact employee well-being or attitudes. These findings ran counter to researchers’ expectations, as job crafting was hypothesized to promote employee well-being and attitudes by helping employees manage and meet work demands. Finding that reducing hindrance demands was not related to employee well-being or attitudes 57 was instead attributed to the fact that eliminating work demands is difficult to accomplish and be captured in research. Shortly thereafter, however, more job crafting studies emerged in alignment with Tims and colleagues. In their dissertation, Bruning (2015) proposed two types of job crafting. They proposed active job crafting, which features work and social role expansion, and withdrawal crafting, which features work role reduction. Results suggested active job crafting showed mixed relationships with performance, personal development, and work experience. Withdrawal crafting, on the other hand, was only associated with increased work/home conflict. Bipp and Demerouti (2016) were the first to formally investigate between approach-avoid temperaments and job crafting behaviors, operationalized by seeking resources, seeking job challenges, and reducing work demands. They found that seeking resources and increasing job challenges were positively related to an approach temperament while reducing demands was positively related to an avoidance temperament. This aligned with Bruning’s results that also suggested job crafting can be split between employees’ tendency to expand work roles and challenge demands or reduce hindrance demands. Licthenthaler and Fischbach (2016) formally proposed a distinction between promotion- and prevention-oriented job crafting behaviors themselves. Promotion crafting encompassed job crafting aimed to increase job resources and challenge demands. Prevention crafting encompassed job crafting aimed to reduce hindrance demands. This framework served to organize Tims et al.’s (2013) and Bipp and Demerouti’s (2016) findings, which both observed outcomes differences featuring the exact crafting behavior distinction. Lichtenthaler and Fischbach’s (2016) study served to validate prior observations suggesting that promotion crafting is associated with greater job performance, motivation, and health outcomes while prevention crafting led to worsened outcomes. Later, Lichtenthaler and 58 Fischbach’s (2018) second meta-analysis further showed promotion-crafting behaviors were positively related to work engagement. They also showed promotion-crafting is positively related to job performance and negatively related to burnout through work engagement. Prevention- crafting showed the opposite patterns, it was negatively related to work engagement, job performance, and positively related to burnout. It was Bruning and Campion (2018) who later used the terms approach and avoidance crafting to refer to promotion and prevention job crafting. Importantly, their paper integrated job crafting and coping research by investigating differences between approach-avoidance crafting to understanding how employees manage and cope with work related demands and challenges. This expanded avoidance crafting motives beyond the reduction of hindrance demands, and includes behaviors that reduce employees’ job roles, tasks, and/or resources. Again, findings suggested approach crafting promoted employee well-being (i.e., enrichment, engagement, and reduced strain) and that avoidance crafting is associated with higher employee withdrawal (operationalized by work boredom, general neglect, and turnover intentions; Bruning & Campion, 2018). However, departing from prior studies, their results also revealed avoidance crafting led to increased employee enrichment. This may be because the reduction of work tasks allows employees to better commit to meaningful work tasks, but at the risk of also promoting employee withdrawal. Zhang and Parker (2019) argue prevention crafting engenders negative outcomes because this style does not create favorable, tangible differences in work role boundaries. They argue that prevention crafting also promotes unfavorable changes in work attitudes. On the other hand, approach crafting is characterized by desirable employee-led changes in their work environment and by an increase in tasks which facilitate resource generation and personal development (Bruning & Campion, 2018; Lichtenthaler & Fischbach, 2016; Tims et al., 2013). Although some 59 studies suggest avoidance crafting could be beneficial in specific circumstances (e.g., promoting employee enrichment or reducing work roles to manage work-family conflict; De Bloom et al., 2020), the consensus is that approach job crafting promotes employee well-being and functioning at work while avoidance crafting undermines these variables. Approach-avoid motivations are thus critical for organizing and understanding job crafting behaviors. Emotion Regulation and Approach-Avoidance Motivations There are few cases where approach-avoid motives are applied to regulation models and theories within the organizational sciences. This framework is instead indirectly underscored across many contemporary emotion regulation studies, within both organizational science and beyond. Just like job crafting, approach-avoidance motivations can help explain emotion regulation at work. Approach-oriented emotion regulation can be characterized by an underlying motive to experience or seek out a specific emotion or emotion-eliciting stimuli. Avoidance- oriented emotion regulation can be characterized by the motive to reduce or eliminate the possibility of another emotion or emotion-eliciting stimuli (Tamir, 2016). Both approach and avoidance emotion regulation may also be characterized by motives to move toward or away real or imagined stimuli to regulate emotions. This clause is important because emotion regulation strategies vary on whether they change internal or external circumstances and whether they are employed in reaction or anticipation to an emotion. These definitions align with the themes of approach-avoid motivations and are specific enough to consider as self-regulation and goal striving processes. Researchers then ought to consider the underlying motives and context for the strategy’s application. This study then seeks to integrate approach-avoid distinctions for emotion regulation behaviors to better understand how emotion regulation impacts employees, similar to contemporary job crafting research. 60 Display rules already show how approach-avoidance motives and behaviors do not only originate from personal factors. Rather, approach-avoidance oriented behaviors can be driven by the type of goal adopted. However, emotion goals as distinct as display rules do not typically occur outside of specific occupations like retail or nursing. Instead, employees themselves can set their own emotion goals and regulate their emotions accordingly. These emotions goals are important drivers of the behaviors used to reach desired emotions (Tamir, 2005; 2009). However, emotion regulation strategies encompass behaviors that can meet emotion goals that are approach (i.e., increase the likelihood to experience a desired emotion) or avoidance (i.e., decrease the likelihood of experiencing an undesired emotion) oriented. For instance, situation selection can be enacted to approach pleasurable stimuli or avoid threatening stimuli. Situation modification can involve adding aspects to a situation (e.g., bringing notes to a presentation) or removing them (e.g., discouraging friends and family from attending a presentation because large crowds make them anxious). Attentional deployment can be used to avert gaze away from or toward unpleasant stimuli or toward positive stimuli. Thus, organization of profiles by emotion regulation strategies can be tricky since one strategy can offer several ways to accomplish approach or avoidance goals. This is reflected across studies by measures of emotion regulation items. Examples include Webb’s (2017) situation selection items which reference selecting and steering clear of activities; Diefendorff et al.’s (2007) emotion regulation survey which includes situation modification items like “try to solve…” “remove myself …” and attentional deployment items like “turn attention toward…” “turn attention away…”; and Schutte’s (2009) situation modification items which ask about behaviors aiming to avoid negative feelings or to feel emotions they desire. Altogether, the classification of a strategy as approach or avoid is not 61 clean or unambiguous because strategies can be used for both purposes. However, it can be argued that some strategies by nature are better suited for approach or avoid goals. Cognitive change strategies and response-focused strategies most clearly align with approach and avoid motives, respectively. Response modulation (and surface acting in emotional labor research) can be categorized as an avoidance strategy because it does not change the underlying emotion experience but seeks to hide or inhibit its development. This is because response modulation consists of strategies featuring suppression of authentically felt emotions or masking with a different emotion (Gross, 1998). Moreover, response modulation strategies align with avoidance-oriented goals like suppressing or hiding emotions. Accordingly, response- focused strategies are employed more frequently in negative customer interactions under positive display rules (Grandey et al., 2004; Sliter et al., 2010). Cognitive change strategies (and deep acting in emotional labor research), on the other hand, align with approach-oriented motivations. Cognitive change strategies encompass methods which alter the meaning of the expected emotion experience (Gross, 1998; 2007). This occurs either by changing the interpretation of an emotion or situation to increase positive emotions or by viewing the event neutrally to remove its emotional impact. Both circumstances are argued to reflect approach motivations because individuals are working to change their emotions in the desired direction, rather than avoiding the occurrence or expression of an emotion altogether. Although some cognitive change scales include items that hint at avoidance (e.g., “I change the way I think about things to prevent me from feeling emotions I do not want to have.”), one could argue the underlying motive for reappraisal is to alter an emotion’s development toward a specific state - rather than inhibit its occurrence or development - aligning it with approach motives. 62 The distinction between approach and avoid becomes less clear for the remaining strategies. The next clearest distinction is made for situation modification. Situation modification can involve adding to or removing from the environment or situation to manage one’s anticipated emotions. Regardless, situation modification requires deliberate action to change one’s surroundings and circumstances toward a desired emotion state. This closely aligns with approach motives because individuals are investing significant effort to increase or decrease the probability of encountering stimuli that affect the likelihood of reaching their desired emotion. This behavior aligns with approach motives because individuals are engaging with their environment, as opposed to accepting the circumstances. Some situation modification items hint toward avoidance themes. Schutte’s (2009) situation modification item “I change situations so they don’t let me feel negative emotions” and Diefendorff et al.’s (2008) item “remove myself from a situation” both feature modification strategies that center around eliminating or moving away from circumstances on the basis of their anticipated outcomes. However, considering these items as examples of avoidance-oriented situation modification may not be totally appropriate. First, as Gross and Thompson (2006) note, the line between situation selection and modification is blurry because modifying a situation can create a new, different situation. Removing oneself from a situation is not closely aligned with definitions of situation modification, which emphasize manipulation of the environment. To leave an environment is to choose another, not to modify or alter the current environment. So, removal of oneself from a situation is closer related to situation selection than modification. Changing a situation to avoid negative emotions, however, does seem to align with avoidance themes. But, another way to interpret this item is that it is preventative, similar to Higgin’s (1997) prevention orientation. Prevention centers around negative emotions and safety behaviors but is still considered a form of approach- 63 oriented behavior because individuals are moving toward desired states. From this standpoint, changing a situation to avoid negative emotions does not necessarily mean movement away from a situation, rather a modification that minimizes the likelihood of encountering an emotion that counters one’s emotion goal. Lastly, situation selection and attentional deployment strategies can be used for either approach or avoid purposes. Situation selection is about opting in or out of an anticipated situation or event depending on the anticipated emotional outcomes (Gross, 1998). Research featuring situation selection effectiveness also shows that individuals use situation selection for both approach and avoidance-oriented purposes. For instance, Catalino et al. (2014) found those who organize their day to day lives to ‘prioritize positivity’ by pursuing events that make them happy also hold greater levels of positive emotions and affect. Work from Tamir and colleagues similarly show individuals prepare for activities like negotiations or video games by engaging in activities that promote fear or listen to music that excites them (Tamir et al., 20008; Tamir & Ford, 2009). Another stream of research shows older individuals are more likely to regulate their emotions through situation selection than younger individuals. Specifically, older adults are more selective about the individuals they socialize with (Cartensen et al., 1997) and the media they consume (Urry & Gross, 2010). While these examples of approach-oriented situation selection suggest it is an effective form of emotion regulation, avoidance forms of situation selection have been investigated mostly within clinical psychology. The term ‘safety behavior’ refers to avoiding situations that produce anxiety or discomfort (Salkovskis, 1991), like socially anxious individuals who stay home to avoid social outings. This behavior is considered maladaptive because individuals do not learn effective strategies for coping with these and are more likely to experience distress when they find themselves in the situations they try to avoid (Goetz et al., 64 2016). Other research shows that avoidance-oriented situation selection is popular amongst older individuals and those with low emotion regulation competency and can be effective for decreasing negative affect, but not for increasing positive affect (Livingstone & Isaacowitz, 2015; Webb et al., 2017). Attentional deployment similarly functions as an approach or avoidance-oriented behavior. Webb et al.’s (2012) meta-analysis of emotion regulation effectiveness considered two primary forms of attentional deployment. Distraction refers to shifting attention to stimuli away from the situation by shifting gaze or conjuring thoughts inconsistent with the undesirable emotion (Gross & Thompson, 2007). Concentration refers to drawing attention to the emotional features of a situation, like one’s feelings, the causes of these feelings, and/or the implication of these feelings (Gross & Thompson, 2007; Webb et al., 2012). Distraction aligns with avoidance themes considering that the direction of attention is away from current stimuli represents psychological movement away a situation. Concentration, alternatively, can be considered approach-oriented because this strategy moves gaze or attention toward emotion eliciting stimuli and/or current feelings (Webb et al., 2012). This represents psychological movement toward the emotional event through engagement with current stimuli, which immerses individuals closer to a situation. Interestingly, distraction may be more beneficial than concentration based on prior reviews and meta-analyses (e.g., Augustine & Hemenover, 2009; Webb et al., 2012). This may be because drawing attention toward the emotion (i.e., concentration) amplifies the emotion experience altogether, leading to more substantial negative effects. Distraction, alternatively, mitigates the negative impact of the emotion and filters away some of its effects. It should be noted that majority of these studies investigated the regulation of negative emotions and not 65 positive emotions. This is important because the valence of the emotion regulated may change or moderate effectiveness of a strategy and its impact (Lennard et al., 2019; Webb et al., 2012), which may mean that the difference between concentration as a beneficial or harmful strategy rests in the emotion regulated. Nevertheless, attentional deployment has shown to be an effective strategy in managing emotions and their demands, through concentration or distraction which varies on the form of attentional deployment employed. Proactive and Reactive Approaches The second distinction anticipated to emerge across profiles is whether the emotion regulation or job crafting behavior is used proactively or reactively. Job demands and resources are posited vary across time, meaning that job crafting and emotion regulation are continually employed to balance meeting demands and acquiring resources. Accordingly, contemporary job crafting and emotion regulation research are increasingly interested in research questions and designs that help researchers understand how workers accomplish this never-ending balancing act. These questions have spurred significant interest in proactive job behaviors. Theories of proactive behaviors are driven largely by motivation and self-regulation research which shows that some employees set and strive for work goals by anticipating their demands (Parker et al., 2010). Proactivity research also follows from general interest in job design research featuring ‘active workers,’ which refer to employees who construct job roles and tasks to best suit themselves (Parker et al., 2017). Accordingly, job crafting research is based on work featuring these active workers. In fact, Wrzsniewski and Dutton’s (2001) seminal job crafting paper coins the phrase active crafters partly based on these workers. Research on proactive employees and work behaviors overwhelmingly suggest proactivity leads to favorable work outcomes. Proactive behaviors are positively linked to significant individual level outcomes (e.g., self-rated and 66 supervisor-rated performance, initiative, job satisfaction), team-level outcomes (e.g., effectiveness, productivity, job satisfaction, commitment, learning), and even organizational outcomes (e.g., firm success and engagement in sustainable environmental practices; Bindl & Parker, 2011). The value of proactivity at work is well established. Proactive behaviors guide attention toward desired goal end states, as well as promote resilience and perseverance during goal pursuit (Parker et al., 2010). In other words, proactive goals streamline effort toward successful goal pursuit by keeping attention on desired goals and promoting plans for reaching the goal and overcoming obstacles. This study proposes a distinction between proactive and reactive emotion regulation and job crafting approaches to uncover unique profiles. Unlike approach-avoidance motivations, the proactive-reactive distinction is not one consistently or frequently applied. However, the following sections discuss reflect research that emphasizes the importance of these approaches for understanding employee well-being and managing emotional demands. Job Crafting and Proactive-Reactive Approaches Job crafting is considered a proactive behavior because it requires individuals to identify problems or opportunities and subsequently plan, self-initiate, and implement change to themselves or their environment (Bindl & Parker, 2019; Parker & Collins, 2010). The contrast between proactive employees who job craft to passive non-crafters in prior studies suggest that proactivity is critical for effectively managing demands and resources and employee functioning and well-being (see previous discussion on Rudolph et al. 2017 and related meta-analyses). However, the actual distinction between proactive and reactive is not exactly delineated. Is job crafting only proactive or can it also be reactive? Are proactive individuals those who enact job crafting exclusively? Makigangas (2018) helps resolve some of these questions. She conducted 67 an LPA featuring four job crafting styles: (1) increase structural resources, (2) increase social resources, (3) increase challenge demands, and (4) decrease hindrance demands. Two profiles emerged – active job crafters, which reported higher levels of all job crafting behaviors, and passive job crafters, which reported low levels of all job crafting behaviors save for moderate levels of reducing hindrance demands. Her results were consistent with prior research that job crafting promotes job engagement, as the active profile displayed significantly higher engagement compared to the passive profile across two time points. These findings are intriguing since it suggests individuals can be divided based on whether they engage in much job crafting or little job crafting. In a sense, a proactive and reactive distinction can be drawn – some individuals enact proactive behaviors and others do to a much lower degree. One interpretation is the lower job crafting group gravitates toward reducing hindrance demands because it is a reaction to negative stimuli but do not enact proactive behaviors to prevent those stimuli. However, some study limitations should be noted. Makigangas’s (2018) investigation of job crafting was fairly limited. Newer theories of job craft differentiate across approach and avoid crafting for both resources and demands, and whether the crafting occurs behaviorally or cognitively (Bruning & Campion, 2018; Zhang & Parker, 2019). Finding profiles that reflect crafters and non-crafters may be a function of omitting more specific job crafting indicators. For example, distinguishing between behavioral and cognitive crafting can help decipher across active-passive profiles. Behavioral job crafting requires more effort and proactivity compared to cognitive crafting because individuals need to initiate changes in their environment which further necessitates identifying, planning, and striving for this change (Bindl & Parker, 2009; Parker et al., 2010). For this, employees will need to invest a large amount of social and structural resources also. This is in contrast to cognitive crafting, which occurs internally and requires far 68 less planning and effort. While Makigangas (2018) offers some evidence that a proactive- reactive distinction could be made by differentiating between employees who engage in job crafting versus those who do not, there is also reason to believe this distinction may not fully capture this spectrum. Other studies imply reactive job crafting because job crafting is studied in reaction to significant change. For example, Petrou et al.’s (2016) paper investigated the role of job crafting using a sample of police officers undergoing organizational change. Because job crafting is being investigated as a mechanism in response and coping to change, the use of job crafting in this study is reactive because employees do not enact or initiate every change themselves. They found ‘seeking challenges’ was linked to higher levels of adaptivity and that ‘seeking resources’ was linked to higher engagement. Job crafting can thus function as a coping behavior for stressors like organizational change and does not necessarily have to be evoked to manage anticipated demands. Bowling (2012) showed that employees job craft whenever their job satisfaction levels drop below a certain threshold or if they perceive a drop in job satisfaction. Employees also job crafted to maintain high levels of job satisfaction. These circumstances show that job crafting occurs both to ensure that job satisfaction levels maintain at high levels (proactive) or to restore a decrease in job satisfaction (reactive). Demerouti and Peeters (2018) showed that optimizing and minimizing demands were positively related at the day level and not general level. They also noted that job crafting occurred according to daily levels of demands, which the authors propose uncover different reactive and constructive motives. Lastly, Lazazzara et al.’s (2020) meta-synthesis of qualitative job crafting research formally proposes that job crafting motives can be distinguished between proactive and reactive. Job crafting could be energized by the wish to reach desirable goals 69 (proactive motives; e.g., improve work performance, achieve work-life balance, craft one’s occupational identity) or cope with structural or job-related adversity (reactive; e.g., manage multiple demands at once, manage conflict, or diminish felt inauthenticity). Their definitions of proactive and reactive mirror the distinction between proactive and reactive coping, such that behaviors can be targeted toward anticipated demands or stressors, or toward immediately present demands. Through this lens, the difference between proactive and reactive job crafting behaviors is then not whether there is the absence of job crafting or not, but whether the underlying motives of these. This perspective also importantly acknowledges that both proactive and reactive job crafting can offer benefits. Emotion Regulation and Proactive-Reactive Approaches Gross’s model of emotion regulation already distinguishes between proactive and reactive emotion strategies. Specifically, each strategy is organized in order of distance from the emotion experience. Situation selection is the most distal strategy followed by situation modification (Gross, 1998; 2008). Both alter the emotion experience by changing or modifying the physical environment, far before the emotion is expected. Attentional deployment follows and represents an important shift from exerting control over external to internal ‘situations’ (Gross & Thompson, 2006). Attentional deployment helps filter the information available to an individual to impact the intensity or meaning of the later emotion experience (Sheppes, 2011; 2014). Lastly, cognitive reappraisal serves to reimagine the meaning of the emotion and its experience. These four strategies are labeled antecedent-focused strategies specifically because they occur before response tendences are activated and behavior is changed (Gross, 2008). Response modulation is the only response-focused strategy because it occurs following the emotion experience and behavioral responses. 70 Based on this, there are two ways to delineate proactive and reactive strategies. One is to follow the antecedent versus response-focused distinction, whereby proactive strategies refer to strategies which alter the emotion experience before it occurs and reactive strategies following. Papers comparing cognitive change and response modulation (or deep acting and surface acting) inherently feature this distinction by comparing outcomes of interest. This is because cognitive change and response-focused strategies are the strategies most proximal to the emotion but distinguished based on when they are employed (antecedent vs. response). However, this distinction may not be appropriate. Gabriel et al.’s (2015) momentary assessment of emotional labor during a customer service episode show that both strategies occur almost simultaneously, and that surface acting could even occur at the start of an encounter. Randolph and Dahling (2013) further showed that proactive personality is positively related to surface acting and deep acting, suggesting proactive individuals may not exhibit clear preferences between antecedent- or response-focused strategies. The second way to distinguish proactive and reactive strategies is to consider changes between internal and external environments. Proactive strategies would thus refer to situation selection and situation modification because these strategies involve anticipating, opting for, and changing of one’s external environment. This aligns with definitions of proactive work behaviors that are characterized by taking initiative to control and change one’s environment to anticipate and create desired outcomes (Bindl & Parker, 2018; Cunningham & De La Rosa, 2008). Reactive strategies are then attentional deployment, cognitive change, and response-focused strategies. These strategies are reactive in that they modify an ‘internal’ environment and are employed for managing real (as opposed to imaginary) demands. This aligns with the coping literature which characterizes reactive coping by efforts to deal with past or present stressors 71 and/or react to prior harm or loss (Schwarzer et al., 2002). The benefit of using this distinction for emotion regulation is that it clearly divides strategies temporally (when a strategy is employed), qualitatively (what and how a strategy works), and can be tied to specific motivations easily. Gross originally proposed antecedent-focused strategies are more effective compared to response-focused strategies (Gross, 1998; Gross & Thompson, 2007; Sheppes & Gross, 2012). This is because individuals can exert greater control over their emotions’ unfolding and subsequent experience. However, evidence supporting this idea is mixed. In Webb et al.’s meta- analysis (2012), reappraisal emerged as the most effective emotion regulation strategy (d = .36), followed by response modulation (d = .16), and attentional deployment (d = .00). Distraction (d = .27) but not concentration (d = -.26) – two forms of attentional deployment – was found to be effective. While the effects of attentional deployment are not stronger than reappraisal, response- focused strategies indeed emerge as far less effective compared to antecedent-focused strategies. Unfortunately, no meta-analytic studies have investigated situation selection or modification together alongside the rest of Gross’s proposed strategies. However, Schutte et al. (2009) compared the relationship of emotion regulation strategies on well-being outcomes (i.e., life satisfaction and mood). They found antecedent-focused strategies predicted subjective well- being and that response-focused strategies did not account for variance in well-being above and beyond antecedent-focused strategies. But, response-focused strategies were found to be positively related to life satisfaction and positive mood. When considering situation selection and modification alone, situation selection was negatively related to negative moods while situation modification positively related to life satisfaction, positive moods, and negatively related to negative moods. Moreover, the effect sizes of situation selection and modification were 72 comparable to other antecedent-focused strategies. While situation selection and modification may not be more effective than more proximal strategies, these strategies nevertheless emerged as effective regulation strategies. Evaluating similarities and differences in emotion regulation according to the proposed framework (proactive = situation selection, modification; reactive = all other strategies) is difficult because studies featuring situation and selection are sparse. Hence why Webb et al. (2012) could not calculate meta-analytic estimates for these strategies. However, studies have emerged showcasing the circumstances where situation selection or modification are especially effective. For instance, situation selection is particularly effective for individuals who struggle to regulate their emotions (Webb et al., 2018). They showed that situation selection was associated with greater subjective well-being, happiness, and positive affect and lower negative affect and depression. Further, these relationships were much stronger for those with higher reactivity and lower emotion regulation efficacy. Another experiment showed that individuals with low emotion regulation efficacy opted out of situations featuring negative stimuli more often, suggesting proactive management of emotions and self-awareness of their regulation skills (Rovenpor et al., 2013). Situation selection and modification strategies also help individuals effectively employ later emotion regulation strategies, like attentional deployment, cognitive change, and response modulation. Van Bockesteale et al. (2019) showed situation selection was an effective strategy for downregulating negative experiences and the strategies following their modification was contingent on the perceived impact and effectiveness of their change. Further, Thulliard (2021) found situation selection reduced the impact of negative emotions in an experiment by an estimated 20 percent, suggesting less effort required for later emotion regulation strategies. Thus, the ordering of different regulation strategies can also impact the 73 effectiveness of later strategies, suggesting another benefit to proactive emotion regulation. Modifying one’s emotions through attentional deployment or reappraisal, for instance, can be much easier or effective when opting into a less intense situation. Contemporary emotion regulation choice work agrees with this assertion, suggesting that individuals’ choices of emotion regulation strategy – and its effectiveness – depends on an individual’s ability to identify and change a situation before engaging in other strategies (Sheppes et al., 2014; Aldao et al., 2015). Distinct Latent Profile Combinations Lastly, the application of approach-avoidance and proactive-reactive distinctions to form profiles suggest four distinct groups. These four groups are labeled approach-proactive, avoidance-proactive, approach-proactive, and approach-reactive. Based on prior discussed theory and results used to compare dimensions, the following segments are dedicated to characterizing the expected behaviors, resources, and outcomes for each profile. Each profile will also feature examples of this approach based on previous qualitative and quantitative emotion regulation and job crafting research. This conveys that, despite no previous integration efforts, the overlap between job crafting and emotion regulation is incredibly apparent and prevalent across research. Moreover, these segments hope to support future use of approach-avoid and proactive-reactive distinctions in future organizational science research by showing the utility of viewing these behaviors through this lens. This leads to the first hypothesis: H1a: Four profiles will emerge based on proposed approach-avoidance and proactive- reactive distinctions. H1b: Two profiles will be significantly higher than the remaining two profiles on approach temperament. 74 H1c: Two profiles will be significantly higher than the remaining two profiles on avoidance temperament. H1d: Two profiles will be significantly higher than the remaining two profiles on proactive personality. Approach-proactive The approach-proactive profile is characterized by emotion regulation and job crafting behaviors centered around approaching real or imagined stimuli. Further, this profile’s expected behaviors reflect motivated changes to one’s environment that facilitate the achievement of desired emotional states. This group’s behavior is driven by their anticipating of the workday’s emotional demands and the goals formed to proactively meet these demands. These employees are in-tune with the demands of their workplace and employ job crafting and emotion regulation to reach high levels of work-related well-being and functioning. Specifically, this profile is expected to manage resources in anticipation to emotionally taxing work events by job crafting (i.e., maximizing social and structural resources) or emotion regulation (i.e., opting into situations, modifying situations, or employing strategies that promote desired emotions) that energize their performance. For instance, a nurse may wish to maximize their positive impact and emotions at work. They may challenge themselves by volunteering to work hospice/ICU units and set a personal goal to spread cheer or comfort to patients because this makes the nurse feel happy and accomplished. This reflects an approach-oriented strategy because volunteering to work in a different unit reflects expansion of work-related roles and demands, in alignment with approach-oriented crafting. There are also approach-oriented task, skill, and relational crafting behaviors reflected in these behaviors, which make managing emotional demands easier over time. Approach-oriented situation selection is also represented considering their work promotes 75 positive emotions through interactions with patients or the ability to savor the positive impact they have on patients’ lives. However, this goal also includes negative aspects like seeing patients die, watching their family grieve, and responding to emergencies suddenly and quickly. An approach-proactive individual would consider these circumstances too and prepare to manage these demands, despite their unpleasantness. Because of this, approach-proactive employees are willing to expose themselves to both hedonic and contra-hedonic work circumstances while goal striving. The nurse’s behaviors also exemplify proactivity because they are self-initiated changes to their environment that impact their emotions and the pursuit of happiness and accomplishment is occurring in anticipation of non-presented stimuli. Several examples of proactive-approach behaviors can be found in qualitative job crafting and emotion regulation studies. One excellent example is from Berg’s (2010) job crafting content study: “I often liken teaching to being a musician because … as soon as I enter that classroom, [Guitarist Gary] the performer is on. … It’s entertaining education: edu-tainment. And I’m doing that all the time. I’m trying to make class time interesting and fun and entertaining because research on education demonstrates when people are in a good mood, they tend to learn better and learn more! I remember when I was performing and I had my rock band and my other bands, the high which I got from playing in front of people was very similar to the high which I get from performing teaching in front of people.” This quote showcases how one participant employs approach-oriented task and relational crafting whenever he teaches music. It can be argued this also reflects a shared form of cognitive crafting and reappraisal because he compares teaching to being a rock band performer, which he enjoys significantly. Situation modification is also highlighted because he seeks to make class time more interesting and fun, which he notes helps him and his students achieving a good mood. Another example comes from Haver (2014), who interviewed hospitality leaders: “I’ve been given feedback that everyone knows when I’m in a bad mood. That has been an important lesson for me, and I chose to start with a personal coach 76 at an early stage so I could learn. It was entirely about having to clean up my own backyard before starting to build in others, in a way. (L4)” In this example, one leader describes how they hired a coach to help them manage their mood so that it is not obvious whenever they are upset. This situation is incredibly different from the quote above, but still showcases an approach-proactive strategy. The leader proactively acknowledges that their bad mood may be demoralizing or counterproductive at work, which interferes with their goal of coaching others. This reflects approach-oriented relational and skill crafting because the leader is expanding their emotion regulation skills to optimally manage and develop their employees. There is no specific emotion regulation strategy showcased here, but it can be argued this a unique form of situation selection and modification considering that the leader is learning how manage work situations with their coworkers. Despite no clear form of emotion regulation displayed, the leader is proactively learning how to manage their emotions to anticipated work events. This profile should thus be characterized by job crafting and emotion regulation behaviors that reflect proactive and approach-oriented motives and behavior. Because this profile is approach-oriented, high levels of job crafting behaviors (i.e., structural, task, relational, cognitive) are expected. Specifically, approach-oriented job crafting behaviors will be exceptionally high for this profile because job crafting is a proactive behavior and because this group is expected to alter their situation and circumstances to reach their desired affect at work and meet subsequent emotional demands. Another expectation is that this profile has access to several resources and face high demands. This aligns with Makigangas (2018), who showed that approach-oriented crafting overwhelmingly represents job crafting behaviors. The anticipated emotion regulation behaviors for the approach-proactive profile should reflect these individuals’ propensity to enact changes to their environment to strive toward desired emotional states. Thus, 77 this profile is expected to display high levels of situation selection and situation modification. Because this group is concerned with proactive approaches to manage emotional demands, the need for other strategies will be lower. This will be especially true for response focused strategies because these individuals will be opting into situations they wish. This profile is expected to face high demands. Approach-orientations are characterized by the willingness to take on more demands because these individuals enjoy developing themselves and watching themselves grow. It follows they will take on greater responsibilities and roles at work, which would increase the level of emotional demands they face. However, this group should be ready to face these demands since they are expected to use job crafting to acquire their necessary resources. In other words, these individuals will be continually crafting their jobs to foster the social support and autonomy necessary to prepare themselves for these emotional demands. Lastly, this suggests that this group should also experience high levels of engagement and low level of emotions because they have the sufficient resources to meet demands. This leads to the following set of hypotheses: H2a: The approach-proactive profile will report high levels of approach-oriented task, relational, cognitive, and task crafting and low levels of avoidance-oriented task, relational, cognitive, and task crafting. H2b: The approach-proactive profile will report high levels of situation selection and selection modification, moderate levels of attentional deployment, cognitive change, and low levels of response-focused strategies. H2c: The approach-proactive profile will report high levels of emotional job demands relative to other profiles. H2d: The approach-proactive profile will report high levels of social support and autonomy relative to other profiles. H2e: The approach-proactive profile will report high levels of work engagement and low levels of emotional exhaustion relative to other profiles. 78 Avoidance-Proactive This profile is similar to the approach-proactive profile, but their behaviors are instead oriented toward avoiding undesirable tasks and situations featuring high emotional demands. Moreover, this group similarly anticipates the demands ahead of them in each workday but organize their tasks and demands to avoid encountering upsetting and frustrating work events. These work events can range between completing boring or frustrating work, settling conflict, or encountering a colleague they do not like. This could occur in several different forms. For instance, individuals may offload undesired work responsibilities to others or eliminate tasks altogether. An employee could proactively set strict communication boundaries (e.g., not responding to e-mails after work or refusing to discuss certain topics with coworkers) or refuse to handle certain clients. These individuals proactively regulate emotional demands according to their anticipated reactions and are expected to be particular with the situations they encounter and their circumstances. In other words, if they engage in a situation, it will how they like it. Because of this, these individuals are not anticipated to encounter situations that offer anything more than hedonic well-being at work. This group is only concerned with ensuring they feel pleasant at work and nothing more. For this reason, this profile’s expected behaviors should feature avoidance-oriented task, relational, cognitive, and skill job crafting because these individuals are concerned with moving away from emotional job demands. This aligns partly with Makiganga’s (2018) job crafting profile paper that ‘passive’ crafters only employ ‘demand reduction’ job crafting, Examples of avoidance-proactive regulation is plentiful across emotion regulation and job crafting studies. Consider this quote from Bruning and Campion (2018): “Some of my prospects, they are not, let’s say, the greatest people in the world. They’re not the nicest people in the world. They’re not the friendliest people. In 79 my mind, I get very stressed out by that because I think they don’t like me, or they’re judging me for X number of reasons. Anyway, they just cause a lot of stress in my job and one way I kind of job craft on that is maybe the infrequent visits I do have ... I don’t try to meet with them frequently.” This quote encapsulates the avoidance-proactive approach perfectly. This participant is incredibly attuned to their (negative) feelings toward their colleagues. Accordingly, they minimize their interaction with their colleagues by visiting infrequently and avoiding their colleagues whenever they do visit. This represents a form of avoidance-oriented relational crafting because they seek to separate themselves from their colleagues as much as possible. Moreover, this represents emotion regulation because the anticipated feelings of anxiety and stress drive this participant’s purpose for situation selection. Another example of avoidance- proactive approach are these excerpts from Lopez (2006, p. 150), who collected qualitative and observational data from two elderly-care nursing homes: “Despite their obvious affection for the residents they cared for, workers at the Lakes [nursing home] could rarely afford to venture far in the direction of serious topics like feelings of loneliness or grief because these are not subjects one can discuss in 10 minutes and then wrap up in time for a neat exit. Managing the interaction from start to finish, keeping things “light” so that timely exit would be possible … was thus a crucial, even self-protective form of emotional labor made necessary by the Lakes’ organizational shortcomings” “Indeed, sticking to safe, superficial topics was a crucial coping mechanism ... Conversation was generally limited to topics such as the weather outside, what was for lunch, what the resident was watching on television, or directive talk about the job at hand. As soon as the job was done—resident toileted, dressed, or fed—the aide or housekeeper delivered a cheery “OK, you’re all set. I’ll see you later” and was gone.” These excerpts highlight how proactive-avoidance approaches do not necessarily reflect disengaged, bitter, or burnt-out workers. In this example, note how emotional demands are managed through relational and task job crafting alongside situation modification emotion regulation. As noted, workers did not harness negative attitude toward patients, but chose to limit 80 their time only to whatever necessary to finish rounds with residents. Conversations were kept superficial and tasks only to what was necessary. This reflects another primary feature of the avoidance-proactive profile, which is the strict setting of boundaries and management of their situations. Despite the opportunity to acknowledge their resident’s limited social interactions and relieve some feelings of isolation, these workers kept a strict boundary between them and residents by focusing only on their tasks. Despite the worker’s approach being an efficient method to complete their jobs, the authors also note how this a protective form of emotional labor via situation modification. Workers anticipated residents desire to express feelings of sadness, grief, or other demanding emotions and limited their conversation to ensure they do not get caught having to manage these feelings themselves. The behaviors of the avoidance-proactive group should thus reflect behaviors that alters one’s environment in anticipation of anticipated stimuli, but also represent a movement away from undesired emotional states. Accordingly, this profile’s job crafting behaviors should be high and characterized by avoidance motives. This is slightly contradictory to Makingangas’s paper because avoidance-oriented job crafting was uncommonly reported. However, this study will expand their investigation by incorporating specific forms of job crafting that do not just concern minimizing work task demands. Rather, this profile is expected to manage emotional demands by changing the tasks they complete, their work relationships, what they are willing to learn, and how they think about their job. Specifically, this group should report lower emotional demands as a function of these specific forms of avoidance crafting. However, this group is also expected to have low social support resources but high autonomy. The high autonomy is expected because these individual’s goals will be to maximize their ability to craft their jobs the way they desire – which is to face the emotional demands they wish to face and minimize all 81 others. Social support will be low because these individuals are simultaneously contracting from their jobs and not working to generate any resources. Lastly, this profile is expected to face low levels of engagement and emotional exhaustion because they are encountering few emotional demands, suggesting they will become exhausted from repeated effort expenditures but also not have much work to keep them engaged. This leads to the following hypotheses: H3a: The avoidance-proactive profile will report low levels of approach-oriented task, relational, cognitive, and task crafting and high levels of avoidance-oriented task, relational, cognitive, and task crafting relative to other profiles. H3b: The avoidance-proactive profile will report high levels of situation selection, low levels of selection modification, attentional deployment, and cognitive change, and moderate levels of response-focused strategies relative to other profiles. H3c: The avoidance-proactive profile will report low levels of emotional job demands relative to other profiles. H3d: The avoidance-proactive profile will report low levels of social support and high levels of autonomy relative to other profiles. H3e: The avoidance-proactive profile will report low levels of work engagement and emotional exhaustion relative to other profiles. Approach-reactive The approach-reactive profile can be thought of as the “opportunists.” This group is drawn to positive emotions at work and will reactively shift their attention toward or work to reappraise stimuli to achieve these emotions. However, this group could also engage in counter- hedonic regulation if deemed appropriate, like empathizing with upset or sad colleagues. This is because this group’s approach to manage emotional demands is to capitalize on whatever present stimulus helps them reach their regulation goals. However, it should be emphasized that these individuals do not necessarily initiate proactive changes to their environment or enter their job with a plan to manage expected emotional demands. This can be either because the individual is not proactive themselves, the work environment does not provide sufficient latitude to proactively react to these events, or the events encountered are not controllable or predictable. However, just because these individuals do not employ proactive strategies to manage demands 82 does not mean they cannot flourish at work. As shown by the coping literature, proactive coping and reactive coping reflect two different but effective strategies (Schwarzer, 2008). This group is expected to exhibit coping and management of demands via reactive, adaptive strategies as opposed to proactive strategies. One comparison to this group may be Gabriel’s (2015) non- regulator group, who exhibit low levels of emotional labor but also report high levels of engagement and job satisfaction. This non-regulator profile notably held high levels of positive affectivity, suggesting this group is not interested – or does not need to engage – in managing their emotions because they already have a high match with expected demands. The approach- reactive group may already perceive a match with their desired affective states and their emotional demands or reappraise situations positively to achieve this fit. An example of an approach-reactive approach is an employee who improvises or customizes their tasks in a way they enjoy, like flight attendants who use their flight safety prompt to entertain passengers and inject humor into an otherwise standard script. This is reactive in that current demands are being the target of management and the changes are occurring on the fly. Examples of reactive-approach strategies are common across qualitative emotion regulation and job crafting studies alike. Note that some examples may showcase job crafting as well. As discussed earlier, some forms of job crafting can be used reactively despite representing a form of proactive behavior (Parker & Bindl, 2011). Consider this quote from Bruning and Campion (2018): “The best way to deal with stress is to just kind of laugh it off. In my opinion. You know, I’m probably the wildest loudest one here as far as that goes ... Laughing makes me feel better. Telling a joke or something funny. Look on the brighter side of things ... Involve other people. We are a very close group.” This quote captures elements of situation modification and reappraisal, in addition to relational crafting. This employee uses humor to reappraise their stress and ‘look on the brighter 83 side of things.’ Using humor can also be considered situation modification considering they mention involving others to spread positive affect and diffuse their colleague’s negative affect too. The emphasis of involving others also suggests this is a form of relational crafting and helps in forming the close relationships in their group. Below is one example from Judges (2021), who collected example emotion regulation behaviors on court judge’s emotion regulation: "Another participant suggested that ‘frequently let[ting] them [the parties] vent their emotions is helpful’." “When I have a disagreement with someone, I focus my attention on something positive to prevent myself from getting upset." These examples both showcase approach-oriented behaviors because they exhibit reactions to current demands, like an upset witness or nursing home resident. The first showcases an example of temporary situation modification because the judge allows parties to vent their emotions in the aim of reducing emotional demands from upset clients. This example also represents task crafting because the judge allows them to vent (to them presumably, but this is not clear), which expands the judge’s demands and tasks temporarily to trade managing more intense emotional demands. Despite situation modification being categorized predominantly as proactive, this change is reactive because the change is temporary and in response to current demands. The second example is also from a court judge, who employs attentional deployment toward positive stimuli whenever they disagree with someone else. This example also reflects relational crafting considering that judges are seeking to remain professional and look impartial in the courtroom. The expected behaviors for this profile should thus lean toward approach-oriented behaviors that are better suited for managing immediate demands. Considering job crafting is a proactive behavior and can require effort to enact permanent environmental change, the reliance 84 of job crafting will be lesser. Although, as noted by the examples, some forms of job crafting like relational may be employed reactively. An interesting exception may be cognitive crafting because it only requires changing the ‘internal’ environment, which can be done reactively or proactively. Nevertheless, enacting job crafting behaviors requires monitoring, attention, and continued proactive goal setting and striving to accomplish long-term goals (Bruning & Campion, 2018; Zhang & Parker, 2019). The emotional demands this group faces is likely going to be high considering these individuals are open to approaching desirable situations, suggesting they are unlikely to turndown opportunities presented to them. However, their resource pool will be moderate. Despite not being high on proactivity, they will still enact job crafting behaviors when possible. But, job crafting requires significant planning and anticipation, which this profile does not engage in. So, their resource crafting efforts may not be totally effective and thus stymied by their lack of proactivity. Because of the mismatch between emotional demands and resources, this profile is expected to be engaged at work but also moderate levels of experience emotional exhaustion. H4a: The approach-reactive profile will report high levels of approach-oriented task, relational, cognitive, and task crafting and low levels of avoidance-oriented task, relational, cognitive, and task crafting. H4b: The approach-reactive profile will report low levels of situation selection and selection modification, high levels of attentional deployment and cognitive change, and low levels of response-focused strategies. H4c: The approach-reactive profile will report high levels of emotional job demands relative to other profiles. H4d: The approach-reactive profile will report moderate levels of social support and autonomy relative to other profiles. H4e: The approach-reactive profile will report high levels of work engagement and moderate levels of emotional exhaustion relative to other profiles. Avoidance-reactive The final profile reflects those who are not proactive and try to minimize their emotional demands by ignoring or avoiding them. In other words, these individuals start their workday 85 without consideration of their emotional demands and plan to eliminate demands as much as possible. These individuals may preserve some resources by avoiding situations that upset, but, without the anticipatory goal setting and striving proactive employees have, this group is easily overwhelmed by demands they fail to prepare for. Avoidance-reactive individuals can work in many different ways. For instance, they may say no to additional responsibilities or distance themselves – physically or cognitive - from immediate tasks whenever possible. This aligns with Makiganga’s (2018) passive job crafter group, who only employ task reduction behaviors. Demerouti and Peeters (2017) similarly found that minimizing demands is a commonly reported form of job crafting, especially when demands are high, that leads to increased burnout (Demerouti, 2014; Petrou et al., 2012). This group may reflect those who rely on maladaptive coping strategies, akin to the ‘safety behaviors’ that socially anxious individuals use to avoid social interaction. However, unlike the proactive group, this group’s reactive approach will not mitigate demands because cannot adeptly forecast demands or enact the changes necessary to manage or eliminate them. Examples of the reactive avoidance-approach are abundant in the emotion regulation space. This is because this approach aligns most closely to the maladaptive ‘surface actors,’ who are considered ineffective regulators. Consider these quotes from Lopez (2006) and Smith (2018): "Now, look, Mr. Rice. I don’t mind cleaning up, that’s my job, but I’m not going to do it while you yellin’ at me like that. If you can’t talk to me civilized, then I’m goin’ out and waiting in the hall till you ready to behave." “Somebody might say something nasty to you and [you] just kind of either turn around and walk away or you hold your breath or say things under your breath or in your mind, so you don't explode.” 86 In both these quotes, note how the employees’ reactions involve their removal or distancing from current demands - emotionally or physically. Like the reactive-approach profile, a proactive strategy like situation selection may still be utilized by this group, as shown in the first quote. The second quote aligns closer with how this group is expected to manage demands they cannot avoid. This is because they are not equipped to manage their demands or default to masking their emotions or suppression - opposed to working to change their internal state. Both quotes also showcase job crafting. In the first example, the employee threatens to abandon their tasks if they are not treated with respect reflecting avoidance-centered task minimization. The second quote also arguably reflects a form of relational crafting. The employee chooses to avoid confrontation and mask their frustration rather than bring up their concerns and issues to their colleague. The reactive-avoidance profile’s job crafting behaviors are expected to be very low because these individuals are not apt to initiate changes to their environments or to engage with their jobs. If these individuals were to employ job crafting, it would be primarily avoidance oriented and centering around minimizing work-related tasks, relationships, skill development, and maximizing psychological distance from their job through cognitive crafting. This aligns with research showing that some individuals tend to employ avoidance-oriented crafting behaviors without other forms of job crafting. The emotion regulation behaviors of this profile will encompass strategies apt for skirting demands or maladaptive management of these demands. This group will be expected to exhibit some situation selection given their primary goal is minimize tasks and avoid situations featuring undesired emotions. As mentioned before, situation selection can align with both approach and avoidance strategies but are most effective for reducing negative emotions but not increasing positive emotions. Because of this, this group 87 will likely opt for situation selection because it aligns with moving away from unpleasant circumstances. Diefendorff et al. (2008) found avoiding and removing themselves from situations was employed most often to manage coworker and customer interactions. This group likely opts for response-focused strategies because it helps maximize the distance between their feelings and current demands. Therefore, these employees are expected to opt for suppression and masking strategies. While the emotional demands of the avoidance-reactive group may be partly mitigated by their avoidance tendencies, they still fail to consider the emotional demands they cannot change. Instead, this group will continue encountering demands they fail to anticipate and try managing them through avoidance strategies as much as possible. These individuals will also have little to no resources because they do not increase their resource pool and continue to employ strategies which reduce their ability to develop these resources, like minimizing social interaction at work. This will finally result in poor well-being indicators given that these individuals will be eliminating their opportunities to engage themselves at work and to develop the resources necessary to meet the demands they will still face. This leads to the following set of hypotheses: H5a: The reactive-avoidance profile will report low levels of approach-oriented task, relational, cognitive, and task crafting and moderate levels of avoidance-oriented task, relational, cognitive, and task crafting. H5b: The reactive-avoidance profile will report low levels of situation selection and selection modification, and cognitive change, and high levels of attentional deployment and response-focused strategies. H5c: The reactive-avoidance profile will report moderate levels of emotional job demands relative to other profiles. H5d: The reactive-avoidance profile will report low levels of social support and autonomy relative to other profiles. H5e: The reactive-avoidance profile will report low levels of work engagement and high levels of emotional exhaustion relative to other profiles. 88 Study 1 The purpose of this study was to observe the emergent profile structure from a latent profile analysis using job crafting and emotion regulation behaviors as profile indicators. This study further tested Hypothesis 1, which centered on the expected numbers of profiles extracted and their characteristics. This study also provided a baseline profile structure for replication during Study 2. As discussed earlier, profile replication is an important step when conducting latent profile analyses due to the exploratory nature of this analysis. Profile replication establishes the validity of observed profile structures and the chance that the profiles interpreted are meaningful. 89 Participants and Procedure Method – Study 1 Data were collected through Amazon’s Mturk and CloudResearch participant pool. Tein et al.’s (2013) recommendation is a sample of at least 500 participants and a minimum of 10 indicators for adequate power when determining class membership, based on an assumption of high-profile separation (d = 0.8). First, a screening survey was posted on both Mturk and CloudResearch sites that compensated participants 5¢. The screening survey asked participants their age, if they worked a part-time job that involves face to face interaction, their average hours worked a week, their average hours worked a week involving face-to-face interaction, and their perception of whether these interactions often demanded emotion regulation. The survey further included a captcha check, a duplicate response identifier, and fraudulent score estimate offered (offered by Qualtrics software) to flag potential bot and duplicate responses. There were several inclusion criteria for the primary study based on the screening survey. Participants must have reported working at least 20 hours on average a week and 10 hours of face-to-face interaction at their job. Respondents were further filtered if their location was outside of the U.S. and if their response was flagged as a duplicate, using Qualtrics’s location coordinates and survey software that analyzed browser, IP address, and location information to flag duplicate responses. CloudResearch participants were also asked to answer, “the last time you had a meal, what did you have?” Qualitative responses have been shown to accurately identify if the respondent is a real person or bot because these questions involve a level of understanding and processing that bots fail to reach. Therefore, blank responses and responses which only provided a time (e.g., “8 am” or “9:30”) were screened out. A total of 2,359 responses were collected using the screening 90 survey, 1,521 (64.5%) responses were collected from Mturk and 838 (35.5%) from CloudResearch. Of these responses, 757 participants (32.1%) were eligible for Study 1 based on the previously mentioned criteria and validity checks. The selection rate for Mturk respondents (26.1%) was notably lower than the rate for CloudResearch respondents (43%). The 757 eligible participants were then sent the study survey which compensated $3 for completion. A total of 581 responses were collected from participants, for a response rate of 76.8%. The same evaluation criteria as the screening survey were applied to account for respondents who provided inaccurate information on the screening survey. A filter was used to remove respondents who reported less than 20 hours worked a week on average or less than 10 hours of face-to-face interaction at work, which reduced the total to 505 participants (n = 76 removed). Duplicate responses were eliminated, which occurred due to submission error from participants or software error (n = 8). An additional 11 participants were dropped due to being flagged as fraudulent or bot responses (based on Qualtrics’s fraud detection score). Lastly, careless responding analyses were conducted utilizing R’s ‘careless’ package. According to Curran’s (2016) recommendations, responses were reverse scored and then average response string, intra-individual response variability (IRV), even-odd consistency, and Mahad’s D statistics were computed. Average string length calculates the longest length of consecutive responses; IRV is the standard deviation of responses across a set of consecutive item responses (Dunn et al., 2018); even-odd consistency is the correlation between corresponding even and odd numbered items in a scale (Johnson, 2005); Mahad’s D is a method of multivariate outlier analysis (Maniaci & Rogge, 2014; Meade & Craig, 2012). Each analysis provided one statistic per test. Because strict cut-off values do not exist for these scores, scores two standard deviations above the sample mean for each careless response statistic were flagged. Responses from 91 participants flagged more than once were removed, to reduce the probability of excluding a valid response when relying on a single metric (Curran, 2016). In total, 17 respondents were removed due to careless responses. This resulted in a final sample of 469. Summaries of these participants’ demographic information can be found in Tables 1 – 3. Participants were overrepresented by women (57%), predominantly White (81%), had obtained a Bachelor’s degree (47%), worked within the education or healthcare industry (13%), and whose job role is a non-supervising employee (47%). Measures Job crafting Job crafting was measured using Bindl et al.’s (2019) job crafting measure. This scale measures promotion and prevention styles of four types of crafting (task, skill, social, and cognitive), for a total of eight job crafting dimensions. Each job crafting type was measured using seven items - four promotion-oriented items and three prevention-oriented items. The total number of items was 28 (refer to Appendix A for items). Respondents were instructed to answer questions regarding the extent to which they performed job crafting behaviors on a scale from 1 (not at all) to 5 (very much). In order to accurately capture whether participants used job crafting to manage work-related emotional demands, the question stem was adapted to specify that these behaviors were performed to “help experience positive and/or display positive emotions at work” and that “the behavior may have occurred either in the moment as necessary or over a longer period of time.” The alpha values for each dimension ranged from .58 to .92. 92 Emotion Regulation Strategies Emotion regulation strategies were measured using items from several emotion regulation measures. The first measure was the emotion regulation survey (Diefendorff et al., 2008), which measured situation selection, situation modification, attentional deployment, cognitive change, and response modulation strategies across 14 items. This measure was developed for Diefendorff et al.’s (2008) study, which investigated the frequency of which each emotion regulation strategy was used. Diefendorff et al. (2008) recruited 7 doctoral students and had them rate and categorize these emotion regulation behaviors based on their representation of Gross’s (2007) emotion regulation strategies from the process model of emotion regulation. The behaviors rated were also included specifically because they could occur at work. Altogether, the measure included 2 situation selection, 2 situation modification, 3 attentional deployment, 5 cognitive change, and 2 response-focused items. Webb’s (2017) situation selection items was included to increase the number of situation selection items. This measure was composed of 6 items that measured situation selection solely. Schutte’s (2009) emotion regulation survey was included because it measured every emotion regulation strategy except for response modulation, and supplemented Diefendorff’s scale which featured a few items for each strategy. Lastly, Gross and John’s (2003) emotional labor scale of cognitive reappraisal and suppression (i.e., deep acting and surface acting) was also included to supplement the total number of emotion regulation items. Refer to appendix B through E for items. As mentioned earlier, each emotion regulation dimension was measured using item composites across these measures. Situation selection was measured by 13 items (3 from Diefendorff et al., 2008, 6 from Webb et al., 2017, and 4 from Schutte, 2009), situation modification by 5 (1 from Diefendorff et al., 2008 and 4 from Schutte, 2009), attentional deployment by 7 (3 from Diefendorff et al., 2008 and 4 from 93 Schutte, 2009), cognitive change by 14 (4 from Diefendorff et al., 2008, 4 from Schutte, 2009, and 6 from Gross & John, 2006), and response modulation by 6 (2 from Diefendorff et al., 2008 and 4 from Gross & John, 2006). The purpose for the composites was two-fold. The first was to increase the number of total emotion regulation behaviors represented across strategies, given the representation of strategies across scales was varied across measures. The other reason was to ensure that approach-avoid dimensions within strategies were adequately represented. The alpha reliabilities for the composites ranged from .75 to .91. In order to investigate whether these scales further measured a single dimension of emotion regulation or two (approach vs. avoidance orientation), confirmatory factor analysis (CFA) was conducted for the situation selection and attentional deployment composites. The purpose of these analyses was to investigate whether the assertations that approach and avoidance dimensions apply to these emotion regulation strategies was supported or not. CFA results showed the single factor situation selection composite exhibited poor overall fit, χ2(54) = 459.92, CFI = .78; RMSEA = .13; SRMR = .09. However, the two-factor model which separated situation selection items by approach-avoidance themes showed improved fit and met satisfactory fit standards, χ2(53) = 178.06, CFI = .94; RMSEA = .07; SRMR = .05. Thus, the situation selection composite variable was split into the approach and avoidance dimensions. For attentional deployment, the single-factor model exhibited good fit altogether, χ2(14) = 60.29, CFI = .94; RMSEA = .09; SRMR = .05. The two-factor model did not improve nor change overall fit, χ2(13) = 60.29, CFI = .94, RMSEA = .09, SRMR = .05. Based off these results, analyses were conducted using the single factor attentional deployment composite variable. Proactive personality. Proactive personality was measured using Bateman and Crant’s (1993) 17-item measure. The reliability of this scale was .91. Responses were recorded on a 5- 94 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Refer to Appendix F for items. Approach-Avoid Temperament (AAT). Motivational orientation was measured using Elliot and Thresh’s (2010) 12 item measure of approach and avoid temperament. Six items measured approach temperament and 6 items measured avoidance temperament. The reliability of this scale was .81 for approach temperament and .88 for avoidance temperament. Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Refer to appendix G for items. BIS/BAS. Dispositional behavioral inhibition and activation system sensitivity was measured using Carver and White’s (1994) measure. BIS using 7 items (α = .83). BAS has three factors, but only the BAS reward responsiveness dimension was included. This is because the BAS drive and fun seeking dimensions include aspects of proactivity and future-orientation that overlap with aspects of proactive personality. BAS was measured using the 5 items from the reward responsiveness dimension (α = .73). Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Display Rules. Display rules perception was measured through Diefendorff et al.’s (2003) “Emotion Display Expectations at Work” scale. This measure was a total of 8 items and the reliability was .77. Responses for every scale were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Emotional Demands Emotional demands were measured using Strazdin’s (2000) emotional work scale and Xanthapolous et al.’s (2012) emotional demands measure. Strazdin’s (2000) measure captures 95 three dimensions of emotional demands: [resultant from] companionship, helping, and regulating. Each factor was measured using 7 items. The reliability for companionship, helping, and regulating factors were .88, .91, and .96, respectively. Xanthapolous et al.’s (2012) measure captures general emotional demands from work and is composed of 6 items (α = .89). Responses for every scale were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Analyses The total number of indicators included was 14, including six emotion regulation strategy indicators (i.e., situation selection – approach, situation selection – avoid, situation modification, attentional deployment, cognitive change, and response modulation) and eight job crafting indicators (two approach and avoidance factors for relational, task, skill, and cognitive job crafting). The latent profile analyses were conducted using R studio using the MCLUST and tidyLPA packages. Analyses were conducted according to Nylund et al.’s (2007) guidelines, which was to first specify two latent profiles and continue increasing the number of latent profiles until model fit does not improve. Model fit was evaluated through several fit indices, in accordance with Foti et al. (2012). The specific indices were log-likelihood (LL), Aikake information criterion (AIC), Bayesian Information Criterion (BIC), sample size adjusted BIC (SA-BIC), bootstrapped likelihood test (BLRT), and entropy. Lower values of LL, AIC, BIC, SA-BIC, and higher entropy in comparison to other model solutions indicated better fit. BLRT tests should be significant (p < .05) because this suggests the k+1 profile solution did not significantly improve fit beyond the k profile solution based on the log-likelihood value. Lastly, fit was assessed using theoretical rationale. Auxiliary variables (i.e., antecedents and outcomes) were modeled using the automatic three-step approach (Asparaouhov & Muthen, 2013). The first 96 step was to determine the number of profiles that best fit the data. The second step was to assess profile membership (i.e., the profile to which an individual most likely belongs to) after accounting for the probability any given individual belongs to any other profiles (Morin et al., 2011). The final step was to assess auxiliary variables according to profile solution, most likely class membership, and classification error rate (Wang & Hanges, 2011). TidyLPA’s get_estimates() function was used to calculate mean, variance, and standard error for each profile’s indicators by profile in a given model. Follow-up analyses were conducted to test the profile’s hypothesized approach-avoidance and proactive-reactive scores. First, profile means of approach-avoidance temperament and proactive personality was tested using one-way ANOVA. The same process was used to examine group differences across auxiliary variables. To investigate relative differences across profiles, dummy variables were created for each profile. A t-test was then conducted to investigate whether a profile’s score on a variable was significantly different compared to every other profile. Standardized mean differences were also calculated to assess mean difference effect size. A summary of correlations, means, and standard deviations can be found in Table 4. 97 Latent Profile Analysis Results – Study 1 Results suggested the best solution was a four-profile model. Table 5 contains LL, AIC, BIC, SA-BIC, and BLRT values for this analysis. The rationale for this solution was based on several indicators of fit in conjunction with theoretical and practical rationale. First, the model was evaluated for any errors or theoretically implausible profiles - which were not present. Second, AIC, BIC, SA-BIC, and BLRT values were evaluated. The four-profile model had a significant BLRT test while the five-profile model did not, suggesting that a four-profile solution significantly improve fit beyond the previous model, while the five-profile solution did not. The four-profile model also displayed the lowest AIC (11,914.91) and SA-BIC (12,089.75) values, and second lowest BIC value (12,657.86) behind the three-profile model. Lastly, the four-profile model had the second highest entropy value (0.77) behind only the five-profile model. H1a was thus supported. Profile Descriptions Profiles were then compared across indicators by conducting t-tests using dummy-coded group membership (0 = respective profile, 1 = remaining profiles) to determine which indicators were reported higher or lower relative to other groups. Hedge’s g was calculated to observe the extent a profile’s indicator mean deviated from other profiles. Effect size guidelines suggest effect sizes ranging from 0.2 – 0.3, 0.5 – 0.6, and 0.8+ represent small, medium, and large effect sizes, respectively. A summary of t-tests is presented in Table 6. Figure 2 shows a bar plot of the Hedge’s g for these tests by profile. The observed patterns across profiles’ indicators suggested unique strategies to emotional demand management. These emotion regulation behaviors are theoretically differentiated based 98 on whether they reflect an approach-avoidance behavior and/or proactive-reactive behavior. This distinction helps characterize these profiles’ behaviors by observing employees’ tendencies to rely on approach-avoidance oriented and proactive behaviors, and how these tendencies align with their personality. Approach-Avoidance oriented behaviors are labeled as such in results and discussion. Proactive behaviors can be identified by those that alter external environments and features, are future oriented, and better suited for managing non-present demands and resources (e.g., avoiding unpleasant tasks, making one’s environment more relaxing). Specifically, all job crafting behaviors (except for cognitive crafting), situation selection and selection modification behaviors are considered proactive. Reactive behaviors refer to behaviors rooted in the present, that manage current demands and resources, and alter internal environments and features (e.g., hiding frustration whilst helping a customer, concentrating on a pleasant photo in the office). Cognitive crafting, attentional deployment, cognitive change, and response-focused regulation strategies were considered reactive. Profile 1 represented 32.2% of the total sample and was the second largest group. Compared to the other 3 profiles, Profile 1 displayed significantly lower levels of approach- oriented relational crafting (M =2.99; t = 4.28, p < .001), approach-oriented skill crafting (M =3.67; t = 4.97, p < .001), avoidance-oriented skill crafting (M =3.6; t = 7.43, p < .001), approach-oriented situation selection (M =3.84; t = 9.71, p < .001), avoidance-oriented situation selection (M =3.62; t = 4.66, p < .001), situation modification (M =3.62; t = 8.12, p < .001), attentional deployment (M =3.75; t = 8.11, p < .001) and cognitive change behaviors (M =3.64; t = 6.52, p < .001). This profile also displayed significantly higher levels of avoidance-oriented relational crafting (M =2.87; t = -2.57, p < .001), approach-oriented task crafting (M =2.98; t = - 2.32, p < .001) and avoidance-oriented task crafting (M =3.25; t = -3.47, p < .001). This profile 99 did not significantly differ from the other profiles on approach-oriented cognitive crafting (M =3.41; t = .78, p = .43), avoidance-oriented cognitive crafting (M =3.39; t = .13, p = .90), and response-modulation behaviors (M =3.24; t = .40, p = .69). Profile 2 was the largest group and represented 32.6% of the total sample. Compared to the other three profiles, Profile 2 displayed significantly lower levels of avoidance-oriented relational crafting (M =2.11; t = 9.78, p < .001), avoidance-oriented task crafting (M =2.63; t = 7.65, p < .001), and significantly lower levels of response modulation (M =2.99; t = 4.96, p < .001). This profile also displayed significantly higher levels of approach-oriented relational crafting (M =3.68; t = -6.47, p < .001), approach-oriented skill crafting (M =4.28; t = -6.59, p < .001), avoidance-oriented skill crafting (M =4.25; t = -7.4, p < .001), approach-oriented situation selection (M =4.42; t = -9.16, p < .001), situation modification (M =4.12; t = -6.19, p < .001), approach-oriented cognitive crafting (M =4.00; t = -10.46, p < .001), attentional deployment (M =4.23; t = -6.34, p < .001), and cognitive change (M =4.04; t = -4.01, p < .001). Lastly, no significant differences were found for approach-oriented task crafting (M =2.74; t = 1.19, p = .23), avoidance-oriented situation selection (M =3.83; t = -.12, p = 0.9), and avoidance-oriented cognitive crafting (M =4.04; t = -0.91, p < .36). Profile 3 represented 24.7% of the total sample. Profile 3 displayed significantly lower levels of approach-oriented relational crafting (M =2.11; t = 8.41, p < .001), approach-oriented skill crafting (M =3.66; t = 4.22, p < .001), avoidance-oriented skill crafting (M =3.80; t = 2.44, p < .001), and approach-oriented task crafting (M =2.11; t = 9.38, p < .001). This profile displayed significantly lower levels of approach-oriented cognitive crafting (M =2.45; t = 18.96, p < .001) and avoidance-oriented cognitive crafting (M =3.11; t =4.05, p < .001). This profile displayed significantly higher levels of avoidance-oriented situation selection (M =3.95; t = -2.3, 100 p < .001). No significant differences were found for avoidance-oriented relationship crafting ( M =2.77; t = -.92, p = .36), avoidance-oriented task crafting (M =2.91; t = 1.9, p = .06), approach- oriented situation selection (M =4.13; t =.32, p =.75), situation modification (M =3.86; t =.77, p = .44), attentional deployment (M =3.94; t =1.86, p < .06), cognitive change (M =3.84; t = .86, p < .39), and response-modulation (M =3.25; t = .17, p < .87) strategies. Lastly, profile 4 represented 10.5% of the total sample. This profile showed significantly higher levels approach-oriented relational crafting (M =4.34; t = -8.57, p < .001), avoidance- oriented relational crafting (M =3.82; t = -9.18, p < .001), approach-oriented skill crafting (M =4.34; t = -3.63, p < .001), avoidance-oriented skill crafting (M =4.25; t = -3.47, p < .001), approach-oriented task crafting (M =4.27; t = -11.94, p < .001), avoidance-oriented task crafting (M =4.06; t = - 9.21, p < .001), approach-oriented cognitive crafting (M =4.27; t = -7.25, p < .001), avoidance-oriented cognitive crafting (M =3.95; t = -4.5, p < .001), avoidance-oriented situation selection (M =4.14; t = -3.59, p < .001), situation modification (M =4.17; t = -3.63, p < .001), attentional deployment (M =4.36; t = -5.02, p < .001), cognitive crafting (M =4.26; t = - 4.81, p < .001), and response modulation (M =4.2; t = -8.94, p < .001). No significant differences were found for approach-oriented situation selection (M =4.21; t =-1.06, p = .29). Profile Predictors and Outcomes Profiles were next compared across variables for hypothesis testing and exploratory profile characterization. Three one-way ANOVAs followed by post-hoc Tukey tests were conducted to test Hypotheses 1b, 1c, and 1d. Significant differences were found for approach temperament, F(3,465) = 33.04, p <.001, avoidance temperament, F(3,465) = 14.44, p <.001, and proactivity, F(3,465) = 49.29, p <.001 across profiles. These results suggest at least two group mean’s differences were significantly different within the profile factor. Refer to Table 6 101 for both omnibus ANOVA test and post-hoc Tukey comparison statistics for approach temperament, avoidance temperament, proactive personality, BIS, and BAS. Figure 3 displays a line graph of mean scores for each of these variables by profile. Approach temperament, avoidance temperament, and proactive personality Profile means of approach temperament from highest to lowest was profile 4 (M =4.24, SD = .4), followed by profile 2 (M =4.12, SD = .53), profile 3 (M =3.67, SD = .65), and profile 1 (M =3.6, SD = .57). No significant differences were found between profiles 4 and 2 (p = .57) , significant differences were found between profiles 4 and 3 (p < .001), significant differences were found between profiles 4 and 1 (p < .001), significant differences were found between profiles 2 and 3 (p < .001), significant differences were found between profiles 2 and 1 (p < .001), and no mean differences were found between profiles 1 and 3 (p = .73) .A pattern emerged where the profiles with the two highest means (profile 4 and profile 2) significantly differed from the remaining profiles but did not significantly differ from each other. Similarly, the profiles with the two lowest approach temperament (profile 3 and profile 1) did not significantly differ. Thus, Hypothesis 1b was supported. Profile means of avoidance temperament from highest to lowest was profile 4 (M =3.93, SD = .76), followed by profile 1 (M =3.39, SD = .86), profile 3 (M =3.24, SD = .97), and profile 2 (M =2.95, SD = 1.06). Significant differences were found between profiles 4 and 1 (p < .001), profiles 4 and 3 (p < .001), and profiles 4 and 2 (p < .001). No significant differences were found between profile 1 and profile 3 (p = .6), significant differences were found between profiles 1 and 2 (p < .001). No significant differences were found between profiles 2 and 3 (p = .07). Although the two lowest profile means (profile 2 and profile 3) did not differ, profile 1’s mean did not differ from profile 3. Further, profile 4 was significantly different from all other profiles 102 (and notably higher). Based on these post-hoc comparisons, the expected pattern of two profiles significantly higher – but not significantly different from each other - than the remaining two profiles did not emerge. Thus, Hypothesis 1c was not supported. Profile means of proactive personality from highest to lowest were profile 4 (M =4.34, SD = .28), followed by profile 2 (M =4.02, SD = .53), profile 1 (M =3.58, SD = .55), and profile 3 (M =3.45, SD = .6). Significant differences emerged for profiles 4 and 2 (p < .001), profiles 4 and 1 (p < .001), and profiles 4 and 3 (p < .001). Significant differences emerged between profiles 2 and profile 1 (p < .001) and profiles 2 and 3 (p < .001). No significant difference emerged between profiles 1 and 3 (p = .21) The two lowest profile means (profile 1 and profile 3) did not differ – similar to approach temperament. However, the second highest profile (profile 2) was significantly different from the two lowest profiles and the highest profile (profile 4). Similar to avoidance temperament, the hypothesized pattern did not emerge across profiles’ proactive personality. Thus, Hypothesis 1d was not supported. Activation system (BIS/BAS) Additional constructs underlying general approach-avoidance motivations were included to complement the approach-avoidance temperament measures. These constructs were activation system reactions. The behavioral activation system measures reflect approach motives while the behavioral inhibition system measures reflect avoidance motives. Figure 4 shows a bar plot of the Hedge’s g for these tests by profile. Profile means for behavioral activation from highest to lowest were profile 2 (M =4.34, SD = .48), profile 4 (M =4.3, SD = .37), profile 3 (M =4.04, SD = .63), and profile 1 (M =3.77, SD = .64). No mean difference was found between profiles 2 and 4 (p = .96). Significant 103 differences emerged between profiles 2 and 3 (p < .001), profiles 2 and 1 (p < .001), profiles 4 and 3 (p < .001), profiles 4 and 1 (p < .001), and profiles 3 and 1 (p < .001). Profile means for behavioral inhibition from highest to lowest were profile 3 (M =3.64, SD = .79), profile 1 (M =3.45, SD = .67), profile 4 (M =3.38, SD = .50), and profile 2 (M =3.32, SD = .94). No significant mean differences were found between profiles 3 and 1 (p = .2), and profiles 3 and 4 (p = .19). A significant difference was found between profiles 3 and 2 (p < .001). No significant mean difference was found between profiles 1 and 4 (p = .93), profile 1 and 2 (p = .43), and profile 4 and 2 (p = .97). Figure 3 displays a bar plot of the hedge’s g effect sizes for each of these variables by profile. Emotional Demands Lastly, each profile was compared across their reported emotional demands. Because the hypothesized structure of the personality variables (e.g., proactive personality, approach temperament, behavioral activation sensitivity, etc.) did not emerge as expected, the discussion of emotional demand results is per profile and exploratory. Profile 1 showed significantly lower levels of compassion emotional demands (t = 2.62, p < .001) and display rule perceptions (t = 7.89, p = < .001). No significant differences emerged for helping (t = 1.30, p = .19), regulation (t = -.76, p = .45), and general emotional demands (t = .91, p =.36). Profile 2 showed significantly higher levels of compassion emotional demands (t = -3.75, p < .001), helping demands (t = -3.13, p < .001), and display rule perceptions (t = -8.47, p < .001); No significant differences emerged for regulation (t = 0.74, p = .46), and general emotional demands (t = .23, p =.82). 104 Profile 3 showed significantly lower levels of compassion-related (t = 7.03, p < .001), helping-related (t = 7.58, p < .001), regulation-related (t = 8.18, p < .001), and general emotional demands (t = 2.19, p < .001). No significant differences emerged for display rule perceptions (t = 1.12, p < .26). Lastly, profile 4 showed significantly higher levels of compassion-related (t = -8.3, p < .001), helping-related (t = -7.79, p < .001), regulation-related (t = -12.41, p < .001), and general (t = -4.93, p < .001) emotional demands. No significant differences were found for display rule perceptions (t = -.81, p = .42). Demographics Next, profiles were compared along several different demographic variables. Table 8 reports gender and race by profile. Table 9 reports industry and role by profile. ANOVA analyses suggested profiles differed in terms of age, F(3,465) = 5.02, p <.05. The mean age of profile 1 was 37.21 years (SD = 11.21), profile 2 was 41.72 years (SD = 12.5), profile 3 was 38.36 years (SD = 10.21), and profile 4 was 36.82 years (SD = 9.11). Profiles also differed regarding the average number of face-to-face hours per week reported, F(3,465) = 3.09, p <.05. The mean FtF hours for profile 1 was 29.3 hours (SD = 10.54), profile 2 was 31.2 hours (SD = 10.95), profile 3 was 28.6 hours (SD = 8.73), and profile 4 was 26.35 hours (SD = 12.64). Profiles did not differ, however, on the average number of hours worked per week, F(3,465) = 2.01, p = .11. Profiles did not differ regarding proportions of gender [χ2 (12, 469) = 20.43, p = .06] or race, χ2 (24, 464) = 27.73, p = .27. Profiles did differ in regard to their industry [χ2 (51, 469) = 93.13, p < .01] and work role, χ2 (18, 469) = 48.54, p < .01. 105 Regarding participant’s self-report job industry, profile 1 seemed to be more represented by retail (13.91%) and healthcare (14.57%) industries compared to other profiles. Profile 2’s participants reported belonging to the education (17%) and manufacturing sectors (10.46%). Profile 3’s participants were more to belong to the Business support/logistics (9.48%) and Hotel/food services (13.79%). Lastly, profile 4 was represented most by those in Technology (36.73%) and Healthcare (16.33%). Results also suggest profile’s varied in terms of job roles. Profile 1 was most represented by non-supervising employees (50.33%) and managers (26.49%) but did not hold the highest percentage on any of these groups across classes. Profile 2 had a higher percentage of vice- president/directors (3.27%) and supervisors (19.95%) compared to other profiles. Profile 3 was mostly represented by non-supervising employees (60.34%). Lastly, profile 4 was largely represented by supervisors (38.78%) and managers (44.19%). Summary of analyses Altogether, the best-fitting solution was a four-profile model, in support of Hypothesis 1a. Hypothesis 1b was also supported because two profiles emerged with significantly different approach temperament mean scores from the two other profiles. However, this structure was not supported for avoidance temperament and proactive personality, hence failing to support Hypotheses 1c and 1d. Altogether, despite the mean scores of emergent profile structure not aligning with the proposed hypotheses, these four distinct profiles showed notable differences across approach-avoidance temperament, proactivity, emotional demands, and emotion regulation behaviors. Profile 1 displayed the lowest levels of approach-oriented temperament, high avoidance- oriented temperament, and low proactivity. Based on this group’s also high BIS scores and low 106 BAS scores, these individuals react strongly to negative stimuli and events and do not react strongly to positive events. Profile 2 displayed high levels of approach-oriented temperament, proactivity, and the lowest avoidance-oriented temperament. This profile further had the highest behavioral activation and lowest behavioral inhibition tendencies. These values suggest individuals in this group are approach-oriented, proactive, and experience strong reactions to positive stimuli but not negative stimuli. Profile 3 displayed the highest levels of behavioral inhibition and low levels of approach- oriented temperament, avoidance-oriented temperament, proactivity, and behavioral activation. These values suggest individuals in this group are non-proactive and prevention-focused who react strongly to negative stimuli. Profile 4 displayed high levels of every single variable except for behavioral inhibition. These individuals seem to be highly proactive, both approach- and avoidance-oriented, and strongly reactive to positive stimuli but not negative stimuli. 107 Discussion – Study 1 This first study sought to test whether the hypothesized number of profiles emerge from latent profile analyses including emotion regulation and job crafting indicators. These profiles were further compared across hypothesized approach-avoid motives and proactive-reactive strategies. Although support for the hypothesized profile differences only emerged for approach- oriented motives, significant differences for avoidance- oriented motives and proactivity nevertheless emerged across profiles. Altogether, one profile was characterized by high approach-oriented temperament, high avoidance-oriented temperament, and high proactivity, one profile by high approach-oriented temperament and proactivity, one by high avoidance temperament and low approach-oriented temperament and proactivity, and a last profile by moderate approach-oriented and avoidance-oriented temperament that is low on proactivity. The most distinct profile is the fourth profile. This profile exhibits high levels across all hypothesis variables, save for BIS values. Moreover, these participants further report the lowest level of face-to-face interaction time and high levels of all emotional demands simultaneously. This profile would align with results from Gabriel et al. (2015) and Makigangas (2018), which similarly show a group of individuals who engage in high level of emotional labor and job crafting (donned the “active crafters”). When considering this profile’s only low score relative to other profiles is BIS, the combination of high emotion regulation behaviors – with a preference for approach behaviors --- alongside low face-to-face interaction suggest this a group focused on crafting an assumedly meaningful work environment for themselves or seeking to advance in their job. This would align with the significant proportion of supervisors and managers. However, this group’s small sample size should be noted before interpretation. 108 Profile 3 is also notable given this group holds the lowest level of proactivity along with low to moderate levels of approach and avoidance-oriented motives. Consistent with their relatively-low proactivity, these individuals also report the lowest levels of job crafting across the board. They are especially low on promotion-oriented styles of cognitive crafting, task crafting, and relationship crafting, with a small, higher than average score for prevention- oriented situation selection. One explanation for these behaviors may be the much lower levels of emotional demands, but there is insufficient information to rule out which direction this relationship may be, if true. Given their preference for avoiding undesirable situations, this profile may be reporting lower demands resultant from avoiding or deflecting emotional burden. However, this profile seems consistent with Makigangas’s (2018) low job crafting profile given their notably lower reliance on job crafting compared to other profiles and emotion regulation. This profile also does align with the expected avoidant-reactive profile given their very low scores on approach-oriented strategies. Participants in profile 1 were low on approach-oriented temperament, high on avoidance- oriented temperament, and low on proactivity. However, they reported much lower emotion regulation and job crafting, except for higher levels of task crafting and avoidance-relationship crafting. Notably, this was despite reporting emotional demands comparable to the remaining profiles. The “low actor” profile from Gabriel’s (2015) study is the best analogue for this group. One plausible explanation is that this group faced few emotional demands to manage and preferred to keep an environment where they only perform the work they like. This would explain why they reported lower skill crafting altogether. This group’s behaviors did align with their low proactivity and avoidance-orientated temperament, and could be considered an avoidant-proactive group for its preference for task crafting and prevention-oriented relationship 109 crafting.Profile 2, on the other hand, reports high levels of proactivity and approach-motives. Consistent with high levels of proactivity, this profile does engage in job crafting and emotion regulation, suggesting these individuals are responsive to their work environment’s characteristics. Their high approach motives are also congruent with this profile’s preference for approach-oriented strategies. This would also correspond to the “active” crafters in Makingangas (2018), those who employ job crafting at work similar to profile 4. But, the distinct combination of high proactivity and approach motives in addition to low avoidance characterizes this as a “super crafter” group. These are individuals who are extremely proactive at work and seem motivated and interested in molding it to suit themselves to the best of their ability. Despite the emergence of four profiles, the hypothesized proactivity, approach-motive and avoidance-motive differences were not supported. But, these variables nevertheless proved helpful for understanding the characteristics of each group and their distinct patterns of emotion regulation behavior. Moreover, each profile’s emotion regulation behaviors seemed to manifest distinct preferences for different job crafting and emotion regulation strategies that align with personality variables, further informed by each profile’s reported emotional demands. However, the variables included in this study were limited and only included a single timepoint. This limits the amount of nuance in profile interpretation, making any conclusions regarding this profile structure precarious. Replicating this profile structure in addition to expanding the number of factors investigated is necessary to ensure this study’s results warrant further consideration and do not just represent sample-specific patterns. 110 Study 2 The purpose of the second study is two-fold. The first goal was to replicate the profile structure found in Study 1 using data collected with a time-lagged research design. Next, Study 2 plans to examine the predictors and outcomes of emergent groups in order to uncover differences in employees’ resources and well-being as they relate to profile membership. Study 1 provided an overview of the profiles’ standings on approach-avoidance motives, proactivity, emotional demand strategy usage, and emotional demands. However, these groups should be next compared across their emotional regulation related resources (e.g., affect, social support, emotional intelligence, etc.) to observe how resource differences vary along levels of approach- avoidance motives and proactivity. Further, several well-being outcomes (e.g., engagement, exhaustion) are included to investigate whether levels of approach-avoidance motives and proactivity are linked to differing levels of employee functioning, in accordance with previous research. Comparisons across timepoints also establish whether theorized combinations of proactivity and approach-avoidance motives indeed result in longitudinal differences in well- being, also previously noted by research. A number of personal resources were added in this study to complement the previously investigated factors. Beyond autonomy and social support, emotional intelligence, dispositional affect, consideration for future consequences, cognitive flexibility, and context sensitivity are resources previously shown to predict emotion regulation and job crafting behaviors. These resources are further discussed below. Dispositional Affect The degree to which an individual is prone to feeling a specific range of emotions and reacting to their environment pleasantly or negatively is an individual difference know as dispositional affectivity (Watson & Clark, 1984; Watson, Clark, & Tellegen, 1988). Dispositional 111 affectivity, or affectivity, is considered bipolar because it is differentiated between independent positive and negative continuums (Watson, 2000). Positive affect (PA) is characterized by emotional states that are active, engaged, and enthusiastic, while negative affect (NA) features hostility, guilt, and anger (Cropanzano et al., 2003; Watson & Clark, 1984). Positive affectivity is featured as a critical resource within JD-R and COR models—both posit that positive affectivity is imperative for well-being at work and mitigating the effects of negative demands on employees (Demerouti & Bakker, 2011; Hobfoll et al., 2011; Nowotny, 1981; Zellars et al., 2006). Counter to expectations, the effects of negative affectivity do not work to moderate the stressor-strain relationship in the same way that positive affect does (Schaubroeck et al., 1992; Spector, 1991). However, negative affectivity can still function like an ‘anti-resource’ through its detrimental influence on how employees manage demands at work. For instance, those with high negative affectivity are both less likely to seek social support (Boland & Cappeliez, 1997) and receive it (Coyne, 1976), and face lower social status perceptions from others (Gordon, 1990; Kemper, 1990). On the other hand, positive affectivity has been linked to higher levels of social support and more accurate social network perceptions (Casciaro et al., 1996; Yoon & Thye, 2000). Moreover, positive affectivity is linked to increased proactive behaviors, proactive goal setting and striving, self-efficacy, and job control (Fritz et al., 2009; Li et al., 2019; Mazzetti et al., 2016; Parker et al., 2006; Parker et al., 2010; Rouxel et al., 2016), which suggests that positive affectivity helps manage job demands at work and mitigate the impact of work demands on employee well-being. Affectivity is also linked to proactive behavior at work (Bindl & Parker, 2019; Wu et al., 2013). Rogala and Cieslak (2019) found that positive affect was positively related to structural crafting (i.e., increasing challenging demands) and relational crafting (i.e., increasing social 112 resources). Similarly, Kwon and Kim (2019) also showed that daily levels of positive affect predicted job crafting, and that this relationship was strengthened when employees reported lower levels of social resources (i.e., LMX). Lastly, Makigangas et al. (2017) found that positive affect also helps predict team-level job crafting behaviors over time (i.e., increasing structural and relational resources, increased challenge demands). Emotional labor research has linked affectivity to differences in surface acting and deep acting behaviors. Specifically, employees higher on positive affectivity report greater levels of deep acting while those higher on negative affectivity report greater surface acting (Kammeyer-Mueller et al., 2013; Mesmer-Magnus et al., 2012). Gabriel et al.’s (2015) emotional labor profiles also suggest a link between emotional labor and affectivity. The ‘non actor’ profile, characterized by the lowest levels of both deep and surface acting across profiles, showed the highest levels of positive affectivity as well as lowest levels of negative affectivity and display rule perceptions. Non-actors also reported the second highest job satisfaction and second lowest levels of emotional exhaustion. Positive affectivity seemingly functions as a way to circumvent the effects of display rules because one can naturally leverage their affect to meet display rules and buffer the detrimental effects of emotional labor. Dispositional affectivity, specifically positive affectivity, thus functions as a resource for both job crafting and emotion regulation behaviors. This suggests positive affectivity functions as a resource for employees employing job crafting and emotion regulation to manage emotional demands at work. Negative affectivity, on the other hand, can function as a resource for some proactive behaviors – such as helping identify problems or directing attention and energy toward undesirable stimuli – but can also be harmful via individual’s propensity to avoid resource generation opportunities (e.g., lower social support, lower challenge demands). 113 Emotional Intelligence Emotional intelligence is a dispositional resource that has been shown to help individuals anticipate, understand, and manage emotional demands. Emotional intelligence is understood as the ability to perceive and understand emotional information, as well as to generate and regulate emotions that promote personal growth (Mayer & Salovey, 1997). Specifically, the structure of emotional intelligence is composed of four dimensions according to Salovey and colleagues (Brackett & Salovey, 2006; Mayer & Salovey, 1997). These dimensions are 1. Perception of emotion, 2. Emotional facilitation of thought, 3. Understanding emotion, and 4. Managing emotion. Accordingly, individuals high in emotional intelligence have been shown to exhibit several qualities critical for managing emotional demands at work. Lopes et al. (2006) showed that high emotional intelligence was linked to adopting emotion regulation strategies adaptively, use cognitive reframing more often, and employ other methods consistent with their desired emotional response. Brotheridge (2006) showed that high emotional intelligence was also linked to correctly perceiving the situational demands presented, which then led to the emotional labor strategy adopted. High Eemotional intelligence was also linked to effective management of these situational demands because individuals were able to identify the frequency of emotional displays necessary and perform deep acting. Emotional intelligence has emerged as a variable of interest within job crafting as well. Sloan and Geldenhuys (2021) investigated the impact of emotional intelligence on task crafting and in-role and extra-role performance. They showed that other-focused emotion appraisal was positively related to relational crafting and that self-focused emotion appraisal was positively related to task crafting. Altogether, results supported the idea that components of emotional intelligence (i.e., self- and other-focused emotion appraisal) aided workers in their ability to 114 complete both in- and extra-role performance. Similarly, Pekaar et al. (2018) investigated the role of emotional intelligence in a group of trainees. They found that self-focused and other- focused emotion appraisal led to active learning behavior and energy levels. This relationship occurred through self-focused emotion regulation, other-focused emotion regulation, and social job crafting behaviors. They were able to show that the weeks employees engaged in higher other-focused emotion appraisal they engaged in greater other-focused emotion regulation and sought more help from colleagues. Weeks marked by higher levels of self-focused emotion appraisal were linked to higher self-focused emotion regulation and seeking help from colleagues. Interestingly, social crafting only occurred in weeks where individuals engaged in high levels of self-focused emotion regulation – via seeking more social support or supervisory coaching (Peekar et al., 2018). This further aligns with prior work suggesting that both emotion regulation and emotional intelligence facilitates proactive behaviors at work (Parker et al., 2010). Consideration for Future Consequences Time orientation is defined as a general orientation toward present or future thinking (Mello, Finan, & Worrell, 2013). Time orientation is similar to another construct known as time perspective, which is the cognitive process whereby individuals assign temporal categories to the continuous flow of personal and social experiences (Zimbardo & Boyd, 1999). However, time orientation is different than time perspective because the focus is on the cognitive weight that individuals assign to the present or the future, rather than the general attitude an individual holds toward the past, present, or future (Murphy & Dockray, 2018). Consideration for Future Consequences (CFC) is a specific conceptualization of time orientation that is concerned with the temporal anchor an individual considers when evaluating the consequences of their behavior (Strathman et al., 1994). CFC has garnered interest especially in health psychology research 115 (Joireman & King, 2016) because it is related to health behaviors, such as lower risky behavior (e.g., substance use, risky driving, alcohol consumption), health promotive behaviors (e.g., sleep hygiene, physical exercise, oral hygiene) and illness preventative and/or detective behavior (e.g., screening, sunscreen use, helmet use, vaccination; Murphy & Dockray, 2018). However, there is growing literature that links CFC to proactive coping and emotion regulation behaviors. Dwivedi and Rastogi (2017) found that future time perspective was positively correlated to proactive coping and preventative coping, which are similar to job crafting due to being proactive and future-oriented forms of demands management. Ortner et al. (2018) found that participants who focused more on future consequences adopted effective strategies over maladaptive strategies for regulating negative emotions specifically. Altogether, research suggests CFC drives behavior by placing emphasis either between immediate or future consequences. CFC was thus added as a complement to proactive personality since it functions as another factor that shapes how proactive or reactive individuals are in their emotion regulation behaviors. Coping Flexibility Coping flexibility is another personal resource that was investigated in this study. Coping flexibility refers to one’s ability to effectively modify coping behavior according to the nature of a stressful situation (Lazarus, 1999). Kato (2012) proposes an alternative definition, which is the ability to discontinue an ineffective approach and implement an alternative coping strategy. Both authors presume that flexible coping will lead to more adaptive outcomes, presumably because different coping approaches are more or less suited for different demands and stressors (Lazarus, 1999; Kato, 2012). Moreover, flexible coping requires an individual to assess their environment and monitor the consequences of their coping to decide whether to continue with the currently employed strategy or to abandon it for a different strategy (Kato, 2012). This underlying meta- 116 cognitive, problem-solving process is why flexible coping is effective, because employing several different strategies does not necessarily lead to more adaptive outcomes (Compas et al., 1988; Dolan and White, 1988). A similar logic can be applied to emotion regulation and job crafting strategies because they are a form of coping with emotional demands. Theoretically, an individual who chooses an emotion regulation strategy following an evaluation of its appropriateness and effectiveness is likely to be less burnt out and more engaged because they can meet their emotional demands. Accordingly, similar flexibility models for emotion regulation have been proposed. Several studies have found that regulatory flexibility is linked to lower depression, anxiety, distress (Kato, 2012) and serves as a buffer for stress (Bonanno et al., 2011; Westphal et al., 2010). Although the bulk of research on regulatory flexibility lies within clinical or cognitive research, some organizational researchers have applied this theory to the workplace. Lenzo et al. (2020) found a cross-sectional negative relationship between regulatory flexibility and emotional exhaustion in a sample of palliative home care workers. In a sample of not-for-profit service workers, Biron and Veldhoven (2012) conducted a diary study and found that person-level psychological flexibility was associated with daily lower emotional exhaustion measured at bedtime. Person-level psychological flexibility further attenuated the daily-level relationship between emotional demands and emotional exhaustion. So, based on theory and prior research, coping flexibility is investigated across profiles in this study. Context Sensitivity Following the thread of regulatory flexibility, context sensitivity is included as a personal resource for emotion regulation behaviors. Context sensitivity is the ability to read contextual clues (Bonanno & Burton, 2013). The growing interest in regulatory flexibility precipitated 117 research on context sensitivity given the moderating role of situational context and the importance of matching regulation strategy to the context surrounding the demands (Aldao, 2013; Aldao & Sheppes, 2015; Cheng et al., 2012; Troy et al., 2013). In response to the limited measures of context sensitivity, Bonanno et al. (2020) developed a scenario-based measure that captures an individual’s sensitivity to contextual cues present in a scenario (cue sensitivity) or the relative absence of cues (cue absence). They found cue presence predicted flexible coping and emotional regulation. Lower cue absence, which is described as the ability to accurately identify, for instance, when there is negligible threat, a response is not urgent, there is little control, was lower in participants with clinically significant levels of depression, anxiety, and stress. In other words, participants more likely to recognize when a situation is missing a key cue fared better psychologically (Bonanno et al., 2020). Contextual sensitivity has also been linked to emotional exhaustion in a study of palliative care workers (Lenzo et al., 2020), suggesting context sensitivity is a promising factor to consider when evaluating the outcomes of emotion regulation behaviors. Given that employees must be acutely aware of their work surroundings, identify whether their environment contains desirable or undesirable features, and adjust them as necessary, profile differences in context sensitivity may explain patterns of emotion regulation. 118 Participants and Procedure Method – Study 2 Participants were recruited on the CloudResearch platform using the same Qualtrics screening survey in Study 1. These participants were also compensated 5¢ for the screening survey and told that the primary study includes two surveys two weeks apart, and their compensation would be $4 for each survey completed. The screening survey asked participants their age, if they worked a job involving face to face interaction at least part-time, the number of hours worked a week on average, the number of hours worked a week involving face-to-face interaction on average, the medium of interaction with others at work, and whether they feel their interpersonal interactions often demand emotion regulation. The survey further included a captcha check, a duplicate response identifier, and fraudulent score estimate offered by Qualtrics software to flag bots and duplicate responses. The same filter criteria as Study 1 were then applied to this screening survey. That is, participants must have reported working at least 20 hours on average a week and 10 hours of face-to-face interaction at their job. Respondents’ location was also checked to see if they resided outside of the U.S. (based off Qualtrics’s location coordinates) or if their response was flagged as a duplicate (using Qualtrics fraud prevention software). Lastly, careless responding analyses were conducted. A total of 2,261 responses were collected from the screening survey and 1,044 participants total were eligible for this study based on the previously mentioned criteria (46.2%). These participants were sent an invitation to participate in the time 1 survey, where 649 participants responded for a response rate of 62.2%. These 649 participants were then sent an invitation to participate in the time 2 survey two weeks later. A total of 452 responses were collected from this survey for a 69.6% response rate. A total of 1,101 responses were collected 119 across time 1 and time 2 surveys. Responses were then dropped if flagged as fraudulent or duplicate responses (N = 18), reported working fewer than 20 hours a week or 10 hours of face- to-face interaction (N = 62), and for careless responding (N = 16). This resulted in a final sample of 1,005 responses consisting of 621 time 1 responses and 384 time 2 responses. Given the central analyses are time-lagged, only responses matched to one participant per time point were retained, which resulted in a final time-lagged matched sample size of 370. In other words, there were a total of 740 total responses from 370 participants across time 1 and time 2 surveys. Demographics are reported for time 1 individuals only in Tables 10 – 12 because time 1 class was used for comparisons in analyses. Measures The same job crafting, approach-avoidance temperament, proactive personality, BAS/BIS, and emotional demands measures from Study 1 were used for this study. Each measure was assessed each time point. Table 13 and Table 14 report the Cronbach’s alpha for these variables for both time 1 and time 2. Given the size of the correlation table containing every value across time 1 and time 2 measures, it is provided as supplemental material. Social Support Social support was measured using twelve items from Rousseau and Aube’s (2010) social support measure. Their measure is composed of 6 items regarding coworker social support and 6 items regarding supervisor social support. A sample item is “My coworkers/supervisor helps me to develop my skills and competencies. Responses ranged from 1 (strongly disagree) to 5 (strongly agree). Coworker social support αtime1 = .9 and αtime2 = .9. Supervisor social support αtime1 = .92 and αtime2 = .92. Refer to appendix L for items. 120 Job Autonomy Work autonomy was measured using Breaugh’s (1985) three-dimensional measure. This scale measures work autonomy 3 factors: work method, work scheduling, and work criteria. Each factor was measured using 3 items, for a total of 9 items. Autonomy αtime1 = .91 and αtime2 = .93. Responses were scored from 1 (strongly disagree) – 5 (strongly agree). Refer to appendix M for items. Emotional Exhaustion Emotional exhaustion was measured using Maslach and Jackson’s (1986) 9 item emotional exhaustion sub-scale. Emotional exhaustion αtime1 = .92 and αtime2 = .92. Responses were asked to read each statement and respond according to how often the statement described their emotional exhaustion levels before, during, or after work. Responses were scored from 1 (never) to 5 (everyday) scale. A sample item is “[How often have …] You felt used up at the end of the workday.” Refer to appendix N for items. Engagement Engagement was measured using Schaufeli et al.’s (2002; 2006) employee work engagement scale. This scale is composed of three dimensions of engagement: absorption, dedication, and vigor. There was a total of 17 items: 6 items for absorption, 5 for dedication, and 6 for vigor. Engagement αtime1 = .92 and αtime2 = .94. Responses varied from 1 (never) to 5 (always). A sample item is “When I am working, I forget everything else around me.” Refer to appendix O for items. 121 Emotional Intelligence (EI) Peekar et al.’s (2017) Rotterdam Emotional Intelligence Scale was used to measure emotional intelligence. Their scale measures 4 dimensions of emotional intelligence (EI), which are self-focused emotion appraisal (SA), other-focused emotion appraisal (OA), self-focused emotion regulation (SR), and other-focused emotion regulation (OR). Each dimension was measured using 7 items, for a total of 28 items. SA αtime1 = .92 and αtime2 = .92. OA αtime1 = .92 and αtime2 = .92. SR αtime1 = .88 and αtime2 = .87. OR αtime1 = .91 and αtime2 = .92. Responses for every scale varied from 1 (strongly disagree) to 5 (strongly agree). A sample item is “I am in control of my own emotions.” Refer to Appendix P for items. Dispositional Affect Dispositional affect was measured using the PANAS (Watson et al., 1988). Their scale is composed of 20 items that each reflect an emotion: 10 items reflect positive emotions, and 10 items reflect negative emotions. Positive affect αtime1 = .92 and αtime2 = .92. Negative affect αtime1 = .92 and αtime2 = .93. The stem was “Indicate to what extent you generally feel this way. That is, how you feel on average.” This stem was selected to ensure participants’ responses reflect their general disposition to positive or negative emotions, and not current or recent states. Responses varied from 1 (very slightly or not at) to 5 (extremely). Refer to Appendix Q for items. Coping Flexibility (CF) Coping flexibility is a measure of one’s emotion regulation efficacy perceptions and whether they employ different emotion regulation strategies. Coping flexibility was measured using Kato’s (2012) Coping Flexibility Scale, which is composed of 10 total items. Coping flexibility αtime1 = .68 and αtime2 = .69. Participants were shown statements and asked to rate how applicable the statements are to them using a scale from 1 (not applicable) to 4 (very applicable). 122 A sample item is “If I unsuccessfully try to change my emotions, I use other ways to manage them.” Refer to Appendix T for items. Context Sensitivity Index (CSI) Context sensitivity was measured using Bonanno et al.’s (2020) Context Sensitivity Index. This measure captures the extent that an individual is sensitive to the presence of contextual cues (cue presence) or the absence of contextual cues (cue absence). The scale first presents a scenario followed by several questions regarding perceptions of different contextual factors. An example scenario is “You are walking down a street when you see a person slip and fall. They hit their head when they land.” An example question for this scenario is “How much control do you have over this situation?” Cue presence αtime1 = .67 and αtime2 = .66. Cue absence αtime1 = .73 and αtime2 = .77. Responses varied on a scale from 1 (not at all) to 5 (very much). Refer to Appendix U for scale scenarios and items. Consideration for Future Consequences (CFC) This scale captures the degree to which one’s behaviors are oriented toward immediate or future consequences. Joireman et al.’s (2012) CFC-14 scale was used, which is composed of 14 total items. CFC αtime1 = .86 and αtime2 = .88. Participants were asked to rate the degree to which the statement described them using a scale from 1 (strongly Disagree) to 5 (strongly Agree). A sample item is “I think that sacrificing now is usually unnecessary since future outcomes can be dealt with at a later time.” Refer to Appendix V for items. 123 Analyses First, latent profile analyses were conducted using the same 14 emotion regulation and job crafting behaviors as study 1and R-package ‘TidyLPA.’ Given this study is time-lagged, there are two options for enumerating latent profiles across time points. The first option is to estimate profile structure for each time point independently and the second option is to estimate profiles concurrently. Only one study has examined whether one strategy is preferred over another. Talley (2020) found that independent enumeration may lead to lower a likelihood of enumeration bias for time-specific class variables. However, this was tested using a simulation where the assumption of measurement invariance was violated. Further, the sample sizes used for simulation started at 500. Prior studies have, however, stated that concurrent profile enumeration is acceptable, but without any empirical evidence to support this (Collins & Lanza, 2009; Lanza et al., 2013). Ultimately, profile enumeration was completed for each time point in this study in alignment with prior the simulation study’s recommendation. Next, mixed-model ANOVAs were conducted to investigate profile mean differences across variables of interest. The mixed-model ANOVAs were run using R studio. The function lmer() from the LME4 package (Bates et al., 2014). The lmer function fits mixed models according to the specified model. Models were specified so each outcome was the dependent variable and class, time, and their interaction were specified as independent variables. A random intercept was included to account for repeated measures. 124 Latent Profile Analysis Results – Study 2 Table 15 summarizes the results of this study’s latent profile analysis results for time 1 and time 2 data. Participants who completed both time 1 and time 2 survey were used for analyses. Participants’ responses were split into two separate datasets according to time point, then latent profile analyses were conducted. Similar to the previous study, AIC, BIC, SA-BIC, and BLRT values and theory were used to determine the number of profiles. Time 1 Profile Enumeration Time 1 results suggest that the four-profile solution exhibited better fit than the previous model based on the significance of the BLRT test while the five-profile model did not, suggesting fit did not improve beyond the prior model. The four-profile solution also held the best AIC (17,805.87) and SA-BIC values (18,012.73). However, this solution also held the second-lowest BIC (18,533.4) and entropy values (.73). This solution was ultimately selected as the final model based on the BLRT test, SA-BIC value, and theoretical rationale. Time 1 Profile Comparisons Next, time 1 profiles were compared across indicator variables. Similar to Study 1, profiles were assessed using t-tests to assess whether there was a significant mean difference between a respective profile score and other profiles. Hedge’s g was further calculated to evaluate the magnitude of any significant differences. In analyses, the profile of interest was coded 0 and the remainder profiles were coded 1 to facilitate interpretation of test statistics and Hedge’s g. These results are presented in Table 16. Figure 5 shows a bar plot with the Hedge’s g for each indicator variable by profile for time 1 profiles. 125 Profile 1 represented 42.7% of the total sample (n = 158) and was the largest class. Profile 1 displayed significantly lower levels of promotion-oriented relationship crafting [M =2.67; t(366.88) = -2.17, p < .05], prevention-oriented relationship crafting [M =2.03; t(365) = - -4.39, p < .05], promotion-oriented task crafting [M =2.11; t(318.59) = -2.44, p < .05], and prevention-oriented cognitive crafting [M =2.71; t(361.45) = -2.67, p < .05]. Profile 1 displayed significantly higher levels of promotion-oriented skill crafting [M =3.68; t(326.22) = -5.74, p < .05]. This group did not significantly differ on prevention-oriented skill crafting [M =3.62; t(364) = 1.95, p = 0.05], prevention-oriented task crafting [M =2.49; t(366.36) = -0.21, p = 0.83], promotion-oriented cognitive crafting [M =2.94; t(363.66) = 0.18, p = 0.86], approach-oriented situation selection [M =3.39; t(367.85) = -1.85, p = 0.07], situation modification [M =3.17; t(362.42) = -0.03, p = 0.98], attentional deployment [M =3.37; t(358.54) = -1.72, p = 0.09], cognitive change [M =3.3; t(362.72) = 0.21, p = 0.83], and response modulation [M =2.88; t(365.99) = -1.89, p = 0.06]. Profile 2 represented 13% of the total sample (n = 48) and was the smallest class. Profile 2 displayed significantly lower levels of promotion-oriented relationship crafting [M =2; t(77.59) = -7.95, p < .05], prevention-oriented relationship crafting [M =1.94; t(66.28) = -2.86, p < .05], promotion-oriented skill crafting [M =1.68; t(92.76) = -18.64, p < .05], prevention-oriented skill crafting [M =2.22; t(61.78) = -14.33, p < .05], promotion-oriented task crafting [M =1.25; t(181.53) = -16.44, p < .05], prevention-oriented task crafting [M =1.85; t(64.78) = -5.93, p < .05], promotion-oriented cognitive crafting [M =1.89; t(64.9) = -9.67, p < .05], prevention- oriented cognitive crafting [M =2.01; t(71.18) = -9.12, p < .05], approach-oriented situation selection [M =2.53; t(56.28) = -7.9, p < .05], avoidance-oriented situation selection [M =2.6; t(55.15) = -3.8, p < .05], situation modification [M =2.52; t(56.27) = -5.03, p < .05], attentional 126 deployment [M =2.86; t(54.78) = -4.67, p < .05], cognitive change [M =2.65; t(59.52) = -5.72, p < .05], and response modulation [M =2.57; t(56.65) = -3.05, p < .05]. Profile 3 represented 26.7% of the total sample (n = 99). Profile 3 displayed significantly higher levels of promotion-oriented relationship crafting [M =3.69; t(181.19) = 12.81, p < .05], prevention-oriented relationship crafting [M =2.56; t(165) = 3.46, p < .05], promotion-oriented skill crafting [M =4.31; t(288) = 14.49, p < .05], prevention-oriented skill crafting [M =3.99; t(236.93) = 7.72, p < .05], promotion-oriented task crafting [M =3.42; t(201.88) = 24.79, p < .05], prevention-oriented task crafting [M =2.75; t(176.98) = 3.31, p < .05], promotion-oriented cognitive crafting [M =3.51; t(212.19) = 8.56, p < .05], prevention-oriented cognitive crafting [M =3.44; t(207.04) = 9.47, p < .05], approach-oriented situation selection [M =3.91; t(211.54) = 6.83, p < .05], avoidance-oriented situation selection [M =3.55; t(231.22) = 5.47, p < .05], situation modification [M =3.61; t(260.22) = 7.41, p < .05], attentional deployment [M =3.8; t(221.89) = 5.87, p < .05], cognitive change [M =3.69; t(235.57) = 6.85, p < .05], and response modulation [M =3.19; t(175.02) = 2.96, p < .05]. Lastly, profile 4 represented 17.6% of the total sample (n = 65). Profile 4 displayed significantly lower levels of promotion-oriented relationship crafting [M =2.31; t(98.43) = -4.67, p < .05], promotion-oriented skill crafting [M =2.2; t(145.49) = -13.19, p < .05], and promotion- oriented task crafting [M = 1.46; t(171.86) = -11.57, p < .05]. Profile 4 displayed significantly higher levels of prevention-oriented relationship crafting [M =2.66; t(98.43) = 3.4, p < .05], approach-oriented situation selection [M =3.75; t(145.49) = 3.12, p < .05], and avoidance- oriented situation selection [M =3.54; t(171.86) = 3.43, p < .05]. Lastly, profile 4 did not significantly differ from other profiles on prevention-oriented skill crafting [M =3.57; t(107.11) = 0.45, p = 0.65], prevention-oriented task crafting [M =2.62; t(83.68) = 1.08, p = 0.28], 127 promotion-oriented cognitive crafting [M =2.78; t(102.95) = -1.52, p = 0.13], prevention- oriented cognitive crafting [M =2.89; t(96.96) = 0.46, p = 0.65], situation modification [M =3; t(87.47) = -1.69, p = 0.09], attentional deployment [M =3.57; t(101.49) = 1.35, p = 0.18], cognitive change [M =3.13; t(85.12) = -1.62, p = 0.11], and response modulation [M =3.17; t(90.58) = 2, p = 0.05]. Time 2 Profile Enumeration Time 2 results were similar to the time 1 results. Specifically, the four-profile model exhibited better fit than the three-profile model while the five-profile solution did not, based on BLRT tests. The four-profile solution also held the lowest SA-BIC value (11,378.88). However, the four-profile solution held the second highest AIC (11,251.32), highest BIC (11,899.22), and the second highest entropy value (.78). The four-profile solution was also selected as the most appropriate solution based on the SA-BIC and BLRT values. This model also exhibited an entropy value close to the suggested threshold (.80) and aligns with Time 1’ and Study 1’s LPA results. Time 2 Profile Comparisons Time 2 profiles were compared across indicator variables similar to Time 1 analyses. That is, t-tests were conducted to assess mean differences between a profile’s mean score and other profile’s mean scores on indicator variables. Hedge’s g was further calculated to evaluate the effect size of differences. For each t-test, respective profiles were coded 0 and the remainder profiles were coded 1. The DVs in these analyses (i.e., profile indicators) were measured using the same scales as time 1. These results are in Table 17. Figure 6 shows a bar plot with the Hedge’s g for each indicator variable by profile for time 2 profiles. 128 Profile 1 represented 54% of the total sample (n = 200) and was the largest class. Profile 1 displayed significantly lower levels of prevention-oriented relationship crafting [M =2.13; t(319.25) = -3.62, p < .05], promotion-oriented task crafting [M =1.87; t(243.61) = -7.82, p < .05], prevention-oriented task crafting [M =2.45; t(313.18) = -2.56, p < .05], and avoidance- oriented situation selection [M =3.01; t(325.55) = -2.52, p < .05]. This group did not significantly differ on promotion-oriented relationship crafting [M =2.62; t(322.03) = -0.81, p = 0.42], promotion-oriented skill crafting [M =3.09; t(313.94) = -1.34, p = 0.18], prevention- oriented skill crafting [M =3.45; t(290.92) = 0.23, p = 0.82], promotion-oriented cognitive crafting [M =2.84; t(348.4) = -0.76, p = 0.45], prevention-oriented cognitive crafting [M =2.72; t(330.84) = -0.77, p = 0.44], approach-oriented situation selection [M =3.46; t(307.08) = -0.25, p = 0.8], situation modification [M =3.12; t(307.48) = 0.22, p = 0.83], attentional deployment [M =3.38; t(316.38) = -0.32, p = 0.75], cognitive change [M =3.32; t(326.51) = 1.98, p = 0.05], and response modulation [M =2.98; t(311.33) = 0.28, p = 0.78]. Profile 2 represented 7.6% of the total sample (n = 28) and was the smallest class. Profile 2 displayed significantly lower levels of promotion-oriented relationship crafting [M =1.79; t(31.71) = -5.08, p < .05], prevention-oriented relationship crafting [M =1.43; t(43.07) = -8.67, p < .05], promotion-oriented skill crafting [M =1.38; t(41.05) = -15.75, p < .05], prevention- oriented skill crafting [M =1.76; t(31.53) = -12.36, p < .05], promotion-oriented task crafting [M =1.18; t(81.39) = -14.6, p < .05], prevention-oriented task crafting [M =1.71; t(32.62) = -5.82, p < .05], promotion-oriented cognitive crafting [M =1.99; t(32.51) = -5.81, p < .05], prevention- oriented cognitive crafting [M =1.98; t(31.47) = -5.34, p < .05], approach-oriented situation selection [M =2.02; t(30.2) = -9.14, p < .05], avoidance-oriented situation selection [M =1.87; t(31.55) = -7.78, p < .05], situation modification [M =1.74; t(31.66) = -9.9, p < .05], attentional 129 deployment [M =2.62; t(29.21) = -4.25, p < .05], and cognitive change [M =2.42; t(29.71) = - 4.54, p < .05]. This profile did not significantly differ on response modulation [M =2.55; t(29.44) = -2.02, p = 0.05]. Profile 3 represented 29.2% of the total sample (n = 108). Profile 3 displayed significantly higher levels of promotion-oriented relationship crafting [M =3.24; t(209.74) = 7.96, p < .05], prevention-oriented relationship crafting [M =2.48; t(210.55) = 2.6, p < .05], promotion-oriented skill crafting [M =3.9; t(276.83) = 10.73, p < .05], prevention-oriented skill crafting [M =3.9; t(277.5) = 7.91, p < .05], promotion-oriented task crafting [M =3.35; t(191.99) = 23.15, p < .05], promotion-oriented cognitive crafting [M =3.31; t(234.09) = 6.57, p < .05], prevention-oriented cognitive crafting [M =3.13; t(198.08) = 5.87, p < .05], approach-oriented situation selection [M =3.7; t(238.72) = 3.6, p < .05], avoidance-oriented situation selection [M =3.48; t(248.77) = 5.11, p < .05], situation modification [M =3.53; t(251.69) = 6.98, p < .05], attentional deployment [M =3.66; t(224.28) = 4.65, p < .05], and cognitive change [M =3.5; t(242.33) = 4.23, p < .05]. This profile did not significantly differ on prevention-oriented task crafting [M =2.69; t(204.66) = 1.83, p = 0.07], and response modulation [M =3.07; t(203.25) = 1.4, p = 0.16]. Lastly, profile 4 represented 9.2% of the total sample (n = 34). Profile 4 displayed significantly lower levels of promotion-oriented relationship crafting [M =1.77; t(49.47) = -7.92, p < .05], promotion-oriented skill crafting [M =2.68; t(41.49) = -2.9, p < .05], promotion- oriented task crafting [M =1.44; t(67.08) = -9, p < .05], promotion-oriented cognitive crafting [M =2.4; t(41.15) = 3.35, p < .05], prevention-oriented cognitive crafting [M =2.38; t(43.41) = - 3.26, p < .05], and cognitive change [M =2.62; t(39.23) = -4.44, p < .05]. Profile 4 displayed significantly higher levels of prevention-oriented relationship crafting [M =3.34; t(49.47) = 6.99, 130 p < .05], prevention-oriented task crafting [M =3.5; t(41.49) = 6.54, p < .05], approach-oriented situation selection [M =4; t(67.08) = 5.15, p < .05], and avoidance-oriented situation selection [M =3.74; t(41.15) = 3.81, p < .05]. Lastly, profile 4 did not significantly differ from other profiles on prevention-oriented skill crafting [M =3.28; t(40.45) = -1.09, p = 0.28], situation modification [M =2.85; t(39.35) = -1.77, p = 0.08], attentional deployment [M =3.24; t(38.41) = -1.07, p = 0.29], and response modulation [M =2.93; t(36.3) = -0.23, p = 0.82]. Profile Stability Both time 1 and time 2 LPA results suggest that a four-profile solution best described the data, suggesting consistency in profile numbers across time points. Moreover, the four-profile solutions align with study 1’s profile structure, suggesting further profile generalizability across time points. Following profile enumeration, further analyses were conducted to investigate whether profile percentages were comparable across time points. Profile proportions were compared across time points using crosstabs and chi-square tests to evaluate whether profile breakdown was similar across time points. This was accomplished by recording each participant’s assigned profile across respective time points. In other words, two variables were created to record a participant’s assigned profile for time 1 and time 2 for each profile. A graph displaying the mean score of profile’s indicators grouped by profile and time point was generated to first examine whether emergent profiles can be interpreted similarly across time points. This graph helps visually establish whether profile 1 be interpreted across time points due to the similarity of this profile’s scores on the indicators. Figure 1, in the appendix, plots mean indicator score by profile and time point for each indicator. In Figure 1, profile 1 and profile 3 have the clearest overlap across time. Profile 2’s scores across time also suggest a similar pattern with the exception of much lower situation 131 selection and situation modification between time 1 and time 2. Profile 4, on the other hand, does overlap across time for several of the indicators. However, their prevention-oriented task crafting and prevention-oriented relationship crafting seem to change across time points, suggesting that participants engaged much more in these behaviors from time 1 to time 2. This pattern should be interpreted cautiously, however, given this group has the lowest sample and is the most variable across time points (see next paragraph). This figure altogether suggests that these profiles are comparable across time. Next, the percentage of participants shared between time 1 and time 2 for each profile was determined by calculating the proportion of responders who belonged to same profile between studies. Regarding Time 1, profile 1 was comprised of 158 participants (42.7%), profile 2 of 48 (13%), profile 3 of 99 (26.7%), and profile 4 of 65 (17.6%). Time-2 profile 1 was comprised of 200 (54%) participants, 28 (7.6%) profile 2 participants, 108 (29.2%) profile 3 participants, and 34 (9.2%) profile 4 participants. Cross-tabulations (Table X.) show the percentage of participants classified as the same profile between time 1 and time 2. 38.8% of profile 1 participants, 31% of profile 2 participants, 41.8% of profile 3 participants, and 17.8% of profile 4 participants were classified as the same profile across time points. Further, χ2 analyses were conducted to investigate whether differences in expected proportion of profile distribution were present. Results suggested differences in proportion existed across time, χ2(9, 370) = 155.88, p < .001. Despite the χ2 test being an omnibus test, the profile breakdown in the beginning of the paragraph shows that profiles 2 and 4’s proportional representation were substantially different across time points (13% vs. 7.6% for profile 2; 17.6 vs. 9.2% for profile 4). 132 Time-Lagged Profile Comparisons The following comparisons investigated whether time 1 class membership predicted time 2 outcomes. These analyses served to investigate whether time 1 profile predicts time 2 outcomes. This set of results help link differences in emotion regulations strategy reliance, employee proactivity and personality, and resource development and expenditure across profiles which can result in different employee well-being, job demands, and job resources across time. Hypothesis Variables A line graph that plots each of the hypothesis variables’ means by profile can be found in Figure 7. Proactivity Significant differences emerged for Time 2 proactive personality across profiles, F(3,365) = 24.44, p < .05. The means from highest to lowest were profile 3 (M = 3.82, SD = 0.51), Profile 1 (M = 3.65, SD = 0.52), Profile 4 (M = 3.32, SD = 0.6), and Profile 2 (M = 3.1, SD = 0.62). No significant difference emerged between profiles 3 and 1 (p = .06), a significant difference emerged between profiles 3 and 4 (p < .05), and a significant difference emerged between profiles 3 and 2 (p = .05). A significant difference emerged between profiles 1 and 4 (p < .05) and profiles 1 and 2 (p < .05). No significant difference emerged between profiles 4 and 2 (p = 0.15). Avoidance Temperament No significant differences emerged for time 2 avoidance temperament across profiles, F(3,365) = 2.31, p = .08. Approach Temperament Significant differences emerged for time 2 approach temperament across profiles, F(3,365) = 9.37, p < .05. The means from highest to lowest were profile 3 (M = 3.82, SD = 0.66), Profile 1 (M = 3.7, SD = 0.64), Profile 4 (M = 3.6, SD = 0.8), and Profile 2 (M = 3.19, SD = 0.8). No significant difference emerged between profiles 3 and 1 133 (p = 0.57), profiles 3 and 4 (p = 0.21), and a significant difference emerged between profiles 3 and 2 (p < .05). No significant difference emerged between profiles 1 and 4 (p = 0.75) and a significant difference emerged between profiles 1 and 2 (p < .05). A significant difference emerged between profiles 4 and 2 (p < .05). Behavioral Activation Significant differences emerged for time 2 behavioral activation across profiles, F(3,365) = 5.11, p < .05. The means from highest to lowest were profile 3 (M = 3.93, SD = 0.52), Profile 1 (M = 3.86, SD = 0.48), Profile 4 (M = 3.78, SD = 0.59), and Profile 2 (M = 3.58, SD = 0.61). No significant difference emerged between profiles 3 and 1 (p = 0.75), profiles 3 and 4 (p = 0.28), and a significant difference emerged between profiles 3 and 2 (p < .05). No significant difference emerged between profiles 1 and 4 (p = 0.7) and a significant difference emerged between profiles 1 and 2 (p < .05). No significant difference emerged between profiles 4 and 2 (p = 0.21). Behavioral Inhibition Significant differences emerged for time 2 behavioral inhibition across profiles, F(3,365) = 4.22, p < .05. The means from highest to lowest were profile 4 (M = 3.9, SD = 0.85), Profile 2 (M = 3.56, SD = 0.87), Profile 1 (M = 3.46, SD = 0.91), and Profile 3 (M = 3.45, SD = 0.91). No significant difference emerged between profiles 4 and 2 (p = 0.19), a significant difference emerged between profiles 4 and 1 (p < .05) and profiles 4 and 3 (p < .05). No significant difference emerged between profiles 2 and 1 (p = 0.91) and profiles 2 and 3 (p = 0.92). No significant difference emerged between profiles 1 and 3 (p = 1). 134 Well-being variables A line graph that plots each of the well-being variables’ means by profile can be found in Figure 8. Emotional Demands Significant differences emerged for time 2 compassion demands across profiles, F(3,365) = 24.34, p < .05. The means from highest to lowest were profile 3 (M = 3.18, SD = 0.88), Profile 1 (M = 2.62, SD = 0.69), Profile 4 (M = 2.51, SD = 0.72), and Profile 2 (M = 2.13, SD = 0.75). A significant difference emerged between profiles 3 and 1 (p < .05), profiles 3 and 4 (p < .05), and profiles 3 and 2 (p < .05). No significant difference emerged between profiles 1 and 4 (p = 0.77) and a significant difference emerged between profiles 1 and 2 (p < .05). A significant difference emerged between profiles 4 and 2 (p < .05). Significant differences emerged for time 2 helping demands across profiles, F(3,365) = 28.63, p < .05. The means from highest to lowest were profile 3 (M = 3.28, SD = 0.93), Profile 1 (M = 2.7, SD = 0.87), Profile 4 (M = 2.41, SD = 0.91), and Profile 2 (M = 1.96, SD = 0.71). A significant difference emerged between profiles 3 and 1 (p < .05), profiles 3 and 4 (p < .05), and profiles 3 and 2 (p < .05). No significant difference emerged between profiles 1 and 4 (p = 0.11) and profiles 1 and 2 (p < .05). A significant difference emerged between profiles 4 and 2 (p < .05). Significant differences emerged for time 2 regulation demands across profiles, F(3,365) = 40.43, p < .05. The means from highest to lowest were profile 3 (M = 2.65, SD = 1.13), Profile 1 (M = 1.82, SD = 0.82), Profile 4 (M = 1.5, SD = 0.69), and Profile 2 (M = 1.22, SD = 0.36). A significant difference emerged between profiles 3 and 1 (p < .05), profiles 3 and 4 (p < .05), and profiles 3 and 2 (p < .05). A significant difference emerged between profiles 1 and 4 (p < .05) 135 and profiles 1 and 2 (p < .05). No significant difference emerged between profiles 4 and 2 (p = 0.34). Significant differences emerged for time 2 general emotional demands across profiles, F(3,365) = 9.65, p < .05. The means from highest to lowest were profile 3 (M = 3.04, SD = 0.88), Profile 4 (M = 2.9, SD = 0.97), Profile 1 (M = 2.79, SD = 0.9), and Profile 2 (M = 2.2, SD = 0.84). No significant difference emerged between profiles 3 and 4 (p = 0.77), profiles 3 and 1 (p = 0.14), and a significant difference emerged between profiles 3 and 2 (p < .05).No significant difference emerged between profiles 4 and 1 (p = 0.84) and a significant difference emerged between profiles 4 and 2 (p < .05). A significant difference emerged between profiles 1 and 2 (p < .05). Emotional Exhaustion Significant differences emerged for time 2 exhaustion across profiles, F(3,365) = 3.72, p < .05. The means from highest to lowest were profile 4 (M = 2.84, SD = 1.04), Profile 3 (M = 2.72, SD = 0.97), Profile 2 (M = 2.51, SD = 1.17), and Profile 1 (M = 2.42, SD = 0.89). No significant difference emerged between profiles 4 and 3 (p = 0.85), profiles 4 and 2 (p = 0.27), and a significant difference emerged between profiles 4 and 1 (p < .05). No significant difference emerged between profiles 3 and 2 (p = 0.61) and profiles 3 and 1 (p = 0.08). No significant difference emerged between profiles 2 and 1 (p = 0.95). Job Engagement Significant differences emerged for time 2 job engagement across profiles, F(3,365) = 11.98, p < .05. The means from highest to lowest were profile 3 (M = 3.1, SD = 0.88), Profile 1 (M = 2.88, SD = 0.7), Profile 4 (M = 2.62, SD = 0.84), and Profile 2 (M = 2.34, SD = 0.73). No significant difference emerged between profiles 3 and 1 (p = 0.12), a significant difference emerged between profiles 3 and 4 (p < .05), and profiles 3 and 2 (p < .05). No significant difference emerged between profiles 1 and 4 (p = 0.13) and a significant 136 difference emerged between profiles 1 and 2 (p < .05). No significant difference emerged between profiles 4 and 2 (p = 0.21). Display Rules Significant differences emerged for time 2 display rules across profiles, F(3,365) = 8.41, p < .05. The means from highest to lowest were profile 4 (M = 3.69, SD = 0.55), Profile 3 (M = 3.6, SD = 0.59), Profile 1 (M = 3.54, SD = 0.47), and Profile 2 (M = 3.2, SD = 0.65). No significant difference emerged between profiles 4 and 3 (p = 0.78), profiles 4 and 1 (p = 0.24), and a significant difference emerged between profiles 4 and 2 (p < .05). No significant difference emerged between profiles 3 and 1 (p = 0.76) and a significant difference emerged between profiles 3 and 2 (p < .05). A significant difference emerged between profiles 1 and 2 (p < .05). Dispositional A line graph that plots each of the dispositional variables’ means by profile can be found in Figure 9. Positive and Negative Affect Significant differences emerged for positive affect across profiles, F(3,365) = 9.38, p < .05. The means from highest to lowest were profile 3 (M = 3.5, SD = 0.82), Profile 1 (M = 3.32, SD = 0.73), Profile 4 (M = 3.15, SD = 0.9), and Profile 2 (M = 2.78, SD = 0.9). No significant difference emerged between profiles 3 and 1 (p = 0.31), and a significant difference emerged between profiles 3 and 4 (p < .05), and profiles 3 and 2 (p < .05). No significant difference emerged between profiles 1 and 4 (p = 0.47) and a significant difference emerged between profiles 1 and 2 (p < .05). No significant difference emerged between profiles 4 and 2 (p = 0.07). No significant differences emerged for negative affect across profiles, F(3,365) = 1.41, p = .24. 137 Emotional Intelligence No significant differences emerged for time 2 self-appraisal across profiles, F(3,365) = 0.03, p = .99. No significant differences emerged for time 2 self-regulation across profiles, F(3,365) = 1.79, p = .15. Significant differences emerged for other appraisal across profiles, F(3,365) = 6.27, p < .05. The means from highest to lowest were profile 3 (M = 4, SD = 0.55), Profile 4 (M = 3.91, SD = 0.66), Profile 1 (M = 3.81, SD = 0.65), and Profile 2 (M = 3.5, SD = 0.88). No significant difference emerged between profiles 3 and 4 (p = 0.83), profiles 3 and 1 (p = 0.13), and a significant difference emerged between profiles 3 and 2 (p < .05). No significant difference emerged between profiles 4 and 1 (p = 0.76) and a significant difference emerged between profiles 4 and 2 (p < .05). A significant difference emerged between profiles 1 and 2 (p < .05). Significant differences emerged for other regulation across profiles, F(3,365) = 8.19, p < .05. The means from highest to lowest were profile 3 (M = 3.69, SD = 0.7), Profile 1 (M = 3.48, SD = 0.77), Profile 2 (M = 3.32, SD = 0.88), and Profile 4 (M = 3.09, SD = 0.8). No significant difference emerged between profiles 3 and 1 (p = 0.17), a significant difference emerged between profiles 3 and 2 (p < .05), and profiles 3 and 4 (p < .05). No significant difference emerged between profiles 1 and 2 (p = 0.59) and a significant difference emerged between profiles 1 and 4 (p < .05). No significant difference emerged between profiles 2 and 4 (p = 0.41). Coping Flexibility Significant differences emerged for coping flexibility across profiles, F(3,365) = 4.09, p < .05. The means from highest to lowest were profile 3 (M = 2.93, SD = 0.43), Profile 1 (M = 2.88, SD = 0.42), Profile 4 (M = 2.81, SD = 0.37), and Profile 2 (M = 2.68, SD = 0.45). No significant difference emerged between profiles 3 and 1 (p = 0.77), profiles 3 and 4 (p = 0.32), and a significant difference emerged between profiles 3 and 2 (p < .05). No significant 138 difference emerged between profiles 1 and 4 (p = 0.73) and a significant difference emerged between profiles 1 and 2 (p < .05). No significant difference emerged between profiles 4 and 2 (p = 0.36). CFC No significant differences on Time 2 CFC emerged for class, F(3,365) = .17, p = .91. Context Sensitivity Significant differences emerged for time 2 cue presence across profiles, F(3,365) = 3.9, p < .05. The means from highest to lowest were profile 4 (M = 4.03, SD = 0.5), Profile 2 (M = 3.93, SD = 0.57), Profile 1 (M = 3.83, SD = 0.53), and Profile 3 (M = 3.75, SD = 0.55). No significant difference emerged between profiles 4 and 2 (p = 0.77), profiles 4 and 1 (p = 0.06), and a significant difference emerged profiles 4 and 3 (p < .05). No significant difference emerged between profiles 2 and 1 (p = 0.65) and profiles 2 and 3 (p = 0.24). No significant difference emerged between profiles 1 and 3 (p = 0.7). Significant differences emerged for time 2 cue absence across profiles, F(3,365) = 13.88, p < .05. The means from highest to lowest were profile 3 (M = 2.39, SD = 0.74), Profile 1 (M = 2.09, SD = 0.55), Profile 4 (M = 1.88, SD = 0.41), and Profile 2 (M = 1.86, SD = 0.51). A significant difference emerged between profiles 3 and 1 (p < .05), profiles 3 and 4 (p < .05), and profiles 3 and 2 (p < .05). No significant difference emerged between profiles 1 and 4 (p = 0.07) and profiles 1 and 2 (p = 0.07). No significant difference emerged between profiles 4 and 2 (p = 1). 139 Resources A line graph that plots each of the resource variables’ means by profile can be found in Figure 10. Autonomy Significant differences emerged for time 2 autonomy across profiles, F(3,365) = 8.01, p < .05. The means from highest to lowest were profile 1 (M = 3.64, SD = 0.77), Profile 3 (M = 3.62, SD = 0.96), Profile 4 (M = 3.15, SD = 0.97), and Profile 2 (M = 3.14, SD = 0.98). No significant difference emerged between profiles 1 and 3 (p = .99), a significant difference emerged between profiles 1 and 4 (p < .05), and a significant difference emerged between profiles 1 and 2 (p < .05). A significant difference emerged between profiles 3 and 4 (p < .05) and profiles 3 and 2 (p < .05). No significant difference emerged profiles 4 and 2 (p = .99). Social Support Significant differences emerged for time 2 coworker support across profiles, F(3,365) = 5.98, p < .05. The means from highest to lowest were profile 3 (M = 3.79, SD = 0.77), Profile 1 (M = 3.74, SD = 0.73), Profile 4 (M = 3.62, SD = 0.78), and Profile 2 (M = 3.24, SD = 1.05). No significant difference emerged between profiles 3 and 1 (p = 0.96), profiles 3 and 4 (p = 0.49), and a significant difference emerged between profiles 3 and 2 (p < .05). No significant difference emerged between profiles 1 and 4 (p = 0.69) and a significant difference emerged between profiles 1 and 2 (p < .05). No significant difference emerged between profiles 4 and 2 (p = 0.07). Significant differences emerged for supervisor social support across profiles, F(3,365) = 5.37, p < .05. The means from highest to lowest were profile 3 (M = 3.79, SD = 0.84), Profile 1 (M = 3.78, SD = 0.79), Profile 4 (M = 3.66, SD = 0.92), and Profile 2 (M = 3.24, SD = 1.02). No significant difference emerged between profiles 3 and 1 (p = 0.99), profiles 3 and 4 (p = 0.81), and a significant difference emerged between profiles 3 and 2 (p < .05). No significant difference 140 emerged between profiles 1 and 4 (p = 0.82) and a significant difference emerged between profiles 1 and 2 (p < .05). A significant difference emerged between profiles 4 and 2 (p < .05). Demographics ANOVA analyses suggested profiles differed in terms of age, F(3,366) = 6.15, p <.05. The mean age of profile 1 was 40.92 years (SD = 10.06), profile 2 was 46.08 years (SD = 9.13), profile 3 was 38.57 years (SD = 11.11), and profile 4 was 42.74 years (SD = 10.96). Profiles did not differ regarding the average number of face-to-face hours per week reported, F(3,366) = .43, p <.05. The mean FtF hours for this sample was 41.28 hours (SD = 10.61). Differences on average work hours could not be analyzed due to an error in collecting the data on Qualtrics. Profiles did not differ regarding proportions of gender [χ2 (12, 370) = 9, p = .7], race [χ2 (24, 464) = 28.76, p = .76], or industry, χ2 (51, 469) = 47.93, p = .6. Profiles did differ in regard to work role, χ2 (18, 469) = 33.1, p < .05. Regarding participant’s self-report work role, Profile 1 did not represent the majority of any positions across profiles but was comprised most of non-supervising employees (47.5%) and managers (29.1%). Profile 2 had the second largest percentage of non-supervising profiles across profiles (60.42%) and was comprised slightly less of supervisors compared to other profiles (8.33%). Profile 3 had the largest representation of supervisors across profiles (21.21%), but also included a large amount of non-supervising employees (35.35%), and managers (31.31%). Lastly, profile 4 comprised the most of non-supervising employees (70.77%), the largest percentage by far across profiles. Table 18 reports frequencies and percentage by class for gender and race. Table 19 reports the same information for industry. Table 20 reports the same information for role. 141 Discussion – Study 2 The purpose of Study 2 was to replicate the structure of latent profiles from the previous study in addition to testing time-lagged relationships between profile membership and outcomes of interest. This study also investigated whether profiles were associated with various different resources, antecedents, and outcomes grounded in emotion regulation and job crafting theory. Using a time-lagged study design across time points separated two weeks apart, a four-profile structure was found in both time-points using approach-oriented and avoidance-oriented emotion regulation and job crafting indicators. These profiles demonstrated similar levels of mean-scores on indicator variables across time points and relative to other profiles. These results suggest that consistent patterns of emotion regulation behaviors can emerge across time, strengthening the argument that an emotion regulation behavior co-occur and that some strategies occur more often with some than others (Gross, 2007). However, those who comprised profiles were inconsistent across time. This finding is not totally surprising, however, given that emotion regulation and job crafting strategies are so closely tied to context and emotion regulation goals. Thus, despite that occupations’ emotional demands and employees’ dispositions can lead to different mean-score levels and patterns in emotion regulation strategies, employees nevertheless face varying work tasks and demands, interact with different people, and pursue multiple emotion regulation goals every workday. The four profiles displayed similar levels of indicator scores across time points, supporting LPA as a promising tool for understanding longitudinal patterns of emotion regulation behaviors. Profile 1’s emotion regulation behaviors revolved predominantly around promotion-oriented skill crafting and a small degree of prevention-oriented skill crafting. This group also scored much lower prevention-oriented relationship crafting and lower prevention- 142 oriented situation selection. Altogether, however, this group seemed to enact typical emotional regulation behaviors relative to the other profiles. This profile had high proactivity and moderately high approach temperament, which aligned with their high promotion-oriented skill crafting. They notably rely less on prevention-oriented strategies and emotion regulation strategies like situation selection, response modulation, and attentional deployment. This may be indicative of a good fit with their work environment if the predominant emotion regulation behaviors they use are skill-oriented job crafting to continue developing skills they enjoy. Despite their seemingly low emotion regulation behaviors, these individuals reported high emotional demands across the board, alongside profile 3. Moreover, they reported moderate exhaustion and job engagement relative to other profiles. Notably, profile 1 had the highest autonomy in addition to moderately high levels of social support from both coworkers and supervisors. So, despite their reported high levels of emotional demands, this group was able to meet these demands given they had necessary resources, especially autonomy, to enact desired changes at work. In other words, this profile was able to regulate and craft toward their desired emotion goals and meet demands. This could explain why they relied heavily on skill crafting modification. Altogether, this group’s scores likely reflect individuals who are well established at their jobs and have accumulated sufficient resources and leverage to manage their emotional demands and craft a work environment that suits them. Another interpretation is this group has crafted a work role where they often interact with others at work, given their self-reported high emotional intelligence for regulating other’s emotions. This supports the idea these individuals formed a work role that fits them well. Altogether, this profile was adept at managing their environment to meet their emotional demands, and successfully crafted an environment that provided them the sufficient resources to remain engaged and not emotionally exhausted. 143 Profile 2 could be considered almost context agnostic. Their low BAS and moderate BIS scores suggest they are not particularly responsive to positive or negative stimuli. However, profile 2 also had low proactivity, further suggesting these individuals were indifferent to changing their work environment. However, profile 2 also reported the lowest amount of job crafting and emotion regulation behaviors, in addition to the lowest emotional demands (i.e., regulation demands, compassion demands, and general demands, display rules) relative to the other groups. This profile seems to encompass individuals at work who do not face many perceived emotional demands and have little need or desire to regulate their emotions. However, given they faced little to no emotional demands, they likely exerted insufficient effort to become emotionally exhausted. With such few emotional demands to manage, their low emotion regulation behaviors explain why their exhaustion levels are similar to the other profiles. On the other hand, this group did not engage in many emotion regulation behaviors (job crafting or emotion regulation strategies), which likely led them to a work environment they do not find engaging. Given their little engagement, few emotional demands, and especially low skill crafting and task crafting emotion regulation behaviors, which also function to maintain or accumulate job resources, this could explain why their social support was the lowest across profiles. One interpretation is this profile reflects individuals who believe --- possibly accurately --- they are not great at understanding and managing other’s emotions. This would align with their other-appraisal and other-regulation emotional intelligence. Compounded with their low positive affect, these individuals may have landed themselves in a role where they interact with those around them just enough to meet their job requirements. But, are ultimately disinterested in their work’s social context and managing its demands. An alternative explanation is this group’s low social support (from both colleagues and supervisors) and low autonomy reflects a group 144 that has isolated themselves emotionally given they do not feel supported or appreciated at work. This profile’s behaviors align with the JDR model, where their low emotional demands may lead to lower exhaustion, but also to low job engagement and insufficient resource allocation and accumulation (Demerouti & Bakker, 2001). Altogether, this profile reflects employees who are incredibly passive at work and are likely to withdraw from their job. Despite their low emotional demands, their other characteristics suggest this profile did not employ low emotion regulation just because they lacked emotional demands. Profile 3 showed a similar pattern to profile 2 but in the opposite direction. These individuals reported very high emotion regulation behaviors across all strategies. Subsequently, this profile also reported the highest degree of emotional demands across the board. Further, this group was also profile 2’s antipode in its characteristics. Profile 3 was highly proactive and in addition had a high approach temperament and BAS. This aligns with expectations, given this group employed all emotion regulation behaviors, especially promotion-oriented task crafting, skill crafting, and relationship crafting. Accordingly, they reported moderate emotional exhaustion and also the highest job engagement. This is not surprising given the amount of emotional demands they reported incurs a lot of effort. But, they also reaped rewards by holding a job they found highly engaging and rewarding over time. This profile supports the tenants of the JDR model in that their demands both engendered costs but also led to greater job engagement when provided sufficient resources (Demerouti & Bakker, 2001). Profile 3 further had high positive affect and high other-focused emotion regulation (a facet of emotional intelligence). This aligns with the large amount of research that has identified positive affect and emotional intelligence as important emotion regulation resources (Bindl & Parker, 2019; Demerouti & Bakker, 2011; Hobfoll et al., 2011; Kammeyer-Mueller et al., 2013; Mesmer- 145 Magnus et al., 2012; Nowotny, 1981; Parker et al., 2010; Peekar et al., 2018; Wu et al., 2013; Zellars et al., 2006). Notably, this profile reported very high cue absence relative to other profiles. Cue absence is the ability to perceive, for instance, when there is negligible threat, no urgent response is required, or little control (Bonano et al., 2020). This profile thus reflects those who were capable situation readers and who reacted accordingly to their circumstances with the appropriate degree of emotion regulation behaviors. Their high cue absence sensitivity is likely why this profile exhibited moderate level of exhaustion, despite their high emotional demands. If these individuals knew when to preserve or expend energy on emotion regulation behaviors, these individuals were then able to preserve energy and save themselves from being emotionally exhausted. Moreover, this profile reported high social support from both supervisors and coworkers alongside high autonomy. These resources granted these employees the ability to enact emotion regulation behaviors to meet their emotional demands. Altogether, profile 3 reflects a high proactive, approach-oriented group of individuals who were capable of meeting the large amount of emotional demands at work through high resources and knowledge of when to expend energy on emotion regulation. Finally, profile 4 is characterized by low proactivity, moderately low approach temperament and BAS, and high BIS. This profile employed much lower promotion-oriented skill crafting, task crafting, and relationship crafting strategies. This profile also reported higher prevention-oriented relationship crafting, promotion-oriented situation selection, and prevention- oriented situation selection, and higher response modulation. They also reported moderate to low emotional demands compared to other profiles. However, this profile exhibited low job engagement and moderately high emotional exhaustion. Considering they relied little on promotion-oriented forms of job crafting and enacted more prevention-oriented relationship 146 crafting and situation selection, this characterizes these individuals as situationally reactive and interested in avoiding tasks and people they disliked, and not interested in approaching the skills, tasks, and people they enjoy. Profile 4’s reactive, preventative tendency is reflected by their relatively poor well-being. Because they did not change their work context to suit their emotional demands, this resulted in their low job engagement and high emotional exhaustion across time. However, these employees also reported low positive affect, autonomy, and social support. Alongside their moderately high cue presence, these characteristics likely explain why this profile also exhibited much higher prevention- and promotion-oriented situation selection, and prevention-oriented relationship crafting. Altogether, this profile somewhat reflected the hedonistic regulators, who are driven to minimize the likelihood they encounter anything they dislike in sacrifice of developing a better set of skills and work tasks that suit them. Another perspective is these individuals are making the best of a difficult situation by sticking close to what they like as often they can. The results of this study paint a more mixed picture of emotion regulation profiles compared to the prior study. Although the four-profile structure replicated across time points, they exhibited characteristics similar, but not necessarily exact, across time points. Moreover, the hypothesized proactivity and approach-avoid temperament splits across profiles did not emerge as distinct as expected. A surprising finding was the variability of profile membership. No profile kept a majority of its members across time points. Given the hypothesized relationship between dispositional factors (i.e., proactivity and approach-avoidance temperament) and other research linking these dispositions to emotion regulation behaviors, the expectation was that individuals’ behaviors would be similar across time. However, prior emotional labor research, like Gabriel et al. (2015) and Diefendorff et al. (2008), also suggests that reliance on multiple strategies is not 147 uncommon. Moreover, as acknowledged previously, context is a strong determinant of emotion regulation behavior. The transience of profiles likely reflects the shifting nature of demands. For example, an employee may interact with their boss or manager one week more than usual or successfully avoid interacting with a particularly grouchy colleague. So, the strategies employed most likely vary to suit the situation and their emotional demands. In total, a four-profile pattern was replicated across two time points using emotion regulation and job crafting behaviors as latent profile indicators. In addition, this study’s profile structure replicated the previous study’s four-profile structure, further supporting the use of LPA to understand emotion regulation strategies. In this case, LPA helped illuminate how emotion regulation behaviors co-occur, the different dispositional factors associated with these different behavior configurations, and the outcome and resources associated with these unique profiles across time. The findings ultimately suggest that emotional demands are typically managed using four unique clusters of behavior that reflect a group’s propensity to engage in job crafting or emotion regulation and their context around their emotional demands. 148 General Discussion The purpose of this study was to investigate whether patterns of emotion regulation and job crafting behaviors can cluster together using latent profile analysis. Moreover, this study sought to link emergent profiles’ behaviors to a hypothesized two-by-two configuration of high- low proactivity and approach-avoidance temperament. Finally, the emergent profiles were compared on well-being outcomes, emotional demands, and job resources grounded in JDR theory. This is the first study to distinguish between long-term and short-term forms of emotion regulation when investigating differences on emotional demands, outcomes, and resources. Further, emotion regulation and job crafting were theoretically integrated to explicate these profiles’ behaviors, emotional demands, demands, and resources. The hypothesized differences in proactivity and approach temperament were partly supported, but the hypothesized differences in avoidance temperament were not. However, the four-profile structure emerged in both study 1 and study 2 as hypothesized. But because the two-by-two proactivity and approach-avoidance temperament differences did not emerge as hypothesized, the characterization of profiles was then exploratory. There was one profile across studies characterized by high emotional demands, emotion regulation behaviors, proactivity, and approach motive. Specifically, profile 4 in study 1 and profile 3 in study 2. As discussed, their emotion regulation behaviors align with their emotional demands reported. However, their high proactivity and approach motives suggest this group is self-aware of their wherewithal to meet many emotional demands, or that they found meeting them engaging and rewarding. Supporting coworkers emotionally can incur emotional demands, but these employees also enjoy the benefits of helping others, which can engender a stronger connection to their coworkers and build social support. Accordingly, this group also exhibits a 149 high level of social support from coworkers and supervisors in addition to high coping flexibility. So, these individuals likely have the resources and emotion regulation strategy repertoire to manage greater emotional demands than other profiles. A title for this profile may be the “demands conquerors” since their significant emotional demands are met with various proactive and reactive emotion regulation behaviors, all while sustaining the resources necessary to meet them. Another profile that emerged across studies was characterized by low emotional demands and low emotion regulation behaviors. These profiles were study 1’s profile 3 and study 2’s profile 2. Both profiles reported substantially lower emotion regulation behaviors and demands. Study’s 2 low regulation profile was, however, a substantially more extreme version. This profile reflected a group of individuals with little to no resources to enact much emotion regulation altogether. Study 1 did not collect autonomy scores, but profile 3’s behaviors also reflect the very low autonomy reported by profile 2 (study 2) given their substantially promotion-oriented forms of relationship crafting, task crafting, and cognitive crafting. Both of their proactivity levels and higher BIS also aligned with their low proactive behaviors. In study 2, this profile’s low job engagement and moderate emotional exhaustion likely reflected the little to no emotion regulation behaviors they engaged, in addition to their poor ability to develop and maintain resources. This profile could be titled the “low regulators” similar to the Gabriel et al. (2015) profile. However, this may not even be appropriate because this group is especially disinterested in promotion-oriented forms of job crafting but employ slightly less than average emotion regulation. But, as shown, their demands, behaviors, and resources are markedly lower than the other profiles, corresponding to their lower job engagement and emotional exhaustion. 150 Beyond indicator and hypothesis variables, emotional demands were incredibly helpful for understanding profiles and their behaviors. The distinct combination of emotional demands with the types and degree of emotional demands reportedly faced, and comparisons relative to other profiles, help characterize why certain profiles seem to perform much lower or higher emotion regulation behaviors than others. However, the sample showed much lower interpersonal demands compared to typical emotion regulation and job crafting studies. The sample’s low emotional demands likely accounts for the variability of profiles across time. Profiles are also more difficult to distinguish when the driver of emotion regulation behaviors are infrequent or require less emotion regulation to meet. However, these results also uncovered markedly different well-being outcomes according to reported emotional demands that were reflected by resources and emotion regulation behaviors. For instance, every profile’s emotional exhaustion and job engagement levels seemed to align with the expectations of the JDR model (Demerouti & Bakker, 2001). For instance, study 2’s profile 2 and 3 both reported well-being (i.e., job engagement and emotional exhaustion) that reflected the level of demands they faced and their access to resources. Moreover, profile 1 reported the low exhaustion despite demands similar to profile 3, which aligned with this profile’s substantially greater autonomy, social support, and positive affect. Profile 4, on the other hand, consistently reported the lowest social support and autonomy, which corresponded to their moderately high emotional exhaustion and low job engagement. Regarding resources, differences across social support, autonomy, positive affect, and context sensitivity helped explain some of the observed emotion regulation behavior patterns. Social support from coworkers and supervisors was highest for profiles high on emotion regulation behaviors, and demands, and lowest for those low on emotion regulation behaviors 151 and behaviors. Moreover, low social support was also associated with profiles employing more avoidance-oriented behaviors. These results align with previous job crafting research that show approach-oriented job crafting is associated with greater resource generation while avoidance- oriented crafting does not. Since social support is a critical resource for helping meet their emotional demands and to enact emotion regulation behaviors, notably proactive behaviors like job crafting and situation modification that altered one’s work context, the pattern of profile differences aligned with previous research. Unsurprisingly, autonomy mirrored the pattern of social support scores. Given that autonomy is a critical resource for enacting change in one’s environment, profiles that reported the most autonomy also reported the greatest well-being. Autonomy also did not necessarily mean that individuals would engage in lesser emotional demands, but rather more. Again, this is not surprising because autonomy allows one to leverage the environment around them to meet emotional demands and engender other resources, like social support. Lastly, context sensitivity helped explain some of these profile’s behaviors. Profile 3’s high cue absence sensitivity suggests these individuals are highly capable of distinguishing when to exert effort on emotion regulation behaviors or to preserve their energy. This explains why their emotional exhaustion is comparable to other profiles with much lower emotional demands. Cue presence, on the other hand, helped explain profile 4’s emotion regulation behaviors which focused on approaching and avoiding specific tasks and/or people. Their sensitivity to these emotional cues, in combination with their low autonomy, helped understand why this profile exhibited the highest emotional exhaustion despite their moderate- low emotional demands. This study underscores the importance of supplementing LPA with theory to better understand patterns in the data. Although LPA helped organize a large amount of information 152 that would normally be too much to consider altogether in a traditional variable-centered approach, the patterns in LPA are still open to the researcher’s interpretation. The subjective evaluation of profiles can present issues for researchers because there are no good criteria for what constitutes a meaningful or distinct pattern of behaviors within or across profiles. This can lead to interpretation of spurious profiles with little meaning or profiles with limited practical or theoretical significance. However, providing adequate theoretical grounding for why some behaviors may co-occur with certain behaviors more than others mitigates the impact of these subjective evaluations. In this study, the enumeration of profiles helped identify unique patterns of behaviors that corresponded to theoretically consistent differences across profiles in accordance to JDR theory. The application of the 2x2 proactive-reactive and approach-avoidance frame was helpful for understanding why, for instance, some profiles performed proactive behaviors more than reactive behaviors. This was also helpful for cross-profile comparison, where differences in proactive behaviors and approach-avoidance temperaments did signal a greater mean of corresponding behaviors. This suggests LPA can be helpful for synthesizing two phenomena that had never been integrated. LPA allows for an efficient, broader look at co- occurring patterns of behaviors and to map them to theoretically shared dispositions, resources, and outcomes. In this sense, LPA can be a tool for theory integration and advancement. However, what these unique patterns of behaviors and their sources are still ultimately unclear. Moreover, the replication of profiles can be volatile and hypothesis testing based on profiles is hard to do given the empirical nature of the method. For these profiles to meaningfully contribute to theory, future research should seek to replicate this structure across samples. Broadly, future studies should continue to identify and investigate the parallels between job crafting and emotion regulation by understanding how they each shape the employees’ well-being and emotional 153 demands. Research should also continue thinking about the ways these behaviors work together and affect each processes’ trajectory at the workplace. This study provides a predominantly person-centered perspective for how these processes occur at the workplace which can inform future studies interested in the interplay between people, the situation, and emotion regulation strategy. This study’s findings have several implications for emotion regulation and job crafting research based on the composition of emergent profiles and their auxiliary variables. First, the profiles’ emotion regulation and job crafting behaviors suggest that emotion regulation (often studied under emotional labor) and job crafting co-occur. Individuals who frequently perform emotion regulation likely also perform more job crafting behaviors and vice-versa. This is especially true for approach and avoidance-oriented situation selection and situation modification. This makes sense given that individuals who modify their environment via job crafting are likely also to be selective about the situations they do or do not encounter at work. Emotion regulation researchers ought to incorporate job crafting behaviors into models to account for the co-occurrence of these behaviors and the way they can shape the frequency and nature emotional demands faced. Given that job crafting enacts semi-stable changes into one’s work environment across social, structural, and cognitive domains, the implications of these changes on emotion regulation should be considered in future studies. Moreover, these behaviors often co-occur according to the approach or avoidance nature of the strategy. Given this distinction is already present in job crafting research, emotion regulation researchers should similarly adopt this split given the differences in employee resources observed. The findings across profile comparisons also align with the tenants of the JDR model, which is often employed to investigate emotion regulation behaviors. Profiles with greater 154 emotional demands also employed more emotion regulation behaviors. Further, higher or lower resources across profiles correspond to their preference for approach or avoidance-oriented behaviors. This all suggests that extant emotion regulation models in the organizational sciences could benefit from applying the approach-avoidance distinction to these behaviors to understand differences in resource development and expenditure. This is especially true for the results concerning emotional exhaustion and job engagement discussed earlier. Across every study 2 profile, the emotional demands reported by employees were reflected in their well-being according to their resources. Altogether, profiles with much higher emotional demands fared better on job engagement and emotional exhaustion than those with lower demands. This observation corresponded to the tenants of the JDR model (Demerouti & Bakker, 2001), given the former profiles also had sufficient access to resources necessary to meet these demands. However, it should be noted the levels of emotion demands and emotion regulation behaviors are lower than typical emotion regulation studies. This is likely because participants were recruited online and were not required to work very emotionally demanding jobs. But, more work should explore the role of emotion regulation behaviors and outcomes in jobs where the emotional demands are not as high as nurses or service workers, but where emotional demands nevertheless occur and impact employees. For instance, emotionally charged phenomena like incivility and conflict have been shown to impact employee performance or work attitude. Although these demands may not be as severe as those in nursing or service work, employees often must regulate their emotions in response to these events. Models should expand and elaborate on the emotional regulation process for these jobs to better link them to other outcomes of interest within the workplace. Despite this sample’s lower emotional demands and emotion 155 regulation behaviors, this study provides insight into the emotion regulation behaviors of employees which are not typically featured in emotional regulation research. Limitations and Future Directions One of the largest limitations in this study was the sample. Participants were recruited using an online participant pool. Each participant is vetted, according to the site, so there are no duplicate participants, and the information participants provide about themselves is accurate, like their current location, demographics, and other background variables. However, there was no way to verify whether the information reported by participants in the initial screening survey is accurate. Despite screening participants who did not meet the study criteria of at least part time employment and 10 hours of face-to-face interaction, several responses were removed from the data after the participant reported working online only or less than 20 hours a week. So, the veracity of responses, despite several rounds of data cleaning and insufficient effort analyses, is not certain. Another sample limitation is that participation is likely skewed toward those who choose to and can participate in online surveys to supplement their income. These individuals may have a greater degree of autonomy or flexibility compared to the average worker. These workers may also have a greater level of proactivity given these surveys are completed beyond another full- time job (based on the average hours worked), which incurs additional work and effort. Moreover, the work roles reported by participants show there is a substantial percentage who are supervisors, managers, or self-employed. Beyond the higher level of autonomy inherent to these roles, the emotional demands of these jobs may not be representative of typical work roles given their greater power and access to resources. These jobs further do not reflect those most featured in emotion regulation research, which are service workers, nurses, or teachers. Because these 156 workers experience such high levels of emotional demands, the job crafting and emotion regulation behaviors in this type of work may not be adequately represented. So, despite it being important to investigate the nature of emotional demands and emotion regulation behaviors within jobs that do not feature high emotional demands, those jobs were nevertheless underrepresented despite their frequent feature in emotion regulation and job crafting research. Another limitation is the sample size. A sample size of 500 is suggested for a latent profile analysis with four-profiles, ten indicators, and a moderate degree of separation. The analyses ran in study 1 and study 2 did not meet this sample size criteria and also included more indicators. So, this study likely did not have sufficient power to detect more profiles if present in the data. The number of time points and their separation are another limitation. First, establishing the consistency of profiles is limited when there are only two time points. Despite profile replication, several more time points are likely necessary to assess whether these behavioral patterns are consistent over time. This is also true for assessing profile membership variability. The two-week separation is another limitation because prior work has already shown how variable emotion regulation and job crafting behaviors are across even shorter time scales. Because the situation and emotional demands that evoke emotion regulation behaviors are mutable and shifting, establishing a connection between these behaviors and outcomes two- weeks later is difficult. This is compounded by the fact that participants were asked to report on their behavior and work circumstances from the previous two weeks. Memory biases and insufficient memory recollection likely impacted participants’ responses, which makes finding these relationships more challenging. 157 Future studies should include a more diverse sample of participants that cover the range of emotional demands across jobs. Service work, hospital workers, and teachers should be represented much more given the high level of emotion regulation necessary to manage emotional demands. This would reflect prior emotion regulation work, which has consistently linked emotion regulation behaviors and emotional demands to well-being and other factors. However, future studies should also feature jobs with moderate emotional demands in order to understand how these demands, and the behaviors employed to manage them, have on employees’ well-being and performance. The demands these jobs face may be lower than those typically featured in the literature, but they nevertheless exist, and studies continue to show that employees enact emotion regulation behaviors at work to manage their emotions and related demands. In future research, the outcomes investigated should be expanded. This study focused on emotional demands because of its application of the JDR theory. A particular focus was to understand how patterns of emotion regulation and job crafting behaviors relate to the resources and outcomes (i.e., well-being) relevant to managing these demands. Moreover, emotional demands were chosen to best match the content of the demands faced to the behaviors used to generate profiles. Other studies should investigate outcomes such as job performance, OCBs, or CWBs, and withdrawal since prior literature has linked factors to emotions and affect. As individuals regulate their emotions to meet emotional demands, the impact of their regulation likely has spillover effects into these other outcomes. Future studies should continue on investigating how context influences these behaviors. There is a growing literature that emphasizes the interaction between emotion regulation strategy and context, which can impact a strategy’s effectiveness and perceived appropriateness. Given that dispositional factors explained 158 some of the differences between profiles, more research should be done to investigate how context shapes employees’ emotion regulation altogether. Lastly, the practical implications of LPA and the emergent profiles are limited. Although, as noted earlier, there are limitations to the theoretical practicality of profiles, the uncovering of behavioral patterns has indeed been replicated and lead the expansion of these profiles to other outcomes (see prior discussion of Grandey’s emotional labor profiles) in previous research. However, the use of these profiles from an applied perspective is unclear. Selection based on profile categorization may not be practical given the uncertain meaning of profiles’ behaviors and their utility for job performance. Moreover, selection systems likely already include many of the auxiliary variables investigated that would be compared across profiles, making them somewhat redundant. Future studies ought to consider the practical utility of these profiles to organizations. One possibility is that profiles may function as a vehicle for employee performance introspection. Discussing an employee’s tendencies at work versus their expected behaviors (according to a profile) may open a window to understanding the source of the discrepancy. For example, an individual may come to terms with the fact they are not as adept at emotion regulation as they thought or there is a barrier hindering the effectiveness of their emotion regulation at work. In this case, the employee is encourages to consider themselves and their qualities situated in the workplace to identify rooms for improvement. 159 REFERENCES Abraham, R. (1998). Emotional dissonance in organizations: Antecedents, consequences, and moderators. Genetic, social, and general psychology monographs, 124(2), 229. Adams, R. B., Ambady, N., Macrae, C. N., & Kleck, R. E. (2006). Emotional expressions forecast approach-avoidance behavior. Motivation and emotion, 30(2), 177-186. Aldao, A., Sheppes, G., & Gross, J. J. (2015). Emotion regulation flexibility. Cognitive Therapy and Research, 39(3), 263-278. Allen, J. A., Diefendorff, J. M., & Ma, Y. (2014). Differences in emotional labor across cultures: A comparison of Chinese and US service workers. Journal of Business and Psychology, 29(1), 21-35. Amirkhan, J. H. (1990). A factor analytically derived measure of coping: The Coping Strategy Indicator. Journal of personality and social psychology, 59(5), 1066. Aspinwall, Lisa G., and Shelley E. Taylor. "A Stitch in Time: Self-Regulation and Proactive Coping." Psychological Bulletin, vol. 121, no. 3, 1997, pp. 417-436. ProQuest, http://ezproxy.msu.edu/login?url=https://www.proquest.com/scholarly-journals/stitch- time-self-regulation-proactive-coping/docview/614401767/se-2?accountid=12598, doi:http://dx.doi.org/10.1037/0033-2909.121.3.417. Augustine, A. A., & Hemenover, S. H. (2009). On the relative effectiveness of affect regulation strategies: A meta-analysis. Cognition and Emotion, 23(6), 1181-1220. Bakker, A. B., & Demerouti, E. (2007). The job demands‐resources model: State of the art. Journal of managerial psychology. Bakker, A. B., & Sanz-Vergel, A. I. (2013). Weekly work engagement and flourishing: The role of hindrance and challenge job demands. Journal of Vocational Behavior, 83(3), 397- 409. Bakker, A. B., & Schaufeli, W. B. (2008). Positive organizational behavior: Engaged employees in flourishing organizations. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 29(2), 147-154. Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2014). Burnout and work engagement: The JD–R approach. Annu. Rev. Organ. Psychol. Organ. Behav., 1(1), 389-411. Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2005). The crossover of burnout and work engagement among working couples. Human relations, 58(5), 661-689. Bakker, A. B., Demerouti, E., & Verbeke, W. (2004). Using the job demands‐resources model to predict burnout and performance. Human Resource Management: Published in 160 Cooperation with the School of Business Administration, The University of Michigan and in alliance with the Society of Human Resources Management, 43(1), 83-104. Bakker, A. B., Tims, M., & Derks, D. (2012). Proactive personality and job performance: The role of job crafting and work engagement. Human relations, 65(10), 1359-1378. Balducci, C., Schaufeli, W. B., & Fraccaroli, F. (2011). The job demands–resources model and counterproductive work behaviour: The role of job-related affect. European Journal of Work and Organizational Psychology, 20(4), 467-496. Barrick, M. R., & Mount, M. K. (1991). The big five personality dimensions and job performance: a meta‐analysis. Personnel psychology, 44(1), 1-26. Bartolucci, F., Farcomeni, A., Pandolfi, S., & Pennoni, F. (2015). LMest: an R package for latent Markov models for categorical longitudinal data. arXiv preprint arXiv:1501.04448. Basch, J., & Fisher, C. D. (1998). Affective events-emotions matrix: A classification of work events and associated emotions (pp. 1-20). Bond University. Bateman, T. S., & Crant, J. M. (1993). The proactive component of organizational behavior: A measure and correlates. Journal of organizational behavior, 14(2), 103-118. Bechtoldt, M. N., Rohrmann, S., De Pater, I. E., & Beersma, B. (2011). The primacy of perceiving: emotion recognition buffers negative effects of emotional labor. Journal of Applied Psychology, 96(5), 1087. Benita, M., Kehat, R., Zaba, R., Blumenkranz, Y., Kessler, G., Bar-Sella, A., & Tamir, M. (2019). Choosing to regulate emotions: Pursuing emotion goals in autonomy-supportive and controlling contexts. Personality and Social Psychology Bulletin, 45(12), 1666-1680. Ben-Naim, S., Hirschberger, G., Ein-Dor, T., & Mikulincer, M. (2013). An experimental study of emotion regulation during relationship conflict interactions: the moderating role of attachment orientations. Emotion, 13(3), 506. Bennett Murphy, L. M., Laurie‐Rose, C., Brinkman, T. M., & McNamara, K. A. (2007). Sustained attention and social competence in typically developing preschool‐aged children. Early Child Development and Care, 177(2), 133-149. Berg, J. M., Dutton, J. E., Wrzesniewski, A., & Baker, W. E. (2013). Job crafting exercise. Ann Arbor: University of Michigan. Berg, J. M., Wrzesniewski, A., & Dutton, J. E. (2010). Perceiving and responding to challenges in job crafting at different ranks: When proactivity requires adaptivity. Journal of Organizational Behavior, 31(2‐3), 158-186. Bindl, U. K., & Parker, S. K. (2011). Proactive work behavior: Forward-thinking and change- oriented action in organizations. In APA handbook of industrial and organizational 161 psychology, Vol 2: Selecting and developing members for the organization. (pp. 567- 598). American Psychological Association. Bindl, U. K., & Parker, S. K. (2011). Proactive work behavior: Forward-thinking and change- oriented action in organizations. In APA handbook of industrial and organizational psychology, Vol 2: Selecting and developing members for the organization. (pp. 567- 598). American Psychological Association. Bindl, U. K., Unsworth, K. L., Gibson, C. B., & Stride, C. B. (2019). Job crafting revisited: Implications of an extended framework for active changes at work. Journal of Applied Psychology, 104(5), 605. Bindl, U. K., Unsworth, K. L., Gibson, C. B., & Stride, C. B. (2019). Job crafting revisited: Implications of an extended framework for active changes at work. Journal of Applied Psychology, 104(5), 605. Bipp, T., & Demerouti, E. (2015). Which employees craft their jobs and how? Basic dimensions of personality and employees' job crafting behaviour. Journal of Occupational and Organizational Psychology, 88(4), 631-655. Blanchflower & Oswald, 1999; Saks & Gruman, 2014) Blanchflower, D. G., & Oswald, A. J. (1999, May). Well-being, insecurity and the decline of American job satisfaction. Paper presented at a Cornell University conference, Ithaca, NY. Retrieved from http://www .dartmouth.edu/~blnchflr/papers/JobSat.pdf Boekhorst, J. A., Singh, P., & Burke, R. (2017). Work intensity, emotional exhaustion and life satisfaction: The moderating role of psychological detachment. Personnel Review. Bohlmann, C., Rudolph, C. W., & Zacher, H. (2021). Effects of proactive behavior on within- day changes in occupational well-being: the role of organizational tenure and emotion regulation skills. Occupational Health Science, 5(3), 277-306. Boland, A., & Cappeliez, P. (1997). Optimism and neuroticism as predictors of coping and adaptation in older women. Personality and Individual Differences, 22(6), 909-919. Bonanno, G. A., Papa, A., Lalande, K., Westphal, M., & Coifman, K. (2004). The importance of being flexible: The ability to both enhance and suppress emotional expression predicts long-term adjustment. Psychological science, 15(7), 482-487. Bonanno, G. A., Papa, A., Lalande, K., Westphal, M., and Coifman, K. (2004). The importance of being flexible: The ability to both enhance and suppress emotional expression predicts long-term adjustment. Psychol. Sci. 15, 482–487. doi: 10.1111/j.0956-7976.2004.00705.x Borst, R. T., Kruyen, P. M., Lako, C. J., & de Vries, M. S. (2020). The attitudinal, behavioral, and performance outcomes of work engagement: A comparative meta-analysis across the public, semipublic, and private sector. Review of Public Personnel Administration, 40(4), 613-640. 162 Bowling, N. A., Beehr, T. A., Wagner, S. H., & Libkuman, T. M. (2005). Adaptation-level theory, opponent process theory, and dispositions: an integrated approach to the stability of job satisfaction. Journal of Applied Psychology, 90(6), 1044. Breaugh, J. A. (1985). The measurement of work autonomy. Human relations, 38(6), 551-570. Brief, A. P., & Weiss, H. M. (2002). Organizational behavior: Affect in the workplace. Annual review of psychology, 53(1), 279-307. Brotheridge, C. M., & Grandey, A. A. (2002). Emotional labor and burnout: Comparing two perspectives of “people work”. Journal of vocational behavior, 60(1), 17-39. Bruning, P. F., & Campion, M. A. (2018). A role–resource approach–avoidance model of job crafting: A multimethod integration and extension of job crafting theory. Academy of Management Journal, 61(2), 499-522. Bruning, Patrick F. The Structure and Function of Job Crafting, Purdue University, Ann Arbor, 2014. ProQuest, http://ezproxy.msu.edu/login?url=https://www.proquest.com/dissertations- theses/structure-function-job-crafting/docview/1615364073/se-2?accountid=12598. Castellano, E., Muñoz Navarro, R., Toledo, M. S., Spontón, C., & Medrano, L. A. (2019). Cognitive processes of emotional regulation, burnout and work engagement. Psicothema, 2019, vol. 31, num. 1, p. 73-80. Catalino, L. I., Algoe, S. B., & Fredrickson, B. L. (2014). Prioritizing positivity: An effective approach to pursuing happiness?. Emotion, 14(6), 1155. Cavanaugh, M. A., Boswell, W. R., Roehling, M. V., & Boudreau, J. W. (2000). An empirical examination of self-reported work stress among US managers. Journal of applied psychology, 85(1), 65. Cellar, D. F., Stuhlmacher, A. F., Young, S. K., Fisher, D. M., Adair, C. K., Haynes, S., ... & Riester, D. (2011). Trait goal orientation, self-regulation, and performance: A meta- analysis. Journal of Business and Psychology, 26(4), 467-483. Cerasoli, C. P., Nicklin, J. M., & Nassrelgrgawi, A. S. (2016). Performance, incentives, and needs for autonomy, competence, and relatedness: A meta-analysis. Motivation and Emotion, 40(6), 781-813. Chi, N. W., & Grandey, A. A. (2019). Emotional labor predicts service performance depending on activation and inhibition regulatory fit. Journal of Management, 45(2), 673-700. Chi, N. W., Grandey, A. A., Diamond, J. A., & Krimmel, K. R. (2011). Want a tip? Service performance as a function of emotion regulation and extraversion. Journal of Applied Psychology, 96(6), 1337. 163 Coyne, J. C. (1976). Depression and the response of others. Journal of abnormal psychology, 85(2), 186. Crant, J. M. (1995). The proactive personality scale and objective job performance among real estate agents. Journal of applied psychology, 80(4), 532. Crant, J. M. (2000). Proactive behavior in organizations. Journal of management, 26(3), 435- 462. Cropanzano, R., Rupp, D. E., & Byrne, Z. S. (2003). The relationship of emotional exhaustion to work attitudes, job performance, and organizational citizenship behaviors. Journal of Applied psychology, 88(1), 160. Cunningham, C. J., & De La Rosa, G. M. (2008). The interactive effects of proactive personality and work-family interference on well-being. Journal of Occupational Health Psychology, 13(3), 271. Daniels, K., Beesley, N., Wimalasiri, V., & Cheyne, A. (2013). Problem solving and well-being: Exploring the instrumental role of job control and social support. Journal of Management, 39(4), 1016-1043. De Bloom, J., Vaziri, H., Tay, L., & Kujanpää, M. (2020). An identity-based integrative needs model of crafting: Crafting within and across life domains. Journal of Applied Psychology, 105(12), 1423. Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of research in personality, 19(2), 109-134. Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian psychology/Psychologie canadienne, 49(3), 182. Demerouti, E. (2014). Design your own job through job crafting. European psychologist. Demerouti, E., & Peeters, M. C. (2018). Transmission of reduction‐oriented crafting among colleagues: A diary study on the moderating role of working conditions. Journal of Occupational and Organizational Psychology, 91(2), 209-234. Demerouti, E., Bakker, A. B., & Halbesleben, J. R. (2015). Productive and counterproductive job crafting: A daily diary study. Journal of Occupational Health Psychology, 20(4), 457. Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands- resources model of burnout. Journal of Applied psychology, 86(3), 499. Demerouti, E., Peeters, M. C., & Heuvel, M. V. D. (2019). Job crafting interventions: do they work and why?. In Positive psychological intervention design and protocols for multi- cultural contexts (pp. 103-125). Springer, Cham. 164 Demerouti, Evangelia, & Bakker, Arnold B.. (2011). The Job Demands-Resources model: challenges for future research. SA Journal of Industrial Psychology, 37(2), 01-09. Retrieved January 26, 2022, from http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2071- 07632011000200001&lng=en&tlng=en. Diefendorff, J. M., Richard, E. M., & Croyle, M. H. (2006). Are emotional display rules formal job requirements? Examination of employee and supervisor perceptions. Journal of occupational and organizational psychology, 79(2), 273-298. Diefendorff, J. M., Richard, E. M., & Yang, J. (2008). Linking emotion regulation strategies to affective events and negative emotions at work. Journal of Vocational behavior, 73(3), 498-508. Dierdorff, E. C., & Jensen, J. M. (2018). Crafting in context: Exploring when job crafting is dysfunctional for performance effectiveness. Journal of Applied Psychology, 103(5), 463. Diestel, S., Rivkin, W., & Schmidt, K. H. (2015). Sleep quality and self-control capacity as protective resources in the daily emotional labor process: results from two diary studies. Journal of Applied Psychology, 100(3), 809. Dittmar, H., Bond, R., Hurst, M., & Kasser, T. (2014). The relationship between materialism and personal well-being: A meta-analysis. Journal of personality and social psychology, 107(5), 879. Doré, B. P., Silvers, J. A., & Ochsner, K. N. (2016). Toward a personalized science of emotion regulation. Social and Personality Psychology Compass, 10(4), 171-187. Duke, A. B., Goodman, J. M., Treadway, D. C., & Breland, J. W. (2009). Perceived organizational support as a moderator of emotional labor/outcomes relationships. Journal of Applied Social Psychology, 39(5), 1013-1034. Elliot, A. J. (1999). Approach and avoidance motivation and achievement goals. Educational psychologist, 34(3), 169-189. Elliot, A. J. (2006). Approach and avoidance motivation. Motivation and Emotion, 30, 111-116. Elliot, A. J., & Thrash, T. M. (2002). Approach-avoidance motivation in personality: approach and avoidance temperaments and goals. Journal of personality and social psychology, 82(5), 804. Elliot, A. J., & Thrash, T. M. (2010). Approach and avoidance temperament as basic dimensions of personality. Journal of personality, 78(3), 865-906. Erez, A., & Isen, A. M. (2002). The influence of positive affect on the components of expectancy motivation. Journal of Applied psychology, 87(6), 1055. 165 Erickson, R. J. (1991). When emotion is the product: Self, society, and (in) authenticity in a postmodern world (Doctoral dissertation, Washington State University). Fischer, R., & Boer, D. (2011). What is more important for national well-being: money or autonomy? A meta-analysis of well-being, burnout, and anxiety across 63 societies. Journal of personality and social psychology, 101(1), 164. Flaherty, J., & Richman, J. (1989). Gender differences in the perception and utilization of social support: Theoretical perspectives and an empirical test. Social Science & Medicine, 28(12), 1221-1228. Foti, R. J., Bray, B. C., Thompson, N. J., & Allgood, S. F. (2012). Know thy self, know thy leader: Contributions of a pattern-oriented approach to examining leader perceptions. The Leadership Quarterly, 23(4), 702-717. Fritz, C., & Sonnentag, S. (2009). Antecedents of day-level proactive behavior: A look at job stressors and positive affect during the workday. Journal of management, 35(1), 94-111. Fuller, A., & Unwin, L. (2017). Job crafting and identity in low-grade work: How hospital porters redefine the value of their work and expertise. Vocations and Learning, 10(3), 307–324. https://doi.org/10.1007/s12186-017-9173-z. Gable, S. L., & Reis, H. T. (2010). Good news! Capitalizing on positive events in an interpersonal context. In Advances in experimental social psychology (Vol. 42, pp. 195- 257). Academic Press. Gabriel, A. S., & Diefendorff, J. M. (2015). Emotional labor dynamics: A momentary approach. Academy of management Journal, 58(6), 1804-1825. Gabriel, A. S., Daniels, M. A., Diefendorff, J. M., & Greguras, G. J. (2015). Emotional labor actors: a latent profile analysis of emotional labor strategies. Journal of applied psychology, 100(3), 863. Gallup, G., & Castelli, J. (1989). The people's religion: American faith in the 90's. Macmillan. Gordon, R. M., & Gordon, R. M. (1990). The structure of emotions: Investigations in cognitive philosophy. Cambridge University Press. Gorman, C. A., Meriac, J. P., Overstreet, B. L., Apodaca, S., McIntyre, A. L., Park, P., & Godbey, J. N. (2012). A meta-analysis of the regulatory focus nomological network: Work-related antecedents and consequences. Journal of Vocational Behavior, 80(1), 160- 172. Grandey, A. A. (2000). Emotional regulation in the workplace: A new way to conceptualize emotional labor. Journal of occupational health psychology, 5(1), 95. Grandey, A. A., & Gabriel, A. S. (2015). Emotional labor at a crossroads: Where do we go from here?. Annu. Rev. Organ. Psychol. Organ. Behav., 2(1), 323-349. 166 Grandey, A. A., & Melloy, R. C. (2017). The state of the heart: Emotional labor as emotion regulation reviewed and revised. Journal of occupational health psychology, 22(3), 407. Grandey, A. A., Dickter, D. N., & Sin, H. P. (2004). The customer is not always right: Customer aggression and emotion regulation of service employees. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 25(3), 397-418. Grandey, A. A., Fisk, G. M., & Steiner, D. D. (2005). Must" service with a smile" be stressful? The moderating role of personal control for American and French employees. Journal of applied psychology, 90(5), 893. Gross, J. J. (1998). Antecedent-and response-focused emotion regulation: divergent consequences for experience, expression, and physiology. Journal of personality and social psychology, 74(1), 224. Gross, J. J. (2008). Emotion regulation. Handbook of emotions, 3(3), 497-513. Gross, J. J. (2015). Emotion regulation: Current status and future prospects. Psychological inquiry, 26(1), 1-26. Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of personality and social psychology, 85(2), 348. Gross, J. J., & Thompson, R. (2007). Handbook of emotion regulation (pp. 3-24). New York: Guilford. Gross, J. J., Carstensen, L. L., Pasupathi, M., Tsai, J., Götestam Skorpen, C., & Hsu, A. Y. (1997). Emotion and aging: experience, expression, and control. Psychology and aging, 12(4), 590. Gruman, J. A., & Saks, A. M. (2011). Performance management and employee engagement. Human resource management review, 21(2), 123-136. Gu-Ne, K., & Lee, Y. M. (2016). Towards high performance organization: The impacts of job characteristics and job crafting. International Journal of u and e-Service, Science and Technology, 9(2), 85-100. Hackman, J. R., & Oldham, G. R. Work redesign. Reading, Mass.: Addison-Wesley, 1980. Hakanen, J. J., Seppälä, P., & Peeters, M. C. (2017). High job demands, still engaged and not burned out? The role of job crafting. International Journal of Behavioral Medicine, 24(4), 619-627. Halbesleben, J. R. (2010). A meta-analysis of work engagement: Relationships with burnout, demands, resources, and consequences. Work engagement: A handbook of essential theory and research, 8(1), 102-117. 167 Halbesleben, J. R., Neveu, J. P., Paustian-Underdahl, S. C., & Westman, M. (2014). Getting to the “COR” understanding the role of resources in conservation of resources theory. Journal of management, 40(5), 1334-1364. Hallberg, U. E., Johansson, G., & Schaufeli, W. B. (2007). Type A behavior and work situation: Associations with burnout and work engagement. Scandinavian Journal of Psychology, 48(2), 135-142. Han, K. M., Shin, C., Yoon, H. K., Ko, Y. H., Kim, Y. K., & Han, C. (2018). Emotional labor and depressive mood in service and sales workers: Interactions with gender and job autonomy. Psychiatry research, 267, 490-498. Harter, J. K., Schmidt, F. L., & Hayes, T. L. (2002). Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: a meta- analysis. Journal of applied psychology, 87(2), 268. Harter, J. K., Schmidt, F. L., Killham, E. A., & Agrawal, S. (2009). Q12 meta-analysis: The relationship between engagement at work and organizational outcomes. Omaha, NE: Gallup. Harter, J. K., Schmidt, F. L., Killham, E. A., & Agrawal, S. (2009). Q12 meta-analysis: The relationship between engagement at work and organizational outcomes. Omaha, NE: Gallup. Haver, A., Akerjordet, K., & Furunes, T. (2014). Wise emotion regulation and the power of resilience in experienced hospitality leaders. Scandinavian Journal of Hospitality and Tourism, 14(2), 152-169. Heatherton, T. F., & Nichols, P. A. (1994). Personal accounts of successful versus failed attempts at life change. Personality and Social Psychology Bulletin, 20(6), 664-675. Heckhausen, J., Wrosch, C., & Schulz, R. (2010). A motivational theory of life-span development. Psychological review, 117(1), 32. Hersey, R. B. (1932). Workers' emotions in shop and home. University of Pennsylvania Press. Herzberg, F. M., & Mausner, B. (1959). B. and Snyderman, BB (1959) The motivation to work. Aufl., NewYork-London.. Heuven, E., Bakker, A. B., Schaufeli, W. B., & Huisman, N. (2006). The role of self-efficacy in performing emotion work. Journal of vocational behavior, 69(2), 222-235. Higgins, E. T. (1997). Beyond pleasure and pain. American psychologist, 52(12), 1280. Hobfoll, S. E. (2001). The influence of culture, community, and the nested‐self in the stress process: Advancing conservation of resources theory. Applied psychology, 50(3), 337- 421. 168 Hochschild A. R. (1983). The Managed Heart: Commercialization of Feeling. Berkeley: University of California Press. Hochschild, A. R. (1979). Emotion work, feeling rules, and social structure. American Journal of Sociology, 85, 551-575. Hornung, S., & Rousseau, D. M. (2007). Active on the job—proactive in change: How autonomy at work contributes to employee support for organizational change. The Journal of Applied Behavioral Science, 43(4), 401-426. Huang, L. C., Su, C. H., Lin, C. C., & Lu, S. C. (2019). The influence of abusive supervision on employees’ motivation and extra-role behaviors: The daily-basis investigation. Chinese Management Studies. Hülsheger, U. R., & Schewe, A. F. (2011). On the costs and benefits of emotional labor: a meta- analysis of three decades of research. Journal of occupational health psychology, 16(3), 361. Hülsheger, U. R., Lang, J. W., & Maier, G. W. (2010). Emotional labor, strain, and performance: Testing reciprocal relationships in a longitudinal panel study. Journal of occupational health psychology, 15(4), 505. Humphrey, S. E., Nahrgang, J. D., & Morgeson, F. P. (2007). Integrating motivational, social, and contextual work design features: a meta-analytic summary and theoretical extension of the work design literature. Journal of applied psychology, 92(5), 1332. Huppertz, A. V., Hülsheger, U. R., De Calheiros Velozo, J., & Schreurs, B. H. (2020). Why do emotional labor strategies differentially predict exhaustion? Comparing psychological effort, authenticity, and relational mechanisms. Journal of occupational health psychology, 25(3), 214. Hwa, M. A. C. (2012). Emotional labor and emotional exhaustion: does co-worker support matter?. Journal of management research, 12(3), 115. Isaacowitz, D. M., Toner, K., & Neupert, S. D. (2009). Use of gaze for real-time mood regulation: effects of age and attentional functioning. Psychology and aging, 24(4), 989. Jacobson, N. S., Martell, C. R., & Dimidjian, S. (2001). Behavioral activation treatment for depression: returning to contextual roots. Clinical Psychology: science and practice, 8(3), 255. Janssen, O., Lam, C. K., & Huang, X. (2010). Emotional exhaustion and job performance: The moderating roles of distributive justice and positive affect. Journal of organizational behavior, 31(6), 787-809. Johns, M., Inzlicht, M., & Schmader, T. (2008). Stereotype threat and executive resource depletion: examining the influence of emotion regulation. Journal of Experimental Psychology: General, 137(4), 691. 169 Johnson, H. A. M. (2004). The story behind service with a smile: The effects of emotional labor on job satisfaction, emotional exhaustion, and affective well-being. Jolly, P. M., Kong, D. T., & Kim, K. Y. (2021). Social support at work: An integrative review. Journal of Organizational Behavior, 42(2), 229-251. Joseph, D. L., Dhanani, L. Y., Shen, W., McHugh, B. C., & McCord, M. A. (2015). Is a happy leader a good leader? A meta-analytic investigation of leader trait affect and leadership. The Leadership Quarterly, 26(4), 557-576. Joseph, Dhanani, Shen, McHugh, & McCord, 2015 Judge, T. A. (1992). The dispositional perspective in human resources research. Research in personnel and human resources management, 10(1), 31-72.\ Judge, T. A., Martocchio, J. J., & Thoresen, C. J. (1997). Five-factor model of personality and employee absence. Journal of applied psychology, 82(5), 745. Kammeyer‐Mueller, J. D., Rubenstein, A. L., Long, D. M., Odio, M. A., Buckman, B. R., Zhang, Y., & Halvorsen‐Ganepola, M. D. (2013). A meta‐analytic structural model of dispositonal affectivity and emotional labor. Personnel Psychology, 66(1), 47-90. Karasek Jr, R. A. (1979). Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative science quarterly, 285-308. Kemper, T. D. (1990). Social relations and emotions: A structural approach. Research agendas in the sociology of emotions, 207-237. Kent de Grey, R. G., Uchino, B. N., Trettevik, R., Cronan, S., & Hogan, J. N. (2018). Social support and sleep: A meta-analysis. Health Psychology, 37(8), 787. Kiefer, T., & Barclay, L. J. (2012). Understanding the mediating role of toxic emotional experiences in the relationship between negative emotions and adverse outcomes. Journal of Occupational and Organizational Psychology, 85(4), 600-625. Kober, H., & Bolling, D. (2014). Emotion regulation in substance use disorders. In J. Gross (Ed), Handbook of Emotion Regulation (pp. 428-46). New York: Guilford Publications. Lanaj, K., Chang, C. H., & Johnson, R. E. (2012). Regulatory focus and work-related outcomes: a review and meta-analysis. Psychological bulletin, 138(5), 998. Laursen, B., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly (1982-), 377-389. Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. Springer publishing company. 170 Lazazzara, A., Tims, M., & De Gennaro, D. (2020). The process of reinventing a job: A meta– synthesis of qualitative job crafting research. Journal of Vocational Behavior, 116, 103267. Leana, C., Appelbaum, E., & Shevchuk, I. (2009). Work process and quality of care in early childhood education: The role of job crafting. Academy of Management Journal, 52(6), 1169-1192. Lee, R. T., & Ashforth, B. E. (1996). A meta-analytic examination of the correlates of the three dimensions of job burnout. Journal of applied Psychology, 81(2), 123. Leiter, M. P., & Maslach, C. (2004). Areas of worklife: A structured approach to organizational predictors of job burnout. In P. Perrewe´ & D. C. Ganster (Eds.), Research in occupational stress and well being (Vol. 3, pp. 91–134). Oxford, United Kingdom: Elsevier Lennard, A. C., Scott, B. A., & Johnson, R. E. (2019). Turning frowns (and smiles) upside down: A multilevel examination of surface acting positive and negative emotions on well- being. Journal of Applied Psychology, 104(9), 1164. LePine, J. A., LePine, M. A., & Jackson, C. L. (2004). Challenge and hindrance stress: relationships with exhaustion, motivation to learn, and learning performance. Journal of applied psychology, 89(5), 883. LePine, J. A., Podsakoff, N. P., & LePine, M. A. (2005). A meta-analytic test of the challenge stressor–hindrance stressor framework: An explanation for inconsistent relationships among stressors and performance. Academy of management journal, 48(5), 764-775. Lewin, K. (1935). A dynamic theory of personality. New York: McGraw HillBook Company. Development, Factor Analysis, and Validation. Lichtenthaler, P. W., & Fischbach, A. (2016). The conceptualization and measurement of job crafting. Zeitschrift für Arbeits-und Organisationspsychologie A&O. Lichtenthaler, P. W., & Fischbach, A. (2018). Leadership, job crafting, and employee health and performance. Leadership & Organization Development Journal. Lichtenthaler, P. W., & Fischbach, A. (2019). A meta-analysis on promotion-and prevention- focused job crafting. European Journal of Work and Organizational Psychology, 28(1), 30-50. Lindsay, E. K., Chin, B., Greco, C. M., Young, S., Brown, K. W., Wright, A. G., ... & Creswell, J. D. (2018). How mindfulness training promotes positive emotions: Dismantling acceptance skills training in two randomized controlled trials. Journal of personality and social psychology, 115(6), 944. 171 Liu, J., Cho, S., & Putra, E. D. (2017). The moderating effect of self-efficacy and gender on work engagement for restaurant employees in the United States. International Journal of Contemporary Hospitality Management. Livingstone, K. M., & Isaacowitz, D. M. (2015). Situation selection and modification for emotion regulation in younger and older adults. Social psychological and personality science, 6(8), 904-910. Locke, E. A., & Latham, G. P. (1990). A theory of goal setting & task performance. Prentice- Hall, Inc. Lopez, S. H. (2006). Emotional labor and organized emotional care: Conceptualizing nursing home care work. Work and Occupations, 33(2), 133-160. Lyons, P. (2008). The crafting of jobs and individual differences. Journal of Business and Psychology, 23(1), 25-36. Mäkikangas, A. (2018). Job crafting profiles and work engagement: A person-centered approach. Journal of Vocational Behavior, 106, 101-111. Maslach, C., & Jackson, S. E. (1986). MBI: Maslach Burnout Inventory; manual research edition. University of California, Palo Alto, CA. Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). Maslach Burnout Inventory manual (3rd ed.). Palo Alto, CA: Consulting Psychologists Press Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual review of psychology, 52(1), 397-422. Masyn, K. E. (2013). 25 latent class analysis and finite mixture modeling. The Oxford handbook of quantitative methods, 2, 551. Mazzetti, G., Robledo, E., Vignoli, M., Topa, G., Guglielmi, D., & Schaufeli, W. B. (2021). Work Engagement: A meta-Analysis Using the Job Demands-Resources Model. Psychological Reports, 00332941211051988. McCauley, C. D., Ruderman, M. N., Ohlott, P. J., & Morrow, J. E. (1994). Assessing the developmental components of managerial jobs. Journal of applied psychology, 79(4), 544. Mesmer-Magnus, J. R., DeChurch, L. A., & Wax, A. (2012). Moving emotional labor beyond surface and deep acting: A discordance–congruence perspective. Organizational Psychology Review, 2(1), 6-53. Mesmer-Magnus, J. R., DeChurch, L. A., Wax, A., & Anderson, K. T. (2011, January). Dissonance matters: Meta-analytic examination of the antecedents and consequences of emotional labor. In Academy of management proceedings (Vol. 2011, No. 1, pp. 1-6). Briarcliff Manor, NY 10510: Academy of Management. 172 Michielsen, H. J., Willemsen, T. M., Croon, M. A., De Vries, J., & Van Heck, G. L. (2004). Determinants of general fatigue and emotional exhaustion: A prospective study. Psychology & Health, 19(2), 223-235. Montgomery, A. J., Peeters, M. C. W., Schaufeli, W. B., & Ouden, M. D. (2003). Work‐home interference among newspaper managers: Its relationship with burnout and engagement. Anxiety, Stress, and Coping, 16(2), 195-211. Morin, A. J., Meyer, J. P., Creusier, J., & Biétry, F. (2016). Multiple-group analysis of similarity in latent profile solutions. Organizational Research Methods, 19(2), 231-254. Morris, W. N., & Reilly, N. P. (1987). Toward the self-regulation of mood: Theory and research. Motivation and emotion, 11(3), 215-249. Mroz, J., & Kaleta, K. (2016). Relationships between personality, emotional labor, work engagement and job satisfaction in service professions. International journal of occupational medicine and environmental health, 29(5), 767. Muthén, B., & Asparouhov, T. (2013). BSEM measurement invariance analysis. Mplus web notes, 17, 1-48. Newsom, J. (2021a) Latent Class Analysis. Personal Class blog. http://web.pdx.edu/~newsomj/cdaclass/ho_lca.pdf Newsom, J. (2021b) Latent Transition Analysis. Personal Class blog. http://web.pdx.edu/~newsomj/cdaclass/ho_lta.pdf Nguyen, N., & Stinglhamber, F. (2020). Workplace mistreatment and emotional labor: A latent profile analysis. Motivation and emotion, 44(3), 474-490. Nielsen, K., Nielsen, M. B., Ogbonnaya, C., Känsälä, M., Saari, E., & Isaksson, K. (2017). Workplace resources to improve both employee well-being and performance: A systematic review and meta-analysis. Work & Stress, 31(2), 101-120. Niessen, C., Weseler, D., & Kostova, P. (2016). When and why do individuals craft their jobs? The role of individual motivation and work characteristics for job crafting. Human relations, 69(6), 1287-1313. Nils, F., & Rimé, B. (2012). Beyond the myth of venting: Social sharing modes determine the benefits of emotional disclosure. European Journal of Social Psychology, 42(6), 672-681. Nowotny, H. (2018). Women in public life in Austria. In Access to power (pp. 147-156). Routledge. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural equation modeling: A multidisciplinary Journal, 14(4), 535-569. 173 Oberski, D. (2016). Mixture models: Latent profile and latent class analysis. In Modern statistical methods for HCI (pp. 275-287). Springer, Cham. Oberski, D. (2016). Mixture models: Latent profile and latent class analysis. In Modern statistical methods for HCI (pp. 275-287). Springer, Cham. O'Connor, E. J., Eulberg, J. R., Peters, L. H., & Watson, T. W. (1984). Situational Constraints in the Air Force. Identification, Measurement, and Impact on Work Outcomes. TEXAS UNIV AT DALLAS RICHARDSON SCHOOL OF MANAGEMENT AND ADMINISTRATION. Oprea, B., & Iliescu, D. (2015). Burnout and job insecurity: the mediating role of job crafting. Psihologia Resurselor Umane, 13(2), 232-244. Parker, S. K., & Collins, C. G. (2010). Taking stock: Integrating and differentiating multiple proactive behaviors. Journal of management, 36(3), 633-662. Parker, S. K., Bindl, U. K., & Strauss, K. (2010). Making things happen: A model of proactive motivation. Journal of management, 36(4), 827-856. Parker, S. K., Van den Broeck, A., & Holman, D. (2017). Work design influences: A synthesis of multilevel factors that affect the design of jobs. Academy of Management Annals, 11(1), 267-308. Parker, S. K., Williams, H. M., & Turner, N. (2006). Modeling the antecedents of proactive behavior at work. Journal of applied psychology, 91(3), 636. Pekrun, R., & Frese, M. (1992). Emotions in work and achievement. International review of industrial and organizational psychology, 7, 153-200. Pekrun, R., Frenzel, A. C., Goetz, T., & Perry, R. P. (2007). The control-value theory of achievement emotions: An integrative approach to emotions in education. In Emotion in education (pp. 13-36). Academic Press. Pelosi, E. M. (2015). Staying engaged when you're on stage: The effects of emotional labor, competence, and relatedness on work engagement (Doctoral dissertation, Seattle Pacific University). Peng, J., He, Y., Deng, J., Zheng, L., Chang, Y., & Liu, X. (2019). Emotional labor strategies and job burnout in preschool teachers: Psychological capital as a mediator and moderator. Work, 63(3), 335-345. Petrou, P., Demerouti, E., & Schaufeli, W. B. (2015). Job crafting in changing organizations: Antecedents and implications for exhaustion and performance. Journal of occupational health psychology, 20(4), 470. 174 Petrou, P., Demerouti, E., & Schaufeli, W. B. (2018). Crafting the change: The role of employee job crafting behaviors for successful organizational change. Journal of Management, 44(5), 1766-1792. Petrou, P., Demerouti, E., & Schaufeli, W. B. (2018). Crafting the change: The role of employee job crafting behaviors for successful organizational change. Journal of Management, 44(5), 1766-1792. Petrou, P., Demerouti, E., Peeters, M. C., Schaufeli, W. B., & Hetland, J. (2012). Crafting a job on a daily basis: Contextual correlates and the link to work engagement. Journal of Organizational Behavior, 33(8), 1120-1141. Podsakoff, N. P., LePine, J. A., & LePine, M. A. (2007). Differential challenge stressor- hindrance stressor relationships with job attitudes, turnover intentions, turnover, and withdrawal behavior: a meta-analysis. Journal of applied psychology, 92(2), 438. Rafaeli, A. (1989). When cashiers meet customers: An analysis of the role of supermarket cashiers. Academy of management Journal, 32(2), 245-273. Rafaeli, A., & Sutton, R. I. (1989). The expression of emotion in organizational life. Research in organizational behavior, 11(1), 1-42. Randolph, K. L., & Dahling, J. J. (2013). Interactive effects of proactive personality and display rules on emotional labor in organizations. Journal of Applied Social Psychology, 43(12), 2350-2359. Rehman, A. U., Bhuttah, T. M., & You, X. (2020). Linking burnout to psychological well-being: The mediating role of social support and learning motivation. Psychology Research and Behavior Management, 13, 545. Reinhold, S., Gegenfurtner, A., & Lewalter, D. (2018). Social support and motivation to transfer as predictors of training transfer: testing full and partial mediation using meta‐analytic structural equation modelling. International Journal of Training and Development, 22(1), 1-14. Renkema, E., Broekhuis, M. H., Ahaus, K., & Tims, M. (2018). Working your way around: Job crafting in a public and professional accountability context. (Unpublished paper). Rich, B. L., Lepine, J. A., & Crawford, E. R. (2010). Job engagement: Antecedents and effects on job performance. Academy of management journal, 53(3), 617-635. Richards, J. M., & Gross, J. J. (2000). Emotion regulation and memory: the cognitive costs of keeping one's cool. Journal of personality and social psychology, 79(3), 410. Rigg, J., Day, J., & Adler, H. (2013). Emotional exhaustion in graduate students: The role of engagement, self-efficacy and social support. Journal of Educational and Developmental Psychology, 3(2), 138. 175 Rimé, B. (2007). Interpersonal emotion regulation. Handbook of emotion regulation, 1, 466-468. Rippere, V. (1977). What's the thing to do when you're feeling depressed: A pilot study. Behaviour Research and Therapy. Rofcanin, Y., Bakker, A. B., Berber, A., Gölgeci, I., & Las Heras, M. (2019). Relational job crafting: Exploring the role of employee motives with a weekly diary study. Human relations, 72(4), 859-886. Roseman, I. J. (1984). Cognitive determinants of emotion: A structural theory. Review of personality & social psychology. Rosenberg JM, Beymer PN, Anderson DJ, Van Lissa CJ, Schmidt JA (2018). “tidyLPA: An R Package to Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software.” Journal of Open Source Software, 3(30), 978. doi: 10.21105/joss.00978, https://joss.theoj.org/papers/10.21105/joss.00978. Rothbart, M. K., Ziaie, H., & O'boyle, C. G. (1992). Self? regulation and emotion in infancy. New directions for child and adolescent development, 1992(55), 7-23. Rousseau, Vincent, & Aubé, Caroline. (2010). Social support at work and affective commitment to the organization: The moderating effect of job resource adequacy and ambient conditions. The Journal of Social Psychology, Vol 150(4), 321-340. doi: https://dx.doi.org/10.1080/00224540903365380, © 2010 by Taylor & Francis. Reproduced by Permission of Taylor & Francis Rovenpor, D. R., Skogsberg, N. J., & Isaacowitz, D. M. (2013). The choices we make: an examination of situation selection in younger and older adults. Psychology and aging, 28(2), 365. Rudolph, C. W., Katz, I. M., Lavigne, K. N., & Zacher, H. (2017). Job crafting: A meta-analysis of relationships with individual differences, job characteristics, and work outcomes. Journal of Vocational Behavior, 102, 112-138. Russell Cropanzano, K. J., & Citera, M. (1993). A goal hierarchy model of personality, motivation, and leadership. Res. Organ. Behav, 15, 267-322. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1), 54-67. Ryan, R. M., & Deci, E. L. (2006). Self‐regulation and the problem of human autonomy: Does psychology need choice, self‐determination, and will?. Journal of personality, 74(6), 1557-1586. Saks, A. M., & Gruman, J. A. (2014). What do we really know about employee engagement?. Human resource development quarterly, 25(2), 155-182. 176 Salkovskis, P. M. (1991). The importance of behaviour in the maintenance of anxiety and panic: A cognitive account. Behavioural and Cognitive Psychotherapy, 19(1), 6-19. Schachter, S. (1959). The psychology of affiliation: Experimental studies of the sources of gregariousness. Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi‐sample study. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 25(3), 293-315. Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and psychological measurement, 66(4), 701-716. Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness studies, 3(1), 71-92. Schutte, N. S., Manes, R. R., & Malouff, J. M. (2009). Antecedent-focused emotion regulation, response modulation and well-being. Current Psychology, 28(1), 21-31. Schwarzer, R., & Luszczynska, A. (2008). Reactive, anticipatory, preventive, and proactive coping: A theoretical distinction. The Prevention Researcher, 15(4), 22-25. Schwarzer, R., & Taubert, S. (2002). Tenacious goal pursuits and striving toward personal growth: Proactive coping. Beyond coping: Meeting goals, vision, and challenges/ed. E. Frydenberg.–London: Oxford University Press. Scrucca L, Fop M, Murphy TB, Raftery AE (2016). “mclust 5: clustering, classification and density estimation using Gaussian finite mixture models.” The R Journal, 8(1), 289– 317. https://doi.org/10.32614/RJ-2016-021. Seibert, S. E., Kraimer, M. L., & Crant, J. M. (2001). What do proactive people do? A longitudinal model linking proactive personality and career success. Personnel psychology, 54(4), 845-874. Sezen-Gultekin, G., Bayrakcı, M., & Limon, İ. (2021). The mediating role of organizational commitment on the relationship Between emotional labor and work engagement of teachers. Frontiers in Psychology, 12. Sheppes, G., & Gross, J. J. (2012). Emotion regulation effectiveness: What works when. Handbook of psychology, 5, 391-406. Sheppes, G., Scheibe, S., Suri, G., & Gross, J. J. (2011). Emotion-regulation choice. Psychological science, 22(11), 1391-1396. 177 Sheppes, G., Scheibe, S., Suri, G., Radu, P., Blechert, J., & Gross, J. J. (2014). Emotion regulation choice: a conceptual framework and supporting evidence. Journal of Experimental Psychology: General, 143(1), 163. Shin, I., & Jeung, C. W. (2019). Uncovering the turnover intention of proactive employees: The mediating role of work engagement and the moderated mediating role of job autonomy. International journal of environmental research and public health, 16(5), 843. Shin, Y., & Hur, W. M. (2022). Having control or lacking control? Roles of job crafting and service scripts in coping with customer incivility. Journal of Occupational Health Psychology, 27(1), 104. Sliter, M., Jex, S., Wolford, K., & McInnerney, J. (2010). How rude! Emotional labor as a mediator between customer incivility and employee outcomes. Journal of occupational health psychology, 15(4), 468. Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. Journal of personality and social psychology, 48(4), 813. Smith, C. A., & Pope, L. K. (1992). Appraisal and emotion: The interactional contributions of dispositional and situational factors. Solomon, R. L., & Corbit, J. D. (1974). An opponent-process theory of motivation: I. Temporal dynamics of affect. Psychological review, 81(2), 119. Spector, P. E. (1986). Perceived control by employees: A meta-analysis of studies concerning autonomy and participation at work. Human relations, 39(11), 1005-1016. Steinberg, R. J., & Figart, D. M. (1999). Emotional demands at work: A job content analysis. The Annals of the American Academy of Political and Social Science, 561(1), 177-191. Strazdins, L. M. (2000). Integrating emotions: Multiple role measurement of emotional work. Australian Journal of Psychology, 52(1), 41-50. Sutton, R. I., & Rafaeli, A. (1988). Untangling the relationship between displayed emotions and organizational sales: The case of convenience stores. Academy of Management journal, 31(3), 461-487. Tamir, M. (2005). Don't worry, be happy? Neuroticism, trait-consistent affect regulation, and performance. Journal of personality and social psychology, 89(3), 449. Tamir, M. (2009). What do people want to feel and why? Pleasure and utility in emotion regulation. Current Directions in Psychological Science, 18(2), 101-105. Tamir, M., & Ford, B. Q. (2012). When feeling bad is expected to be good: emotion regulation and outcome expectancies in social conflicts. Emotion, 12(4), 807. 178 Tamir, M., Mitchell, C., & Gross, J. J. (2008). Hedonic and instrumental motives in anger regulation. Psychological science, 19(4), 324-328. Taylor, S. E., Klein, L. C., Lewis, B. P., Gruenewald, T. L., Gurung, R. A., & Updegraff, J. A. (2000). Biobehavioral responses to stress in females: tend-and-befriend, not fight-or- flight. Psychological review, 107(3), 411. Tein, J. Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural equation modeling: a multidisciplinary journal, 20(4), 640-657. ten Brummelhuis, L. L., & Bakker, A. B. (2012). Staying engaged during the week: the effect of off-job activities on next day work engagement. Journal of occupational health psychology, 17(4), 445. Thayer, R. E., Newman, J. R., & McClain, T. M. (1994). Self-regulation of mood: strategies for changing a bad mood, raising energy, and reducing tension. Journal of personality and social psychology, 67(5), 910. Thuillard, S., & Dan-Glauser, E. S. (2017). The regulatory effect of choice in situation selection reduces experiential, exocrine and respiratory arousal for negative emotional stimulations. Scientific reports, 7(1), 1-14. Thuillard, S., & Dan-Glauser, E. S. (2021). Situation selection for the regulation of emotion responses: Non-meaningful choice options retain partial physiological regulatory impact. International Journal of Psychophysiology, 162, 130-144. Tims, M., Bakker, A. B., & Derks, D. (2013). The impact of job crafting on job demands, job resources, and well-being. Journal of occupational health psychology, 18(2), 230. Toppinen‐Tanner, S., Ahola, K., Koskinen, A., & Väänänen, A. (2009). Burnout predicts hospitalization for mental and cardiovascular disorders: 10‐year prospective results from industrial sector. Stress and Health: Journal of the International Society for the Investigation of Stress, 25(4), 287-296. Totterdell, P., & Holman, D. (2003). Emotion regulation in customer service roles: testing a model of emotional labor. Journal of occupational health psychology, 8(1), 55. Troy, A. S., Shallcross, A. J., & Mauss, I. B. (2013). A person-by-situation approach to emotion regulation: Cognitive reappraisal can either help or hurt, depending on the context. Psychological science, 24(12), 2505-2514. Troy, A. S., Shallcross, A. J., and Mauss, I. B. (2013). A person-by-situation approach to emotion regulation: cognitive reappraisal can either help or hurt, depending on the context. Psychol. Sci. 24, 2505–2014. doi: 10.1177/0956797613496434 Urry, H. L., & Gross, J. J. (2010). Emotion regulation in older age. Current Directions in Psychological Science, 19(6), 352-357. 179 Uy, M. A., Lin, K. J., & Ilies, R. (2017). Is it better to give or receive? The role of help in buffering the depleting effects of surface acting. Academy of management Journal, 60(4), 1442-1461. Van Bockstaele, B., Atticciati, L., Hiekkaranta, A. P., Larsen, H., & Verschuere, B. (2020). Choose change: Situation modification, distraction, and reappraisal in mild versus intense negative situations. Motivation and Emotion, 44(4), 583-596. Van de Ven, B., van den Tooren, M., & Vlerick, P. (2013). Emotional job resources and emotional support seeking as moderators of the relation between emotional job demands and emotional exhaustion: A two-wave panel study. Journal of Occupational Health Psychology, 18(1), 1. Van den Broeck, A., De Cuyper, N., De Witte, H., & Vansteenkiste, M. (2010). Not all job demands are equal: Differentiating job hindrances and job challenges in the Job Demands–Resources model. European journal of work and organizational psychology, 19(6), 735-759. Van den Broeck, A., Vansteenkiste, M., De Witte, H., & Lens, W. (2008). Explaining the relationships between job characteristics, burnout, and engagement: The role of basic psychological need satisfaction. Work & stress, 22(3), 277-294. Van Dierendonck, D., Schaufeli, W. B., & Buunk, B. P. (1998). The evaluation of an individual burnout intervention program: The role of inequity and social support. Journal of applied psychology, 83(3), 392. Van Dijk, P. A., & Brown, A. K. (2006). Emotional labour and negative job outcomes: An evaluation of the mediating role of emotional dissonance. Journal of Management & Organization, 12(2), 101-115. Van Kleef, G. A. (2009). How emotions regulate social life: The emotions as social information (EASI) model. Current directions in psychological science, 18(3), 184-188. Van Ruysseveldt, J., Verboon, P., & Smulders, P. (2011). Job resources and emotional exhaustion: The mediating role of learning opportunities. Work & Stress, 25(3), 205-223. Viswesvaran, C., Sanchez, J. I., & Fisher, J. (1999). The role of social support in the process of work stress: A meta-analysis. Journal of vocational behavior, 54(2), 314-334. Wagner, D. T., Barnes, C. M., & Scott, B. A. (2014). Driving it home: How workplace emotional labor harms employee home life. Personnel Psychology, 67(2), 487-516. Walk, M., & Handy, F. (2018). Job crafting as reaction to organizational change. The Journal of Applied Behavioral Science, 54(3), 349-370. Wallbott, H. G., & Scherer, K. R. (1989). Assessing emotion by questionnaire. In The measurement of emotions (pp. 55-82). Academic Press. 180 Wang, H. J., Chen, X., & Lu, C. Q. (2020). When career dissatisfaction leads to employee job crafting: The role of job social support and occupational self-efficacy. Career Development International. Wang, H., Demerouti, E., & Bakker, A. B. (2016). A review of job crafting research. Proactivity at work: Making things happen in organizations, 77. Wang, M., & Hanges, P. J. (2011). Latent class procedures: Applications to organizational research. Organizational Research Methods, 14(1), 24-31. Webb, T. L., Lindquist, K. A., Jones, K., Avishai, A., & Sheeran, P. (2018). Situation selection is a particularly effective emotion regulation strategy for people who need help regulating their emotions. Cognition and Emotion, 32(2), 231-248. Webb, T. L., Miles, E., & Sheeran, P. (2012). Dealing with feeling: a meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. Psychological bulletin, 138(4), 775. Weiss, H. M., & Cropanzano, R. (1996). Affective events theory. Research in organizational behavior, 18(1), 1-74. Weiss, H. M., Suckow, K., & Cropanzano, R. (1999). Effects of justice conditions on discrete emotions. Journal of applied Psychology, 84(5), 786. Wharton, A. S. (1993). The affective consequences of service work: Managing emotions on the job. Work and occupations, 20(2), 205-232. Williams, G. A., & Kibowski, F. (2016). Latent class analysis and latent profile analysis. Handbook of methodological approaches to community-based research: Qualitative, quantitative, and mixed methods, 143-151. Wright, T. A., & Cropanzano, R. (1998). Emotional exhaustion as a predictor of job performance and voluntary turnover. Journal of applied psychology, 83(3), 486. Wróbel, M. (2013). Can empathy lead to emotional exhaustion in teachers? The mediating role of emotional labor. International Journal of Occupational Medicine and Environmental Health, 26(4), 581-592. Wrzesniewski, A., & Dutton, J. E. (2001). Crafting a job: Revisioning employees as active crafters of their work. Academy of management review, 26(2), 179-201. Xanthopoulou, D., Bakker, A. B., & Fischbach, A. (2013). Work engagement among employees facing emotional demands. Journal of Personnel Psychology. Xanthopoulou, D., Bakker, A. B., & Ilies, R. (2012). Everyday working life: Explaining within- person fluctuations in employee well-being. Human relations, 65(9), 1051-1069. 181 Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2009). Reciprocal relationships between job resources, personal resources, and work engagement. Journal of Vocational behavior, 74(3), 235-244. Yeo, G., Loft, S., Xiao, T., & Kiewitz, C. (2009). Goal orientations and performance: Differential relationships across levels of analysis and as a function of task demands. Journal of Applied Psychology, 94(3), 710. Yoo, J. (2016). Perceived customer participation and work engagement: the path through emotional labor. International Journal of Bank Marketing. Yoo, J. J., & Arnold, T. J. (2014). Customer orientation, engagement, and developing positive emotional labor. The Service Industries Journal, 34(16), 1272-1288. Yoo, J., & Jeong, J. (2017). The effects of emotional labor on work engagement and boundary spanner creativity. Asia Pacific Journal of Innovation and Entrepreneurship. Yuan, L., Yu, Y., & Liu, P. (2019). Are teams experiencing relationship conflicts destined to fail: The role of emotions and work engagement. Chinese Management Studies. Zahoor, A., & Siddiqi, M. A. (2021). Customer incivility and service recovery performance: Job crafting as a buffer. Vision, 0972262921991963. Zaki, J., & Williams, W. C. (2013). Interpersonal emotion regulation. Emotion, 13(5), 803. Zellars, K. L., Perrewé, P. L., Hochwarter, W. A., & Anderson, K. S. (2006). The interactive effects of positive affect and conscientiousness on strain. Journal of Occupational Health Psychology, 11(3), 281–289. https://doi.org/10.1037/1076-8998.11.3.281 Zhang, F., & Parker, S. K. (2019). Reorienting job crafting research: A hierarchical structure of job crafting concepts and integrative review. Journal of organizational behavior, 40(2), 126-146. 182 APPENDIX A: JOB CRAFTING SCALE (BINDL ET AL., 2019) Please rate how often you engaged in the behavior at work (1 = not at all – 5 = a great deal). Promotion Oriented Relationship crafting. 1. I actively sought to meet new people at work. 2. I made efforts to get to know other people at work better. 3. I sought to interact with other people at work, regardless of how well I knew them. 4. I tried to spend more time with a wide variety of people at work Prevention Oriented Relationship crafting 1. I minimized my interactions with people at work that I did not get along with. 2. I changed my work so that I only interacted with people that I felt good about working with. 3. I tried to avoid situations at work where I had to meet new people. Promotion Oriented Skill crafting. 1. I actively tried to develop wider capabilities in my job. 2. I tried to learn new things at work that went beyond my core skills. 3. I actively explored new skills to do my overall job. 4. I sought out opportunities for extending my overall skills at work. Prevention Oriented Skill crafting. 1. I channeled my efforts at work towards maintaining a specific area of expertise. 2. I sought to develop those skills in my job that helped prevent negative work outcomes. 3. I made sure I stayed on top of knowledge in core areas of my job. Promotion-oriented Task Crafting 1. I actively took on more tasks in my work. 2. I added complexity to my tasks by changing their structure or sequence. 3. I changed my tasks so that they were more challenging. 4. I increased the number of difficult decisions I made in my work. Prevention Oriented Task Crafting 1. I actively reduced the scope of tasks I worked on. 2. I tried to simplify some of the tasks that I worked on. 3. I sought to make some of my work mentally less intense. Promotion-oriented Cognitive Crafting 1. I tried to think of my job as a whole, rather than as separate tasks. 2. I thought about how my job contributed to the organization’s goals. 3. I thought about new ways of viewing my overall job. 4. I thought about ways in which my job as a whole contributed to society. Prevention Oriented Cognitive Crafting 1. I focused my mind on the best parts of my job, while trying to ignore those parts I didn’t like. 2. I assessed the different elements of my job to determine which parts were most meaningful. 3. I tried to think of my job as a set of separate tasks, rather than as a ‘whole.’ 183 APPENDIX B: EMOTION REGULATION QUESTIONNAIRE (GROSS & JOHN, 2003) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree). Cognitive Reappraisal 1. When I want to feel more positive emotion (such as joy or amusement), I change what I’m thinking about. 2. When I want to feel less negative emotion (such as sadness or anger), I change what I’m thinking about. 3. When I’m faced with a stressful situation, I make myself think about it in a way that helps me stay calm 4. When I want to feel more positive emotion, I change the way I’m thinking about the situation. 5. I control my emotions by changing the way I think about the situation I’m in. 6. When I want to feel less negative emotion, I change the way I’m thinking about the situation. Expressive Suppression 1. When I am feeling positive emotions, I am careful not to express them. 2. I control my emotions by not expressing them. 3. When I am feeling negative emotions, I make sure not to express them. 4. I keep my emotions to myself. 184 APPENDIX C: SITUATION SELECTION SCALE (WEBB ET AL., 2017) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree). 1. I select activities that help me to feel good 2. If a situation makes me feel good, then I try to stick around 3. I gravitate towards people, situations, and activities that put me in a good mood 4. I keep doing something if it seems to be improving my mood, 5. I shy away from situations that might upset me 6. I steer clear of people who put me in a bad mood 185 APPENDIX D: EMOTION REGULATION SURVEY (DIEFENDORFF ET AL., 2008) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree). Situation Selection 1. Avoid a situation that I know will make me feel bad 2. Seek out individuals that make me feel good Situation Modification 1. Try to solve the problem 2. Remove myself from the situation Attentional Deployment 1. Turn my attention to something that doesn’t bother me 2. Keep myself busy working on other things 3. Do something enjoyable to improve my mood Cognitive change 1. Think about how the other person feels 2. Find humor in the situation 3. Remind myself that I can’t control everything 4. Reinterpret the situation in a more positive light Response modulation 1. Hide how I really feel 2. Pretend I am in a good mood 186 APPENDIX E: EMOTION REGULATION QUESTIONNAIRE (SCHUTTE, 2009) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree). Selection of situations 1. I spend time in situations that help me to feel emotions I want to have. 2. I spend time in situations that prevent me from feeling emotions I do not want to have. 3. I seek out situations that help me feel positive emotions. 4. I avoid situations that lead me to feel negative emotions. Situation Modification 1. I change situations so that they help me to feel emotions I want to have. 2. I change situations that lead me to feel emotions I do not want to have. 3. I change situations so they lead me to feel positive emotions. 4. I change situations so they do not lead me to feel negative emotions. Attention deployment 1. I pay attention to the things around me that help me to feel emotions I want to have. 2. I pay attention to the things around me that prevent me from feeling emotions I do not want to have. 3. I concentrate on things that help me feel positive emotions. 4. I concentrate on things that block negative emotions. Cognitive change 1. I change the way I think about things to help me to feel emotions I want to have. 2. I change the way I think about things to prevent me from feeling emotions I do not want to have. 3. I change my perspective on events to create positive emotions. 4. I change my perspective on events so that I do not develop negative emotions. 187 APPENDIX F: PROACTIVE PERSONALITY (BATEMAN & CRANT, 1993) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree). 1. I am constantly on the lookout for new ways to improve my life. 2. I feel driven to make a difference in my community, and maybe the world. 3. I tend to let others take the initiative to start new projects. 4. Wherever I have been, I have been a powerful force for constructive change. 5. I enjoy facing and overcoming obstacles to my ideas. 6. Nothing is more exciting than seeing my ideas turn into reality. 7. If I see something I don’t like, I fix it. 8. No matter what the odds, if I believe in something I will make it happen. 9. I love being a champion for my ideas, even against others’ opposition. 10. I excel at identifying opportunities. 11. 1 1. I am always looking for better ways to do things. 12. If I believe in an idea, no obstacle will prevent me from making it happen. 13. I love to challenge the status quo. 14. When I have a problem, I tackle it head-on. 15. I am great at turning problems into opportunities. 16. I can spot a good opportunity long before others can. 17. If I see someone in trouble, I help out in any way I can. 188 APPENDIX G: APPROACH-AVOID TEMPERAMENT (ELLIOT & THRASH, 2010) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree). 1. By nature, I am a very nervous person. 2. Thinking about the things I want really energizes me. 3. It doesn’t take much to make me worry. 4. When I see an opportunity for something I like, I immediately get excited. 5. It doesn’t take a lot to get me excited and motivated. 6. I feel anxiety and fear very deeply. 7. I react very strongly to bad experiences. 8. I’m always on the lookout for positive opportunities and experiences. 9. When it looks like something bad could happen, I have a strong urge to escape. 10. When good things happen to me, it affects me very strongly. 11. When I want something, I feel a strong desire to go after it. 12. It is easy for me to imagine bad things that might happen to me. 189 APPENDIX H: BAS/BIS (CARVER & WHITE, 19944) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree)… 1. If something unpleasant is going to happen I usually get pretty “worked up.” 2. I worry about making mistakes. 3. Criticism or scolding hurts me quite a bit. 4. I feel pretty worried or upset when I think or know somebody is angry at me. 5. Even if something bad is about to happen to me, I rarely experience fear or nervousness. (R) 6. I feel worried when I think I have done poorly at something. 7. I have very few fears compared to my friends. (R) 8. When I get something I want, I feel excited and energized. 9. When I’m doing well at something, I love to keep at it. 10. When good things happen to me, it affects me strongly. 11. It would excite me to win a contest. 12. When I see an opportunity for something I like, I get excited right away. 190 APPENDIX I: EMOTION DISPLAY EXPECTATIONS AT WORK (DIEFENDORFF ET AL., 2003) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree)… 1. I remain positive at work even when I may be feeling otherwise. 2. I keep a positive attitude despite obstacles or difficulties. 3. I let negative events affect my mood. (reverse coded) 4. I display excitement and enthusiasm at work. 5. I monitor my emotions to make sure they are appropriate. 6. I conceal negative feelings about tasks or others. 7. I display emotions appropriate for work. 8. I display emotions consistent with my job requirements. 191 APPENDIX J: EMOTIONAL DEMANDS (STRAZDINS, 2000) How often have you… [1 = not at all in the previous month (or does not apply), 2 = some or a little of the time (about once a month or more), 3 = occasionally or a moderate amount of time (about once a week or more), 4 = often or a lot of the time (about once a day), and 5 = frequently or most of the time (more than once u day)]. Companionship 1. Shown verbal affection, speaking warmly, expressed endearments, etc. to your coworkers? 2. Praised, acknowledged, or expressed appreciate to your coworkers? 3. Shared your innermost thoughts and feelings with your coworkers? 4. Enquired about the thoughts, feelings or wellbeing of your coworkers? 5. Made the effort to spend time or do things together with your coworkers during work? 6. Initiate “play” – e.g., games, jokes, humor with your coworkers? 7. Organized social occasions, e.g., parties, get togethers, sports for coworkers? Help 1. Listened attentive to the problem or worries of coworkers? 2. Done things to soothe or calm coworkers? 3. “Taken the load off” coworkers because you knew they were stressed? 4. Done things to protect coworkers from becoming stressed (e.g., done things to help them face difficult situations)? 5. Tried to about any problems in your relationship with coworkers? 6. Tried to change or compromise in order to improve your relationship with coworkers? 7. Acted as a third party to resolve conflict between coworkers (e.g., helped both sides listen to each other and come up with solutions)? Regulating 1. Suggested to coworkers they take steps to improve their well-being? 2. Tried to help coworkers think through the consequences of their behavior? 3. Persuade coworkers to stop doing something that was harmful? 4. Stopped coworkers from doing something that could be harmful? 5. Discussed with coworkers’ rules and guidelines for socially acceptable behavior? 6. Pointed out to coworkers that they may be upsetting others (not just yourself)? 7. Tried to change how coworkers behave to make them more socially acceptable to others? 192 APPENDIX K: EMOTIONAL DEMANDS (XANTHAPOLOU ET AL., 2012) Please read each statement and then mark the appropriate answer (1 = never to 5 = always). 1. Is your work emotionally demanding? 2. In your work, are you confronted with things that personally touch you? 3. Do you face emotionally charged situations in your work? 4. In your work, do you deal with clients who incessantly complain, although you always do everything to help them? 5. In your work, do you have to deal with demanding clients? 6. Do you have to deal with clients who do not treat you with the appropriate respect and politeness? 193 APPENDIX L: SOCIAL SUPPORT (ROUSSEAU & AUBE, 2010) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree). Coworker 1. My coworkers help me emotionally when I need them. 2. My coworkers care about my physical and mental well-being. 3. My coworkers recognize my contributions and my strengths. 4. My coworkers communicate work-related information to me. 5. My coworkers help me to develop my skills and my competencies. 6. My coworkers share their knowledge and their work experience with me. Supervisor 1. My supervisor helps me emotionally when I need them. 2. My supervisor cares about my physical and mental well-being. 3. My supervisor recognizes my contributions and my strengths. 4. My supervisor communicates work-related information to me. 5. My supervisor helps me to develop my skills and my competencies. 6. My supervisor shares their knowledge and work experience with me. 194 APPENDIX M: WORK AUTONOMY (BREAUGH, 1985) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree). Work Method 1. I am allowed to decide how to go about getting my job done (i.e., the methods to use). 2. I am able to choose the way to about my job (i.e., the procedures to utilize). 3. I am free to choose the methods to use in carrying out my work. Work Scheduling 1. I have control over the scheduling of my work. 2. I have some control over the sequencing of my work activities (i.e., when I do what). 3. My job is such that I can decide when to do particular work activities. Work Criteria 1. My job allows me to modify the normal way we are evaluated so I can emphasize some aspects of my jobs and play down others. 2. I am able to modify what my job objectives are (i.e., what I am supposed to accomplish). 3. I have some control over what I am supposed to accomplish (i.e., what my supervisor sees as my job objectives). 195 APPENDIX N: MASLACH BURNOUT INVENTORY – EMOTIONAL EXHAUSTION SUB-SCALE (MASLACH & JACKON, 1986) Please report how often… [1 (Never) – 5 (Every day)] 1. I feel emotionally drained from my work. 2. I feel used up at the end of the workday. 3. I feel fatigued when I get up in the morning and have to face another day on the job. 4. Working with people all day is really a strain for me. 5. I feel burned out from my work. 6. I feel frustrated by my job. 7. I feel I'm working too hard on my job. 8. Working with people directly puts too much stress on me. 9. I feel exhilarated after work at my job. 196 APPENDIX O: WORK ENGAGEMENT SCALE (SCHAUFELI ET AL., 2002) Please rate the degree to which you agree with the statement (1 = strongly disagree – 5 = strongly agree). 1. At my work, I feel that I am bursting with energy* (Vi) 2. I find the work that I do full of meaning and purpose (De) 3. Time flies when I’m working (Ab) 4. At my job, I feel strong and vigorous (Vi)* 5. I am enthusiastic about my job (De)* 6. When I am working, I forget everything else around me (Ab) 7. My job inspires me (De)* 8. When I get up in the morning, I feel like going to work (Vi)* 9. I feel happy when I am working intensely (Ab)* 10. I am proud on the work that I do (De)* 11. I am immersed in my work (Ab)* 12. I can continue working for very long periods at a time (Vi) 13. To me, my job is challenging (De) 14. I get carried away when I’m working (Ab)* 15. At my job, I am very resilient, mentally (Vi) 16. It is difficult to detach myself from my job (Ab) 17. At my work I always persevere, even when things do not go well (Vi) 197 APPENDIX P: ROTTERDAM EMOTIONAL INTELLIGENCE SCALE (REIS) (PEEKAR ET AL., 2017) Please rate the degree to which you agree with the statement (1 = strongly disagree – 7 = strongly agree)… Self-focused emotion appraisal 1. I always know how I feel. 2. I can distinguish my own emotions well. 3. I am aware of my own emotions. 4. I understand why I feel the way I feel. 5. I know which emotions I experience. 6. Mostly, I am able to explain exactly how I feel. 7. I can judge well if events touch me emotionally. Other-focused emotion appraisal 1. I am aware of the emotions of the people around me. 2. I know which feelings others experience. 3. When I look at other people, I can see how they feel. 4. I can empathize with the people around me. 5. I understand why other people feel the way they feel. 6. I can distinguish well between other people's emotions. 7. I can judge well if events touch others emotionally. Self-focused emotion regulation 1. I am in control of my own emotions. 2. I can suppress my emotions easily. 3. I do not let my emotions take over. 4. I only show my emotions when it is appropriate. 5. Even when I am angry, I can stay calm. 6. If I want to, I put on my poker face. 7. I adjust my emotions when necessary. Other-focused emotion regulation 1. I can make someone else feel differently. 2. I can alter another person's emotional state. 3. I can boost or temper the emotions of others. 4. I have great influence on how others feel. 5. I know what to do to improve people's mood. 6. I know how to influence people. 7. I am able to calm others down. 198 APPENDIX Q: PANAS – WATSON ET AL. (1988) Please read each word and then mark the appropriate answer. Indicate to what extent you generally feel this way. That is, how you feel on average. 1 (very slightly or not at all) – 5 (extremely). Positive (odd numbers); Negative (even numbers) … 1. Interested 2. Distressed 3. Excited 4. Upset 5. Strong 6. Guilty 7. Scared 8. Hostile 9. Enthusiastic 10. Proud 11. Irritable 12. Alert 13. Ashamed 14. Inspired 15. Nervous 16. Determined 17. Attentive 18. Jittery 19. Active 20. Afraid 199 APPENDIX R: COPING FLEXIBILITY SCALE (KATO, 2012) We try to use various actions and thoughts to change or manage our emotions. The following items describe specific situations. Please indicate how applicable these statements are to you. (1-very applicable 2-applicable 3-somewhat applicable 4-not applicable). 1. When my emotions don’t change how I expected, I try to think of other ways to change them. 2. I only use certain strategies to manage my emotions. (R) 3. I use several strategies to manage my emotions. 4. When I haven't managed my emotions in a situation well, I use other strategies to change my emotions. 5. If I unsuccessfully try to change my emotions, I use other ways to manage them. 6. I am aware of how successful or unsuccessful my attempts to control my emotions have been. 7. I fail to notice when I have been unable to manage my emotions. (R) 8. If I feel that I have been unable to manage my emotions, I try another strategy of changing them. 9. After attempting to manage my emotions, I think about how well other strategies of managing emotions have worked or did not work previously. 10. If I fail to control my emotions, I think of other ways to manage them. 200 APPENDIX S: CONTEXT SENSITIVITY INDEX (CSI) (BONANNO ET AL., 2020) Read the following short descriptions of situations that may arise in your everyday life. For each situation, first spend a few moments to try to imagine that you are actually in the situation, and then answer the questions that follow by circling the number that best corresponds with your response. (1 = strongly disagree – 7 = strongly agree) 1. A friend calls and asks you to do a favor for their partner, whom you don’t like. a. How much control do you have over what happens next? b. How much control do others have over what happens next? c. How much cooperation do you need from others to respond to this situation? d. How threatening is this situation? 2. Your partner is at risk for diabetes and has been told by his/her doctor to go on a diet. He/she is refusing. a. How much control do you have over what happens next? b. How much control do others have over what happens next? c. How much cooperation do you need from others to respond to this situation? d. How threatening is this situation? 3. You are walking down a street when you see a person slip and fall. They hit their head when they land. a. How much control do you have over what happens next? b. How urgently do you need to respond in this situation? c. How much cooperation do you need from others to respond to this situation? d. How threatening is this situation? 4. You take a medicine and it makes your nauseous. Your doctor tells you that it is not serious and that you just have to “wait it out”. Not at all Very much a. a. How much control do others have over what happens next? b. How urgently do you need to respond in this situation? c. How much cooperation do you need from others to respond to this situation? 5. You are reading a book while you wait for a flight. When the plane begins to take off you realize you have left the book in the waiting area. Not at all Very much a. How much control do you have over what happens next? b. How urgently do you need to respond in this situation? c. How threatening is this situation? 6. You see somebody on a street suddenly punch another person. They turn to you angrily saying “What are you looking at?” a. How much control do others have over what happens next? b. How urgently do you need to respond in this situation? c. How threatening is this situation? 201 APPENDIX T: CFC-14 SCALE (JOIREMAN ET AL., 2016) Please read each statement and indicate how much the statement is characteristic of you. (1 = Not at all – 5 = Very much). 1. I consider how things might be in the future, and try to influence those things with my day to day behavior. 2. Often I engage in a particular behavior in order to achieve outcomes that may not result for many years. 3. I only act to satisfy immediate concerns, figuring the future will take care of itself. 4. My behavior is only influenced by the immediate (i.e., a matter of days or weeks) outcomes of my actions. 5. My convenience is a big factor in the decisions I make or the actions I take. 6. I am willing to sacrifice my immediate happiness or wellbeing in order to achieve future outcomes. 7. I think it is important to take warnings about negative outcomes seriously even if the negative outcome will not occur for many years. 8. I think it is more important to perform a behavior with important distant consequences than a behavior with less important immediate consequences. 9. I generally ignore warnings about possible future problems because I think the problems will be resolved before they reach crisis level. 10. I think that sacrificing now is usually unnecessary since future outcomes can be dealt with at a later time. 11. I only act to satisfy immediate concerns, figuring that I will take care of future problems that may occur at a later date. 12. Since my day-to-day work has specific outcomes, it is more important to me than behavior that has distant outcomes. 13. When I make a decision, I think about how it might affect me in the future. 14. My behavior is generally influenced by future consequences. 202 APPENDIX U: LIST OF TABLES Table 1. Sample Gender and Race Demographics – Study 1 Gender Female Male Non-Binary/Agender Trans-man DNA Missing Race White/Caucasian Black Latino/a Native American or American Indian or Alaskan Native Native Hawaii or Pacific Islander East Asian South Asian Middle Eastern or Arab Multiracial Other DNA Missing Note. DNA = did not answer. Frequency 268 194 4 1 2 0 Frequency 384 32 23 14 1 21 6 4 25 1 4 5 Percent 57.14 41.37 0.85 0.21 0.43 0.00 Percent 81.9 6.8 4.9 3 0.2 4.5 1.3 0.9 5.3 0.2 0.9 1.1 203 Table 2. Sample education level demographics – study 1 Education level No Schooling Elementary - 8th Some high school Highschool/Diploma equivalent Some college Assoc. degree Bachelor's degree Master's degree Ph.D. Professional school Don't know DNA Missing Note. DNA = did not answer. Self Frequency 0 1 1 35 77 47 223 60 10 7 1 2 5 Percent 0 0.21 0.21 7.46 16.42 10.02 47.55 12.79 2.13 1.49 0.21 0.43 1.07 Mother Frequency 3 16 27 159 71 54 100 27 3 3 3 1 2 Percent 0.64 3.41 5.76 33.90 15.14 11.51 21.32 5.76 0.64 0.64 0.64 0.21 0.43 Father Frequency 3 24 25 147 61 48 102 30 7 8 8 1 5 Percent 0.64 5.12 5.33 31.34 13.01 10.24 21.75 6.40 1.49 1.71 1.71 0.21 1.07 204 Table 3. Sample industry and work role demographics – study 1 Industry Advertising & Marketing Agriculture Airlines & Aerospace (including Defense) Arts, Entertainment & Recreation Business Support, Logistics, Transportation & Delivery Construction Education Finance, Banking, Insurance and Financial Services Government & Military Healthcare & Pharmaceuticals Hotel & Food/Beverage Services Journalism & Publishing Manufacturing Nonprofit Professional Consulting Services Retail Technology (hardware, software, internet, social media, etc.) Utilities, Energy & Extraction Missing Role CEO/Owner Manager Non-supervising employee Self-employed Senior Mgmt. or Executive (e.g., COO; CFO) Frequency 9 4 2 11 28 12 63 53 15 60 35 4 40 13 11 55 49 5 0 Frequency 2 123 221 8 13 Percent 1.92 0.85 0.43 2.35 5.97 2.56 13.43 11.30 3.20 12.79 7.46 0.85 8.53 2.77 2.35 11.73 10.45 1.07 0.00 Percent 0.43 26.23 47.12 1.71 2.77 205 Table 3. (cont’d) Supervisor VP/Director Head Missing 95 7 0 20.26 1.49 0.00 206 Table 4. Means and Correlations – study 1 Mean RC - App. RC - Av. SC - App. SC - Av. TC - App. TC - Av. CC - App. CC - Av. SS - App. SS - Av. SM SD 0.99 — 0.99 0.83 0.69 1.02 0.88 0.87 0.92 0.50 0.67 0.57 0.53 0.59 0.84 0.61 0.99 0.32 — 0.15 0.49 0.28 0.43 0.15 0.35 0.38 0.42 -0.03 0.50 -0.10 0.67 — 0.48 0.05 0.60 0.29 0.42 0.07 0.42 0.42 0.49 0.00 0.58 -0.10 -0.13 — -0.06 0.15 0.47 -0.03 0.32 0.07 0.44 0.08 0.05 -0.12 0.40 -0.06 0.52 -0.24 — 0.50 0.37 0.50 0.12 0.51 0.25 0.27 -0.09 0.36 0.28 0.46 0.04 0.58 -0.15 RC - App. 3.27 RC - Av. 2.70 SC - App. 3.94 SC - Av. 3.93 TC - App. 2.82 TC - Av. 3.05 CC - App. 3.46 3.40 CC - Av. 4.14 SS - App. 3.82 SS - Av. 3.89 SM 4.02 AD 3.88 CCh 3.26 RM 3.77 PP 3.27 AAT - App. AAT - Av. 3.86 0.50 3.20 Comp. 0.31 3.10 Help 0.31 2.32 Reg. 0.25 3.19 Gen. 0.14 3.45 BIS -0.08 4.08 BAS 0.47 0.44 3.85 DR Note. App = approach-oriented; AV = avoidance-oriented; RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection; SM = situation modification; AD = attentional deployment; CCh = Cognitive Change; RM = response modulation; PP = proactive personality; Comp. = compassion demands; Help = helping demands; Reg. = regulation demands; Gen. = general demand; BIS = behavioral inhibition sensitivity; BAS = behavioral activation sensitivity; DR = display rules. 0.21 — 0.50 0.40 0.13 0.00 0.27 0.23 0.32 0.28 0.50 0.05 0.20 — 0.47 0.18 0.30 0.17 0.18 0.12 0.34 0.17 0.39 0.37 — 0.35 0.07 0.43 0.42 0.50 0.04 0.61 0.01 0.30 — 0.28 0.34 0.29 0.34 0.26 0.34 0.27 0.47 — 0.66 0.67 0.54 -0.04 0.46 0.05 0.42 — 0.49 0.17 0.28 0.11 0.36 0.46 0.65 0.58 0.52 0.19 -0.28 0.26 0.37 0.31 0.50 0.53 0.59 0.20 -0.18 0.18 0.19 0.50 0.52 0.53 0.44 0.21 -0.16 0.36 0.41 -0.05 -0.03 0.01 0.20 0.19 0.29 -0.03 -0.25 0.47 0.37 0.35 0.20 0.18 -0.13 0.44 0.37 0.16 0.26 0.29 0.42 0.23 0.22 0.15 -0.08 0.48 0.45 0.45 0.31 0.16 -0.19 0.40 0.45 0.32 0.36 0.42 0.47 0.22 0.12 0.23 0.10 0.56 0.31 0.28 0.12 0.14 0.07 0.64 0.46 0.18 0.04 0.10 0.10 0.16 0.32 0.27 0.10 0.62 0.85 0.94 1.16 0.90 0.79 0.62 0.50 0.66 0.62 0.04 0.53 0.00 207 Table 4. (cont’d) AD CC RM PP AAT - App. AAT - Av. Comp. Help Reg. Gen. BIS BAS RC - App. RC - Av. SC - App. SC - Av. TC - App. TC - Av. CC - App. CC - Av. SS - App. SS - Av. SM AD CCh RM PP AAT - App. AAT - Av. Comp. Help Reg. Gen. BIS BAS DR Note. App = approach-oriented; AV = avoidance-oriented; RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection; SM = situation modification; AD = attentional deployment; CCh = Cognitive Change; RM = response modulation; PP = proactive personality; Comp. = compassion demands; Help = helping demands; Reg. = regulation demands; Gen. = general demand; BIS = behavioral inhibition sensitivity; BAS = behavioral activation sensitivity; DR = display rules. — 0.65 — 0.06 0.50 0.08 0.52 0.35 0.33 0.21 0.13 0.04 0.51 0.49 0.00 — 0.61 -0.12 0.56 0.49 0.44 0.28 0.12 -0.17 0.47 0.58 0.04 — 0.34 -0.03 0.08 0.16 0.32 0.22 0.18 -0.06 -0.01 -0.10 — 0.70 0.56 0.57 0.48 0.22 -0.25 0.48 0.50 — 0.77 — 0.59 0.33 -0.07 0.36 0.35 0.73 — 0.35 -0.06 0.30 0.34 0.31 — -0.08 0.12 0.10 — 0.44 0.40 0.25 0.21 -0.03 0.74 0.51 0.00 0.06 0.07 0.19 0.29 0.70 0.08 -0.26 0.23 — 0.22 0.16 0.12 — -0.16 0.46 208 Table 5. Results of latent profile analyses – study 1 Classes 2 3 4 5 Note. SABIC = sample-adjusted BIC; min and max = the proportion of sample assigned to the smallest and largest class based on most likely class membership; BLRT = bootstrapped likelihood ratio test. Loglikelihood AIC -5869.98 -5829.43 -5791.87 -5778.45 BLRT_val 45.68 81.09 75.13 26.83 BIC 12564.14 12575.31 12592.44 12657.86 SABIC 12138.85 12102.41 12071.93 12089.75 Entropy 0.74 0.76 0.76 0.77 12007.96 11956.87 11911.74 11914.91 BLRT_p 0.02 0.01 0.01 0.52 min 0.36 0.14 0.1 0.11 max 0.64 0.46 0.33 0.3 209 Table 6. Profile T-tests and effect sizes – Study 1 Profile 1 Profile 2 t t t p p Profile 4 p Hedges' g RC - App. -4.67 < .001 0.65 RC - Av. 0.01 2.82 -0.98 SC - App. -4.97 < .001 0.68 SC - Av. -7.49 < .001 0.77 TC - App. 2.54 0.01 -0.12 TC - Av. 3.9 < .001 -0.74 CC - App. -0.94 0.35 1.15 CC - Av. -0.14 0.89 0.09 SS - App. -9.32 < .001 0.95 SS - Av. -4.66 < .001 0.01 SM -8.07 < .001 0.61 AD -7.89 < .001 0.65 CCh -6.56 < .001 0.4 RM 0.65 -0.46 -0.45 Comp. 0.01 -2.74 0.39 Help 0.17 -1.36 0.32 Reg. 0.42 0.81 -0.07 Gen. -0.91 0.36 -0.02 DR 0.87 -7.7 < .001 Note. App = approach-oriented; AV = avoidance-oriented; RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection; SM = situation modification; AD = attentional deployment; CCh = Cognitive Change; RM = response modulation; PP = proactive personality; Comp. = compassion demands; Help = helping demands; Reg. = regulation demands; Gen. = general demand; BIS = behavioral inhibition sensitivity; BAS = behavioral activation sensitivity; DR = display rules. 12.84 < .001 < .001 9.87 < .001 5.72 4.72 < .001 22.26 < .001 < .001 11.3 12.19 < .001 < .001 5.77 0.19 1.32 < .001 4.5 < .001 4.63 < .001 6.4 7.41 < .001 14.62 < .001 10.58 < .001 10.33 < .001 16.71 < .001 < .001 5.94 0.35 0.94 6.65 < .001 -9.97 < .001 6.91 < .001 < .001 7.86 -1.22 0.22 -7.48 < .001 11.67 < .001 0.88 0.38 9.67 < .001 0.12 0.91 6.19 < .001 6.62 < .001 4.03 < .001 -4.6 < .001 3.96 < .001 3.23 < .001 0.45 -0.76 -0.23 0.82 8.87 < .001 Hedges' g -0.46 0.28 -0.49 -0.74 0.25 0.39 -0.09 -0.01 -0.92 -0.46 -0.8 -0.78 -0.65 -0.05 -0.27 -0.13 0.08 -0.09 -0.76 Hedges' g -0.95 0.11 -0.43 -0.25 -1.15 -0.22 -2.09 -0.41 -0.04 0.26 -0.09 -0.22 -0.09 -0.02 -0.76 -0.83 -1.09 -0.22 -0.13 t -8.86 0.99 -4.01 -2.34 -10.7 -2.01 -19.5 -3.86 -0.35 2.43 -0.83 -2.01 -0.85 -0.17 -7.14 -7.74 -10.14 -2.1 -1.18 Hedges' g 1.94 1.49 0.86 0.71 3.36 1.71 1.84 0.87 0.2 0.68 0.7 0.97 1.12 2.21 1.6 1.56 2.52 0.9 0.14 Profile 3 p < .001 0.32 < .001 0.02 < .001 0.05 < .001 < .001 0.73 0.02 0.41 0.05 0.4 0.86 < .001 < .001 < .001 0.04 0.24 210 Table 7. Omnibus and Post-hoc Tukey Difference Effect Sizes – study 1 Proactive Personality 49.29 F AAT - Approach 14.44 AAT - Avoidance 33.04 BIS 3.98 BAS 29.14 Profile No. 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 M 3.58 4.02 3.45 4.34 3.39 2.95 3.24 3.93 3.60 4.12 3.67 4.24 3.45 3.32 3.64 3.38 3.77 4.34 4.04 4.30 SD 0.55 0.53 0.60 0.28 0.86 1.06 0.97 0.76 0.57 0.53 0.65 0.40 0.67 0.94 0.79 0.50 0.64 0.48 0.63 0.37 – 1 — 1.5 -0.39 3.05 — -0.47 -0.18 0.78 — 1.71 0.19 2.22 — -0.2 0.36 -0.19 — 1.8 0.68 1.55 – 2 — -1.8 1.4 — 0.28 0.98 — -1.31 0.48 — 0.43 0.08 – 3 — 3.19 — 0.83 — 1.63 — -0.53 — -1 -0.21 — 0.81 Note. Effect sizes larger than .3 are significant at p < .05. AAT = approach-avoidance temperament; BIS = behavioral inhibition sensitivity; BAS = behavioral activation sensitivity. 211 Table 8. Sample self-reported education level demographics by profile – Study 1 Gender Female Male Non-Binary/Agender Trans-man DNA Missing Profile 3 Profile 2 Profile 4 Profile 1 Frequency Percent Frequency Percent Frequency Percent Frequency 66.67 78 32.68 70 0.65 2 0.00 0 0.00 1 0.00 0 51.66 46.36 1.32 0.00 0.66 0.00 59.48 37.93 0.86 0.86 0.86 0.00 102 50 1 0 0 0 19 30 0 0 0 0 69 44 1 1 1 0 Frequency Percent Frequency Percent Frequency Percent Frequency 79.08 115 4.58 11 2.61 7 1.31 3 78.26 3.48 3.48 0.87 121 7 4 2 90 4 4 1 37 6 0 1 76.16 7.28 4.64 1.99 Race White/Caucasian Black Latino/a Native American or American Indian or Alaskan Native Native Hawaii or Pacific Islander 6 East Asian 1 South Asian 0 Middle Eastern or Arab 8 Multiracial 2 DNA Missing 0 Note. Profile 1 n = 151; profile 2 n = 153; profile 3 n = 116; profile 4 n = 49 3.97 0.66 0.00 3.31 0.66 1.32 3.92 1.31 0.00 6.54 0.00 0.65 6 2 0 10 0 1 6 1 0 5 1 2 0.00 0.00 0 0 0 0.00 5.22 0.87 0.00 6.96 1.74 0.00 0 0 2 0 1 0 2 Percent 38.78 61.22 0.00 0.00 0.00 0.00 Percent 75.51 12.24 0.00 2.04 0.00 0.00 4.08 0.00 2.04 0.00 4.08 212 Table 9. Sample industry and work role demographics – Study 1 Profile 1 Profile 3 Profile 3 Profile 4 Industry Advertising & Marketing Agriculture Airlines & Aerospace (including Defense) Arts, Entertainment & Recreation Business Support, Logistics, Transportation & Delivery Construction Education Finance, Banking, Insurance and Financial Services Government & Military Healthcare & Pharmaceuticals Hotel & Food/Beverage Services Journalism & Publishing Manufacturing Nonprofit Professional Consulting Services Retail Technology (hardware, software, internet, social media, etc.) Utilities, Energy & Extraction Missing Role CEO/Owner Manager Non-supervising employee Frequency Percent Frequency Percent Frequency Percent Frequency Percent 5 3 1 1.72 0.00 0.00 3.31 1.99 0.66 1.31 0.65 0.65 0.00 0.00 0.00 0 0 0 2 0 0 2 1 1 2 9 4 18 16 5 22 9 0 11 5 5 21 15 0 0 1.32 5.96 2.65 11.92 10.60 3.31 14.57 5.96 0.00 7.28 3.31 3.31 13.91 9.93 0.00 0.00 5 7 4 26 17 7 17 10 3 16 6 2 17 11 1 0 3.27 4.58 2.61 16.99 11.11 4.58 11.11 6.54 1.96 10.46 3.92 1.31 11.11 7.19 0.65 0.00 4 11 3 15 12 3 12 16 1 10 2 2 14 5 4 0 3.45 9.48 2.59 12.93 10.34 2.59 10.34 13.79 0.86 8.62 1.72 1.72 12.07 4.31 3.45 0.00 0 1 1 4 8 0 9 0 0 3 0 2 3 18 0 0 0.00 2.04 2.04 8.16 16.33 0.00 18.37 0.00 0.00 6.12 0.00 4.08 6.12 36.73 0.00 0.00 Frequency Percent Frequency Percent Frequency Percent Frequency Percent 0 40 76 0.00 18.97 60.34 0.00 44.90 12.24 0.00 26.49 50.33 1.31 25.49 45.10 0 22 70 0 22 6 2 39 69 213 Table 9 (cont’d) Self-employed Senior Mgmt. or Executive (e.g., COO; CFO) Supervisor VP/Director Head Missing Note. Profile 1 n = 151; profile 2 n = 153; profile 3 n = 116; profile 4 n = 49 17.22 0.00 0.00 1.99 3.31 29 5 0 26 1 0 4 5 3 5 2.61 3.27 18.95 1.31 0.00 1 2 21 0 0 0.86 1.72 18.10 0.00 0.00 0 1 19 1 0 0.00 2.04 38.78 0.00 0.00 214 Table 10. Sample Gender and Race demographics – study 2 Gender Male Female Non-Binary/Agender Trans-man DNA Missing Race White/Caucasian Black Latino/a Native American or American Indian or Alaskan Native Native Hawaii or Pacific Islander East Asian South Asian Middle Eastern or Arab Multiracial Other DNA Frequency 149 216 3 1 1 0 Frequency 289 24 15 1 1 9 1 1 25 1 3 Percent 40.27 58.38 0.81 0.27 0.27 0 Percent 78.11 6.49 6.76 0.27 0.27 2.43 0.27 0.27 6.76 0.27 0.81 215 Table 11. Sample education level demographics – study 2 Education level No Schooling Elementary - 8th Some high school Highschool/Diploma equivalent Some college Assoc. degree Bachelor's degree Master's degree Ph.D. Professional school Don't know DNA Missing Self Mother Father Frequency 1 0 0 33 74 45 122 54 9 9 0 3 20 Percent 0.27 0.00 0.00 8.92 20.00 12.16 32.97 14.59 2.43 2.43 0.00 0.81 5.41 Frequency 1 10 16 142 53 42 59 36 1 1 0 3 6 Percent 0.27 2.70 4.32 38.38 14.32 11.35 15.95 9.73 0.27 0.27 0.81 1.62 0.27 Frequency 1 16 24 140 37 34 61 26 5 14 3 3 6 Percent 0.27 4.32 6.49 37.84 10 9.19 16.49 7.03 1.35 3.78 0.81 0.81 1.62 216 Table 12. Sample industry and work role demographics – study 2 Industry Advertising & Marketing Agriculture Airlines & Aerospace (including Defense) Arts, Entertainment & Recreation Business Support, Logistics, Transportation & Delivery Construction Education Finance, Banking, Insurance and Financial Services Government & Military Healthcare & Pharmaceuticals Hotel & Food/Beverage Services Journalism & Publishing Manufacturing Nonprofit Professional Consulting Services Retail Technology (hardware, software, internet, social media, etc.) Utilities, Energy & Extraction Role Non-supervising Employee Supervisor Manager Vice President/Director Head Senior mgmt./Executive (e.g., CFO, COO) CEO/Owner Self-employed Frequency 1 3 1 16 16 18 52 22 24 56 17 2 36 15 14 47 23 6 Frequency 185 61 96 7 3 8 10 Percent 0.27 0.81 0.27 4.32 4.32 4.86 14.05 5.95 6.49 15.14 4.59 0.54 9.73 4.05 3.78 12.7 6.22 1.62 Percent 50 16.49 25.95 1.89 0.81 2.16 2.7 217 Table 13. Reliabilities of Study 2 Indicator Variables Measure RC - Approach-Oriented RC – Prevention-Oriented SC - Approach-Oriented SC - Prevention-Oriented TC - Approach-Oriented TC - Prevention-Oriented CC - Approach-Oriented CC - Prevention-Oriented SS – Approach-Oriented SS – Avoidance-Oriented Situation Modification Attentional Deployment Cognitive Change Response Modulation Note. RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection. Time-1 Cronbach’s α 0.9 0.7 0.95 0.7 0.84 0.74 0.77 0.55 0.91 0.85 0.83 0.87 0.93 0.8 Time-2 Cronbach’s α 0.9 0.72 0.95 0.75 0.85 0.73 0.78 0.55 0.92 0.88 0.85 0.88 0.94 0.84 218 Table 14. Reliabilities of Study 2 Personality and Emotional Demands Variables Measure Proactive Personality Approach Temperament Avoidance Temperament BIS BAS Display Rules Compassion Demands Helping Demands Regulation Demands General Demands Note. BIS = behavioral inhibition sensitivity; BAS = behavioral activation sensitivity. Time-1 Cronbach’s α 0.89 0.82 0.88 0.85 0.71 0.7 0.88 0.91 0.94 0.9 Time-2 Cronbach’s α 0.9 0.86 0.89 0.88 0.7 0.73 0.87 0.92 0.94 0.89 219 Table 15. Latent Profile Analysis Results for Time 1 and Time 2 Samples Classes 2 3 4 5 Classes 2 3 4 5 LL -8821.1 -8785.13 -8738.94 -8720.07 LL -5517.66 -5489.49 -5461.66 -5441.19 AIC 17910.19 17868.26 17805.87 17798.13 AIC 11303.31 11276.98 11251.32 11240.37 BIC 18504.64 18529.25 18533.4 18592.2 BIC 11832.7 11865.62 11899.22 11947.54 Time 1 LPA SABIC 18079.21 18056.2 18012.73 18023.9 Time 2 LPA SABIC 11407.54 11392.87 11378.88 11379.6 Entropy 0.75 0.7 0.73 0.76 Entropy 0.73 0.72 0.78 0.84 Min 0.11 0.11 0.12 0.06 Min 0.28 0.29 0.08 0.08 Max 0.89 0.6 0.46 0.36 Max 0.72 0.41 0.54 0.62 BLRT 41.44 71.93 92.39 37.74 BLRT 42.09 56.34 55.66 40.95 p 0.04 0.01 0.01 0.08 p 0.08 0.02 0.01 0.09 Note. Bolded values represent the best fit according to LL, AIC, BIC, SABIC, and entropy statistics. p values are bolded at < .05. 220 Table 16. Profile T-tests and effect sizes – Study 2 Time 1 Profile 2 Profile 1 Profile 3 p Profile 4 p t t t t p p -4.67 3.4 Hedges' g -0.23 -0.46 0.6 0.2 -0.26 -0.02 0.02 -0.28 -0.19 -0.41 0 -0.18 0.02 -0.2 -2.17 <.001 -4.39 <.001 5.74 <.001 1.95 0.05 -2.44 <.001 0.83 -0.21 0.18 0.86 -2.67 <.001 0.07 -1.85 <.001 -3.9 0.98 -0.03 0.09 -1.72 0.83 0.21 0.06 -1.89 Hedges' g RC - App. -0.64 RC - Av. 0.46 SC - App. -1.8 SC - Av. 0.06 TC - App. -1.58 TC - Av. 0.15 CC - App. -0.21 CC - Av. 0.06 SS - App. 0.43 SS - Av. 0.47 SM -0.23 AD 0.18 CC -0.22 RM 0.27 Note. 0 = respective profile; 1 = remaining profiles. App = approach-oriented; AV = avoidance-oriented; RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection; SM = situation modification; AD = attentional deployment; CCh = Cognitive Change; RM = response modulation; PP = proactive personality; Comp. = compassion demands; Help = helping demands; Reg. = regulation demands; Gen. = general demand; BIS = behavioral inhibition sensitivity; BAS = behavioral activation sensitivity; DR = display rules. <.001 <.001 -13.19 <.001 0.45 0.65 -11.57 <.001 0.28 1.08 0.13 -1.52 0.65 0.46 <.001 3.12 <.001 3.43 0.09 -1.69 0.18 1.35 0.11 -1.62 0.05 2 -7.95 <.001 -2.86 <.001 -18.64 <.001 -14.33 <.001 -16.44 <.001 -5.93 <.001 -9.67 <.001 -9.12 <.001 <.001 -7.9 -3.8 <.001 -5.03 <.001 -4.67 <.001 -5.72 <.001 -3.05 <.001 Hedges' g -1.23 -0.44 -2.88 -2.22 -2.54 -0.92 -1.5 -1.41 -1.22 -0.59 -0.78 -0.72 -0.89 -0.47 Hedges' g 1.5 0.41 1.7 0.91 2.91 0.39 1.01 1.11 0.8 0.64 0.87 0.69 0.8 0.35 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 12.81 3.46 14.49 7.72 24.79 3.31 8.56 9.47 6.83 5.47 7.41 5.87 6.85 2.96 221 Table 17. Profile T-tests and effect sizes – Study 2 Time 2 Profile 2 Profile 1 Profile 3 p Profile 4 p t t t t p p Hedges' g -0.08 -0.38 -0.14 0.02 -0.82 -0.27 -0.08 -0.08 -0.03 -0.26 0.02 -0.03 0.21 0.03 0.42 -0.81 -3.62 <.001 0.18 -1.34 0.82 0.23 -7.82 <.001 0.01 -2.56 0.45 -0.76 0.44 -0.77 0.8 -0.25 0.01 -2.52 0.83 0.22 0.75 -0.32 0.05 1.98 0.78 0.28 Hedges' g RC - App. -1.43 RC - Av. 1.26 SC - App. -0.52 SC - Av. -0.2 TC - App. -1.62 TC - Av. 1.18 CC - App. -0.6 CC - Av. -0.59 SS - App. 0.93 SS - Av. 0.69 SM -0.32 AD -0.19 CC -0.8 RM -0.04 Note. 0 = respective profile; 1 = remaining profiles. App = approach-oriented; AV = avoidance-oriented; RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection; SM = situation modification; AD = attentional deployment; CCh = Cognitive Change; RM = response modulation; PP = proactive personality; Comp. = compassion demands; Help = helping demands; Reg. = regulation demands; Gen. = general demand; BIS = behavioral inhibition sensitivity; BAS = behavioral activation sensitivity; DR = display rules. -5.08 <.001 -8.67 <.001 -15.75 <.001 -12.36 <.001 -14.6 <.001 -5.82 <.001 -5.81 <.001 -5.34 <.001 -9.14 <.001 -7.78 <.001 -9.9 <.001 -4.25 <.001 -4.54 <.001 0.05 -2.02 Hedges' g 0.91 0.3 1.23 0.9 2.65 0.21 0.75 0.67 0.41 0.58 0.8 0.53 0.48 0.16 Hedges' g -1 -1.7 -3.1 -2.43 -2.87 -1.14 -1.14 -1.05 -1.8 -1.53 -1.95 -0.84 -0.89 -0.4 <.001 <.001 0.01 0.28 <.001 <.001 <.001 <.001 <.001 <.001 0.08 0.29 <.001 0.82 <.001 <.001 <.001 <.001 <.001 0.07 <.001 <.001 <.001 <.001 <.001 <.001 <.001 0.16 7.96 2.6 10.73 7.91 23.15 1.83 6.57 5.87 3.6 5.11 6.98 4.65 4.23 1.4 -7.92 6.99 -2.9 -1.09 -9 6.54 -3.35 -3.26 5.15 3.81 -1.77 -1.07 -4.44 -0.23 222 Table 18. Sample self-reported gender and Race demographics by time 1 Profiles – Study 2 Profile 1 Profile 2 Profile 3 Profile 4 Gender Female Male Non-Binary/Agender Trans-man DNA Missing Frequency Percent Frequency Percent Frequency Percent Frequency 33.33 73 66.67 82 0.00 1 0.00 1 0.00 1 33.33 73 39.39 59.60 0.00 1.01 0.00 39.39 46.20 51.90 0.63 0.63 0.63 46.20 16 32 0 0 0 16 39 59 0 1 0 39 21 43 0 1 0 21 Frequency Percent Frequency Percent Frequency Percent Frequency 66.67 126 10.42 7 2.08 8 0.00 1 78.79 9.09 3.03 0.00 79.75 4.43 5.06 0.63 53 3 3 0 32 5 1 0 78 9 3 0 Race White/Caucasian Black Latino/a Native American or American Indian or Alaskan Native Native Hawaii or Pacific Islander East Asian South Asian Middle Eastern or Arab Multiracial DNA Missing Note. Profile 1 n = 158; profile 2 n = 48; profile 3 n = 99; profile 4 n = 65; N = 370 6.25 0.00 0.00 14.58 0.00 0.00 1.90 0.63 0.00 6.33 0.00 1.27 3 1 0 10 0 2 2 0 0 4 1 1 3 0 0 7 0 0 0.00 0.00 0 0 1 1.01 2.02 0.00 0.00 4.04 1.01 1.01 0 1 0 1 4 0 0 Percent 32.31 66.15 0.00 1.54 0.00 32.31 Percent 81.54 4.62 4.62 0.00 0.00 1.54 0.00 1.54 6.15 0.00 0.00 223 Table 19. Sample self-reported industry demographics by time 1 Profiles – Study 2 Industry Advertising & Marketing Agriculture Airlines & Aerospace (including Defense) Arts, Entertainment & Recreation Business Support, Logistics, Transportation & Delivery Construction Education Finance, Banking, Insurance and Financial Services Government & Military Healthcare & Pharmaceuticals Hotel & Food/Beverage Services Journalism & Publishing Manufacturing Nonprofit Professional Consulting Services Retail Technology (hardware, software, internet, social media, etc.) Utilities, Energy & Extraction Missing Profile 1 Profile 3 Profile 3 Profile 4 Frequency Percent Frequency Percent Frequency Percent Frequency Percent 0 3 0 7 10 8 22 7 12 27 10 0 13 6 8 12 11 2 0 0.00 1.90 0.00 4.43 6.33 5.06 13.92 4.43 7.59 17.09 6.33 0.00 8.23 3.80 5.06 7.59 6.96 1.27 0.00 1 0 0 2 1 1 5 4 3 8 2 1 7 2 2 8 1 0 0 2.08 0.00 0.00 4.17 2.08 2.08 10.42 8.33 6.25 16.67 4.17 2.08 14.58 4.17 4.17 16.67 2.08 0.00 0.00 0 0 1 4 4 6 12 7 3 15 1 1 11 4 2 16 8 3 1 0.00 0.00 1.01 4.04 4.04 6.06 12.12 7.07 3.03 15.15 1.01 1.01 11.11 4.04 2.02 16.16 8.08 3.03 1.01 0 0 0 3 1 3 13 4 6 6 4 0 5 3 2 11 3 1 0 0.00 0.00 0.00 4.62 1.54 4.62 20.00 6.15 9.23 9.23 6.15 0.00 7.69 4.62 3.08 16.92 4.62 1.54 0.00 224 Table 20. Sample self-reported Role demographics by time 1 Profiles – Study 2 Profile 2 Profile 1 Profile 3 Profile 4 Frequency Percent Frequency Percent Frequency Percent Frequency Percent 75 26 46 3 1 35.35 21.21 31.31 2.02 1.01 70.77 15.38 12.31 0.00 0.00 60.42 8.33 22.92 4.17 2.08 47.47 16.46 29.11 1.90 0.63 Role Non-supervising Employee Supervisor Manager Vice President/Director Head Senior mgmt./Executive (e.g., CFO, COO) 2.08 CEO/Owner 0.00 Self-employed Missing 60.42 Note. Profile 1 n = 158; profile 2 n = 48; profile 3 n = 99; profile 4 n = 65; N = 370 2.53 1.90 47.47 35 21 31 2 1 29 4 11 2 1 46 10 8 0 0 1 0 29 4 3 75 3 6 35 3.03 6.06 35.35 0 1 46 0.00 1.54 70.77 225 APPENDIX V: LIST OF FIGURES FIGURE 1. Indicator Scores by Profile and Time Note. Pro = promotion-oriented; Prev = prevention-oriented; RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection; SM =situation modification; AD = attentional deployment; CCH = cognitive change; RM = response modulation. 226 FIGURE 2. Bar Plot Graph of Hedge’s g Values for Indicators by Profile – Study 1 Note. Pro = promotion-oriented; Prev = prevention-oriented; RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection; SM =situation modification; AD = attentional deployment; CCH = cognitive change; RM = response modulation. 227 FIGURE 3. Line Graph of Mean Values for Hypothesis Variables by Profile – Study 1 Note. T. = temperament; BAS = behavioral activation sensitivity; BIS = behavioral inhibition sensitivity. 228 FIGURE 4. Bar Plot Graph of Hedge’s g Values for Emotional Demands by Profile Study 1 229 FIGURE 5. Bar Plot Graph of Hedge’s g Values for Time 1 Indicators by Time 1 Profile – Study 2 Note. Pro = promotion-oriented; Prev = prevention-oriented; RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection; SM =situation modification; AD = attentional deployment; CCH = cognitive change; RM = response modulation. 230 FIGURE 6. Bar Plot Graph of Hedge’s g Values for Time 2 Indicators by Time 2 Profile – Study 2 Note. Pro = promotion-oriented; Prev = prevention-oriented; RC = relationship crafting; SC = skill crafting; TC = task crafting; CC = cognitive crafting; SS = situation selection; SM =situation modification; AD = attentional deployment; CCH = cognitive change; RM = response modulation. 231 FIGURE 7. Line Graph of Time 2 Mean Values for Hypothesis Variables by Time 1 Profile – Study 2 Note. T. = temperament; BAS = behavioral activation sensitivity; BIS = behavioral inhibition sensitivity. 232 FIGURE 8. Line Graph of Time 2 Mean Values for Well-Being Variables by Time 1 Profile – Study 2 Note. ED = emotional demands; EE = emotional exhaustion; DR = display rules. 233 FIGURE 9. Line Graph of Time 2 Mean Values for Dispositional Variables by Time 1 Profile – Study 2 Note. EI = emotional intelligence; SA = self-appraisal; OA = other-appraisal; SR = self-regulation; OR = other-regulation; PA = positive affect; NA = negative affect; CF = coping flexibility; CSI = context sensitivity index; CP = cue presence; CA = cue absence. 234 FIGURE 10. Line Graph of Time 2 Mean Values for Resource Variables by Time 1 Profile – Study 2 Note. SS = social support. 235