THE RIGHT SUPPORT: HOW THE EFFECTS OF HELP VARY BASED ON INDIVIDUAL PERCEPTIONS AND NEEDS By Nathan Baker A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Psychology – Doctor of Philosophy 2025 ABSTRACT Workplace help is widely recognized for enhancing employee well-being and performance, yet its potential to yield negative outcomes remains poorly understood due to the lack of a unified theoretical framework, passive views of help recipients, and limited focus on recipients’ key psychological factors. This dissertation addresses these issues by integrating Conservation of Resources (COR) theory and Self-Determination Theory (SDT) to examine how help perceptions influence acceptance and outcomes through basic psychological needs (autonomy, competence, relatedness). Two complementary studies tested this model: Study 1 utilized a scenario-based design with 1,163 undergraduates to assess manipulated help features (proactive vs. reactive, disempowering versus empowering, prosocial versus impression management motives) on help acceptance intentions, while Study 2 employed a 5-week multi-wave survey with 99 full-time employees to explore real-world help dynamics and outcomes (task progress, affect, self- esteem). Findings revealed that disempowering help and helper motives were key predictors of basic psychological needs and had downstream effects on reactions to help. Study 2 highlighted the importance of perceived help quality in predicting workplace well-being, as well as the key role of competence needs and status perceptions in forming reactions to help. These results advance COR and SDT by framing help as a dynamic resource exchange contingent on recipient appraisals, offering a novel framework to unify positive and negative support effects. By clarifying the active role of help recipients’ responses to support, this work provides insights into how and why help may backfire. Practically, this work informs organizational policies and training to optimize help delivery, minimizing resource threats and fostering supportive cultures. Future research should explore nuanced appraisal patterns and relational contexts to refine this model. ACKNOWLEDGEMENTS As with all my work during graduate school, I would like to first thank my family for their continual support of my work and dreams. My parents, for their life wisdom and guidance. Rhianna, for her encouragement and support, and even little Declan for inspiring me to dream bigger for him. Second, I want to express my gratitude for Dr. Chang’s mentorship during graduate school. Her good advice and support were instrumental in both my job search and in my development as a researcher. iii TABLE OF CONTENTS INTRODUCTION......................................................................................................................... 1 LITERATURE REVIEW ............................................................................................................ 8 STUDY 1 ...................................................................................................................................... 31 STUDY 2 ...................................................................................................................................... 48 GENERAL DISCUSSION ......................................................................................................... 77 CONCLUSION ........................................................................................................................... 90 TABLES ....................................................................................................................................... 91 REFERENCES .......................................................................................................................... 107 APPENDIX A: SCENARIOS .................................................................................................. 123 APPENDIX B: STUDY ITEMS .............................................................................................. 125 APPENDIX C: MEASUREMENT EQUIVALENCE ANALYSES..................................... 137 iv INTRODUCTION Receiving social support and help at work has significant impact on employees’ well- being, stress, and relationships (Gonzalez-Mulé & Yuan, 2022; Pluut et al., 2018; Thoits, 2011). Receiving help from others at work is associated with outcomes including lower turnover and work stress (De Clercq et al., 2020; Dormann & Zapf, 1999; Organ et al., 2006), as well as increased goal progress and performance (Bakker et al., 2023; Lee et al., 2023). The exchange of various types of support in organizations is one of the most essential drivers of employee well- being and performance. As a result, research has focused extensively on the benefits and availability of social support for workers (Lim et al., 2020; Viswesvaran et al., 1999). Critically, research has also revealed that workplace social support has the potential to backfire—either failing to benefit an individual or even worsening their situation (Deelstra et al., 2003; Gray et al., 2023; Hughes et al., 2022). The proposition that social support protects against the negative effects of stressors on well-being has received mixed support in research, with reviews finding both positive and negative moderating effects of social support, suggesting that support can worsen the negative effects of stressors in certain situations (Mathieu et al., 2019; Nurulluh, 2012). Such research highlights that not all forms of help benefit the recipients (Pluut et al., 2018). Studies focused on the negative consequences of helping and social support have revealed that receiving help can be detrimental to self-esteem, coping skills, social image, and self-efficacy (Koo et al., 2023; Nadler, 2015). Research increasingly reveals that help in organizations is often poorly matched to the needs of workers (Cohen & McKay, 1984; Dalal & Sheng, 2019), is unwanted by its recipients (Beehr et al., 2010; Gray et al., 2020) and fails to reduce stress or increase performance (Pluut et al., 2018; Thompson & Bolino, 2018). 1 Although the negative consequences of help have received increasing attention in research, work in this area is hamstrung by three critical issues: (1) the lack of a theoretical framework to explain when and why help will have negative outcomes for its recipients, (2) a passive view of support recipients that ignores their psychological processes, and (3) a failure to identify the key psychological factors that influence recipients’ reactions to characteristics of help. This study aims to address these three crucial issues in the area of workplace support and help. Research on the downsides of helping and social support focuses on how individuals respond to support – both in terms of rejecting or accepting help and the impact of receiving support. Research on these issues has largely studied isolated factors that decrease the benefits of helping. This work has identified important variables such as perceived helper competence (Tai et al., 2023), the difference between requested and proactively granted support (Harari et al., 2022; Nurulluh, 2012), and the extent to which help is (dis)empowering (Lee et al., 2023) as key factors that influence help recipients’ responses. Aside from longstanding evidence that self- esteem may be a critical mediator of reactions to help (DePaulo et al., 1981; Nadler, 2015), there has been little theoretical work to unify explanations regarding why receiving help with certain features sometimes triggers negative reactions. Developing a framework that addresses these issues would enable more general predictions regarding when the provision of social support is likely to result in negative outcomes for potential or actual recipients. Similarly, it is crucial to understand the processes underlying how the recipients of help interpret and respond to the help available to them. Research on helping generally focuses on the degree to which individuals receive support and then links that support to outcomes for the individual (Nadler et al., 2015; Thoits, 2011). However, the effects of help are theoretically 2 mediated through the responses of help recipients (Gray et al., 2020). Individuals actively form perceptions of the availability and quality of the support they can utilize (Newark et al., 2017). Such perceptions influence both the seeking of help and responses to received help. Support is more effective when individuals are willing to accept the help provided to them (Thompson & Bolino, 2018). As a result, the effects of help are best considered by understanding help recipients as active agents who decide how to respond to available support. Reactions to help are likely to impact how beneficial social support is for a given individual. Failure to account for how individuals evaluate help reflects a missed opportunity to understand when and why help harms or benefits employees (Kassirer & Kouchaki, 2023). Additionally, help research has often ignored key features of support. For instance, help research rarely explicitly measures the quality of help available to individuals. One reason that individuals reject available support is that they anticipate receiving poor or ineffective support (Gray et al., 2020). Although evidence that receiving help leads to negative outcomes for employees is often interpreted as due to self-esteem threats (e.g., Deelstra et al., 2003), such interpretation may be an oversimplification as it fails to account for instances where individuals are receiving social support that is, in fact, unhelpful (Hughes et al., 2022). Similarly, research on helping and support often ignores factors that theoretically influence how beneficial help is likely to be. For instance, the motivations of helpers are frequently posited to impact how help recipients respond to support (Nadler, 2015; Dalal & Sheng, 2018), however these are rarely accounted for in empirical work on help. Given these factors, theory aimed at understanding why receiving help can negatively impact individuals must account for both the benefits and costs implied by social support. 3 This dissertation examines a theoretical model of how help recipients perceive and react to the help available to them. Drawing on the conservation of resources (COR) model (Hobfoll, 1989), I extend research that examines social support as the exchange of resources (e.g., Hobfoll et al., 1990) by illustrating how receiving help can lead to both a gain and a loss of resources for individuals. While receiving support from others can provide resources in the form of emotional comfort (Thoits, 2011), information (House, 1981), and tangible job resources (Halbesleben & Wheeler, 2015), support can lead to losses of resources including social reputation (Thompson & Bolino, 2018), control over one’s work (Koo et al., 2023), and positive self-image (Nadler, 2015). Specifically, I argue that characteristics of help can pose threats to the fulfillment of an individual’s basic needs for autonomy, competence, and relatedness. These three needs are essential to human functioning (Deci et al., 2017), and from the perspective of COR they can be viewed as universal resources that are important for individuals to maintain to cope with stress (Dreison et al., 2018; Halbesleben et al., 2014). As a result, individuals are likely to react to available help based on the potential gains and losses of resources involved in receiving the help. Understanding help as a process involving risks and benefits to an individual’s resources allows a clearer picture of why employees respond to help in the way they do. Workers often make seemingly irrational decisions not to utilize available help (Newark et al., 2017). However, individuals may be simply making a broader assessment of the risks and rewards associated with help (Kassirer & Kouchaki, 2023). Building upon this view of resources, I develop a model clarifying how perceptions of available help influence an individual’s acceptance of help through their anticipated effect on the basic needs of that individual. Based on these perceptions, individuals interpret the support offered to them as either a potential gain or loss for their 4 resources and react accordingly. As a result, the outcomes of help depend on how individuals perceive and respond to the support available to them. To test this theoretical model, I first conducted a scenario-based study with a student sample to provide an initial validation of the model. This study focused on validating the core aspects of the research model, testing the linkages between help perceptions and the basic psychological needs fulfillment. Participants rated their reactions to different helping scenarios, designed to test the effects of varying levels of key help features upon help acceptance and basic needs. After establishing support for the core aspects of the research model, I performed a second study utilizing a working sample. This study examines the helping process in a multi- wave, multi-week, study. This study provides a more rigorous test of the model in situ for working participants and assesses behavioral outcomes of the helping process. By examining responses to help across several workweeks, this study provides insights into the dynamic nature of responses to support at work. This research offers several contributions to theory and practice. The primary contribution of this work is to provide a unifying framework for understanding positive and negative responses to help. Such a framework clarifies why social support can sometimes worsen rather than alleviate the effects of work stress on individual well-being (Mathieu et al., 2019; Nurulluh, 2012). By providing insights into how and when help can lead to resource loss, this dissertation advances theory regarding social support and help and work. Specifically, it provides a clear theoretical model that unifies the emerging body of research demonstrating specific processes that lead to negative responses to help (Koo et al., 2023; Harari et al., 2022; Lee et al., 2023). Integrating these perspectives lays important groundwork for additional research to 5 address questions including how to develop interventions that increase the likelihood of social support having positive, rather than negative, outcomes. This work also contributes to theory by shedding light on the role played by the cognitive, affective, and motivational processes of help recipients. In parallel to evidence suggesting organizational feedback is only useful when it is accepted by its recipients (Bell & Arthur, 2008; Ilgen et al., 1979), it is important to understand the processes internal to individuals that inform their acceptance of help (Kassier & Kouchaki, 2023; Thompson & Bolino, 2018). This active view of help recipients allows a clearer understanding of the mechanisms underlying the social support process. Much like an active view of learners allowed the training literature to better conceptualize the factors that drive skill acquisition (Bell et al., 2017), it is important to understand help as a process that involves the help recipient as an active component. Individuals respond to help in highly varied ways, each with consequences for the outcomes of that help. To understand this process, identifying predictors of help acceptance is essential. This view of helping also has practical contributions to organizational practices. The resources gained during the helping process enable employees to perform more effectively and to have greater well-being (Bakker et al., 2023; Bowling et al., 2005). As a result, organizations generally want to encourage helping behavior among their employees. However, organizations need to avoid situations where help worsens rather than improves the conditions of their employees (Gray et al., 2023). The model tested in this dissertation allows a clearer understanding of what risks exist during the helping process and how to best manage them. Similarly, this work has the potential to inform policy changes aimed at improving the quality of help through setting standards and norms regarding how help occurs. Understanding what 6 aspects of help can threaten the resources and needs of employees can support efforts to provide aid in a way that individuals can optimally benefit from. 7 Helping and Social Support LITERATURE REVIEW Research on helping and support in psychology has emerged from several distinct sources. While social support can be studied in terms of perceived support available in an organization or other setting (e.g., Eisenberger et al., 1986), research focusing on individuals receiving social support is more similar to how helping is understood (Deelstra et al., 2003). Although the terms helping and social support are often used interchangeably by researchers, there is substantial variation in what is meant by these terms (Beehr et al., 2010; Gray et a., 2020). For the purposes of this review, I will focus on help and social support that is task oriented, or instrumental in nature. Early work by House (1981) suggested that social support occurred through the provision of emotional or empathic support, appraisal support, such as the sharing of opinions and interpretations of events, informational support, such as sharing useful knowledge, and instrumental support, such as tangible task assistance. Contemporary research often focuses specifically on the difference between emotional support and instrumental support (often including factors such as information). Focusing on task-related support (such as the provision of feedback or tangible, instrumental, assistance) allows for similarity to conventional definitions of workplace help which typically focus on the provision of assistance with tasks or problems to coworkers (Choi & Moon, 2016). These types of help and support are most likely to be relevant to performance for employees. Much of the work on social support has developed from research on stress and well- being. Relationships have long been understood to have a protective effect on well-being, as individuals are able to provide assistance to one another that allows for the buffering of stress (Cohen & McKay, 1985). Through the exchange of different types of support and social 8 influence, individuals can aid one another, driving improved well-being and performance (Bakker et al., 2023; Colbert et al., 2016; Pluut et al., 2018). Evidence regarding these positive effects has led to social support being one of the most considered variables across psychological research (Thoits, 2011). Some initial research found that social support did not always serve as a buffer against stress (DePaulo et al., 1984). These findings gave rise to a view suggesting that support was most helpful when it matched the demands faced by an individual (Cohen & McKay, 1984). This concept has proven difficult to test empirically; however, there is some evidence that individuals benefit more from social support that specifically addresses the challenges they are experiencing (Van Veldhoven et al., 2020). Similarly, support can come from a variety of domains, such as family or work (Lim, 1996). The effectiveness of different sources of support may depend on the domain in which stress is occurring (Pluut et al., 2018). For instance, there is mixed evidence to suggest that demands in the work domain are more easily addressed by support at work, rather than in the home domain (Halbesleben et al., 2006; Pluut et al., 2018). Research in this area has revealed that the benefits of help can be varied and context-dependent, yet why these varied effects occur is not always understood. At a broad level, social support can have a substantial positive effect on health (Colbert et al., 2016; Viswesvaran et al., 1999), but the mechanisms underlying how these benefits occur have not been fully clarified (Thoits, 2011). Most researchers have understood social support through a resource lens (Bamberger et al., 2017). Support can have various forms (e.g., informational, emotional, instrumental) but these all fundamentally involve the exchange of resources between individuals (Hobfoll et al., 1990; House, 1981). Through utilizing social connections, individuals can handle more than they could on their own. The conservation of 9 resources theory (COR; Hobfoll, 1989) is the most prominent perspective in this area, conceptualizing resources as valued objects (e.g., a house), conditions (e.g., seniority in a company), characteristics (e.g., self-esteem), and energy (e.g., time, money). In this perspective, resources enable individuals to adapt to stress and respond to their environment. Due to their adaptive value, individuals are motivated to obtain and protect resources. Individuals are protected from the negative effects of stressors if they have sufficient resources. In contrast, COR predicts that individuals become distressed and unwell when their resources are lost or threatened (Halbesleben, 2006; Hobfoll, 1989). For example, the threat of losing one’s position at work is likely to be distressing even if it does not occur. The construct of resources is broad. Some resources appear to be universally valued by individuals, while in other cases there appears to be cultural and individual variation in what is considered a resource (Halbesleben et al., 2014). In the social context, people can exchange resources with one another, enhancing their ability to cope with stressors (Hobfoll et al., 2018). The spread of resources between individuals that occurs during social support allows them to handle demands in the work context (Dreison et al., 2018; Halbesleben, 2006). From a resources perspective, social support is a process that allows individuals to receive additional resources and expand their ability to adapt to the world around them as a result. Such a view helps unify research on the varied types of support by providing a single explanatory mechanism. For example, while emotional support such as listening is different from providing instrumental support in the form of money, both processes involve the provision of needed resources to the support recipient. As a result, the varied types of support and help can be understood in terms of how they impact an individual’s resources. A second stream of helping research has grown out of the study of relationships. Individuals naturally feel pressure to help those who express dependence on them (Berkowitz & 10 Daniels, 1964). Social exchange theory is often evoked to understand this process – individuals who give help to others can expect to receive help from them in the future due to shared norms of reciprocity (Cropanzano & Mitchell, 2005). Employees are likely to help others who have treated them well out of a sense of obligation and are likely to treat others well when they expect that they will receive similar treatment in return (Spitzmuller & Van Dyne, 2013). Over time, some employees will develop trust-based relationships that involve continual reciprocation of assistance to one another beyond one-to-one exchanges (Cropanzano et al., 2017). This process is not merely dyadic, as individuals may help others out of a desire to benefit a broader shared group or organization that they feel has treated them well (Eisenberger et al., 1986; Ladd et al., 2000; Settoon et al., 1996). How helping occurs depends on the broader network within which individuals reside. For instance, both positive and negative interpersonal behaviors are most common among individuals who interact frequently (Venkataramani & Dalal, 2007). As a result, helping and social support must be understood in the context of an individual's social ties (Nahum-Shani & Bamberger, 2011). From a resource perspective, individuals reciprocally invest in relationships to develop a greater amount of shared resources over time (Halbesleben & Wheeler, 2015). As a result, individuals can expect to receive additional resources from their social ties when they need support. This process will be influenced by the degree to which a given organization has norms of reciprocity (Nahum-Shani & Bamberger, 2011), as well as the unique social ties to which a given individual has access (Zagenczyk et al., 2010). Not all relationships will involve equal exchanges of support. For instance, while managers often provide support at work, they may primarily need to seek support outside of work due to their unique organizational position 11 (Lindorff, 2001). Given these views, helping can be understood in the broader context of relational exchanges as individuals reciprocate support in the form of resources over time. Help Risks and Resources Even though receiving help provides additional resources to individuals, the expected positive outcomes of this process do not always occur (Van Veldhoven et al., 2020). Early research found that support often fails to buffer against the negative effects of stressors (Cohen & McKay, 1984) and can result in damage to the recipient’s self-esteem (DePaulo et al., 1981; Nadler & Jeffrey, 1986). Research has also revealed that helping does not always occur optimally. In organizations, employees underestimate and underutilize the help that they could potentially receive (Newark et al., 2017). In addition to failing to utilize available support, employees often report being dissatisfied with the support they receive (Dishop & Awasty, 2023). Research in this area has often focused on employee’s underestimation of the quality and quantity of available support as the explanatory process driving failure to make use of available support (Newark et al., 2017). However, there is some evidence that employees face genuine risks when utilizing help. For instance, individuals who request help can receive worse performance ratings from their supervisors (Liu et al., 2022; Nadler et al., 2003). Similarly, receiving help can associate an individual with negative stereotypes, such as laziness and incompetence (Hall et al., 2014; Kassirer & Kouchaki, 2023). The fact that help involves potential risks, as well as rewards, has important implications when understood through the COR lens. The COR theory posits that individuals are more sensitive to losses of resources than gains (Hobfoll, 1989). Individuals invest effort in preventing the loss of resources and become distressed by threats to their overall resources (Hobfoll et al., 12 2018). As a result, if individuals perceive any risks to their resources during the helping process, they may react negatively. Resource loss can spread through social interactions (such as helping contexts), with important negative effects on well-being (Westman, 2001). Existing research on helping does not typically conceptualize the risk of resource loss during the helping process, yet any given help involves trade-offs. These trade-offs relate to tangible factors such as whether the help offered is sufficient to meet one’s demands or not (Gray et al., 2020), as well as psychological factors including the extent to which receiving help would make one feel obligated to reciprocate help in the future (Thacker & Stoner, 2012). Individuals must weigh these factors when understanding how to respond to the help offered. Due to the motivational salience of resource loss, individuals will likely try to avoid or reject help that carries potential risks to their resources (Hobfoll et al., 2018). The COR theory also proposes that the loss of resources increases motivation to gain new resources (Hobfoll, 1989), highlighting that individuals in need of assistance will be motivated to accept help that appears to offer increased resources. Due to this sensitivity to both losses and potential gains, individuals are highly motivated to understand the costs and benefits of accepting social support. Given the vast array of potential resources, it is important to consider what resources might be most salient to individuals as they react to support. Within the COR framework, some resources are viewed as universally important to individuals (Hobfoll et al., 2018). Central within these universal resources are basic psychological needs for autonomy, competence, and relatedness (Halbesleben et al., 2014). Proposed as part of the broader self-determination theory (SDT), these needs have been widely supported as factors that facilitate social and personal well- being (Ryan & Deci, 2000). The fulfillment of these needs is heavily influenced by the social 13 environment occupied by an individual (Ryan et al., 2022). For instance, opportunities to complete desired tasks facilitate competence needs, while opportunities to bond socially fulfill relatedness needs (Reis et al., 2000). Satisfaction of basic needs provides individuals with enhanced resources, improving well-being and motivation (Dreison et al., 2018). These needs are universal and appear to reflect resources that are common to all individuals (Chen et al., 2015; Halbesleben et al., 2014). As a result, if a helping interaction threatens these basic needs, individuals may perceive a significant loss of resources. Threats or losses to these resources are likely to result in stress, decreased well-being, and loss of motivation (Vansteenkiste et al., 2020), posing a major concern for individuals. The existing research on negative reactions to help has identified several mechanisms driving these negative responses that align closely with the fulfillment or frustration of the basic needs. Anticipated or actual loss of control is a common theme among negative responses to help (Gray et al., 2020). For instance, autonomy needs are fulfilled by engaging in activities of one’s own volition and choice (Deci & Ryan, 2008). Asking for help involves trading off some of one’s independence (Nadler, 2015). Research has found that help is more likely to have negative effects if it removes control from individuals (Bamberger et al., 2017; Lee et al., 2023). For instance, employees are more likely to have negative responses to help, such as increased negative affect and maladaptive coping, if they lose control over their work as a result of receiving support (Koo et al., 2023). Such effects can be understood as factors that thwart the autonomy needs by restricting the help recipient’s ability to decide how they will engage in their work (Ryan & Deci, 2000). Given the negative effects of thwarted need fulfillment (Bartholomew et al., 2011), features of help that threaten autonomy are likely to have detrimental effects on help recipients. 14 Similarly, receiving help impacts perceptions of competence (Harari et al., 2022; Kim et al., 2016; Tai et al., 2023). Being given help can represent a signal of inability (Nadler, 2015), threatening competence need fulfillment by reducing experiences of mastery and efficacy (Reis et al., 2000; Vansteenkiste et al., 2020). Help can be characterized based on the degree to which it empowers an individual to complete activities under their own volition, versus being disempowering and involving another person performing activities for that individual (Bamberger et al., 2017). Perceiving help as disempowering is one factor that triggers negative help outcomes for recipients, including negative self-perceptions regarding one’s own abilities (Lee et al., 2023). Similarly, receiving help can be viewed as a negative signal of competence and status within one’s workplace (Harari et al., 2022). Employees avoid receiving help if they think it will lead to others viewing them as incompetent (Gray et al., 2020; Hughes et al., 2022). Taken together, these studies highlight that receiving support at work can have a substantial impact on competence need fulfillment. Help can also have implications for relatedness need fulfillment. Although receiving help frequently enhances feelings of social connectedness (Nadler, 2015), it can also threaten one’s social position. Receiving help can place an individual into a dependent social relationship with those who have aided them (Halabi et al., 2011), changing how that individual is positioned within their social network. Being dependent on others can reduce one’s social standing, which harms feelings of acceptance and social belonging (Anderson et al., 2015; Greenwood et al., 2013; Komissarouk & Nadler, 2014). Receiving assistance from others can also create a stigmatized ‘aid recipient’ identity, isolating individuals and decreasing their experience of relatedness (Kassirer & Kouchaki, 2023). Such reactions may depend on the relational context within which an employee is operating. For instance, individuals react differently to help 15 depending on how they perceive their relationship with the helpers (e.g., friendship; Nadler, 2015). A needs-based view of helping allows for a clear and unified perspective on the factors that are most likely to drive negative outcomes of helping. Widespread research suggests that threats to autonomy, competence, and relatedness need fulfillment can have a profound negative impact on psychological and physical health (Ryan et al., 2022; Vansteenkiste et al., 2020). Thus, individuals who reject or react negatively to help may do so because of genuine threats to their well-being, perhaps contributing to the frequent avoidance of available help in the workforce (Newark et al., 2017). Based on the combined insights from the COR and SDT perspectives, it is likely that individuals are, at least implicitly, highly sensitive to potential trade- offs of need fulfillment and thwarting during the helping process. An individual’s perception of those trade-offs will in turn impact their reaction to help. Help Acceptance Individuals are not merely passive recipients of help. Instead, they can choose how to react to the support available to them (Halabi et al., 2011). This agency can be observed in the fact that individuals often choose to not utilize help that is readily available to them (Newark et al., 2017; Thompson & Bolino, 2018). Choosing not to accept offers of help is only one way in which individuals actively respond to help. Even when receiving support, individuals can decide how to interact with that support. Such interactions are highlighted by research demonstrating that help is frequently provided proactively in organizations, with one individual providing unrequested support to another (Spitzmuller & Dyne, 2013). Researchers have found that such instances often lead to defensive responses on the part of help recipients, with individuals attempting to reject or resist the help provided to them (Harari et al., 2022). This area of research 16 highlights that the outcomes of support depend upon both the support offered and the reaction to that support. Help recipients have substantial agency in shaping when, and if the helping process occurs. Even high-quality help offered to an individual may backfire if that individual is resistant to being helped in the first place (Thompson & Bolino, 2018). This issue has long been recognized in research on helping within therapeutic contexts, with many researchers and clinicians suggesting that help with issues such as mental health is rarely effective unless the recipient is willing to accept it (Lambert & Barley, 2001). As a result, it is important to take an active view of the recipients of help, acknowledging that their responses to potential support play a critical role. With this active role of help recipients in mind, it is important to explore how individuals decide to respond to help. Feedback and advice can be thought of as different types of support (Lim et al., 2020), and research on feedback and advice-taking often includes the construct of acceptance as a factor that mediates the outcomes of either feedback or advice. For instance, in feedback research, feedback is proposed to be ineffective unless it is judged by an individual as acceptable (Ilgen et al., 1979). Feedback acceptance is the result of several perceptual processes, such as the credibility and status of the feedback source and affective tone (Bell & Arthur, 2008; Christensen-Salem et al., 2018). Failure to accept feedback largely nullifies its potential benefits (Sargeant et al., 2008; Son & Kim, 2016). Research on advice-taking has revealed a similar, active, role for the recipients of advice. Individuals must decide how to respond to advice, considering various social and informational factors (Bonaccio & Dalal, 2006). The effects of helping are likely to be mediated by a process of help acceptance. Helping occurs in the same manner as other types of resource exchange, including advice-taking and 17 feedback acceptance (Lim et al., 2020). Thus, given research in these domains, individuals can be expected to form overall acceptance of the help provided to them. In the feedback and advice literature, acceptance is typically understood as a cognitive construct, mediating the effects of advice or feedback on behavior (Christensen-Salem et al., 2018). Help acceptance can be expected to emerge from how an individual perceives the help that they are provided. Returning to the COR framework, when individuals perceive that help might threaten key resources, they are likely to more negatively respond to help. Perceptions of such threats may be influenced by different features of the support offered within a given circumstance. Research on advice and feedback in organizations highlights that individuals’ acceptance of such support is heavily influenced by perceptions of the situation. For instance, the perceived intentions of an advice giver influence advice acceptance (Son & Kim, 2016) as does the perceived credibility of the advice itself (Reyt et al., 2016). Similarly, acceptance of help is likely to be influenced by perceptions of the helper and the help that they are providing. Help Perceptions Individuals are likely to pay attention to features of help that signal potential risks or benefits to their resources (Lim et al., 2020). Based on the COR framework, individuals will favor helping experiences that are more likely to result in a net gain to resources, and dislike helping that threatens or decreases their overall resources (Hobfoll et al., 2018). As highlighted above, crucial resources like psychological need fulfillment may be the key factors threatened by the provision of support (Nadler, 2015). Individual perceptions of such risks can be expected to influence the likelihood of an individual accepting a rejecting potential help at work. The following sections focus on three unique dimensions of help that are especially likely to influence fulfillment psychological need fulfillment. For each of these dimensions, I explore 18 their theoretical relationship with acceptance and psychological needs. The effects of these help features are likely to be partially mediated through basic psychological needs as these are especially the focal resources for individuals. This mediation path is unlikely to fully account for the effects of help perceptions on help acceptance, however, as a wide range of resources might be impacted by each. To account for this fact, I will also examine some alternative resource paths that may mediate the relationship between help perceptions and help acceptance. Proactive Versus Reactive Help One of the most important situational aspects of helping relates to how the helping process was initiated. Existing helping research has emphasized that proactive help offered without a request from the help recipient is interpreted differently from reactive help provided in response to a request from the help recipient (Spitzmuller & Van Dyne, 2013). Research suggests that proactive helping may be good for helpers, as it enables them to enhance their reputation, solve organizational problems, and fulfill prosocial motivations to support others (Chiaburu et al., 2007; Clary & Orenstein, 1991; Jia et al., 2021). From the help-recipient perspective, however, proactive help appears to almost always be perceived negatively (Harari et al., 2022). Individuals tend to have negative emotional and behavioral responses to unsolicited support, such as freely offered advice (e.g., Landis et al., 2022; Lee et al., 2019). Unsolicited help appears to have an important impact on how acceptable help is to recipients of support. Although proactive support is initiated without the recipient’s request, the recipient can still choose to reject or resist support (Harari et al., 2022), making it important to consider responses to such help. The negative effects of unsolicited support appear to be largely mediated through its negative impact on autonomy need fulfillment. Unrequested help appears to impact feelings of 19 control and autonomy over one’s work and serves to undermine one’s competence (Deelstra et al., 2003). Indeed, concerns over being viewed as less independent are one of the primary reasons that employees avoid or reject offers of help (Thompson & Bolino, 2018). Such research suggests that being given unsolicited help is likely to be perceived a threat to control over one’s work, which can lead to negative emotions and stress (Koo et al., 2023). Similarly, unsolicited help is likely to be viewed as a signal that an individual is unable to handle a task on their own (Harari et al., 2022). Additionally, receiving unrequested help can remove opportunities to try to handle the task oneself (Landis et al., 2022), limiting the independence of employees and their chance to control their environment. All these effects relate to a loss of ownership and discretion over one’s tasks, which is likely to negatively impact autonomy needs (Deci & Ryan, 2008). As a result, proactive support may be associated with decreased help acceptance, as mediated by autonomy need fulfillment. Hypothesis 1: Proactively provided help will negatively relate to help acceptance. Hypothesis 2: Proactively provided help will (a) negatively relate to autonomy need fulfillment, (b) autonomy need fulfillment will positively relate to help acceptance, and (c) autonomy needs will partially mediate the effect of proactively provided help on help acceptance. (Dis)empowering Help Negative beliefs regarding help from employees often center around concerns regarding negative self-image effects, such as appearing incompetent (Thompson & Bolino, 2018). Such beliefs are often posited as unreasonably decreasing an individual’s utilization of help, yet these concerns may be justified. Research by Liu et al. (2022) suggests that individuals who are perceived as being dependent on others are rated as less competent and poorer performers by 20 their superiors. Thus, an important concern for workers is to understand how receiving support might impact their perceived competence. How help occurs appears to be an important driver of these effects. Particularly, help can feel empowering or disempowering to individuals. The extent to which help occurs in a way that allows individuals to remain personally involved in resolving issues appears to be the main factor distinguishing empowering and disempowering help (Lee et al., 2023). Help that provides individuals with the resources to resolve their problems personally, rather than relying on an external source, is perceived differently by individuals (Liu et al., 2022). Although disempowering help may be equally as effective at resolving a given demand, empowering help tends to be more favorably perceived (Lee et al., 2023). Help can easily involve a loss of control and agency (Nadler, 2015). These losses are perceived negatively by individuals as they negatively impact self-perceptions of their abilities and social standing (Tai et al., 2023). If help instead facilitates individual growth and development, it is likely to be perceived more favorably due to its more positive impact on self-perceptions. Disempowering help thwarts competence need fulfillment. If an employee’s coworker steps in to solve a problem for them, the employee receives negative information regarding their own competence (Deelstra et al., 2003; Harari et al., 2022). Empowering help has the opposite effect, as the individual retains agency over their issues while also gaining the resources necessary to solve them. For instance, being taught a new technique or strategy facilitates mastery experiences for an individual and allows them to fulfill their competence needs (Lee et al., 2023; Vansteenkiste et al., 2020). These factors suggest that competence need fulfillment mediates the relationship between help acceptance and empowering versus disempowering help. Hypothesis 3: Disempowering help is negatively related to help acceptance. 21 Hypothesis 4: Disempowering help will (a) negatively relate to competence need fulfillment, (b) competence need fulfillment will positively relate to help acceptance, and (c) competence needs will partially mediate the effect of disempowering help on help acceptance. Perceived Helper Motives Helping occurs for differing reasons. Aid can carry different implications for help recipients depending on the motives of the aid source. For instance, one reason employees offer help is to enhance their reputation (Spitzmuller & Van Dyne, 2013). A coworker might offer their assistance on a project out of a genuine desire to help, or out of a desire to enhance their standing with a supervisor. Research on social motives highlights that helper motives carry implications for the quality and outcomes of support. Help recipients are likely to engage in acceptance of helper intent. Individuals continually attempt to understand the intent behind the actions of those around them (Reeder et al., 2002). These perceptions of intent can have important consequences for behavior. Minnikin et al. (2022) found that managers’ perceptions of why their subordinates requested performance feedback impacted the quality of feedback they offered. In cases where individuals were perceived as requesting feedback to improve their self- image, managers provided less feedback compared to subordinates who were perceived as desiring to improve their performance. Research on organizational citizenship behavior has suggested three main motives underlying engaging in helpful behavior at work – (1) prosocial goals, (2) impression management goals, and (3) goals to support the organization (Rioux & Penner, 2001). Such goals will likely trigger different reactions on the part of help recipients. Organizationally focused support motives are likely to have a neutral effect on how individuals perceive help, given that 22 such motives are simply focused on assisting a coworker without any specific relational implications. In contrast, prosocial and impression management motives are likely to have substantial perceived impacts on how help recipients evaluate helpers. Individuals driven by prosocial motives are likely to help work colleagues because they genuinely desire to help (Bolino & Grant, 2016). Prosocial motives are tied to empathy and caring, which serve as signals that help is being offered out of genuine concern for the help recipient (Choi & Moon, 2016). Such motives are associated with greater social connectedness (Locke, 2018) and are likely to be interpreted positively by help recipients by making them feel more valued (Nadler, 2015). These positive social experiences provide a sense of being cared for and are likely to drive relatedness need fulfillment (Reis et al., 2000). As a result, perceived prosocial helping motivation will likely be related to more positive help acceptance from help recipients. Hypothesis 5: Perceived prosocial support motives of helpers positively relate to help acceptance. Hypothesis 6: Perceived prosocial support motives of helpers will (a) positively relate to relatedness need fulfillment, (b) relatedness need fulfillment will positively relate to help acceptance, and (c) relatedness needs will partially mediate the effect of perceived helper prosocial support motives on help acceptance. Help motivated by a goal of impression management will likely be perceived differently by help recipients. Help recipients are often concerned that helpers will try to degrade their status and image within a company (Harari et al., 2022; Thompson & Bolino, 2018). These concerns appear especially valid when support is provided due to impression management motives. Such support is often driven by motivation to look better than others and obtain workplace rewards 23 (Rioux & Penner, 2001). Individuals generally react negatively to others’ efforts to enhance their reputation at the expense of others (Crocker et al., 2017; Jia et al., 2021; Locke, 2018). Help recipients who perceive that help is being offered by an individual seeking to enhance their image will likely experience a threat to their relatedness need fulfillment. Such motives pose a threat to one’s social standing, and signal that the helper is not concerned for the recipients’ wellbeing (Greenwood et al., 2013). As a result, these motives likely thwart relatedness need fulfillment by making the help recipient feel socially threatened, and uncared for (Bartholomew et al., 2011). Hypothesis 7: Perceived impression management support motives of helpers negatively relate to help acceptance. Hypothesis 8: Perceived impression management support motives of helpers will (a) negatively relate to relatedness need fulfillment, (b) relatedness need fulfillment will positively relate to help acceptance, and (c) relatedness needs will partially mediate the effect of perceived helper impression management support motives on help acceptance. Help Perception Interactions The three help perceptions described above may interact with one another. This interaction can be expected to take the form of a 3-way interaction, as the negative or positive ends of each help feature compound with one another. Help recipients are likely to react differently to proactively or reactively provided support depending on perceived helper motives and the extent to which the help is disempowering or empowering. The negative effects of disempowering help are partly because recipients of such help feel viewed as incompetent and of low worth (Lee et al., 2023). Such perceptions are likely to be worsened or alleviated depending on how help was provided. Proactive support is generally interpreted by the recipient as a sign 24 that the helper views them as unable to handle their problems without assistance (Harari et al., 2022; Lee et al., 2019). When a help recipient feels disempowered by help that they did not ask for in the first place, they will likely react more negatively. In contrast, individuals tend to experience more ownership of reactive help and perceive themselves as more invested in such help (Chou & Chang, 2017; Zhan et al., 2021). In such cases, being disempowered during the helping process is likely to be viewed as less threatening because the recipient requested this help. Help motives can be expected to interact with both how help is initiated and how it is performed in predicting help acceptance. For instance, the perceived motives of a helper will inform reactions to disempowering help and proactive or reactively provided help. Many of the negative effects of both disempowering and proactive support are due to recipient concerns that helpers will try to make them look incompetent during the help process (Jia et al., 2021). Perceived prosocial motivations may alleviate some of these concerns (Choi & Moon, 2016). In contrast, the provision of disempowering help and proactive support are likely more threatening when a helper is perceived as motivated by the goal of impression management. The removal of task control and opportunities to learn during disempowering help (Koo et al., 2023; Lee et al., 2023) are likely to be perceived as malicious in such contexts. Help recipients will justifiably be concerned that the helper has initiated the helping process to make them look bad during the help process (Liu et al., 2022), enhancing the negative effect of disempowering and proactive help. In light of such theoretical evidence, I will test for 3-way interactions between the help perceptions. Specifically, the negative effects of proactive help are expected to be compounded by interactions with high disempowering help and perceived impression management motives. In 25 contrast, the positive effects of reactive help are likely to be enhanced interaction with low disempowering help and high perceived prosocial motivations. Hypothesis 9a: The negative relationship between proactive help and help acceptance is strongest at high levels of both (a) disempowering help and (b) impression management motives. Hypothesis 9b: The positive relationship between reactive help and help acceptance is strongest at low levels of (a) disempowering help and high levels of (b) prosocial motives. The Task Context While autonomy, competence, and relatedness needs may capture many of the resource- related effects of helping, resources take a variety of forms (Halbesleben et al., 2014). Specifically, psychological needs could be considered as personal characteristics in the COR model, but do not address other key resources (Hobfoll, 1989). Other forms of resources are likely to have an important effect on help acceptance. For instance, instrumental support at work may involve more tangible resources such as money, information, and direct task assistance (Hobfoll, 1989; House, 1981). Such resources may serve as important mediators of the effects of help perceptions on help acceptance. For instance, if a helper provides information critical to improving the help recipient’s situation, the recipient may positively evaluate the support despite other unfavorable attributes. Surprisingly, the quality of help is rarely considered in the helping literature (Bolino et al., 2013; Kassirer & Kouchaki, 2023). Despite this, help varies considerably in the degree to which recipients perceive it as effective. Research on why workers reject support has highlighted the importance of help quality. In a qualitative study, Gray et al. (2020) found that employees 26 frequently reported being offered help that is impractical, incomplete, and unreliable. Unhelpful workplace support can be a source of stress for employees as they must devote additional resources to utilize incomplete or unreliable support (Hughes et al., 2022). For example, leader support during times of crisis can make things worse for employees if it is of poor quality (Gray et al., 2023). Such findings can also be understood as the provision of support that is poorly matched to the demands faced by the help recipient. Van Veldhoven et al (2020) suggested that not all resources are always beneficial for all job demands. For instance, if an individual is overwhelmed with work tasks, emotional support would likely be beneficial up to a point, but such support would not directly resolve the task-related issues. Given such evidence, it is important to consider the extent to which potential support would be helpful to a recipient. Failure to account for this variable creates several problems in the existing helping research. Findings suggesting that help carries negative implications for workers’ psychological well-being are often explained in terms of self-esteem threat (DePaulo et al., 1981; Nadler et al., 2015; Thompson & Bolino, 2018), however, it is fully plausible that some of these effects are due to receiving help from others that is of low quality, poorly matched to the demands faced by the individual, or that may even be damaging (Gray et al., 2020). As a result, understanding how individuals perceive help quality is important for understanding their reactions to help. Hypothesis 10: Perceived help quality positively relates to help acceptance. It is important to account for the fact that help occurs in response to situation-specific needs (Lee et al., 2023; Spitzmuller & Van Dyne, 2013). At work, individuals may need help with issues ranging from minor technical assistance to assistance with the completion of complex projects. The demands faced by an individual will influence how much they desire assistance. If 27 an individual is in a highly demanding work context, it is likely that they will more readily accept help. Research on social support generally focuses on its potential to buffer against negative effects of work demands on performance and well-being (Mathieu et al., 2019). This model examines how task-related demands may also alter how likely individuals are to accept support from others. As a result, the study model connects to more general research on the buffering effects of support by clarifying the importance of help acceptance in determining the degree to which support moderates the relationship between demands, stress, and performance. Additionally, it is possible that task demands will interact with help perceptions. For instance, in highly demanding situations, employees may be less bothered by receiving help that is disempowering. This possibly will be examined in an exploratory manner, given the lack of research linking help perceptions and task demands. Hypothesis 11: Perceived task demands positively relate to help acceptance. Research Question 1: Is there an interaction between task demands and (a) proactive/reactive help, (b) disempowering help, (c) impression management/prosocial motives on the effect of each perception on (d) relatedness, (e) competence, and (f) autonomy needs? Outcomes of Help Acceptance Instrumental support at work is best understood as episodic – with help being connected to a specific work demand (Dalal & Sheng, 2019). Dynamic changes to employee outcomes at work are likely to be connected to the demands faced by an employee and their reaction to potential support. More demanding situations at work are likely to have greater negative effects on stress and well-being (Bakker et al., 2023). While an individual’s performance may increase 28 to meet challenge posed by demanding tasks, when they face demands that challenge or surpass their capabilities, they may perform more poorly and experience negative outcomes such as increased negative affect and stress (Bauer et al., 2014; Jostmann & Koole, 2010). Similarly, when the task is beyond an individual’s abilities, they are likely to suffer decreases to their self- esteem (Besser et al., 2008). As a result, high levels of task demands may negatively impact the ability of an employee to make progress on that task and negatively impact both affect and self- esteem. Hypothesis 12: Perceived task demands negatively relates to (a) task progress, (b), state self-esteem, and (c) positive affect, but positively relates to (d) negative affect. Theories of support propose that help can buffer against the negative effect of task demands and lead to improved affective and performance outcomes (Bakker et al., 2023; Mathieu et al., 2019). Social support at work is proposed to both directly lead to greater well- being and provide resources that buffer against stressors (Dormann & Zapf, 1999; Pluut et al., 2018). Research suggests that coworkers can enhance one another’s performance by exchanging resources with one another (Halbesleben et al., 2015). Support grants individuals access to resources such as information that allows them to perform tasks that they could do as effectively alone (Bakker et al., 2020; DePaulo et al., 1981; House, 1981; Mueller & Kamdar, 2011). Given such research on help and demands, it is likely that if an individual accepts support from a coworker they will experience benefits both to their performance and their emotional outcomes. Hypothesis 13: Help acceptance positively relates to (a) task progress, (b), state self- esteem, and (c) positive affect, but negatively relates to (d) negative affect. The full study model is presented in Figure 1, summarizing these hypothesized paths. 29 Figure 1. Proposed Study Model. 30 Method STUDY 1 Study 1 relied on a scenario-based design to provide an initial test of the model’s core hypotheses related to basic psychology needs, help acceptance, and perceptions of help features and task demands. Using a fully crossed analysis of variance (ANOVA), Study 1 tests if varying levels of task demands, helper motivations, disempowering help, and proactive/reactive help significantly predict variation in basic needs and help acceptance. Each scenario described a work task and then some form of instrumental task support from a coworker. Participants Ten pilot participants were first recruited to offer feedback on the clarity and wording of the scenario used in the design. Pilot participants were recruited from personal contacts and early student participants in the study. Participants in the main study were recruited through the university undergraduate research participant pool. A total of 1318 participants completed the study. However, 155 of these participants were removed for skipping questions related to the scenario and providing incomplete data on the measures used in the study. A final sample of 1163 participants were included in the analysis. Participants were randomly assigned to one of 24 possible scenarios reflecting various combinations of help features and task demands with total group sizes ranging from 48 to 54 participants per scenario. Based on analyses conducted in G*Power (Faul et al., 2009), this sample is sufficient to detect moderate group differences with greater than 80% power in each group. The final sample of participants was majority female (75%), white (77%), and between the ages of 18 and 24 (98%). 31 Procedure After consenting to participate, participants completed the study on Qualtrics. They first were presented with a scenario describing a work task and potential instrumental support related to that task. The scenarios were randomly selected and evenly distributed across participants to cover varying combinations of high and low versions of task demands and each help perception. After reading a scenario, all participants first responded to two qualitative questions querying their perceptions of the scenario. They then rated their reaction to the help presented in the scenario using scales measuring basic psychological needs and their intentions to accept help in this scenario. Additionally, each scenario was paired with Likert scale measures of help perceptions and perceived task demands, to serve as a manipulation check for the scenarios and to test the effects of each perception within the model. After completing the scenario portion of the study, participants responded to demographic and control variable questions at the end of the study. To test each of the three help perceptions and the effect of task demands, the study was designed as a 2x2x2x3 fully crossed factorial ANOVA, with four focal independent variables (task demands, disempowering help, proactive versus reactive help, and helper motives). Proactive/reactive help, disempowering help, and task demands each had two levels, reflecting a high versus low version of each construct. Helper motives had three conditions, reflecting prosocial motives, impression management motives, and a neutral scenario that does not mention helper motives. The core relationships tested in Study 1 are presented in Figure 2. 32 Figure 2. Study 1 Empirical Model. Materials Help Scenarios. Each scenario asked participants to imagine that they are an employee of an organization who is trying to complete a specific work task. The scenario then describes assistance on that project, either offered by or requested from a coworker. The exact wording of the scenarios was refined for clarity using 10 pilot participants consisting of graduate students, undergraduate students, and personal contacts. Pilot participants were presented with the potential wording for each combination of variables in the scenario (e.g., a highly demanding task, with help offered proactively by a colleague in a prosocial, but disempowering manner). After each example scenario, pilot participants were asked to respond to open-ended questions asking for their comments on the clarity of the scenario and how they would interpret the statements. Based on feedback from these comments, the final variations of the scenario were developed. The wording of the scenario is presented in Appendix A. Each scenario has the same general structure, first describing a work task (placing either high or low work demands on the participant), then describing help offered or requested from a colleague that has varying features (perceived motives, extent to which help is disempowering). 33 Anticipated RelatednessRequested un requested Help( is)empowering HelpHelper Motives ask emandsAnticipatedCompetenceAnticipated Autonomy Qualitative Questions. I included two open-ended questions to provide additional information regarding participants’ responses to help. The first question asked participants to “Please write 2-3 sentences explaining why or why not you would like to receive this kind of assistance?” The second question asked, “Please write 2-3 sentences describing how you would react to this type of help if you were in this situation”. hese questions were presented prior to the other study scales to avoid any priming of participant responses. Proactive versus Reactive Support. A single item measure asking, “In this scenario, did you ask for help, or did the other person provide it without your request?” was used as a manipulation check for the reactive versus proactive support scenarios. Participants then selected either that they had requested the help, or that it had been offered to them. Perceived Helper Motives. The manipulation check for helper motives was performed using Rioux and Penner’s (2001) help motives scale. he original scale consists of three dimensions, each containing 10 items. For this study, I used adapted versions of the prosocial values and impression management motives, altered to focus on perceived motives rather than personal motives. I also shortened the measures, retaining the five items with the highest factor loading for each of the two motives. See the Appendix B for adapted items used in Study 1. Participants were asked to rate each statement using a five-point of ‘not at all important’ to ‘extremely important’ regarding the potential motivations of the helper. A sample item for prosocial motives was “Because they feel it is important to help those in need,” while an example item for impression management motives was “To avoid looking bad in front of others.” In the current study, these items exhibited acceptable reliability for both prosocial motives (α = .82) and impression management motives (α = .86). 34 Disempowering Help. The disempowering/empowering help descriptions in the scenarios were checked using items based on Lee et al.’s (2023) four item measure of disempowering or empowering help. These items were adapted to the context of this study by rewording them to focus on the specific helping instance in question, as opposed to a general example of helping (see Appendix B for details). Participants responded using a five-point ‘strongly disagree’ to ‘strongly agree’ scale. A sample item for the scale is “Instead of doing it for me, this coworker would teach me how to do it.” his scale exhibited acceptable reliability (α = .80). Work Demands. The task demands manipulation check used five items adapted from Spector and Jex’s (1998) measure of task demands. Participants responded using a five-point ‘strongly disagree’ to ‘strongly agree’ scale. An example item was “I would have to work very hard to complete this task.” he adapted items are contained in Appendix B. The scale had a reliability of α = .87. Psychological Need Fulfillment. Basic needs in the work context were measured using items adapted from Van den Roeck et al.’s (2010) scale. he scales consist of 6 items for each dimension of basic psychological needs. Given that Study 1 asked participants to imagine their responses in the context of specific scenarios, it was necessary to ask participants to consider how they would feel in the scenario. As a result, I adapted the initial framing of the items to ask participants to respond based on how they think they would feel in the situation described in the prompt. Each item was reworded to have a prospective framing, rather than being framed around one’s current experiences (e.g., ‘I wouldn’t really feel competent in my job if I received this help’ instead of ‘I don’t really feel competent in my job’). Participants responded using a five- point ‘strongly disagree’ to ‘strongly agree’ scale. The reworded scale is presented in Appendix 35 B. Relatedness need fulfillment had a reliability of α = .77, competence need fulfillment had a reliability of α = .81, and autonomy need fulfillment a reliability of α = .68. Intentions to Accept Help. Participant willingness to accept the help described in the scenario was assessed using 5 items developed based on items from Harari et al.’s (2022) measures of help acceptance. Participants were prompted to consider the extent to which they would want this help based on the description in the scenario and respond to each item on a 1 (strongly disagree) to 5 (strongly agree) scale. An example item was “I would let this person help me.” The full items are presented in Appendix B. The scale had a reliability of α = .91. Quality. Help quality was measured using eelstra et al.’s (2003) measure of perceived help appropriateness, to control for how effective participants perceived help to be in the different scenarios. The measure asks participants to rate the support they received along five pairs of bipolar adjective combinations using a five-point semantic differential scale. The adjectives include appropriate-inappropriate, effectual-not effectual, useful-not useful, effective- ineffective, and necessary-unnecessary. The scale had a reliability of α = .51. Upon examination of the scale, the necessary-unnecessary adjective scale appeared to be driving low reliability. With this item removed, reliability was α = .85. Demographics. This study assessed several demographic variables due to their potential influence on the helping process. Gender, age, and race were recorded as research suggests that these factors may impact the helping process (Nadler, 2015). For instance, research by Lee et al. (2023) suggests that women may experience greater threats during the helping process as receiving help can be viewed as confirming negative stereotypes regarding their competence. For Study 1, helper race and gender were kept ambiguous in the scenarios. Additionally, the helper 36 presented in each scenario is described as a colleague to control relative seniority within the organization. Analytic Approach The psychometric properties of the measures with significant adaptation from their original wording (basic psychological needs and help acceptance) were examined confirmatory factor analysis (CFA) in Mplus 7 (Muthén & Muthén, 2012) to fit with best practices regarding scale adaptation (Baker & Chang, 2025). Item loadings were examined to gauge relevance to the overall construction, and overall model fit was examined to evaluate the measure as well. The effects of help perceptions and task demands on need satisfaction and intentions to accept help were first evaluated using an analysis of variance (ANOVA) looking at the effect of each help perception at high and low levels for the overall help acceptance and psychological needs scores. Effects of scenario manipulations on basic psychological needs were evaluated separately using a multivariate ANOVA. To test the mediational hypotheses in the model, the ANOVA was followed up using a path model to test each hypothesized path and indirect effects of scenario variables on help acceptance, mediated by basic psychological needs. Indirect effects and their corresponding confidence intervals were then estimated using a Monte Carlo simulation with 20,000 replications (Preacher et al., 2010). Results First, to test the effectiveness of the study manipulations, I performed an analysis of variance (ANOVA) for each manipulation check, predicted by the scenario manipulations. For each manipulation check scale, all scenario manipulations were included to allow for the possibility that various manipulations might have unintended crossover effects on other relevant variables. The results of these tests are reported in Table 1. Supporting each manipulation’s 37 validity, ANOVA analyses revealed that the corresponding scenario manipulation was consistently the strongest dominant predictor of each manipulation check variable and explained the majority of the variance. The task demands manipulation significantly predicted the task demands scale, F(1, 1139) = 181.32, p < .001, η2 = .46, with a mean difference of 0.46 between high and low conditions (Tukey HSD, p < .001, 95% CI [0.50, 0.67]). Results were similar for the disempowering help manipulation check and the corresponding scenario manipulation, F(1, 1139) = 174.01, p < .001, η2 = .41, with a mean difference of 0.65 between high and low conditions (Tukey HSD, p < .001, 95% CI [0.55, 0.74]). The motive manipulation had a significant effect on the prosocial motive manipulation check, F(2, 1139) = 43.43, p < .001, η2 = .38, with a mean difference of 0.45 between prosocial and impression management conditions (Tukey HSD, p < .001, 95% CI [0.32, 0.57]). The same manipulation also had a significant effect on the impression management motive manipulation check, F(2, 1139) = 48.30, p < .001, η2 = .36, with a mean difference of 0.62 between impression management and prosocial conditions (Tukey HSD, p < .001, 95% CI [0.46, 0.77]). While Table 1 illustrates that other manipulation conditions and interactions between conditions were occasionally significant, it is likely that the large sample size may be inflating these minor effects (Cumming, 2014). In each case, these other variables explained far less variance than the corresponding manipulation (average η2 = .04 for all other predictors and interactions when predicting a given manipulation check). Of these additional effects, an interaction between reactive help and helper motives was the most common, with a significant reactive help-perceived helper motives for the task demands, prosocial, and impression management motive outcomes. These results suggest that these two scenario framings may interact to impact the manipulation check variables. Lastly, the proactive-reactive distinction was 38 tested using logistic regression to account for the binary outcome variable. This analysis also suggested that the corresponding proactive-reactive manipulation was the strongest predictor (ϐ = -.90, SE = .16, p <.001). Descriptive statistics and correlations for Study 1 are presented in Table 2. To test the direct effects of scenario manipulations on participant help acceptance, I first performed an analysis of variance (ANOVA). Table 3 summarizes the results of this analysis. Hypothesis 1 was not supported by this analysis as there was no significant link between proactive/reactive help scenario manipulations and help acceptance. Providing support for Hypothesis 3, disempowering help was significantly related to lower help acceptance, F(1, 1139) = 138.83, p < .001, η2 = .13, with a mean difference of -0.52 between high and low conditions (Tukey HSD, p < .001, 95% CI [-0.62, -0.42]). To test Hypotheses 5 and 7, I first tested the overall main effect of perceived motives on help acceptance, F(2, 1139) = 8.81, p < .001, η2 = .02. This effect was followed up by post-hoc Bonferroni test with adjusted alpha levels. These tests revealed a significant mean difference of 0.22 between neutral and impression management motive conditions (Tukey HSD, p < .001, 95% CI [0.07, 0.36]). Prosocial motives were not significantly different from neutral or impression management motives conditions in their acceptance intention, providing support for only Hypothesis 5. Figure 4 displays the means for help acceptance across the three motive conditions. 39 Figure 3. Help Acceptance by Motive Condition. The help quality variable was a significant predictor of help acceptance, F(1, 1139) = 280.00, p < .001, η2 = .23, providing some initial support for Hypothesis 10. Importantly, help quality was not explicitly manipulated in the scenarios, unlike the other focal variables. Lastly, none of the focal variables had significant interactions, providing no support for Hypotheses 9a- b. The majority of other possible interactions between needs and scenario manipulations were not reported in Table 3 and were all nonsignificant. Finally, there were no main effects of task demands on help acceptance, providing no support for Hypothesis 11. o test for violations of the model’s assumptions, I examined both the normality of the residuals and tested for homogeneity of variance in the model. To test for normality, I used a Shapiro-Wilk test (Razali & Wah, 2011) and to test for homogeneity of variance I used Levene’s 40 test (Levene, 1960). The Shapiro-Wilk test indicated a possible violation of normality (W = .98, p < .001), and the Levene test suggested a violation of the homogeneity of variance assumption (F = 2.17, p < .01). However, both tests can become overly sensitive in large sample sizes, detecting differences that may not meaningfully impact ANOVA results (Ghasemi & Zahediasl, 2012; Kim & Cribbie, 2018). To supplement these tests, I plotted the distribution of residuals to examine homogeneity of variance and examined a Q-Q plot to evaluate the normality of the data. The Q-Q plot (Figure 5a) and residual distribution (Figure 5b) illustrate an approximately normal distribution, with some heterogeneity of variance within the data. Additionally, the outcome exhibited relatively normality skew (-.71) and kurtosis (2.94) , suggesting a close-to-normal distribution for the dependent data (Tabachnik et al., 2013). Figure 4a. Normal Q-Q Plot. 41 Figure 4b. Residual Distributions. To test the effect of help features on basic psychological needs (Hypotheses 2a, 4a, and 6a), a multivariate ANOVA (MANOVA) was utilized. Table 4 summarized results of these analyses, first presenting overall MANOVA results, then results for each basic psychological need. In the full MANOVA model, Pillai’s race revealed that all variables explained a significant about of main in needs overall. Examining the needs individually, Hypothesis 2a received no support, as proactive help did not negatively relate to anticipated autonomy need fulfillment. In contrast, task demands lowered autonomy need fulfillment, F(1, 1139) = 11.86, p < .001, η2 = .01, with a mean difference of -.11 between high and low conditions (Tukey HSD, p < .001, 95% CI [-0.19, -.04]), as did disempowering help F(1, 1139) = 111.13, p < .001, η2 = .09, with a mean difference of -.36 between high and low conditions (Tukey HSD, p < .001, 95% CI [-0.43, -.29]). Motives also emerged as a significant predictor of autonomy needs F(1, 1139) = 7.07, p < .001, η2 = .01, with a post hoc Tukey test with a Bonferroni correction indicating that autonomy needs had higher fulfillment in prosocial (mean difference = .14, Tukey HSD, p < .01, 95% CI [0.03, .25]) and neutral conditions (mean difference = .12, Tukey HSD, p = .01, 95% CI [0.02, .22]), compared to helper with impression management motives. 42 Hypothesis 4a received full support, as disempowering help was associated with lower anticipated competence need fulfillment, F(1, 1139) = 91.93, p < .001, η2 = .01, with a mean difference of -.42 between high and low conditions (Tukey HSD, p < .001, 95% CI [-0.50 -.33). Similarly, Hypothesis 6a and 8a received support as motives were significantly related to relatedness need fulfillment F(1, 1139) = 20.54, p < .001, η2 = .03. Follow up post hoc Tukey tests with a Bonferroni correction revealed that relatedness needs had higher fulfillment in neutral (mean difference = .18, Tukey HSD, p < .001, 95% CI [0.10, .26]) and prosocial conditions (mean difference = .17, Tukey HSD, p < .001, 95% CI [0.09, .25]), compared to impression management motives condition. Disempowering help also emerged as a significant predictor of relatedness needs, F(1, 1139) = 85.47, p < .001, η2 = .07, with a mean difference of - .24 between high and low conditions (Tukey HSD, p < .001, 95% CI [-0.29 -.18). Interactions between focal variables were non-significant and were dropped from the model. To provide a test of the basic needs and mediational predictions made in Hypotheses 2b- c, 4b-c, and 6b-c, I also analyzed the data using a path model in Mplus 7 (Muthén & Muthén, 2012). A confirmatory factor analysis (CFA) was first used to evaluate model fit for the focal continuous variables and controls. An initial model including the basic psychological needs, help acceptance, positive and negative affect, help quality, and self-esteem, had relatively poor model fit, χ2(1,246) = 7945.01 comparative fit index (CFI) = .78, root mean square error of approximation (RMSEA) = .07, and standardized root mean square residual (SRMR) = .08. Model fit improved after dropping items from the basic needs scale that had low factor loadings (< .40) and substantial cross-loadings between the needs (two items from the competence scale, three from the relatedness and autonomy scales). The revised model had improved model fit, 43 χ2(638) = 3821.77 comparative fit index (CFI) = .86 root mean square error of approximation (RMSEA) = .06, and standardized root mean square residual (SRMR) = .09. Path model results are presented in Table 5. Figure 6 visualizes the path model. Perceived helper motives were dummy coded separately for Mirroring the ANOVA results, all three basic psychological needs were significant predictors of help acceptance. Direct paths for proactive- reactive help and helper motives were nonsignificant for help acceptance, providing no support for Hypothesis 1, 5, and 7 within the path model. Hypothesis 2a and 2c were not supported as proactive-reactive help did not relate to autonomy need fulfillment, however Hypothesis 2b received support as autonomy needs significantly predicted help acceptance (ϐ = .43, p < .001, 95% CI [.43, .60]). In contrast, disempowering help was a significant direct predictor of help acceptance (γ = -.07, p < .01, 95% CI [-.11, -.02]), providing support for Hypothesis 3. Similarly, Hypothesis 4a, 4b, and 4c, received full support as disempowering help was significant predictor of competence needs (β = -.27, p < .001, 95% CI [-.32, -.22]), competence need fulfillment was a significant predictor of help acceptance (γ = .17, p < .001, 95% CI [.10, .23]), and disempowering help had a significant indirect effect on help acceptance, mediated through competence needs (indirect effect = -.07, p < .001, 95% CI [-.10, -.04]). Perceived helper motives were dummy coded to create two separate codes – ones contrasting impression management motives with the neutral condition, and one contrasting prosocial motives with the neutral condition. Fitting with Hypotheses 6a, perceived prosocial helper motives were associated with higher relatedness need fulfillment, compared to a neutral motive condition, (γ = .31, p < .001, 95% CI [.26, .36]), and relatedness needs fulfillment was a significant predictor of help acceptance (ϐ = .30, p < .001, 95% CI [.24, .36]), supporting 44 Hypotheses 6b and 8b. There was no indirect effect of prosocial motives via relatedness needs, however, providing no support for Hypothesis 6c. Hypothesis 8a received support, as perceived impression management helper motives were associated with decreased relatedness need fulfillment compared to a neutral motive condition (γ = -.26, p < .01, 95% CI [-.17, -.11]). Lastly, Hypothesis 8c was supported as impression management motives had a significant indirect effect on help acceptance, mediated through relatedness needs (indirect effect = -.06, p < .001, 95% CI [-.08, -.05]). Perceived help quality was also a significant predictor (b = .23, p < .001, 95% CI [.18, .28]), providing some initial support for Hypothesis 10. Lastly, task demands emerged as a significant predictor of help acceptance within the path model (γ = .08, p < .01, 95% CI [.04, .12]), providing support for Hypothesis 11. Figure 5. Path Model Results. Note. Figure depicts standardized beta coefficients with standard error estimates in parentheses. 45 AnticipatedRelatednessRequested un requested Help( is)empoweringHelpImpression Motives ask emandsAnticipatedCompetenceAnticipatedAutonomy.30(.0 ) .17(.03) . 5(.0 ) .08 (.0 ) .27 (.0 ) Prosocial Motives.02 (.0 ).31(.02) .26 (.01) Discussion Across the two models, the focal hypotheses of the study received general support, except for Hypotheses 1, 2a, and 2c, predicting that proactive/reactive help would relate to autonomy needs and help acceptance. All three basic psychological needs were found to be significant predictors of help acceptance, as were task demands (in the path model) and help quality. Both impression management and prosocial motives were significant predictors of relatedness needs and helper’s impression management motives had an indirect effect on help acceptance. The effect of motives on help acceptance appear to be fully mediated by relatedness need fulfillment, likely contributing to the different relationships observed in the ANOVA model when compared to the path model. In contrast, all predictions related to competence and disempowering help were supported by the data – as the effect of disempowering help was partially mediated by competence needs yet it still had a significant direct effect on help acceptance. As a whole, these results support a picture of help acceptance as being influenced by perceptions of the level of demands faced by the recipient, the potential benefits offered by the help, and a consideration of the help’s impact on need fulfillment. Impressions of need fulfillment appear to be related to the extent to which help is performed in a disempowering manner, and the perceived motivations of helpers. Overall, the results of Study 1 support the main predictions of the model. These findings provide initial evidence that responses to help at work might be influenced by the perceived effects of help on basic needs. Although the study employed experimental manipulations of help perceptions, the hypothesized mediators and outcome were measured at the same time. Thus, while it provided support for the causal effect of help perceptions on needs fulfillment and help acceptance intentions, the causal direction between the needs fulfillment and help acceptance 46 was less clear. Examining help perceptions over time, testing demands and outcomes separately from help perceptions, would allow for a more dynamic view of help. Additionally, focal help perceptions in study were directly manipulated using scenario descriptions. These results do not reveal if individuals are sensitive to variations in these factors in real work contexts. For instance, help recipients may not naturally notice helper motivations or react to disempowering help in real world settings. Study 1’s results also do not speak to how responses to help might impact worker outcomes and behavior. Study 2 was designed to address key potential issues with the interpretation of Study 1 results by studying help perceptions in relation to actual offers of help, rather than manipulating help variables, and by studying help in a dynamic manner. Study 2 relied on an experience sampling design to assess help experiences over a five-week period. With data collected from a sample of full-time working adults, the study assessed weekly task demands at the start of the week, then measured any help received by participants on Thursday, and finally measured work outcomes at the end of the week. 47 STUDY 2 Method Participants The second study relied on a working sample to test how real-world helping experiences as perceived by employees. Participants were recruited using Prolific Academic. Prolific is an online subject pool with a large range of potential subjects, high-quality data screening procedures, generally low rates of attrition (Palan & Schitter, 2018; Kothe & Ling, 2019). Participants were screened to ensure full-time employment, that they are not employed as a fully remote worker, and to ensure that they work in a job that requires frequent coworker interaction. To estimate the required sample size for the analyses, I used a Monte Carlo simulation to estimate the necessary sample size to estimate the parameters of the path model using a medium effect size for all estimates. (Muthén & Muthén, 2002). These analyses assumed modest relationships (b = .2) for all hypothesized paths and suggested that 500 within-person observations would result in power for the different parameters ranging from .90-.95. These analyses were used to target an initial subject pool of 120, to account for attrition. Of the 120 participants collected, 21 were removed for completing less than 2 days of studies, following recommendations for multilevel data collection (Gabriel et al., 2019). The final sample of 99 participants was 49.5% female, 7% Hispanic, and 16% identified as part of a visible minority group. The majority of the participants (63.3%) were between the ages of 25 and 44. Additionally, participants were screened to only include full-time workers who worked at least partially in the office, as opposed to working fully remote. Participants completed an average of 3.6 weeks of the survey, with 356 observations at the within-persons level and a 72% overall response rate. 48 Procedure Study 2 assessed participant experiences with task demands and potential coworker help at work over 5 weeks. Participants first completed an initial screening study, which also included a description of the weekly portion of the study and compensation structure. Individuals who completed a full week of surveys received a small cash bonus to incentivize participation. Each week, participants could respond to up to 3 surveys. Participants began the weekly studies on Monday following their completion of the screening survey. The Monday survey presented participants with the following prompt “Consider the various tasks or projects you will work on this week. Of those activities, please think about the task that will be most important or will take up most of your time this week. Thinking about that task, please respond to the questions below:” Participants then rated their perception of demands related to that task and answered short qualitative questions regarding the nature of the task. On Thursday, participants responded to a survey asking them (1) if they have asked for any help with the task they described on Monday and (2) if someone offered them help proactively with the task they described on Monday. If they answer no to one or both questions, the questions related to that type of help were replaced with short qualitative questions asking them to describe either why they chose not to ask for help (no reactively provided help) or why they believed no one offered them assistance (no proactively provided help). If they answered yes to either question, they rated their evaluation of the help quality, helper motives, and the extent to which the help is disempowering or empowering. They also rated how asking for/being offered help makes them feel in terms of their basic psychological need fulfillment. Lastly, participants reported if they rejected or accepted the help. The wording of the questions varied 49 depending on if the participant was describing reactive (requested) or proactive (offered) types of help. On Friday, participants received a survey measuring the various outcomes of the study, including task-related progress, self-esteem, and positive and negative affect. The same process was repeated the following week, beginning Monday. The weekly surveys were sent out multiple times, for a total of 5 waves. Surveys were sent out in the morning each day (8:00AM) and participants could complete the surveys any time during that day. Figure 3 visualizes the relationships examined in Study 2. Figure 6. Study 2 Empirical Model. Materials Help Context. In the Monday survey, participants responded to two qualitative questions: “Please describe your most important task project at work this week in 2-3 sentences.” 50 Reactive HelpProactive Help Positive Negative AffectRelatedness oal ask Progress( is)empowering HelpState Self steem ask emandsCompetenceAutonomyProactiveHelp ualityRelatedness( is)empowering HelpHelper MotivesCompetenceAutonomyHelper Motives ReactiveHelp uality “Please use 2-3 sentences to describe what kind of help would be most useful to you on this project”. Work Demands. The Monday survey measured perceived task demands-related to the main work task of the participant using the same task demands measure as Study 1, adapted to focus on the specific task that the participant viewed as the most demanding for that week. Participants responded using a five-point ‘strongly disagree’ to ‘strongly agree’ scale. An example item was “I would have to work very hard to complete this task.” The adapted items are contained in Appendix B. The scale had a between-level reliability of α = .94 and within-level reliability of α = .73. Proactive versus Reactive Support. Proactive versus reactive help was coded in the data based on how participants responded to the daily surveys on Thursday each week. Participants were asked about each type of help separately using a binary ‘yes’ – ‘no’ response scale. For instance, if a participant reports that they requested help, their responses to that survey will be recorded as a reactive helping situation. Participants reported 124 instances of proactively-offered support, and 154 instances of reactive support. In contrast, there were 172 instances where participants reported not receiving help. Perceived Helper Motives. As in Study 1, perceived helper motives were measured using an adapted version of Rioux and Penner’s (2001) help motives scale. he wording of the scale prompt varied slightly between reactive and proactive types of help. See Appendix B for adapted items. Participants were asked to rate each statement using a five-point of ‘not at all important’ to ‘extremely important’ regarding the potential motivations of the helper. A sample item for prosocial motives was “Because they feel it is important to help those in need,” while an example item for impression management motives was “ o avoid looking bad in front of 51 others.” The prosocial motive scale had a between-level reliability of α = .95 and within-level reliability of α = .76 for proactive help and had a between-level reliability of α = .92 and within- level reliability of α = .74 for reactive help. The impression management had a between-level reliability of α = .96 and within-level reliability of α = .82 for proactive help and had a between- level reliability of α = .96 and within-level reliability of α = .57 for reactive help. The reactive-proactive versions of each measure were aggregated together for analyses (see the results section for more details on this decision). The combined versions of these scales had a between-level reliability of α = .95 and within-level reliability of α = .86 for prosocial motives and a between-level reliability of α = .98 and within-level reliability of α = .76 for impression management motives. Disempowering Help. Perceptions that help is disempowering/empowering were measured using items based on Lee et al.’s (2023) measure of disempowering help. As in Study 1, these items were rewritten to focus on the specific helping situation (See Appendix for exact items). Participants responded using a five-point ‘strongly disagree’ to ‘strongly agree’ scale. A sample item for the scale is “this coworker (would have) showed me how to best handle this situation, rather than taking over.” The scale had a between-level reliability of α = .83 and within-level reliability of α = .73 for proactive help and had a between-level reliability of α = .83 and within-level reliability of α = .72 for reactive help. One item was dropped from the scale for having a very poor factor loading to the latent trait. The subsequent combined reactive and proactive versions of this scale had a between-level reliability of α = .87 and within-level reliability of α = .79. Psychological Need Fulfillment. As in Study 1, basic needs in the work context were measured using items adapted from Van den Roeck et al.’s (2010) scale. In the context of Study 52 2, the items were adapted to reflect either how the participants feel about asking for help or being offered help. The exact wording of the items varied based on if the participant identified the help as proactive or reactive. The reworded Study 2 scale is presented in Appendix B. Participants responded using a five-point ‘strongly disagree’ to ‘strongly agree’ scale. An example item for the reactive wording of the relatedness scale is “Requesting this help doesn't make me feel connected with other people at my job.” An example item for the proactive wording of the competence scale is “I don’t really feel competent in my job after requesting this help.” The relatedness scale had a between-level reliability of α = .94 and within-level reliability of α = .61 for proactive help and had a between-level reliability of α = .86 and within-level reliability of α = .47 for reactive help. The competence scale had a between-level reliability of α = .92 and within-level reliability of α = .75 for proactive help and had a between-level reliability of α = .90 and within-level reliability of α = .64 for reactive help. The autonomy scale had a between-level reliability of α = .91 and within-level reliability of α = .74 for proactive help and had a between-level reliability of α = .94 and within-level reliability of α = .58 for reactive help. During aggregation of the basic needs scales, items with particularly low factor loadings were dropped (2-3 items per scale; see results). The aggregated scales had a between-level reliability of α = .95 and within-level reliability of α = .68 for relatedness, a between-level reliability of α = .90 and within-level reliability of α = .74 for competence, and a between-level reliability of α = .95 and within-level reliability of α = .83 for competence. Help Acceptance. After rating help perceptions, participants responded yes/no to a question asking them ‘did you accept this help?’ Acceptance rates for help were 92% across all instances reported by participants. Participants accepted 95% of help that was reactive (requested), and 91% of help that was proactive (offered). 53 Help Quality. Help quality was measured using eelstra et al.’s (2003) measure of perceived help appropriateness for each instance of helping reported by participants. The measure asked participants to rate the support they received along five pairs of bipolar adjective combinations using a five point semantic differential scale. The adjectives included appropriate- inappropriate, effectual-not effectual, useful-not useful, effective-ineffective, and necessary- unnecessary. Fitting with the pattern observed in Study 1, the final ‘necessary-unnecessary’ item appeared to load poorly on the scale and was dropping from the analysis. The scale had a between-level reliability of α = .97 and within-level reliability of α = .86 for proactive help and had a between-level reliability of α = .90 and within-level reliability of α = .73 for reactive help. After aggregation, the help quality scale had a between-level reliability of α = .97 and within- level reliability of α = .76. Affective Outcomes. Participants were also asked to complete the PANAS short form (Mackinnon et al., 1999) to assess their state positive and negative affect at the end of each week. and the full item list is presented in the Appendix. Participants responded to each item using a using a five-point ‘never’ to ‘always’ response scale. An example item for positive affect was “Alert”, while a negative affect example was “Upset.” The positive affect scale had a between- level had a between-level reliability of α = .92 and within-level reliability of α = .73, while the negative affect scale had a between-level omega reliability of had a between-level reliability of α = .96 and within-level reliability of α = .76. Additionally, I included a measure of burnout to provide a complementary evaluation of stress outcomes for participants. I used a nine-item measure of burnout from the Maslach Burnout Inventory (Maslach et al., 1986). Participants responded using a seven-item scale ranging from ‘Not at all’ to ‘to a great extent’. An example item is “I feel emotionally drained 54 from my work.” The burnout measure had a between-level reliability of α = .98 and within-level reliability of α = .91. In the initial survey, I also measured trait-level PANAS. At the between- persons level, positive affect had a reliability of α = .92, while negative affect had a reliability of α = .9 . Performance Outcomes. Participants’ performance on their focal task for the week was assessed using Koopman et al.’s (2016) three item measure of work goal progress. Participants responded on a five point ‘Strongly disagree’ to ‘Strongly agree’ scale. An example item is “I have moved forward with my work goals this week.” Work goal progress had a between-level reliability of α = .98 and within-level reliability of α = .9 . Additionally, given that the performance benefits of support can be understood through its motivational impact on work engagement (Bakker et al., 2023), I included a six-item short measure of work engagement adapted from Bakker & Xanthopoulou (2009). Participants responded on a five point ‘Strongly disagree’ to ‘Strongly agree’ scale. An example item is “I feel strong and vigorous.” The work engagement measure had a between-level reliability of α = .95 and within-level reliability of α = .75. The items for both measures are reported in Appendix B. Self-Esteem. State self-esteem was assessed with the social and performance domain items from Heartherton and Polivy’s (1991) measure of state self-esteem. The scale contains fourteen items, which are presented in Appendix B. Participants responded using a 1 (strongly disagree) to 5 (strongly agree) scale. An example item was “I feel self-conscious.” Self-esteem was assessed at the end of each week. The weekly measure had a between-level reliability of α = .97 and within-level reliability of α = .82. It was also assessed at the between-persons level, with a reliability of α = .92. 55 Relational Control Variables. To account for possible alternative explanations based on the relationship between the participant and the help source, I included an item adapted from Methot et al. (2016); “Is this coworker someone you consider yourself to be friends with (i.e., someone who you occasionally socialize with outside of work) – yes/no.” Additionally, I included an item asking for the relative position of the helper to the participant within the company and asking the participant the type of work relationship they have with the coworker (subordinate, superior, work peer) to allow help perception effects to be distinguished from the organizational hierarchy. Participants reported that 38% of help interactions were with friends. Additionally, 70% of interactions were with a peer, 15% were with a supervisor, and 15% were with a subordinate. Demographics and Exploratory Variables. This study assessed gender, age, and race. Participants also rated perceived changes in their status due to requesting help using an adapted measure of Yu et al.’s (2019) six item measure of perceived workplace status. Participants responded using a 1 (strongly disagree) to 5 (strongly agree) scale. An example item was “I will be less respected if I take this help.” The scale had a between-level reliability of α = .98 and within-level reliability of α = .83 for proactive help and had a between-level reliability of α = .99 and within-level reliability of α = .86 for reactive help. After aggregating across reactive and proactive variations, the scale had a between-level reliability of α = .98 and within-level reliability of α = .90. The reworded scale is available in Appendix B. Lastly, several motivational tendencies and workplace features were explored as possible moderators of responses to help due to their theoretical relevance to the processes under examination. These moderators were explored as second-stage moderating factors – altering how changes to basic needs may relate to subsequent help acceptance. A central tenet of the COR 56 model is that individuals’ perceptions of resources are moderated by the environmental features, such as a workplace’s climate and task features (Hobfoll et al., 2018). Such factors may influence how individuals interpret the fulfillment or thwarting of their basic psychological needs and their reactions towards the interpretation. For instance, organizational features, such as how autonomy-supportive one’s immediate supervisor is, can increase how greatly individuals benefit from need-supportive experiences (Ryan et al., 2022). Additionally, task interdependence, level of competitive climate, and psychological safety were included as potential organizational-level moderators. These factors were chosen as the most likely to influence how individuals perceive resources, as factors related to interdependence and competition between workers have previously been identified as key moderators of COR processes (Hobfoll et al., 2018). Workplace task interdependence was assessed using Van der Vegt and Janssen’s (2003) five item scale (α = .83). he scale used a 1 (strongly disagree) to 7 (strongly agree) scale. An example item was “I need to collaborate with my colleagues to perform my job well.” Competitive climate was assessed using Fletcher et al.’s (2008) four-item scale (α = .88). he scale used a 1 (strongly disagree) to 7 (strongly agree) scale. An example item was “My manager frequently compares my performance with that of my coworkers.” Lastly, psychological safety was assessed using dmondson’s (1999) seven-item (α = .82). he scale used a 1 (strongly disagree) to 7 (strongly agree) scale. An example item was “Members of this team are able to bring up problems and tough issues.” Similarly, individual motivational differences have been identified as moderators in both SDT and COR research. Broadly speaking, individuals appear to differ in the extent to which they value certain resources (Halbesleben et al., 2014). Similarly, SDT perspectives emphasize that motivational orientations influence need fulfillment (Ryan & Deci, 2000; Vansteenkiste et 57 al., 2020). Given these elements of SDT and COR, regulatory foci (promotion and prevention) and goal orientations (learning, performance-prove, performance-avoid) were selected as individual differences likely to moderate how different resources are perceived by participants. For instance, individuals differ in their self-regulatory focus – with promotion focus being defined by tendency to set goals and move towards, while prevention focus is defined by a tendency to avoid undesirable states and outcomes (Lin & Johnson, 2015). Such factors are likely to influence how individuals perceive potential resource losses and gains. General promotion and prevention motivational tendencies were assessed using Lin and Johnson’s (2015) six item scale (promotion, α = 76. prevention, α = .78). Participants responded using a 1 (strongly disagree) to 5 (strongly agree) scale. An example item was “Right now I am focused on achieving positive outcomes.” Goal orientations were assessed using VandeWalle’s (1997) measures. Learning goal orientation had a reliability of α = .91, while performance goal orientation had a reliability of α = .77 and avoidance orientation α = .91. Participants responded using a 1 (strongly disagree) to 5 (strongly agree) scale. An example item was “I often look for opportunities to develop new skills and knowledge.” Analytic Approach The data was analyzed using a multilevel path model in Mplus7 (Muthén & Muthén, 2012). Variables were first assessed using multilevel confirmatory factor analyses to evaluate the measurement model for the data and ensure a reasonable model fit to the data. Then a multilevel path model was estimated between the variables used to test the various direct, mediational, and moderating paths proposed by the hypotheses. Analyses utilized full-information maximum likelihood estimation, to ensure minimum bias in estimates due to missing data (Newman, 2009). In line with previous research, within-person relationships (help perceptions, psychological 58 needs, help acceptance) were modeled as random slopes, while control variables were modeled as fixed slopes (Lin et al., 2021). To test within-person moderator hypotheses, variables were group-mean centered and treated as interactions between the variables. Indirect effects and their corresponding confidence intervals were then estimated using a Monte Carlo simulation with 20,000 replications (Preacher et al., 2010). In order to preserve level 2 observations in the multilevel model and ensure model stability, week 5 was dropped from the current analyses as this week had substantial missingness in the data (Hox et al., 2010). Results Basic descriptive information for the study variables is presented in Table 6. First, to estimate the need for multilevel modeling, null models for each of the outcome variables were estimated. These analyses suggested that a statistically significant portion of variance was located at the within-person level for work goal progress (56%), negative affect (14%), positive affect (17%), burnout (51%), work engagement (23%), and state self-esteem (16%), implying a need to account for multilevel effects within the analyses. To evaluate model fit, multilevel confirmatory factor analyses (MCFA) were estimated for the focal study variables. Given the number of parameters in the MCFA, I analyzed predictor exogenous variables (task demands, motives, help quality, and disempowering help), mediating variables (relatedness, competence, and autonomy), and outcome variables in separate MCFAs to prevent convergence and model stability issues due to the complexity of the model (Geldhof et al., 2014). MCFA results for the exogenous predictor variables displayed good model fit for both the reactive help variables, χ2(220) = 321.58, CFI = .95, RMSEA = .04, and SRMRwithin = .07, and the proactive help variables, χ2(220) = 348.00, CFI = .93, RMSEA = .04, and SRMRwithin = .07, after dropping one poorly loading item from disempowering help for each of the scales. In a 59 model combining both reactive and proactive items for each latent variable, model fit tended to be similar compared to separate estimation of the effect. Initial MCFA results for the mediating variables (i.e., basic needs fulfillment) faced convergence issues and extremely poor model fit. After an examination of the factor loadings, 2 items were dropped from the competence scale and 3 items from the relatedness and autonomy scales were dropped. These were the same items identified as drivers of misfit and dropped from the Study 1 CFA. The poor functioning of these items may have been due to the adaptation of the scales, as they consistently did not load on the corresponding needs scale across studies. The resulting model had modest fit for both the proactive help variables, χ2(32) = 65.36, CFI = .95, RMSEA = .09, and SRMRwithin = .04, and the reactive help variables, χ2(32) = 66.62, CFI = .94, RMSEA = .08, and SRMRwithin = .05. Alternative models with all items loading on a single latent ‘needs’ traits were estimated given the large correlations between the three needs. These models had similar, although slightly lower fit. For example, the reactive needs model had a fit of χ2(45) = 596.14 CFI = .92, RMSEA = .09, and SRMRwithin = .06. This suggests that modeling the basic needs separately is more appropriate than modeling them as a single construct. Lastly, results of the MCFA analysis for outcome variables (goal progress, negative affect, positive affect, and self-esteem) suggested relatively poor model fit for the outcome variables, χ2(183) = 673.75, CFI = .86, RMSEA = .09, and SRMRwithin = .09. Modification indices suggested that this might be primarily resolved through allowing errors to correlate for 3 of the state self-esteem items and 3 of the PANAS items. To account for shared method variance and item wording similarity, error terms for similarly worded positive and negative affect items (e.g., "Inspired" and "Excited" for positive affect, "Afraid" and "Scared" for negative affect) and self-esteem items (e.g., "I am confident about my abilities" and "I feel confident that I understand 60 things") were allowed to correlate in the confirmatory factor analysis. This adjustment was justified as these items likely capture overlapping response patterns due to their semantic similarity and shared affective or evaluative content, which could otherwise inflate model misfit if constrained to be uncorrelated, following recommendations for handling method effects in psychometric analyses (In’nami &Koizumi, 2013). A model allowing for these correlations had improved fit, χ2(177) = 438.36, CFI = .93 RMSEA = .07, and SRMRwithin = .08. Items cut from the scale based on the MCFA results were also dropped from the scale scoring for the subsequent path model analyses. Reliability information in the Methods section is reported separately for both the refined, aggregated, scales and the original scales. Based on an examination of the data, reactive and proactive variations of the scales were aggregated for the analyses, eliminating this distinction from subsequent analyses. For each scale, participant’s responses that week were aggregated and averaged together, to form combined proactive-reactive data. In an initial estimation of the model, proactive and reactive variable did not significantly relate to any other variables (p > .05 for all paths). MCFA results suggested that the scales were comparable for each of the three variables sets examined. For example, the model fit for the exogenous predictor variables was similar in a MCFA estimating reactive and proactive models as distinct latent traits, χ2(164) = 337.68, CFI = .91, RMSEA = .08, and SRMRwithin = .07, versus combined onto a single latent trait χ2(169) = 367.24, CFI = .90, RMSEA = .08, and SRMRwithin = .07. Additionally, the factor loadings were nearly identical in each framing of the variables, suggesting that the nature of help quality, disempowering, and motive perceptions may not differ meaningfully across the two types of help. This decision also maximized the available data, as the number of observations for each variable were far lower when splitting the data (87) versus aggregating the data (173). 61 To provide an additional test of the validity of combining the proactive and reactive scales, Mean and Covariance Structures (MACS) analyses (Stark et al., 2006) were performed on each of the focal scales (disempowering help, perceived motives, basic needs, help quality, and status threat). The results of these analyses are reported in Appendix C, Table 1C. Although the Chi-square Satorra-Bentler scaled difference tests were significant for each level of constraint in the MACS analyses, this was likely due to the sensitivity of the test, as the change in other fit indices was acceptable (e.g., ΔCFI < .01), suggesting that constraining the scale to have identical factor structures, loadings, and intercepts sequentially did not worsen fit, suggesting invariance between the two scales (Cheung & Rensvold, 2002; Nye & Drasgow, 2011; Satorra & Bentler, 2001). One exception was high RMSEA values in some of the models. Overfitting in the less constrained models (e.g., configural invariance testing) may have driven the high RMSEA in contrast to CFI and SRMR values and would also explain the reduction in RMSEA in more constrained models (Hu & Bentler, 1999). After aggregating the separated proactive-reactive variations of the help scales, a multilevel path analysis was used to test the main study model. The multilevel path model results are reported in Table 7. All predictors of help acceptance were nonsignificant in model. Similarly, help acceptance did not significantly relate to any of the proposed outcome variables. Hypotheses related to the basic psychological needs received mixed support. Hypothesis 2a was not supported as proactive-reactive help was unrelated to autonomy needs. Disempowering help did not predict competence needs fulfillment, providing no support for Hypothesis 4a. Perceived prosocial motives significantly predicted relatedness need satisfaction (b = .21, p < .001, 95% CI [.10, .32]), supporting Hypothesis 6a. Impression management motives were not a significant predictor of relatedness needs, providing no support for Hypothesis 8a. Notably, disempowering 62 help was significantly related to both relatedness needs (b = -.11, p < .001, 95% CI [-.19, -.04]) and autonomy needs (b = -.11, p = .02, 95% CI [-.20, -.02]). Also, perceived prosocial motives were a significant predictor of competence needs (b = .37, p < .001, 95% CI [.24, .50]), and autonomy needs (b = .24, p < .001, 95% CI [.10, .38]). These results suggest a general negative effect of disempowering help, and a general positive effect of prosocial motives for need fulfillment. Supplemental Analyses Help acceptance exhibited extremely low variance, as instances of help were accepted 92% of the time (SD = .41). This low variance likely impacted the study results, as suggested by the null correlations of help acceptance in both conditions with other study variables in Table 6. This is perhaps not surprising, as employees may be hesitant to reject whatever help is available to them, given the relational implications carried by help (Nadler, 2015; Thompson & Bolino, 2018). Despite this, participants may still have varying evaluations of how useful and effective help is to them (Gray et al., 2020). To test this proposition, help quality perceptions, as opposed to help acceptance, were explored as a key mediating variable of help perceptions and needs. The exploratory help quality model was also revised in a few additional ways. After an initial test to establish their non-significance in the model, proactive and reactive help, as well as weekly positive and negative affect, were excluded from the model. The alternative mediating variable of status threat was also included in the new model, along with indirect effects for the relevant variables. Additionally, weeks when participants reported that there was no help available to them were coded to note observations where no help occurred. Table 8 presents the results of this modified path model. 63 To test the power of the exploratory analyses, I performed a post-hoc power analysis using the same Monte Carlo simulation technique used to estimate the sample size requirements (Muthén & Muthén, 2002). The initial parameter estimate of b = .2 was refined using the observed coefficients from the data. Despite the reduced within-person sample size (173), analysis suggested that power to identify the significant effects in the model was greater than .80 for all parameters. For the nonsignificant parameters (which each had smaller effect sizes), power ranged considerably from .11-77. In general, the power analysis suggested that the study had sufficient power to detect any significant relationships that had a modest effect (b > .15). Help quality was predicted by both competence need fulfillment (b = .24, p <= .02, 95% CI [.04, .43]), and perceived status threat (b = -.29, p < .001, 95% CI [-.44, -.15]), suggesting that help was perceived as higher quality when it supported competence need fulfillment, but of lower quality when threatening one’s social standing within the company. Help quality, in turn, was associated with both lowered weekly self-esteem (b = -.23, p = .02, 95% CI [-.49, -.03]) and lower work-related burnout (b = -.23, p = .02, 95% CI [-.43, .00), suggesting a nuanced effect for receiving higher quality assistance from others. The only significant predictor of weekly goal progress was if the participant had not received any help, or offers of help, from others that week (b = -2.91, p < .001, 95% CI [-4.32, -1.49]), suggesting that goal progress was significantly lower when no help was perceived as available to the participant. Lastly, a model including coworker friendship and relative position (peer, subordinate, supervisor) as control variables suggested that none of these factors were related to help quality perceptions. Mirroring results of the initial path model, the exploratory results presented in Table 8 highlight a key role for both disempowering help and perceived prosocial helper motivations for predicting basic psychological needs. Relatedness need fulfillment was positively associated 64 with perceived prosocial motivations (b = .20, p < .001, 95% CI [.09, .32]) and was negatively related to disempowering help (b = -.11, p < .001, 95% CI [-.18, -.04]). The only significant predictor of competence need fulfillment was perceived prosocial motivations, which were associated with greater need fulfillment (b = .36, p < .001, 95% CI [.23, .48]). Autonomy need fulfillment exhibited a similar pattern to relatedness needs, as prosocial motivations were positively associated with need fulfillment (b = .23, p < .001, 95% CI [.09, .37]) and disempowering help was negatively associated (b = -.12, p < .01, 95% CI [-.21, -.03]). Lastly, perceived prosocial motives were associated with decreased perceived status threat (b = -.22, p < .01, 95% CI [-.36, -.08), while disempowering help was associated with increased perceptions of status threat (b = .09, p =.04, 95% CI [.01, .19]). Indirect effects were also tested for the significant predictors of competence needs and status threat, testing how these needs might mediate effects on perceived help quality. These analyses suggested that perceived prosocial motivations had an indirect effect on help quality, mediated via competence needs (indirect effect = .08, p = .03, 95% CI [.01, .16]). Similarly, prosocial motivations had an indirect effect on help quality via their impact on status threat (indirect effect = .06, p = .02, 95% CI [.01, .12]). In contrast, disempowering help did not appear to have a significant indirect effect on help quality perceptions via its significant relationship to status threat. Figure 8 visualizes the effects identified within this path analysis. 65 Figure 8. Study 2 Exploratory Help Quality Path Model. *Note. Dashed lines reflect non-significant relationships. Coefficients are only reported for significant paths in the model. To test for possible cross-over effects in the form of individuals differences and workplace variables, several additional models were explored. Attempting to analyze cross-level effects in the full path model lead to serious convergence issues, preventing accurate estimation of parameters. The model was simplified by removing non-significant predictors, however examining the cross-level moderators using random slopes was still impossible in the model. These issues likely emerged sample size constraints from the within-person sample size (McNeish & Stapleton, 2016). Instead defaulting to a simple fixed effects model, ignoring within person slope differences (Bell et al., 2019), I decided to examine cross-level moderators using individual multilevel regressions. These analyses were focused on the competence or status threat to help quality relationships, examining how different between-person variables might shape how perceptions of help quality form across individuals. Moderators were examined at the second stage of the proposed mediation model, given existing research in both COR and SDT 66 Relatedness isempoweringHelpImpressionMotives ask emandsCompetenceAutonomy.2 .09 ProsocialMotivesStatus hreat oal ProgressNo HelpSelf steemBurnoutHelp uality .29 .23 . 23 . 291 . .11 . 22 .12 .20 .36 .23 suggesting that both individual differences and environmental factors can alter perceptions and resource gain and need fulfillment (Hobfoll et al., 2018; Ryan et al., 2022). Table 9 presents the key findings from these analyses. Three multilevel regression models were estimated for the status threat to help quality relationship, and the competence to help quality relationship. Workplace variables (psychological safety, competitive climate, and task interdependence) were tested as cross-level moderators in one model, motivational tendencies (prevention/promotion focus) and goal orientations (learning, performance (prove, avoid) were also estimated in separate models. These analyses provided tentative evidence that psychological safety (b = -.20, p =.02, 95% CI [-.37, -.03]), prevention focus (b = .11, p =.03, 95% CI [.02, .31]), and performance-avoid goal orientation b = .38, p =.01, 95% CI [.08, .71]) moderated the effect of competence needs on help quality. Figure 9a presents the simple slope analysis for the moderating effect of psychological safety. Analyses revealed that at high levels of psychological safety, the relationship between competence need fulfillment and help quality perceptions was null (b = .12, p = .40, 95% CI [-.15, .39]), while at low levels of psychological safety, competence need satisfaction was more strongly linked to help quality perceptions (b = .49, p <.001, 95% CI [.22, .75]). Figure 9b presents the simple slopes for the moderating effect of prevention focus. The relationship between competence need fulfillment and help quality was null at low levels of prevention focus (b = .07, p = .60, 95% CI [-.20, .35]), while it was stronger and significant at higher levels of prevention focus (b = .39, p <.001, 95% CI [.13, .66]). Lastly, Figure 9c presents the simple slopes for the moderating effect of performance-prove goal orientation. The relationship between competence need fulfillment and help quality was null at low levels of performance-prove orientation (b = -.05, p = .78, 95% CI [-.40, .30]), while it was stronger and significant at higher levels (b = .60, p <.001, 95% CI [.20, .91]). 67 Figure 9a. Simple Slope for the Moderating Effect of Psychological Safety on Competence-Help Quality. y t i l a u Q p l e H 5 4 3 2 1 0 Low Competence Needs (-1 SD) High Competence Needs(+1 SD) Low Psychological Safety High Psychological Safety Figure 9b. Simple Slope for the Moderating Effect of Prevention Motivation on Competence- Help Quality. y t i l a u Q p l e H 5 4 3 2 1 0 Low Competence Needs (-1 SD) High Competence Needs(+1 SD) Low Pevention Focus High Prevention Focus 68 Figure 9c. Simple Slope for the Moderating Effect of Performance-Prove Orientation on Competence-Help Quality. y t i l a u Q p l e H 5 4 3 2 1 0 Low Competence Needs (-1 SD) High Competence Needs(+1 SD) Low Performance-Prove High Performance-Prove In contrast, learning goal orientation (b = .57, p <.001, 95% CI [.35, .80]), performance- prove goal orientations (b = -.44, p <.01, 95% CI [-.79, -.10]), and performance-avoid orientation (b = .44, p <.001, 95% CI [.33, .58]) appeared to moderate the effect of status threat. Self- esteem, positive affect, and negative affect were not significant predictors in these analyses and were not reported in Table 9. Figure 10a presents the simple slopes for the moderating effect of learning goal orientation. At a lower level of learning goal orientation, the negative effect of status threat on perceived help quality was more severe (b = -.94, p <.001, 95% CI [-1.22, -.66]), while it was nonsignificant at higher levels of learning goal orientation (b = .10, p = .72, 95% CI [-.45, .65]). Figure 10b presents the simple slopes for the moderating effect of performance- prove goal orientation. At a lower level of performance-prove goal orientation, the negative effect of status threat on perceived help quality was nonsignificant (b = -.02, p = .95, 95% CI [- .45, .65]), while it was more severe at higher levels of performance-prove goal orientation (b = - .91, p <.001, 95% CI [-.70, .66]). Figure 10c presents simple slopes for the moderating effect of performance-avoid goal orientation. At a lower level of performance-avoid goal orientation, the 69 negative effect of status threat on perceived help quality was more severe (b = -.91, p <.001, 95% CI [-1.23, -.59]), while it was nonsignificant at higher levels of performance-avoid goal orientation (b = .07, p = .77, 95% CI [-.41, .48]). Additionally, minority status and gender did not relate to help quality perceptions. Figure 10a. Simple Slope for the Moderating Effect of Learning Goal Orientation on Status Threat-Help Quality. y t i l a u Q p l e H 5 4 3 2 1 0 Low Status Threat (-1 SD) High Status Threat (+1 SD) Low Learning Orientation High Learning Orientation Figure 10b. Simple Slope for the Moderating Effect of Performance-Prove Orientation on Status Threat-Help Quality. y t i l a u Q p l e H 5 4 3 2 1 0 Low Status Threat (-1 SD) High Status Threat (+1 SD) Low Performance-Prove High Performance-Prove 70 Figure 10c. Simple Slope for the Moderating Effect of Performance-Avoid Orientation on Status Threat-Help Quality. y t i l a u Q p l e H 5 4 3 2 1 0 Low Status Threat (-1 SD) High Status Threat (+1 SD) Low Performance-Avoid High Performance-Avoid To further explore the relationship between status threat, needs, and help perceptions, a simplified path model was estimated to explore status threat as a possible mediator of the effects of relatedness need satisfaction. Given research highlighting that help is generally beneficial to social needs (Nadler, 2015) yet can impact one’s social standing and feelings of dependence (Anderson et al., 2015; Greenwood et al., 2013; Komissarouk & Nadler, 2014). The path model was the same as that presented in Table 8, with nonsignificant variables (impression management motives, task demands) dropped in order to simply the model. This model suggested that feelings of relatedness needs fulfillment were associated with decreased perceived status threat (b = -.22, p =.01, 95% CI [-.37, -.06]), and that relatedness and status threat mediated the effect of prosocial motivations on perceived help quality (indirect effect = .02, p = .04, 95% CI [.01, .04]), suggesting that social effects such as perceived impact on relatedness needs may be fully mediated through status threat perceptions of help. To explore additional context for participant responses to help, I examined participant qualitative data. Participants answered up to five open-ended questions each week, with two 71 questions on Monday – one asking about participants’ main task that week and one asking what kind of help would be most useful, up to two questions on Thursday, asking either why the participant didn’t request help (no reactive help) or why they think no one offered them help (no proactive help). Lastly, the Friday survey asked participants to evaluate why or why not they wanted help that week, and their perceptions of any help that they did receive. As a result, the qualitative data included information both from people who received help and those who did not, with more information coming from participants who did not receive assistance. Focusing on the four help-related questions, participants generated approximately 1,250 statements related to their evaluation of help, its availability, and their decision-making regarding help use. I focused on identifying and categorizing consistent themes in the data, coding statements expressing similar content as reflecting a common underlying idea (Braun & Clarke, 2006). Fitting with best practices in qualitative thematic analysis, I first generated themes inductively using a subset of the data and then examined that coding scheme over the broader range of all statements (Fereday & Muir-Cochrane, 2006). Several consistent themes emerged as especially dominant in the data. First, roughly 22% (278) of the statements reflected a common theme of self-sufficiency/independence. These statements expressed either a preference for handling tasks oneself, often highlighting confidence in one’s own abilities or a desire to work alone. For instance, one participant noted “I am keeping up pretty well on my work currently and do not need any help” while another wrote, “I chose not to ask for help this week because I felt confident in handling my tasks independently and wanted to take full responsibility for my work. I also wanted to challenge myself to solve any problems on my own.” Importantly, the framing of the Thursday questions was specifically 72 focused on instances where no help was received or offered, influencing many of the statements to be more closely focused on reasons for not seeking help. A related theme focused on situations where participants perceived a lack of help being offered to them. Participants generated 207 statements (17% of the total) reflecting a perspective that one’s coworkers were too busy or unaware of the participant’s task demands to offer help. For instance, one participant wrote “No one offered because they thought I could manage” while another reported “I think the reason I didn't get any offer this week was that everyone was focused on meeting their own work deadlines.” Similarly, a number of statements (46, 4% of total) reflected a hesitance to pursue help out of a belief that it would burden one’s colleagues, particularly given task demands faced by coworkers. For example, one participant reported “I don't want to impose on anyone and I feel like I am imposing if I ask for help.” Conversely, 4% of the statements reflected personal constraints towards seeking out help, such as exhaustion. For example, one participant reported “I didn’t feel like I had the time or energy to pursue it.” A dominant theme of the qualitative analysis was a desire for a specific type of assistance, which was often perceived as unavailable to the participants. Approximately, 30% of statements (375) reflected this sentiment, focusing on specific support or technical needs in relations to one’s work. xample statements in this category included “[I need] 2 more guys we don’t have enough people on this project,” and “Assistance supervising would be helpful.” Conversely, participants who did receive assistance often commented on its usefulness (16% of statements, 203 total). For example, “This coworker assisted me and it really helped to get things done a lot faster.” Table 10 summarizes these themes. 73 Discussion Although most of the original hypotheses were not fully supported by the results of Study 2, the general pattern of results still suggests an important role for help perceptions and basic psychological needs in predicting responses to help. Study 2 extended the findings of Study 1 by testing the proposed model in a real-world workplace setting. The results provide partial support for the hypotheses, highlighting the nuanced role of help perceptions in shaping basic psychological needs and downstream workplace outcomes. Consistent with Study 1, perceived prosocial motives of the helper positively predicted relatedness, competence, and autonomy needs fulfillment. Disempowering help negatively related to relatedness and autonomy needs fulfillment, aligning with the idea that such perceptions are associated with need fulfillment, but its effect on competence needs was non-significant. Contrary to expectations, the reactive- proactive help distinction did not significantly relate to autonomy needs. The exploratory follow-up model that examined help quality (Table 8) considered it as a key outcome of help perceptions and need fulfillment. Help quality perceptions are especially important to workers as they form reactions to the help that they receive (Bolino et al., 2013). Although different from decisions regarding help acceptance, the findings of this model mirror many of the original study predictions. For instance, resources in the form of social standing and competence need satisfaction were predicted by perceived prosocial motivations of helpers and disempowering help (in the case of status threats). These resources, in turn, significantly related to help quality perceptions. The effects of prosocial motivations on help quality were mediated through competence need fulfillment and status threat perceptions, suggesting that the social implications of helper motives may be primarily due to their effects on perceived social standing and competence needs. 74 Receiving high quality assistance led to a negative effect on self-esteem, which broadly fits with other research highlighting how self-esteem can be threatened by receiving help (Deelstra et al., 2003). Conversely, higher quality help was associated with lower work-related burnout, suggesting that, despite impacts on self-esteem, help perceived as higher quality is a more effective buffer of negative impacts of weekly demands on well-being. The only significant predictor of weekly goal progress was if help was available to the participants that week. During weeks where participants did not request help, or receive an offer of help, perceived weekly goal progress was significantly lower. Supplemental analyses of the participant qualitative data complemented this finding by revealing that many participants desired specific, task-relevant support that they did not perceive as available to them. Such circumstances may have driven lower performance in weeks when participants were unable to access help from others. Task demands did not significantly predict any variables in the model, providing no support in this study for Hypothesis 12. Lastly, individual goal and motivational orientations, as well as workplace psychological safety, moderated the effect of competence needs and status threat on help quality perceptions. Fitting with predictions from the COR model (Hobfoll et al., 2018), perceptions of resource gain or loss/threat may be moderated by individual differences. For instance, individuals with a higher learning orientation did not perceive help quality as lower when the status threat posed by that help was perceived as higher. In contrast, higher performance-prove goal orientations led to both a stronger positive influence of competence needs, and a stronger negative influence of status threat, when predicting help quality perceptions. The various second-stage moderation findings provide initial evidence that certain individuals may be less sensitive to threats or gains to resources. Additionally, the finding that individuals were more strongly influenced by 75 competence need fulfillment in low psychological safety environments extends other research suggesting that effects of perceptions of need fulfillment can be influenced by contextual factors (Ryan et al., 2022). As a whole, the findings of Study 2 support the general notion that threats to resources (competence needs and social status) have implications for how individuals perceive and react to help offered at work. Similarly, how help occurs (e.g., if it is disempowering) and to the extent to which helpers appear to be prosocially motivated appears to have an important to link to these resource perceptions. 76 GENERAL DISCUSSION The findings from Study 1 and Study 2 collectively support parts of the theoretical model proposed in this dissertation, which merges Conservation of Resources (COR) theory and Self- Determination Theory (SDT) to explore how perceptions of help shape basic psychological needs and subsequent workplace outcomes. These results highlight that specific help perceptions—such as whether help is proactive, disempowering, or motive-driven—critically influence the fulfillment or frustration of autonomy, competence, and relatedness needs, driving how help is evaluated, and perceived help quality, self-esteem, and burnout. Although many findings of Study 2 were exploratory, the results generally align with the study’s core framework, which views help as a dynamic resource exchange where recipients actively assess potential resource gains or losses, as outlined by COR (Hobfoll, 1989), while also gauging its impact on their fundamental psychological needs, as underscored by SDT (Ryan & Deci, 2000). A key consistency across both studies is the impact of help features on basic psychological needs. In Study 1, perceptions of prosocial helper motives consistently bolstered relatedness need fulfillment, whereas impression management motives diminished fulfillment across autonomy, competence, and relatedness needs, highlighting the social context’s role in shaping employees’ responses to help. isempowering help similarly undermined all three needs, aligning with prior research linking such support to reduced efficacy, independence, and relational outcomes (Koo et al., 2023; Nadler, 2015). Study 2 echoed these patterns: prosocial motives enhanced all three needs, while disempowering help threatened autonomy and relatedness. These findings reinforce the model’s central premise—that help fostering need fulfillment and recipient agency acts as a resource gain, promoting well-being, whereas help 77 damaging autonomy or social standing functions as a resource loss, thwarting needs and risking adverse employee outcomes (Hobfoll et al., 2018) Despite methodological differences, both studies underscore the mediating role of basic psychological needs in the helping process, though the pathways to outcomes like help acceptance diverged from initial hypotheses. In Study 1, anticipated need fulfillment directly predicted help acceptance intentions, reflecting the controlled setting where participants focused on psychological reactions to hypothetical help scenarios, consistent with expectations that autonomy, competence, and relatedness would drive acceptance (Hypothesis 2b, 4b, 6b). In Study 2, however, employees in real-world contexts rarely rejected help outright, yet displayed significant variance in their perceptions of received help (e.g., Gray et al., 2020; Thompson & Bolino, 2018). The exploratory model in Study 2, incorporating help quality and cross-level moderators, enriches the theoretical framework by capturing individual and contextual variability in help dynamics. These pathways reflect real-world complexities, positioning help quality as a critical link between psychological needs and outcomes like self-esteem and burnout. Across both studies, the findings supported the concept that responses to help depend on recipients’ appraisals of its social and personal costs and benefits (Hobfoll et al., 2018; Nadler, 2015). Theoretical Implications and Future Directions A core theoretical contribution of this dissertation is the development of a unified framework that reconciles the fragmented literature on help’s potential negative outcomes, integrating Conservation of Resources (COR) theory (Hobfoll, 1989) and Self-Determination Theory (SDT) (Deci et al., 2017) to explain why help can sometimes elicit adverse reactions. Across both studies, the results establish a link between basic psychological needs/resources— competence, autonomy, relatedness, and social status—and responses to help, offering a 78 cohesive lens to connect disparate research on workplace support. This perspective captures how help threatens workers when it undermines their sense of control, perceived efficacy, or social connections (Deelstra et al., 2003; Gray et al., 2020; Koo et al., 2023), as seen in Study 1’s controlled scenarios where disempowering help reduced acceptance intentions, and Study 2’s real-world findings where disempowering help undermined competence needs and status perceptions, subsequently shaping help quality judgments in the exploratory follow-up analyses. Notably, Study 2’s exploratory findings related to help quality and the role of cross-level moderators, such as learning goal orientation amplifying the negative effects of status threats, extend the integration of COR and SDT by highlighting the importance of individual differences and contextual variability in resource appraisals (Halbesleben et al., 2014). This finding extends existing research by suggesting that help’s impact is not solely determined by its alignment with universal psychological needs but also is influenced by how recipients’ motivational orientations and workplace environments shape their perceptions of help as a resource (Harari et al., 2022). For instance, the positive effect of competence needs on help quality in Study 2 was moderated by learning goal orientation, aligning with COR’s view that resource valuation varies across individuals (Hobfoll et al., 2018). As a whole, the moderation results suggest that differences in individual’s motivational traits and workplace characteristics can, in some cases, either compound or nullify the influence of various resources. This work offers a versatile framework for interpreting the complex duality of support in organizational contexts, providing a cohesive structure for understanding how antecedent conditions—such as help’s form, intent, or impact on needs—shape its reception and downstream effects. This study identified several antecedent conditions that may contribute to perceptions of resource loss or gain during help. Contributing to COR perspective of support, certain features of 79 help appear to signal potential for resource gains while others signal potential resource threats (Hobfoll et al., 2018). For example, the relationship observed between perceived help quality and help acceptance (Study 1), highlights that individuals place an emphasis on understanding how beneficial the potential help might be for them (Bolino et al., 2013). In contrast, factors like disempowering help or prosocial motives, consistently linked to levels of need fulfillment across both studies, signal implications for personal resources and align with COR model-based predictions that effects on resources influence how help is evaluated (Hobfoll et al., 2018). These findings suggest that help perceived as encroaching on autonomy, competence, or relatedness— such as when help is disempowering or driven by helper impression management motives—may act more as a hindrance stressor than a supportive asset, potentially undermining well-being and performance (Hughes et al., 2023; LePine et al., 2005). Further research is needed to delineate the boundary conditions of these processes. While Study 2 incorporated status threat as an alternative resource mechanism—revealing its key role in shaping help quality perceptions—resources encompass a broad spectrum beyond status (Hobfoll et al., 1989; Hobfoll et al., 2018). Future studies should investigate other resources potentially depleted during helping, such as psychological capital (e.g., resilience or efficacy), which help characteristics such as disempowering help might undermine (Luthans et al., 2006). Additionally, the helper-recipient relationship may moderate these effects. Research indicates that reactions to help vary with factors like helper-recipient similarity, with less threat perceived when help comes from dissimilar others (Nadler, 2015). Although Study 2 controlled for relative hierarchy and friendship, these relational dynamics warrant deeper exploration. For example, prior interactions with the helper could foster trust, mitigating concerns about motives or behavior (Dutton & Heaphy, 2003), and thus influence how help is appraised and received. 80 This study also contributes to helping research by highlighting the active role of help recipients in shaping the manner in which support occurs. Although research often focuses on the active processes of helpers, treating recipients as a passive blackbox that accepts help (Nadler, 2015), individuals appraise help along several dimensions, and these appraisals inform how they respond to the support available to them. This perspective contributes to the integration of COR model into social support (Hobfoll et al., 1990) by highlighting the role of active decision- making on the part of help recipients. Although research on helping often focuses on the role of resource exchange (e.g., Bamberger et al., 2017), other aspects of COR model remain poorly integrated. For instance, individuals respond in an active manner to protect existing resources and attain new ones (Hobfoll et al., 2018). Prior research suggests that individuals may proactively engage in behaviors such as information or help-seeking in order to enhance their autonomy in an organization (Ashford & Black, 1996). Future work should explore how active efforts on the part of employees are used to gain or protect resources through their decisions regarding help use (Gray et al., 2020; Harari et al., 2022). Future work should expand upon the active view of help recipients. It is possible that reactions to help contain more nuanced characteristics that influence decisions regarding accepting/rejecting help. Evaluative reactions, such as attitudes, contain affective and motivational content in addition to cognitive evaluations (Credé, 2018; Dalege et al., 2016). Although Study 1’s measure of help acceptance intentions included items reflecting a range of evaluative reactions, it may be important to further consider the range of ways that employees might respond to offers of help. For instance, given the low variance of actual help acceptance decisions, other evaluations may be especially important in shaping the outcomes of the helping process. Rather than focusing exclusively on cognitive reactions to features of help, as in the 81 case of much research on feedback or advice acceptance (Bell & Aruthur, 2008; Christensen- Salem et al., 2018), future research should increasingly explore how emotional responses to help might shape individual decisions regarding potential support. Research regarding support should also examine how individuals weigh the potential relational consequences of their decisions to accept or reject help. This study did not explicitly examine these factors, yet findings related to status threats in Study 2 highlight the possibility that receiving help may have downstream effects on employees’ social outcomes. For instance, previous research suggests that receiving help has implications for future reciprocity expectations on the part of helpers (Spitzmuller & Van Dyne, 2013; Thompson & Bolino, 2018). These expectations of reciprocity can be beneficial to relationship development (Cropanzano & Mitchell, 2005), yet they may also become burdensome if demands to reciprocate help interfere with other aspects of an employee’s job (Mueller & Kamdar, 2011). Similarly, choosing to reject help might cause offense, leading to negative social implications (Nadler, 2015). Such broader considerations might also weigh into the active decision making on the part of help recipients. Research exploring these factors could help shed light on the way in which individual decide how to respond to potential support. This project’s findings offer significant contributions to workplace stress and performance theories, notably the Job Demands-Resources (JD-R) theory (Bakker & Demerouti, 2007) by illuminating the active role recipients play in shaping the impact of social support. Within the JD-R model, research on job crafting highlights individuals’ capacity to proactively manage resources to optimize well-being and performance (Tims et al., 2013), a principle mirrored in the results suggesting recipients evaluate help in different ways based on its perceived resource implications. Study 1’s controlled scenarios demonstrated that 82 disempowering help is more readily declined when it sufficiently threatens psychological needs, while Study 2’s multi-wave data linked competence and status to high-quality help and, in turn, to reduced burnout, indicating that social support may act as a job resource only when appraised as need-aligned rather than demand-exacerbating. This refines the premise offered by the JD-R model that resources buffer job demands, demonstrating that help’s effectiveness hinges on these appraisals, potentially converting it into a demand when misaligned. Similarly, these findings inform research on helping based on social exchange theory (Cropanzano & Mitchell, 2005). Helping interactions tend to create a certain level of dependence, which can be threatening to individual’s self-perceptions (Nadler, 2015). SET research suggests that asymmetric access to resources can lead to dependency in social exchanges, which individuals attempt to navigate over time through negotiation and reciprocity of help (Molm, 1994). The current work extends understanding of how this process might play out, for instance highlighting how perceived motives of helpers—rather than just exchange frequency—shapes these negotiations, as recipients weigh help’s social costs against its benefits. Study 2’s findings concerning the effects of status threat highlight this possibility by illustrating how employees may feel concerned about how exchanges will impact their social standing over time. This offers a fresh lens for SET, highlighting how dependency influences the trajectory of workplace support exchanges. Future research could explore how trust-building or equitable exchanges mitigate these threats, deepening our understanding of help’s long-term relational impact. This research lays a foundation for advancing theories of stress and well-being, notably the Transactional Theory of Stress and Coping, which positions appraisal as the critical process determining whether an event is perceived as stressful or supportive (Lazarus & Folkman, 1984; 83 Lazarus, 1991). The findings on basic psychological needs and help features align seamlessly with this appraisal framework, casting help as an event subject to dynamic evaluation. When help features—like disempowering delivery or questionable motives—are appraised as threats, they can trigger stress responses, influencing coping processes (Hughes et al., 2022; Tomaka et al., 1997). Study 1 showed that disempowering help diminished acceptance when appraised as need-thwarting, while Study 2 linked perceived threats to autonomy and status with reduced help quality and heightened burnout. Future research could investigate how individuals differentially appraise help features, such as distinguishing between demands perceived as challenges to overcome versus threats to avoid (Blascovich & Mendes, 2000), and identify moderators shaping these appraisals. Practical Implications This study provides valuable insights for crafting effective organizational policies to optimize workplace helping practices. Organizations often aim to boost employee utilization of help to enhance performance (Newark et al., 2017), yet employees frequently perceive available support as poor quality or mismatching to their needs (Dishop & Awasty, 2023; Gray et al., 2020). My findings offer a diagnostic tool to address these challenges by pinpointing why help may be seen as unhelpful (Hughes et al., 2023). For example, organizations could use employee surveys or focus groups to assess support systems, pinpointing threats that reduce acceptance, as seen in Study 1’s rejection of disempowering help and Study 2’s link between need threats and poor help quality perceptions. Grounded in a framework of resource threats to basic needs (autonomy, competence, relatedness), leadership could then devise policies to promote help efforts that minimize these risks. A concrete step might involve establishing transparent performance evaluation guidelines that explicitly value help-seeking as a developmental strength 84 rather than a weakness (Mueller, 2012), alleviating fears that accepting support might impact performance evaluations by signaling dependency or inadequacy (Koo et al., 2023; Thompson & Bolino, 2018). Such policies could reassure employees that utilizing help aligns with organizational goals, fostering a supportive environment while promoting successful performance. Beyond policy changes, this research informs training initiatives to enhance the delivery of effective support. he consistent evidence of disempowering help’s detrimental effects—seen in Study 1’s reduced acceptance and Study 2’s links to lower help quality and burnout—suggests that training programs should prioritize teaching employees to offer assistance that empowers rather than overshadows recipients’ efforts (Liu et al., 2022; Lee et al., 2023). For instance, workshops could teach strategies like guiding coworkers toward independent problem-solving or providing skill-building resources, minimizing perceived threats to competence that fuel negative outcomes such as social undermining (Tai et al., 2023). Similarly, the importance of perceived helper motives highlights the need for training in clear communication. Programs could emphasize articulating genuine support motives transparently to reduce ambiguity, often a source of suspicion and social threat (Lanaj et al., 2019). By equipping employees with these skills, organizations can lessen misinterpretations that undermine trust (Thompson & Bolino, 2018), increasing the chances that help is offered and received constructively. Finally, future work could explore practical aspects of how organizational culture shapes helping practices, with implications for how leaders and peers influence help acceptance norms. Cultural perceptions of help, set by supervisors and coworkers, determine whether it carries a stigma or is embraced as a strength (Thacker & Stoner, 2012). When help-seeking is framed as creating social dependency, employees may avoid it due to anticipated social costs to 85 relatedness, reinforcing a culture of isolation (Kassirer & Kouchaki, 2023; Nadler, 2015). Conversely, minimizing these costs—by normalizing help as a collaborative asset—can encourage its use and positive evaluation (Lim et al., 2020). Leaders play a critical role in any cultural shift, particularly through how they model help-seeking behaviors. Research on transformational leadership highlights how leaders’ openness to receiving help fosters trust and reciprocal support among employees (Owens et al., 2013; Tepper et al., 2018), suggesting that managers who visibly seek assistance can de-stigmatize it across the organization. Organizations should thus prioritize leadership development that encourages managers to demonstrate vulnerability and responsiveness to help, setting a tone that integrates support into the fabric of workplace culture while aligning with employees’ psychological needs. Limitations The two studies, while designed to complement one another, are subject to several limitations that affect the interpretation of their results. Study 1 utilized highly controlled scenario manipulations to test help perceptions in a structured setting. The artificial nature of these scenarios may oversimplify the complexities of actual workplace interactions, potentially reducing the generalizability of their findings to organizational settings (Highhouse, 2009). For instance, the hypothetical context might fail to capture the emotional stakes or social nuances that shape help acceptance in practice, limiting the practical applicability of its conclusions. In contrast, Study 2 examined these processes in real-world work contexts, sacrificing control over the specific help situations participants reported. This unstructured approach introduces substantial noise—unobserved variability in help timing, frequency, source, and task-context— that muddies causal inferences and weakens confidence in its conclusions. For example, participants’ retrospective self-reports in Study 2 may reflect biased recall or inconsistent help 86 experiences, further clouding the links between perceptions, needs, and outcomes like burnout or help quality. Similarly, the working sample used in Study 2 revealed that help was often unavailable, and when it did occur it was generally accepted, regardless of perceived quality. Such factors placed practical limitations on the data, such as reduced within-persons observations and low variance in help acceptance. Overall, the two studies highlight different methodological tensions, which must each be considered when evaluating the study results. Study 1’s cross-sectional design prevents firm conclusions about the temporal precedence of variables in my model (Shadish et al., 2002). To address this, Study 2 adopted a multi-wave approach, collecting data on task demands, help instances, and workplace outcomes across several weeks to establish some temporal separation. However, this design has its limitations as help perceptions, acceptance decisions, and basic needs were measured concurrently within each wave, obscuring the precise ordering of effects. For example, participants who had already accepted or rejected help might retrospectively evaluate its features—such as quality or motives—in a biased manner to affirm their decision, a tendency rooted in confirmation bias (Jonas et al., 2001). Such biases could lead those who accepted help to view it more favorably (e.g., as empowering or high quality) or those who rejected it to perceive it more negatively (e.g., as disempowering or low quality), as individuals often adjust perceptions to validate their choices (De Dreu & Carnevale, 2003). Although the theoretically grounded model posits a sequence from perceptions to acceptance to outcomes, these concurrent measures leave room for alternative processes, such as acceptance influencing help perceptions. Future research employing finer-grained methods, such as experimental manipulations or within-day experience sampling, could better disentangle these causal pathways. 87 A significant limitation lies in the exclusive focus on help recipients’ experiences, sidelining the perspectives of those providing support. While much help research examines helpers’ motivations and behaviors (Bowling et al., 2005; Nadler, 2015), my recipient-focused approach offers valuable insights into how support is perceived and acted upon. Yet, this one- sided perspective prevents a comprehensive understanding of the helping dynamic. Helpers’ actions are shaped by their own realities, such as resource depletion, task overload, or organizational pressures (Lanaj et al., 2016), which likely influence the motives and quality of help they offer. In Study 2, for instance, recipients rated help quality and motives without insight into these helper-side factors, risking misattributions such as labeling genuine support as impression management due to unseen constraints. Integrating helper perspectives in future studies, perhaps through paired dyadic surveys or interviews, would yield a more holistic view of help interactions, potentially refining the model by clarifying how helper and recipient experiences interplay. This study also presents a constrained view of the network of coworker interactions. Both studies adopt a participant-centered dyadic perspective, concentrating solely on interactions between a single helper and recipient. This narrow focus facilitates an in-depth analysis of specific helping exchanges but neglects the broader relational context of an employee’s coworker network. Social ties—such as the frequency and quality of interactions with colleagues—shape how support emerges and evolves within organizations (Nahum-Shani & Bamberger, 2011; Venkataramani & Dalal, 2007). The range of help available to an employee is inherently limited by the knowledge, skills, and availability of colleagues within their network (Borgatti & Cross, 2003). For example, some employees may forgo seeking help if their immediate network lacks individuals qualified to provide support for their specific role (Lindorff, 2001). Such network- 88 driven constraints, unexamined here, likely influence help-seeking behaviors and outcomes. Employees with sparse networks might struggle to identify suitable helpers, whereas those with dense, diverse connections could benefit from a wider array of support options. Thus, understanding how network characteristics—like acquaintance ties or resource distribution (Methot et al., 2018)—affect access to help is crucial for contextualizing these findings, a gap that network analysis in future research could address. Given the episodic and dynamic nature of workplace help, Study 2 sought to track week- to-week variations in job demands and coworker support instances. Despite this longitudinal effort, the study fails to capture the event-level intricacies of help interactions. Responses to help evolve dynamically as individuals continuously appraise and reappraise features like helper motives or help quality (Dalal & Sheng, 2019). Yet, this design records perceptions only after participants have decided to accept or reject help, restricting any ability to explore how these perceptions form amidst the evaluation of diverse cues. Event-level data could illuminate the psychological processes driving help responses, such as how employees weigh conflicting information about help quality or motives in real time (Beal et al., 2005; Bono et al., 2013). Moreover, once help is underway, individuals may resist support they find unappealing (Harari et al., 2022), a dynamic the Study 2 weekly snapshots overlook. Future research using event- contingent methods could better capture these fluid processes, revealing when and why resistance emerges and how it alters help’s perceived value. 89 CONCLUSION This dissertation explored the nuanced effects of workplace help, revealing key insights into how perceptions of resources shape its acceptance and outcomes. Across two studies, the research demonstrated that help is most effective when it poses minimal threats to employees’ basic needs for competence, autonomy, and relatedness. Further, the current research provides evidence that perceptions of helper motives and how disempowering help may play a crucial role in shaping the perceived impact of help on basic needs. Study 1’s scenario-based findings showed that disempowering help and impression management motives diminish acceptance, while Study 2’s exploratory multi-wave data from a working sample suggested that these perceptions influence real-world outcomes like task progress, self-esteem, and affect. Together, these findings emphasize that help is not universally beneficial—its success depends on aligning with recipients’ psychological and situational needs. his work advances our understanding of help as a dynamic resource exchange, offering a framework for organizations to optimize support systems and improve employee resilience and performance. 90 Table 1. Study 1 Manipulation Tests. TABLES Df Sum Square Mean Square F p Demands Manipulation Check Task Demands Reactive Help Disempowering Help Help Motives Reactive Help * Help Motives Disempowering Manipulation Check Task Demands Reactive Help Disempowering Help Help Motives Reactive Help * Disempowering Help Task Demands * Help Motives Reactive Help * Help Motives Prosocial Motive Manipulation Check Task Demands Reactive Help Disempowering Help Help Motives Task Demand * Reactive Help Reactive Help * Help Motives 1 1 1 2 2 1 1 1 2 1 2 2 1 1 1 2 1 2 98.80 0.04 3.53 4.81 3.78 5.26 2.52 121.50 23.17 3.84 4.87 4.25 0.04 1.68 3.38 46.41 2.40 14.44 Impression Management Motive Manipulation Check Task Demands Reactive Help Disempowering Help Help Motives Task Demands * Help Motives 1 1 1 2 2 3.03 0.48 10.81 74.85 5.13 91 98.80 0.04 3.53 2.41 1.89 5.26 2.52 121.50 11.59 3.84 2.44 2.12 0.04 1.68 3.38 23.20 2.40 7.22 3.03 0.48 10.81 37.43 2.57 181.32 0.07 6.47 4.41 3.47 7.53 3.61 174.01 16.59 5.49 3.49 3.04 0.08 3.15 6.33 43.43 4.49 13.52 3.91 0.62 13.94 48.30 3.31 0.00*** 0.80 0.01** 0.01** 0.03* 0.01* 0.06 0.00*** 0.00*** 0.02* 0.03* 0.05 0.78 0.08 0.01* 0.00*** 0.03* 0.00*** 0.05 0.43 0.00*** 0.00*** 0.04* η2 0.46 0.00 0.03 0.04 0.03 0.03 0.01 0.41 0.12 0.02 0.03 0.02 0.00 0.02 0.04 0.38 0.03 0.16 0.02 0.00 0.08 0.36 0.04 f 0.92 0.02 0.17 0.20 0.18 0.17 0.12 0.84 0.37 0.15 0.17 0.16 0.02 0.15 0.21 0.78 0.18 0.44 0.15 0.06 0.29 0.75 0.20 Table 1 (cont’d). Reactive Help * Help Motives 2 Proactive-Reactive Manipulation Check* Intercept Reactive Help Task Demands Disempowering Help Prosocial Motive Impression Management Motive 15.51 β 1.01 -0.90 -0.09 -0.04 0.02 0.00 7.75 SE 0.16 0.13 0.12 0.12 0.15 0.15 10.00 0.00*** Z 6.38 -7.14 -0.75 -0.29 0.11 -0.02 P 0.00*** 0.00*** 0.46 0.77 0.91 0.98 0.11 OR 2.75 0.41 0.91 0.96 1.01 0.99 0.34 R2 0.06 *Note. Tests for the proactive-reactive manipulation check were run using a logistic regression, as the outcome variable for these tests was categorical. * p < .05, ** p < .01. ** p < .001 N = 1139. η2= partial eta squared. f = Cohen’s f. OR = odds ratio. Model R2 for the logistic regression was calculated using Nagelkerke’s pseudo R2. 92 Table 2. Study 1 Descriptive Statistics. Variable 1. Gender 2. Minority Status 3. Task Demands SD N M 0.76 0.43 1201 1 -- 2 3 4 5 6 7 8 9 10 11 12 0.23 0.42 1221 -.07* -- 3.81 0.80 1219 .12** 0.87 -0.03 - .09** 0.01 0.77 0.06 0.81 0.02 0.85 .06* .07* 0.02 -0.02 -0.02 .43** .25** .39** -.08** -.17** .010** 3.00 0.76 1219 3.33 0.64 1217 3.53 0.50 1219 3.46 0.77 1162 4. Help Quality 3.77 0.53 1218 5. Relatedness Needs 6. Competence Needs 7. Autonomy Needs 8. Prosocial Motives 9. Impression Management Motives 10. Disempowering Help 11. Reactive Help 12. Help Acceptance Note. * p < .05, ** p < .001. Cronbach’s alphas are presented along the diagonal for continuous variables. 3.93 0.91 1219 0.61 0.49 1161 2.54 0.93 1162 2.42 0.91 1163 - .32** - .31** - .46** - .22** - .20** - .36** - .29** - .17** - .35** - .41** .62** .13** .12** .55** .30** .28** .10** .47** .08** .31** .69** .36** .28** .68** .37** .09** .54** -0.02 -0.01 -0.04 -0.02 -0.01 -0.02 -0.02 .07* .07* 0.01 0.01 0.82 .06* 0.01 0.04 .07* 0.68 .33** -0.01 - .31** 0.86 0.80 -.06* - .41** -- .15** 0.91 93 Table 3. ANOVA Results for Help Acceptance. Df Sum Square 0.65 0.24 78.50 9.96 Task Demands Reactive Help Disempowering Help Help Motives Help Quality Minority Status Gender Task Demands * Reactive Help Task Demands * Disempowering Help Reactive Help * Disempowering Help Task Demands * Help Motives Reactive Help * Help Motives Disempowering Help * Help Motives Task Demands * Reactive Help * Disempowering Help Task Demands * Reactive Help * Help Motives Task Demands * Disempowering Help * Help Motives Reactive Help * Disempowering Help * Help Motives Task Demands * Reactive Help * Disempowering Help * Help Motives 0.03 Note. * p < .05, ** p < .01. ** p < .001 N = 1139. η2= partial eta squared. f = Cohen’s f. Interactions between control variables are not reported. Mean Square F p 0.65 1.15 0.28 1 0.51 0.43 0.24 1 78.50 138.83 0.00*** 1 2 8.81 0.00*** 4.98 1 158.33 158.33 280.00 0.00*** 0.30 1 0.08 1 0.85 1 0.26 1 0.43 1 0.51 2 0.11 2 0.70 2 0.31 1 0.37 2 0.31 2 0.41 2 η2 0.00 0.00 0.13 0.02 0.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 f 0.04 0.02 0.39 0.14 0.55 0.03 0.05 0.01 0.04 0.03 0.04 0.07 0.03 0.03 0.05 0.05 0.04 1.08 3.14 0.04 1.27 0.64 0.68 2.25 0.36 1.05 1.00 1.17 0.89 0.63 1.84 0.02 0.72 0.36 0.39 1.27 0.21 0.59 0.57 0.66 0.50 0.63 1.84 0.02 0.72 0.36 0.77 2.54 0.41 0.59 1.14 1.32 1.00 0.34 0.51 0.51 0.00 0.90 1 94 Table 4. MANOVA Results for Basic Needs. Overall MANOVA Task Demands Reactive Help Disempowering Help Help Motives Help Quality Minority Status Gender Relatedness ANOVA Task Demands Reactive Help Disempowering Help Help Motives Help Quality Minority Status Gender Competence ANOVA Task Demands Reactive Help Disempowering Help Help Motives Help Quality Minority Status Gender Autonomy ANOVA Task Demands Reactive Help Df Pillai's Trace 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 Df Df Df Approx. F 5.05 0.96 50.48 6.58 64.64 4.06 2.99 0.01 0.00 0.12 0.03 0.15 0.01 0.01 Sum Square Mean Square 0.03 0.01 16.09 7.73 30.36 0.01 1.13 0.03 0.01 16.09 3.87 30.36 0.01 1.13 Sum Square Mean Square 1.12 0.55 50.88 3.19 19.87 5.27 0.23 1.12 0.55 50.88 1.59 19.87 5.27 0.23 Sum Square Mean Square 3.92 0.03 3.92 0.03 95 num df 3 3 3 6 3 3 3 F 0.14 0.08 85.47 20.54 161.29 0.03 6.02 F 2.02 0.99 91.93 2.88 35.91 9.51 0.41 F 11.86 0.10 den df 1130 1130 1130 2262 1130 1130 1130 p 0.70 0.78 0.00 0.00 0.00 0.86 0.01 p 0.16 0.32 0.00 0.06 0.00 0.00 0.52 p 0.00 0.76 p 0.00 0.41 0.00 0.00 0.00 0.01 0.03 η2 0.00 0.00 0.07 0.03 0.12 0.00 0.01 η2 0.00 0.00 0.09 0.01 0.04 0.01 0.00 η2 0.01 0.00 f 0.01 0.01 0.27 0.19 0.37 0.01 0.07 f 0.05 0.03 0.32 0.08 0.20 0.10 0.02 f 0.10 0.01 Table 4 (cont’d). Disempowering Help Help Motives Help Quality Minority Status Gender 1 2 1 1 1 36.72 4.67 42.41 0.65 0.29 36.72 2.34 42.41 0.65 0.29 111.13 7.07 128.35 1.96 0.89 0.00 0.00 0.00 0.16 0.35 0.09 0.01 0.10 0.00 0.00 0.31 0.11 0.34 0.04 0.03 Note. * p < .05, ** p < .01. ** p < .001 N = 1139. η2= partial eta squared. f = Cohen’s f. 96 Table 5. Study 1 Path Model Results. Variable Direct Effects Relatedness Needs → Help Acceptance Competence Needs → Help Acceptance Autonomy Needs → Help Acceptance Task Demands → Help Acceptance Impression Management → Help Acceptance Prosocial → Help Acceptance isempowering Help → Help Acceptance Reactive Help → Help Acceptance Impression Management → Relatedness Needs Prosocial → Relatedness Needs isempowering Help → Competence Needs Reactive Help → Autonomy Needs Indirect Effects Impression Management → Relatedness Needs → HA Prosocial → Relatedness Needs → HA isempowering → Competence Needs → HA b β SE p CI LB CI UB 0.47 0.17 0.53 0.13 -0.03 -0.03 -0.10 -0.05 -0.14 0.20 -0.41 0.02 0.30 0.17 0.45 0.08 -0.04 -0.03 -0.07 -0.03 -0.26 0.31 -0.27 0.02 -0.06 0.09 -0.07 0.04 0.03 0.04 0.04 0.02 0.03 0.04 0.04 0.01 0.02 0.04 0.04 0.01 0.01 0.02 < .001 < .001 < .001 < .001 0.11 0.28 0.01 0.14 < .001 < .001 < .001 0.58 < .001 < .001 < .001 0.25 0.11 0.38 0.04 -0.09 -0.08 -0.11 -0.08 -0.31 0.26 -0.32 -0.04 -0.08 0.07 -0.10 0.36 0.23 0.52 0.12 0.01 0.02 -0.02 0.01 -0.21 0.36 -0.22 0.07 -0.05 0.12 -0.04 Note. N = 1,161. HA = help acceptance. CI LB = lower bound of a 95% confidence interval around the estimate. CI UB = upper bound of the 95% confidence interval. 97 Table 6. Study 2 Descriptive Statistics. Variable Within Person (Level 1) Variables M SD N 1 2 3 4 5 6 7 8 9 10 1. Task Demands 2. Prosocial Motives (reactive) 3. Impression Management Motives (reactive) 4. Prosocial Motives (proactive) 5. Impression Management Motives (proactive) 6. Disempowering Help (reactive) 7. Disempowering Help (proactive) 8. Relatedness (reactive) 9. Competence (reactive) 10. Autonomy (reactive) 11. Relatedness (proactive) 12. Competence (proactive) 13. Autonomy (proactive) 14. Help Quality (reactive) 15. Help Quality (proactive) 16. Help Acceptance (reactive) 17. Help Acceptance (proactive) 18. Work Goal Progress 19. State Self-Esteem 20. Positive Affect 21. Negative Affect Between Person (Level 2) Variables 22. Minority Status 23. Gender 3.56 0.86 331 0.73 3.52 0.93 155 0.09 0.74 2.34 1.17 155 .19* -.36** 0.57 3.69 0.98 125 0.11 .81** -.28** 0.76 2.22 1.17 125 0.05 -.28** .87** -.38** 0.82 2.57 1.08 155 -0.01 -.24** .23** -.36** .26* 0.72 2.57 3.58 3.57 3.83 3.62 3.51 3.69 4.57 4.41 1.06 0.93 0.88 0.88 1.07 1.01 1.08 0.61 0.81 125 154 154 154 124 124 124 154 0.10 0.03 0.04 0.04 0.05 0.10 -0.01 -0.08 -0.14 .47** .38** .33** .53** .55** .52** .26** 0.15 -.33** -.24** -.37** -.30** -.31** -.37** -.30** -.33** .53** .44** .47** .62** .62** .63** .39** .23* -.38** -.29** -.47** -.41** -.38** -0.46** -.37** .63** -.23** -.19* -.26** -.40** -.37** -.38** -0.16 0.73 -.30** -0.20 -.21* -.40** -.31** -.36** -0.18 0.47 .66** .59** .73** .64** .67** 0.64 .76** .68** .81** .80** 0.58 .71** .75** .82** .28** .33** .37** 124 -0.06 .43** -.29** .55** -.41** -.24* -.21* .37** .35** .43** 0.96 0.19 154 0.03 -0.05 0.15 0.06 0.09 -0.12 -0.13 0.08 0.10 0.06 0.88 4.18 3.82 3.09 1.67 0.17 0.51 0.33 1.02 0.99 0.99 0.88 0.38 0.50 124 324 323 323 323 99 99 0.07 0.03 -.11* .13* 0.08 .30** .120* .31** 0.04 -0.13 0.12 -.39** -.57** -.21** .45** 0.11 .27** 0.13 .37** -0.15 0.02 -.39** -.53** -.24** .44** -0.01 -0.12 -0.08 -.18* 0.08 -0.17 -0.03 0.00 -.33** 0.05 0.01 0.05 0.12 .43** .25** .37** -.31** .47** .33** .38** -.36** .52** .41** .35** -.43** 0.05 0.08 -0.08 0.03 .27** -0.09 -.22* .30** 0.05 -0.04 -0.05 -0.10 0.02 -0.18 -0.06 0.04 0.05 0.11 -0.09 0.06 98 Table 6 (cont’d). Variable Within Person (Level 1) Variables 11 12 13 14 15 16 17 18 19 20 21 22 23 11. Relatedness (proactive) 12. Competence (proactive) 13. Autonomy (proactive) 14. Help Quality (reactive) 15. Help Quality (proactive) 16. Help Acceptance 0.61 .81** 0.75 .82** .87** 0.74 .26* .31** .28** 0.73 .46** .52** .54** .66** 0.86 (reactive) 0.19 0.18 0.10 -0.10 0.04 -- 17. Help Acceptance (proactive) 18. Work Goal Progress 0.10 0.11 0.14 -0.11 0.07 .31** -- .38** .53** .51** .23** .33** 19. State Self-Esteem 20. Positive Affect 21. Negative Affect .26** .46** - .32** .35** .56** - .40** .35** .56** - .41** .22** 0.09 - .24** .38** 0.17 - .34** Between Person (Level 2) Variables 0.10 0.13 - 0.04 0.11 0.07 0.13 - 0.01 -0.10 0.94 .32** .51** - .38** 0.82 .29** - .63** 0.73 - .42** 0.76 22. Minority Status 23. Gender -.23* .19* -0.16 0.17 -0.18 0.16 0.04 0.06 -0.05 0.15 -0.10 0.03 -0.10 0.14 -0.10 0.08 -0.08 -0.11 -0.02 0.05 .23** 0.06 -- .15** -- Note. * p < .05, ** p < .001. Within-person Cronbach’s alphas are presented along the diagonal for continuous variables. 99 Table 7. Study 2 Original Model Multilevel Path Analysis Results. Variable Help Acceptance Predictors Relatedness Needs Competence Needs Autonomy Needs Task Demands Impression Management Motives Prosocial Motives Disempowering Help Help Quality Relatedness Needs Predictors Impression Management Motives Prosocial Motives Disempowering Help Competence Needs Predictors Impression Management Motives Prosocial Motives Disempowering Help Autonomy Needs Predictors Impression Management Motives Prosocial Motives Disempowering Help Work Goal Progress Predictors Task Demands Help Acceptance Help Quality Self Esteem Predictors Task Demands Help Acceptance Help Quality Negative Affect Predictors b SE p CI LB CI UB 0.06 0.06 0.06 0.04 0.04 0.05 0.03 0.04 0.06 0.06 0.04 0.07 0.07 0.04 0.07 0.07 0.05 0.09 0.17 0.12 0.08 0.15 0.10 0.48 0.51 0.20 0.99 0.64 0.36 0.49 0.86 0.86 < .001*** < .001*** 0.33 < .001*** 0.25 0.07 < .001*** 0.02* 0.68 0.19 0.64 0.58 0.18 .03* -0.07 -0.08 -0.19 -0.06 -0.10 -0.13 -0.07 -0.08 -0.11 0.10 -0.19 -0.20 0.24 -0.13 -0.28 0.10 -0.20 -0.28 -0.65 -0.18 -0.20 -0.50 -0.43 0.15 0.16 0.04 0.06 0.05 0.06 0.04 0.09 0.13 0.32 -0.04 0.07 0.50 0.04 0.01 0.38 -0.02 0.14 0.11 0.29 0.11 0.10 -0.02 0.04 0.04 -0.08 0 -0.02 -0.04 -0.02 0.01 0.01 0.21 -0.11 -0.07 0.37 -0.05 -0.14 0.24 -0.11 -0.04 -0.22 0.06 -0.04 0.2 -0.22 100 Table 7 (cont’d). Task Demands Help Acceptance Help Quality Positive Affect Predictors Task Demands Help Acceptance Help Quality Model Fit Information AIC BIC loglikelihood value 0.00 -0.02 -0.08 0.06 0.13 0.07 0.95 0.89 0.30 0.04 0.04 0.02 0.05 0.11 0.07 0.46 0.69 0.80 -0.11 -0.27 -0.22 -0.06 -0.17 -0.12 0.12 0.23 0.07 0.14 0.26 0.15 1578.65 1776.94 -726.33 Note. Level 2 N = 76, Level 1 N = 172. CI LB = lower bound of a 95% confidence interval around the estimate. CI UB = upper bound of the 95% confidence interval. All estimates are unstandardized. The path model was estimated simultaneously as one model. 101 Table 8. Study 2 Help Quality Model Multilevel Path Analysis Results. Variable Help Quality Predictors Relatedness Needs Competence Needs Autonomy Needs Task Demands Status Threat Relatedness Needs Predictors Impression Management Motives Prosocial Motives Disempowering Help Competence Needs Predictors Impression Management Motives Prosocial Motives Disempowering Help Autonomy Needs Predictors Impression Management Motives Prosocial Motives Disempowering Help Status Threat Predictors Impression Management Motives Prosocial Motives Disempowering Help Work Goal Progress Predictors Task Demands Help Quality No Help Self Esteem Predictors Task Demands Help Quality No Help b SE p CI LB CI UB 0.09 0.10 0.09 0.05 0.08 0.06 0.06 0.04 0.07 0.07 0.04 0.07 0.07 0.05 0.08 0.07 0.05 0.09 0.12 0.72 0.08 0.10 0.65 0.09 0.24 0.05 0.01 -0.29 0.02 0.2 -0.11 -0.06 0.36 -0.07 -0.12 0.23 -0.12 0.04 -0.22 0.09 -0.05 0.05 -2.91 -0.05 -0.23 -0.04 102 0.31 .02* 0.59 0.89 <.001*** 0.86 <.001*** <.01** 0.40 <.001*** 0.11 0.09 <.001*** <.01** 0.63 <.01** .04* 0.62 0.68 <.001*** 0.52 .02* 0.96 -0.09 0.04 -0.13 -0.09 -0.44 -0.10 0.09 -0.18 -0.19 0.23 -0.15 -0.27 0.09 -0.21 -0.11 -0.36 0.01 -0.22 -0.18 -4.32 -0.20 -0.49 -1.30 0.27 0.43 0.23 0.11 -0.15 0.14 0.32 -0.04 0.08 0.48 0.02 0.02 0.37 -0.03 0.19 -0.08 0.19 0.13 0.28 -1.49 0.10 -0.03 1.23 Table 8 (cont’d). Burnout Predictors Task Demands Help Quality No Help Indirect Paths Prosocial → Competence → H Prosocial → Status hreat → H isempowering → Status hreat→ H Model Fit Information AIC BIC loglikelihood value -0.02 -0.23 -0.04 0.08 0.10 0.65 0.13 0.06 -0.03 0.52 0.02* 0.96 < .01** 0.02* 0.07 -0.19 -0.43 -1.12 0.04 0.01 -0.06 0.14 0.00 1.23 0.22 0.12 0 1762.83 1914.19 -833.41 Note. Level 2 N = 76, Level 1 N = 173. CI LB = lower bound of a 95% confidence interval around the estimate. CI UB = upper bound of the 95% confidence interval. All estimates are unstandardized. The path model was estimated simultaneously as one model. HQ = Help Quality. 103 Table 9. Study 2 Help Quality Model Cross-Level Moderators. Variable Competence → Help uality Moderators Psychological Safety (PS) PS*Competence Competitive Climate (CC) CC*Competence Task Interdependence (INT) INT*Competence Prevention Focus (PRE) PRE*Competence Prevention Focus (PRO) PRO*Competence Learning Goal Orientation (LG) LG*Competence Performance Prove Orientation (PP) PP*Competence Performance Avoid Orientation (PA) PA*Competence Status hreat → Help Quality Moderators Psychological Safety (PS) PS*Status Threat Competitive Climate (CC) CC*Status Threat Task Interdependence (INT) INT*Status Threat Prevention Focus (PRE) PRE*Status Threat Prevention Focus (PRO) PRO*Status Threat Learning Goal Orientation (LG) LG*Status Threat b SE p CI LB CI UB 0.04 0.09 0.04 0.07 0.05 0.10 0.06 0.08 0.09 0.17 0.08 0.15 0.08 0.16 0.07 0.12 0.06 0.15 0.04 0.11 0.05 0.17 0.06 0.17 0.09 0.23 0.08 0.11 <.001*** .02* 0.30 0.10 0.44 0.27 .04* .03* 0.40 0.14 0.68 0.96 0.54 0.01* 0.59 0.65 <.001*** 0.86 0.61 0.93 0.45 0.95 .03* 0.95 0.18 0.28 0.67 <.001*** 0.11 -0.37 -0.05 -0.02 -0.12 -0.06 -0.23 0.02 -0.10 -0.08 -0.13 -0.29 -0.21 0.08 -0.17 -0.28 0.11 -0.33 -0.05 -0.23 -0.13 -0.33 -0.23 -0.32 -0.10 -0.70 -0.18 0.35 0.32 -0.03 0.09 0.27 0.06 0.32 -0.01 0.31 0.25 0.57 0.20 0.30 0.11 0.71 0.09 0.18 0.32 0.28 0.09 0.20 0.06 0.35 -0.01 0.34 6.00 0.20 0.20 0.80 0.2 -0.2 0.02 0.12 -0.04 0.11 -0.12 0.11 0.08 0.25 0.04 0.01 -0.05 0.38 -0.04 -0.05 0.22 -0.03 0.02 -0.01 -0.04 0.01 -0.12 0.01 0.08 -0.25 0.04 0.57 104 Table 9 (cont’d). Performance Prove Orientation (PP) PP*Status Threat Performance Avoid Orientation (PA) PA*Status Threat -0.05 -0.44 -0.04 0.44 0.08 0.18 0.07 0.07 0.53 .01* 0.59 <.001*** -0.21 -0.79 -0.14 0.33 0.11 -0.10 0.09 0.58 Note. Level 2 N = 76, Level 1 N = 172. CI LB = lower bound of a 95% confidence interval around the estimate. CI UB = upper bound of the 95% confidence interval. All estimates are unstandardized. Regression models were estimated separately for workplace moderators (CC, PS, INT), promotion-prevention tendencies, and goal orientations (learning, performance-prove, performance-avoid). 105 Table 10. Qualitative Themes Summary. 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You have [a lot left to complete on // have mostly finished] the report, which includes several pages of writing, preparing a presentation, obtaining and reporting technical information related to the projects, and preparing an update on the financial costs associated with the project. One issue is that you are currently unsure of the proper way to report some aspects of the financial and technical sections of the report. [In addition, you have a great deal of other tasks to complete today // You have most of the day free to work on this task]. [You are worried about how your boss will react if you don’t do a good job on this report, so you really want to complete it in time // You aren’t too worried about the report, but you still want to do a good job for your boss]. [Proactive vs reactive: To get this report done in time, you consider asking one of your coworkers to help you out // Seeing your situation, one of your coworkers offers to help you finish your report]. Based on your knowledge of this colleague, you think that they [Motives: are willing to help you because they genuinely like to help other people out at work // are willing to help you because they think it will help impress their boss]. One of the main areas you need help in is the technical and financial sections which you are not fully sure how to complete. Based on what you know of this coworker, you think that they would [(Dis)empowering help: take the time to explain these sections to you and help you learn how to write them on your own // take over on 123 these sections and complete them for you]. Considering your workload with this report, and the potential help from the coworker, consider how you would respond to the following questions if you were in this scenario: 124 APPENDIX B: STUDY ITEMS Impact on Psychological Needs (Study 1). “ he following statements examine how you think you would feel in this situation.” 1-5 Agree-Disagree scale. Need for Relatedness Satisfaction: 1. Receiving this help would make me feel disconnected with other people at my job 2. At work, I would feel like part of the group if I received this help 3. If I got this help, I don’t think I’d feel like I mix well with other people at my job (r) 4. Receiving this help would likely make me feel able to talk with people about things that really matter to me at work 5. I think I would often feel alone when I am with my colleagues if I received this help (r) 6. Getting this kind of help would make me feel close to my coworkers. Need for Competence Satisfaction 1. I wouldn’t really feel competent in my job if I received this help (r) 2. If I got this kind of kelp, I’d feel like I really have mastered my tasks at my job 3. I’d feel competent at my job if I got this kind of help 4. I would doubt whether I am able to execute my job properly if I received this help (r) 5. If I received this help, I would feel like I’m good at the things I do in my job 6. I would have the feeling that I can even accomplish the most difficult tasks at work after receiving this kind of help Need for Autonomy Satisfaction 125 1. If I got this kind of help, I would feel I can be myself at my job 2. At work, I would feel like I have to follow other people’s commands if I was helped in this way (r) 3. If I could choose, I would do things in this situation differently (r) 4. I would feel like the tasks I have to do at work are in line with what I really want to do even if I received this help 5. If I received this help, I would feel free to do my job the way I think it could be best be done 6. I would feel forced to do things I do not want to do if I got this help (r) Disempowering Help (Study 1). 1-5 Agree-Disagree Scale. 1. This coworker would most likely help me by showing me how to best handle this situation (R). 2. Instead of doing it for me, this coworker would teach me how to do it (R). 3. My coworker would likely help me by doing the work him/herself. 4. Instead of showing me how to handle this situation myself, my coworker would take over. Task Demands (Study 1) Please respond to the following questions, thinking about how you would feel in the scenario described earlier. 1-5 Agree-Disagree 1. I would have to work very hard to complete this task 2. I would have little time to get other things done because of this task. 126 3. I would have to work quickly to get this task done. 4. There is a lot that I would have to do in order to complete this task. 5. I would have to devote much of my time to completing this task. Helper Motives (Study 1). Consider why your colleague is providing you help in the scenario. For each item, rate the extent of how important you think each reason for helping is to this person (1 = not at all important, 6 = extremely important). Prosocial Motives: 1. Because they feel it is important to help those in need. 2. Because they believe in being courteous to others. 3. Because they are concerned about other people's feelings. 4. Because they want to help co-workers in any way they can. 5. Because it is easy for them to be helpful. Impression Management 6. To avoid looking bad in front of others. 7. To avoid looking lazy. 8. To look better than their co-workers. 9. To avoid a reprimand from their boss. 10. Because they fear appearing irresponsible. Help quality (Study 1 control) Please use the following questions to rate what terms you feel best describe the help presented in the scenario above: 1-5 Semantic differential scale: • appropriate-inappropriate 127 • effectual-not effectual • useful-not useful • effective-ineffective • necessary-unnecessary Help Acceptance Intentions 1 (strongly disagree) to 5 (strongly agree) scale. 1. I would let this person to help me. 2. This help is not what I want (r). 3. I would be willing to use this help. 4. I want to accept this person’s assistance. 5. This is not the kind of help I need (r). Positive and Negative Affect (control variable). Please indicate how you tend to feel in general: 5-point scale (1= very slightly or not at all, 2 = a little, 3 = moderately, 4 = quite a bit, 5 = very much) • Positive Affect o Inspired o Alert o Excited o Enthusiastic o Determined • Negative Affect o Afraid o Scared 128 o Nervous o Upset o Distressed State Self-Esteem (Control Variable) Please rate the extent to which you agree with each of the following statements about yourself: Agree-disagree scale. 1. 2. 3. 4. 5. 6. 7. 8. 9. I am confident about my abilities. I am worried about whether I am regarded as a success or failure. I feel frustrated or rattled about my performance. I feel that I am having trouble understanding things that I read. I feel self-conscious. I feel as smart as others. I feel displeased with myself. I am worried about what other people think of me. I feel confident that I understand things. 10. I feel inferior to others at this moment. 11. I feel concerned about the impression I am making. 12. I feel that I have less scholastic ability right now than others. 13. I feel like I’m not doing well. 14. I am worried about looking foolish. Study 2 Monday Survey Help Context (Study 2). 129 “Please describe your most important task project at work this week in 2-3 sentences.” “Please use 2-3 sentences to describe what kind of help would be most useful to you on this project”. Task Demands (Study 2) Please respond to the following questions, thinking about how you feel regarding your most important task this week: 1-5 Agree-Disagree 1. I will have to work very hard this week to complete this task 2. This week, I will have little time to get other things done because of this task. 3. I will have to work quickly to get this task done this week. 4. This week, there is a lot that I will have to do in order to complete this task. 5. I will have to devote much of my time to completing this task. Tuesday-Thursday Survey (Released in the late afternoon). Proactive versus Reactive Help (Study 2). 1. “Have you requested any help from your coworkers on the task you wrote about on Monday?” 2. “Has a coworker offered to help you with the task you wrote about on Monday?” Helper Motives (Study 2). Reactive Context: Consider why this person would be willing to help you. For each item, rate the extent of how important you think each reason for helping is to this person (1 = not at all important, 6 = extremely important). 130 Proactive Context: Consider why this person is offering you help. For each item, rate the extent of how important you think each reason for helping is to this person (1 = not at all important, 6 = extremely important). Prosocial Motives: 1. Because they feel it is important to help those in need. 2. Because they believe in being courteous to others. 3. Because they are concerned about other people's feelings. 4. Because they want to help co-workers in any way they can. 5. Because it is easy for them to be helpful. Impression Management 6. To avoid looking bad in front of others. 7. To avoid looking lazy. 8. To look better than their co-workers. 9. To avoid a reprimand from their boss. 10. Because they fear appearing irresponsible. Disempowering Help (Study 2). 1-5 Agree-Disagree Scale. 1. This coworker (would have) showed me how to best handle this situation, rather than taking over (R). 2. Instead of doing it for me, this coworker (would have) taught me how to do it (R). 3. My coworker (would have) helped me by doing the work him/herself. 4. Instead of showing me how to handle this situation myself, my coworker (would have) took the work over. 131 Basic Need (Study 2) : Reactive Help “How does asking for this help make you feel? Please respond to the questions below considering how requesting your coworker’s help feels to you”. Proactive help: How does being offered this help make you feel? Please respond to the questions below considering how your coworker’s offer of help feels to you”. Need for Relatedness Satisfaction: 1. I don’t really feel connected with other people at my job (r) 2. At work, I feel part of a group 3. I don’t really mix with other people at my job (r) 4. At work, I can talk with people about things that really matter to me 5. I often feel alone when I am with my colleagues (r) 6. Some people I work with are close friends of mine Need for Competence Satisfaction 7. I don’t really feel competent in my job (r) 8. I really master my tasks at my job 9. I feel competent at my job 10. I doubt whether I am able to execute my job properly (r) 11. I am good at the things I do in my job 12. I have the feeling that I can even accomplish the most difficult tasks at work Need for Autonomy Satisfaction 13. I feel I can be myself at my job 14. At work, I often feel like I have to follow other people’s commands (r) 15. If I could choose, I would do things at work differently (r) 132 16. The tasks I have to do at work are in line with what I really want to do 17. I feel free to do my job the way I think it could be best be done 18. In my job, I feel forced to do things I do not want to do (r) Help quality (Study 2) Please use the following questions to rate what terms you feel best describe help from this coworker: 1-5 Semantic differential scale: • appropriate-inappropriate • effectual-not effectual • useful-not useful • effective-ineffective • necessary-unnecessary Help Acceptance Did you accept this help? (yes/no) Friday Survey Positive and Negative Affect (Study 2). Please indicate how you feel right now: 5-point scale (1= very slightly or not at all, 2 = a little, 3 = moderately, 4 = quite a bit, 5 = very much) • Positive Affect o Inspired o Alert o Excited o Enthusiastic o Determined • Negative Affect 133 o Afraid o Scared o Nervous o Upset o Distressed Status. Please rate your agreement with each of the following statements: 1-5 Agree-Disagree 1. Accepting this help will make others less frequently seek my opinion (r) 2. My good reputation might be harmed by using this help (r) 3. I will be less respected if I take this help (r) 4. People will look up to me less if I take this help (r) 5. I will be admired less for my skills at work if I receive this help (r) 6. Coworkers will trust my judgment less if I take this help (r) State Self-Esteem Please respond to the following questions considering how you feel after this week of work. Agree-disagree scale. 1. 2. 3. 4. 5. 6. I am confident about my abilities. I am worried about whether I am regarded as a success or failure. I feel frustrated or rattled about my performance. I feel that I am having trouble understanding things that I read. I feel self-conscious. I feel as smart as others. 134 7. 8. 9. I feel displeased with myself. I am worried about what other people think of me. I feel confident that I understand things. 10. I feel inferior to others at this moment. 11. I feel concerned about the impression I am making. 12. I feel that I have less scholastic ability right now than others. 13. I feel like I’m not doing well. 14. I am worried about looking foolish. Work Goal Progress (Koopman et al., 2016) 1. I have made good progress on my work goals this week. 2. I had a productive week in relation to my work goals. 3. I have moved forward with my work goals this week. Additional Variables Bur u . L k r r g g fr m ‘ ’ (1) ‘ gr x ’ (7). 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 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 like I’m at the end of my rope. 135 Work Engagement. 5-Point Scale (1 = strongly disagree, 5 = strongly agree) In my work this week… 1. …I feel strong and vigorous. 2. …I feel bursting with energy. 3. …I am enthusiastic about my work. 4. …my job is inspiring to me. 5. …I am getting completely immersed in my work. 6. …I am getting carried away with my work. Promotion focus (Lin & Johnson 2015) 1. 2. 3. My major focus right now is to achieve success. Right now I am focused on achieving positive outcomes. I am currently more oriented toward achieving success than preventing failure. Prevention focus (Lin & Johnson 2015) 1. 2. 3. 4. Right now I am focused on preventing negative events. Right now I am anxious about failing short of my responsibilities and obligations. My major focus right now is to avoid failure. to avoid failure. 136 APPENDIX C: MEASUREMENT EQUIVALENCE ANALYSES Table 1C. Results for Mean and Covariance Structure Analyses. Scale Prosocial Impression Management Disempowering Help Relatedness Model Unconstrained Single Group Configural Invariance Model Metric Invariance Model Scalar invariance Model Unconstrained Single Group Configural Invariance Model Metric Invariance Model Scalar invariance Model Unconstrained Single Group Configural Invariance Model Metric Invariance Model Scalar invariance Model Unconstrained Single Group Chi- Square df RMSEA CFI SRMR Loglikelihood Correction Factor Chi- Square Test Chi- Square df 10.65 10 0.02 1 0.03 -2027.27 12.96 10 0.02 0.99 0.03 -1814.33 1.08 1.10 18.03 14 0.05 0.99 0.05 -1815.48 1.21 142.68 19.81 18 0.03 0.99 0.06 -1816.28 1.25 46.23 17.08 10 0.05 0.99 0.03 -2142.61 20.06 10 0.09 0.98 0.02 -1937.23 1.17 1.28 22.12 14 0.06 0.98 0.03 -1937.49 1.31 44.83 25.2 18 0.05 0.99 0.03 -1938.67 1.36 65.52 0 0 0.04 0 1 1 1 1 0 5 5 0.42 0 2.4 2.89 12.59 3 0 2 4 6 0.01 -1547.40 -1342.84 0.97 1.08 -1344.30 1.06 25.14 -1344.42 1.08 21.84 0.06 0.97 0.03 -1851.76 1.14 137 4 4 4 4 2 2 Table 1C (cont’d). Configural Invariance Model Metric Invariance Model Metric Invariance Model Scalar invariance Model Unconstrained Single Group Configural Invariance Model Metric Invariance Model Scalar invariance Model Unconstrained Single Group Configural Invariance Model Metric Invariance Model Scalar invariance Model Unconstrained Single Group Configural Invariance Model Metric Invariance Model 21.15 18.83 3.6 4 7 7 6.41 10 10.67 6.45 13.26 6 4 7 0.18 0.11 0 0 0.05 0.07 0.08 0.9 0.04 -1642.21 1.24 0.93 0.06 -1643.19 1.23 19.70 1 1 0.03 -1429.99 1.55 34.16 0.03 -1431.52 1.64 70.09 0.98 0.03 -1732.89 0.99 0.02 -1517.88 1.32 1.44 0.97 0.09 -1522.59 1.44 4.82 15.6 10 0.06 0.97 0.1 -1523.56 1.51 62.02 31.93 15 0.06 0.97 0.02 -1881.09 31.42 18 0.07 0.97 0.03 -1666.19 2.95 3.05 36.15 23 0.06 0.97 0.05 -1673.31 3.11 37.51 41.68 28 0.06 0.97 0.05 -1674.59 3.53 129.51 16.6 10 0.05 0.99 0.05 -1620.32 29.79 10 0.12 0.95 0.04 -1399.38 1.93 2.00 3 3 3 3 3 3 5 5 29.32 14 0.09 0.96 0.04 -1399.56 2.07 45.81 4 138 Autonomy Status Threat Help Quality Table 1C (cont’d). Scalar invariance Model 34.56 18 0.08 0.96 0.04 -1401.51 2.27 96.22 4 *Note. Chi-square tests were performed using a Satorra-Bentler scaled chi-square difference test. 139