NEEDS FULFILLMENT AND CORE AFFECT AS ANTECEDENTS TO PERSONORGANIZATION FIT: A DYNAMIC APPROACH By Abdifatah Ahmed Ali A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Psychology—Doctor of Philosophy 2017 ABSTRACT NEEDS FULFILLMENT AND CORE AFFECT AS ANTECEDENTS TO PERSONORGANIZATION FIT: A DYNAMIC APPROACH By Abdifatah Ahmed Ali Candidate’s person-organization (PO) fit perceptions are invaluable to organizations because they lead to positive organizational attraction and job acceptance decisions. Yet, the recruitment literature provides a narrow perspective on the factors that contribute to the development of PO fit. Extending recent theorizing on the dynamics of PO fit, I propose and test a multilevel model that seeks to elaborate on the factors that govern candidates’ PO fit perceptions during the recruitment process. At the within-person level, I draw on insights from self-determination theory and core affect to propose that job candidate’s hedonic experiences and needs fulfillment function as proximal determinants of PO fit. At the between-person level, I propose that job candidate’s motivational orientation towards the recruitment process—i.e., a promotion focus versus a prevention focus—operates as a cross-level moderator shaping the effects of core affect on PO fit. Using a longitudinal sample of new labor market entrants and adult job seekers (n = 70), the data showed that at the within-person level, higher levels of needs fulfillment positively related to both activated (e.g., excitement) and deactivated (e.g., contentment) positive affect and negatively related to both activated (e.g., frustration) and deactivated (e.g., sadness) negative affect. Findings also showed that activated positive affect was positively related to PO fit, whereas activated negative affect was negatively related to PO fit. Further, findings also showed that PO fit was positively related to acceptance intentions and negatively related to withdrawal cognitions. Finally, the results did not support the cross-level moderation. These findings are discussed in terms of their theoretical implications for understanding the determinants of PO fit, and practical implications as to how the recruitment process can be managed in order to lead to positive recruitment outcomes. ACKNOWLEDGEMENTS I would like to thank my adviser, Dr. Ann Marie Ryan, for her continual support and guidance throughout my time as a graduate student and while working on this dissertation. I also greatly benefited in terms of support and feedback from my committee members Drs. Daisy Chang, Kevin Ford, and Joseph Cesario. iv TABLE OF CONTENTS LIST OF TABLES........................................................................................................................ vii LIST OF FIGURES ..................................................................................................................... viii CHAPTER 1: INTRODUCTION ................................................................................................... 1 CHAPTER 2: REVIEW OF PO FIT AND HYPOTHESIS DEVELOPMENT............................. 8 PO fit During Recruitment.......................................................................................................... 8 Review of Core Affect .............................................................................................................. 13 Dimensional approach. ......................................................................................................... 13 Categorical approach. ........................................................................................................... 14 The Role of Core Affect in Predicting PO Fit .......................................................................... 16 Affect-as-information. .......................................................................................................... 17 Affective-priming. ................................................................................................................ 17 Recruitment Events as Signals for Needs Fulfillment .............................................................. 21 Consequences of PO Fit............................................................................................................ 25 Moderating Role of Regulatory Focus...................................................................................... 26 PO Fit Predicting Core Affect and Needs Fulfillment.............................................................. 31 CHAPTER 3: METHODS............................................................................................................ 33 Pilot Study................................................................................................................................. 33 Sample and Procedure............................................................................................................... 33 Summary of Findings................................................................................................................ 34 Recruitment time course. ...................................................................................................... 34 Needs fulfillment. ................................................................................................................. 34 Main Study................................................................................................................................ 38 Sample and Procedure............................................................................................................... 38 Sample Characteristics.............................................................................................................. 39 Measures ................................................................................................................................... 40 Initial Survey............................................................................................................................. 40 Perceived alternatives. .......................................................................................................... 40 Trait affect............................................................................................................................. 40 Organizational attraction....................................................................................................... 41 Organizational prestige. ........................................................................................................ 41 Organizational reputation...................................................................................................... 41 PO fit..................................................................................................................................... 41 Regulatory focus. .................................................................................................................. 42 Weekly Survey.......................................................................................................................... 42 Affect. ................................................................................................................................... 42 Psychological needs fulfillment............................................................................................ 42 v PO fit..................................................................................................................................... 43 Acceptance intentions. .......................................................................................................... 43 Withdrawal cognition............................................................................................................ 43 Analytical Strategy and Data Analysis ..................................................................................... 43 Confirmatory Factor Analysis................................................................................................... 43 CFA for the baseline survey. ................................................................................................ 44 CFA for the weekly survey. .................................................................................................. 46 Random Coefficients Modeling................................................................................................ 48 CHAPTER 4: RESULTS.............................................................................................................. 51 Main Effect Hypotheses............................................................................................................ 53 Cross-Level Moderation Hypotheses........................................................................................ 60 CHAPTER 5: DISCUSSION........................................................................................................ 63 Summary of Findings and Contributions.................................................................................. 64 Affect as predictor of PO fit.................................................................................................. 64 Needs fulfillment as predictor of core affect. ....................................................................... 65 PO fit as predictor of acceptance intentions and withdrawal cognition................................ 66 Reciprocal relationship between PO fit, needs fulfillment, and core affect. ........................ 66 Regulatory focus as a moderator........................................................................................... 68 Practical Implications................................................................................................................ 68 Limitations ................................................................................................................................ 71 Directions for Future Research ................................................................................................. 72 Conclusion ................................................................................................................................ 75 APPENDICES .............................................................................................................................. 76 APPENDIX A: Recruitment Information................................................................................. 77 APPENDIX B: Screening Questionnaire.................................................................................. 78 APPENDIX C: Measures.......................................................................................................... 79 REFERENCES ............................................................................................................................. 84 vi LIST OF TABLES Table 1: A List of Recruitment Events as a Function of Recruitment Stage................................ 24 Table 2: Standardized Latent Correlations Between Needs Fulfillment Dimensions. ................. 37 Table 3: Comparison of Alternative Factor Structures for Baseline Measures. ........................... 45 Table 4: Comparison of Alternative Factor Structures for Weekly Measures.............................. 47 Table 5: Random Coefficients Modeling Estimates of Null Models............................................ 50 Table 6: Means, Standard Deviations, Reliabilities, and Correlations Among Variables............ 52 Table 7: Random Coefficients Modeling Results for the Relationships between Focal Study Variables. .............................................................................................................................. 56 Table 8: Results from Random Coefficients Modeling Serial Mediation. ................................... 58 Table 9: Linear and Quadratic Effects for Focal Variables. ......................................................... 62 vii LIST OF FIGURES Figure 1: Proposed Theoretical Model ........................................................................................... 7 Figure 2: Hypothesized Cross-Level Interaction between Promotion Focus, Activated Positive Affect, and PO Fit. ................................................................................................................ 29 Figure 3: Hypothesized Cross-Level Interaction Between Promotion Focus, Deactivated Negative Affect, and PO Fit.................................................................................................. 29 Figure 4: Hypothesized Cross-Level Interaction between Prevention Focus, Deactivated Positive Affect, and PO Fit. ................................................................................................................ 30 Figure 5: Hypothesized Cross-Level Interaction between Prevention Focus, Activated Negative Affect, and PO Fit. ................................................................................................................ 30 viii CHAPTER 1: INTRODUCTION Attracting high quality candidates and influencing their job choice decisions is one step toward firms achieving competitive advantage. Thus, it is no surprise that organizations expend resources and effort toward employee recruitment. The existing literature on recruitment points to a diverse set of factors explaining why candidates are attracted to organizations (see Breaugh, 2013, and Darnold, & Rynes, 2012 for recent reviews). Person-organization (PO) fit—that is, “the congruence between the norms and values of organizations and the value of persons” (Chatman, 1989; p. 339)—is one factor that has gained prominence in recruitment research because of the accumulated evidence linking it with recruitment outcomes (Uggerslev, Fassina, & Kraichy, 2012). During recruitment, organizations and candidates alike have vested interest to establish PO fit. For organizations, PO fit is viewed from a strategic perspective in that candidate characteristics are expected to align with organizational objectives in order to create and sustain organizational resources (Barney, 1991). Indeed, meta-analytic findings illustrate the importance of experiencing fit at work in that it directly contributes to workplace outcomes including job satisfaction, organizational commitment, and task and contextual performance (Kristof-Brown, Zimmerman, & Johnson, 2005). From the candidate perspective, PO fit is viewed from a social identity lens in that candidates are motivated to join organizations they can identify with (Cable & Kay, 2012). From this view, therefore, organizational attraction increases as a function of similarity in fundamental characteristics (e.g., value congruence) (Chatman, 1989; Schneider, 1987). Consequently, when candidates perceive high PO fit, they are attracted to and are more likely to accept a job offer from the recruiting organization (Chapman, Uggerslev, Carroll, 1 Piasentin, & Jones, 2005; Harold, Holtz, Griepentrog, Brewer, & Marsh, 2015; Swider, Zimmerman, & Barrick, 2015; Uggerslev et al., 2012). The existing recruitment literature on PO fit has advanced our understanding of the consequences of experiencing PO fit, therefore clearly establishing its importance (KristofBrown, Reeves, & Follmer, 2014). Given the importance of PO fit, research has moved to understand what potentially shapes PO fit. To this end, research has focused on signals organizations provide to influence PO fit. For instance, formal information sources such as job advertisement (Cable & Judge, 1996) and recruiters (Saks & Ashforth, 1997) have been shown to influence candidates’ perceptions of PO fit. Further, macro organizational features such as corporate social responsibility have also been shown to influence these perceptions. In a recent study, Jones, Willness, and Madey (2014) found that candidates were more attracted to recruiting organizations when they thought they were high in corporate social responsibility; this relationship was in part mediated by candidate’s PO fit perceptions. The findings discussed above, although insightful, largely focus on a slice of the recruitment process, ignoring temporal elements associated with PO fit (Jansen & Shipp, 2013; Shipp, & Jansen, 2011). Given that recruitment is a multi-stage process, organizational characteristics (e.g., advertisement, organization website, reputation) responsible for initial PO fit perceptions may lose their influence as candidates gain additional information and update their PO fit accordingly (Rynes, Bretz, & Gerhart, 1991). Consistent with theorizing in the larger recruitment literature (Uggerslev et al., 2012), the elaboration likelihood model (ELM; Petty & Cacioppo, 1986) would predict initial PO fit as a function of organizational characteristics. According to the model, when candidate have little information about the organization, they are more likely to rely on simple cues from the recruiting environment—that is, they are more likely 2 to base their PO fit using recruitment sources such as websites and advertisement as well as macro firm characteristics including firm reputation and image. As ELM suggests, however, when candidates begin progressing through the recruitment process, they are more likely to engage in systematic processing (i.e., critically evaluating all relevant information and experiences), which may lead candidates to update their PO fit perceptions. For example, using a longitudinal structured interview design, Rynes and colleagues (1991) found that while some candidates maintained their initial PO fit throughout the recruitment process, others had a decline in PO fit because of negative experiences. Therefore, although initial recruitment factors matter, what happens during the recruitment process also deserves consideration when examining PO fit. Thus, in the current study, I examine time-varying antecedents to PO fit dynamics throughout the recruitment process. A key emphasis of the current study is that candidates’ experiences during the hiring process are filled with affective reactions. Scholars who take a job search perspective to studying candidates have recognized the importance of affective reactions in influencing job search outcomes. The job search literature is different from that of the recruitment literature in that the job search literature examines “various outcomes within-subjects across organizations” whereas the recruitment literature examines “similar outcomes between-subjects within an organization” (da Motta Veiga & Turban, in press; p.1). Given the within-person focus, the job search literature has examined the influences of core affect on job search outcomes. For instance, da Motta Veiga and Turban (2014) examined the role of daily positive and negative affective experiences originating from the job search context and their relation with job search intensity. In support of their control theory predictions, they found that negative affect positively related to job search intensity (i.e., the amount of effort exerted toward job search behaviors) whereas positive affect 3 negatively related to job search intensity. Further, Song, Uy, Zhang, and Shi (2009), using a longitudinal design, examined the within-person relationship between negative affect—measured as experienced stress—and effort expenditure. These studies underscore the importance of examining candidates’ affective experiences during the hiring process. Considering the recruitment context, recruitment research has failed to recognize and integrate how affective experiences originating from interactions with recruiting organization can shape candidates’ PO fit perceptions. Take for example an applicant who initially perceives to have good PO fit with the company; upon applying to the company, the applicant first experiences delays in the communication of his/her application status, followed by an interaction with an impersonal organizational representative who updates him/her about next steps. This candidate may experience several unpleasant affect and emotions ranging from anxiety to anger that may in turn diminish their initial positive fit perceptions. Take another applicant who at first is tentative about their fit with the organization but has several recruitment events (e.g., interviewer engages in other-focused enhancement by praising the candidate) that enhances their perception of fit as well as other organizational attributes (e.g., a friendly workplace). Given the potential emotional rollercoaster associated with looking for work, it is suspected that affective experiences during recruitment may serve as an antecedent explaining within-person changes in PO fit. As such, the purpose of this study is to develop and test a model that enhances our understanding of the within-person determinants of PO fit during the recruitment process. First, this model (see Figure 1) utilizes the affect infusion model (Forgas, 1995) in theorizing about the linkages between core affective reactions and perceptions of PO fit. This perspective builds on the existing cognitively oriented theoretical frameworks (e.g., signaling theory, ELM) within the 4 recruitment literature (Uggerslev et al., 2012) by suggesting core affect can shape the type of recruitment information candidates attend to as well as the type of information that is retrieved from memory. From a practical point of view, this suggest organizations need to consider the affective quality of recruitment events as they have consequences for the way recruitment information is processed, which in turn influences recruitment outcomes including PO fit. Second given that applicants experience a range of recruitment events, self-determination theory (Deci & Ryan, 2000) is used as a way to synthesize those experiences to meaningful dimensions that applicants care about. Specifically, the model examines psychological needs fulfillment for autonomy, relatedness, and competence as contributing to applicant’s affective experiences, and indirectly shaping PO fit perceptions. This perspective extends existing social identity frameworks within the recruitment literature (Cable & Kay, 2012) by emphasizing psychological needs fulfillment as another determinant to organizational attraction. Though the focus of the model is on examining the determinants of PO fit, the model also links PO fit to outcomes that have been shown to be important when considering PO fit at the between-person level. In addition, given the within-person nature of the model, a competing model is tested whereby PO fit precedes both needs fulfillment and core affect. Finally, the model draws upon regulatory focus theory (Higgins, 1997) to examine whether individual difference in applicant’s promotion and prevention focus moderates the within-person relationship between core affect and PO fit. The following chapter provides a review of existing theory and evidence for PO fit during the recruitment process. Also this chapter provides a broad review of core affect and the affect infusion model, and I also develop specific hypotheses regarding the relationship between core affect and PO fit. The rest of the chapter provides additional hypotheses pertaining to the other parts of the conceptual model. In chapter 3, I discuss the sample and procedure, measures, and 5 analytical strategy used for testing the hypotheses. Chapter 4 provides a review of the results as well as findings from exploratory analyses. In chapter 5, I summarize the findings and provide a discussion of the contributions of the model; further, I highlight limitations and suggest future research directions for expanding on the current study. 6 Figure 1: Proposed Theoretical Model 7 CHAPTER 2: REVIEW OF PO FIT AND HYPOTHESIS DEVELOPMENT PO Fit During Recruitment Recruitment can generally be thought of as activities organizations engage in to attract job candidates, with candidates either being internal or external to the company (Barber, 1998). Given that most recruitment research focuses on external recruitment, Breaugh (2008) emphasized this aspect by defining recruitment as “an employer’s actions that are intended to (1) bring a job opening to the attention of potential job candidates who do not currently work for the organization, (2) influence whether these individuals apply for the opening, (3) affect whether they maintain interest in the position until a job offer is extended, and (4) influence whether a job offer is accepted” (p. 103-104). Thus, an organization’s ability to acquire human talent largely depends on the extent to which they can generate a pool of suitable candidates, maintain their interest throughout the recruitment process, and influence their job choice decisions (Barber, 1998). Recruitment scholars have conceptualized and operationalized PO fit in many ways. The following paragraphs organize the literature on PO fit during recruitment by examining how PO fit has been conceptualized (i.e., complementary versus supplementary fit) as well as the type of measurement approaches taken to study PO fit. Research concerning fit during pre-organizational entry has taken either a complementary or supplementary perspective to study the concept. Complementary fit is concerned with matching of person’s characteristics and those of the organization, and has largely been studied when considering the match between the person and the job or PJ fit. PJ fit can take on two perspectives, depending on whether the focus is on the individual or the organization. From the individual’s perspective, PJ fit is studied as needs-supplies fit or the “congruence between 8 employees’ needs and the rewards they receive in return for their services and contributions on a job” (Cable & DeRue, 2002, p.875). Needs-supplies fit is contrasted with demands-abilities fit, which is from the organization’s perspective and is concerned with the extent to which the knowledge, skills, and abilities of the individual fits the demands of the job (Cable & DeRue, 2002). Though fewer studies examine PJ fit during recruitment, it has been shown to positively relate to organizational attraction (ρ = .59, k = 13, CI = .53, .65; Uggerslev et al., 2012). Beyond complementary fit, as highlighted in this study, individuals can also fit on the basis of them sharing similar characteristics with the organization. PO fit—which is considered supplementary fit—exists when there is “congruence between the norms and values of the organizations and the values of the person” (Chatman, 1989, p.339). In accordance with theoretical frameworks such as social identity theory (Tajfel & Turner, 1985) and Schneider’s (1987) attraction-selection-attrition model, candidates should pursue and accept job offers from organizations that share similar fundamental characteristics, such as an individual’s core values (Cable & Judge, 1996; Judge & Cable, 1997; Harold et al., 2015; Kausel, & Slaughter, 2011; Ryan, Horvath, & Kriska, 2005; Yu, 2014). Though PO fit has largely been defined as value congruence, PO fit can also be conceptualized along additional content dimensions such as congruence in personality (e.g., Lievens, Decaesteker, Coetsier, & Geirnaert, 2001), goals (e.g., Vancouver, Millsap, & Peters, 1994; Vancouver & Schmitt, 1991), and demographic attributes (Turban & Jones, 1988). In a recent meta-analysis of predictors of applicant attraction to organizations, Uggerslev and colleagues (2012) noted that PO fit is one of the strongest predictors of organizational attraction (ρ = .63, k = 39, CI = .59, .67). PO fit, however, has been less predictive of job choice decisions as evidenced by Chapman et al.’s (2005) meta-analytic review (ρ = .18, k = 3, CI = .00, .37), though recent empirical research suggest PO fit influences 9 job choice when a dynamic approach is taken to studying fit (Swider et al., 2015). Recruitment scholars have also differed in their measurement approach to studying PO fit. For the most part, recruitment research has focused on perceived fit, which according to Edwards, Cable, Williamson, Lambert, and Shipp (2006) is considered the molar approach to studying fit. This approach allows for the most cognitive manipulation since “the compatibility of P [person] and E [environment] can be assessed on any dimensions or across dimensions by the respondent” (Kristof-Brown et al., 2014; p. 439-440). The molar approach is different from the atomistic and molecular approach to measuring PO fit (Edwards et al., 2006). Within the atomistic approach, the person’s values and those of the organization are measured separately and subsequently combined to create an overall PO fit measure. This is also called an indirect approach to measuring and calculating perceived PO fit. Within the molecular approach, the focus is on assessing the discrepancy that exists between a person’s values and those of the organization; this conceptualization emphasizes the directionality of fit (e.g., the persons’ values exceed those of the organization). Findings in the recruitment literature suggest that the direct (molar) approach has the strongest relationship with recruitment outcomes compared to other conceptualizations of fit (Kristof-Brown et al., 2005). In the meta-analysis by Kristof-Brown et al. (2005), direct measure of perceived fit had a stronger relationship with organizational attraction (ρ = .62, k = 10, CI = .42, .62) compared to an indirect measure of fit (ρ = .20, k = 4, CI = .16, .22). Given my interest in the subjective experiences of job candidates as they engage with the recruitment process, the current study adopts the molar approach to studying PO fit. The emphasis on the molar approach is relevant because as the review suggested, such conceptualization is consistent with the extant recruitment literature on PO fit (e.g., Harold & 10 Ployhart, 2008; Swider et al., 2015) and meta-analytic evidence suggests that subjective PO fit has the strongest relationship with recruitment outcomes compared to other conceptualizations of fit (Kristof-Brown et al., 2005). In addition, from a conceptual standpoint, as job seekers engage with the recruitment process they are able to draw upon their experiences and update their PO fit perceptions. In this fashion, candidates’ PO fit perceptions can be updated as new information becomes available. Changes or updating in PO fit perceptions are expected to occur for several reasons. First, changes in PO fit may occur because candidates could hold inaccurate initial beliefs about the recruiting organization (e.g., Cable, Aiman-Smith, Mulvey, & Edwards, 2000). Second, as candidates engage in the recruitment process they acquire additional information about company, which in turn allows for information to be updated (Slaughter, Cable, & Turban, 2014). For example, Saks and Uggerslev (2010) used Hogarth and Einhorn’s (1992) model of belief updating to explain how candidates integrate recruitment information across the recruitment phases. Because subjective experiences of PO fit allows for the most cognitive manipulation, job candidate’s are therefore able to continually integrate new information received from the recruiting organization with existing knowledge. Findings from the review of PO fit during recruitment suggest that it is connected to recruitment outcomes (Chatman et al., 2005; Kristoff-Brown et al., 2005; Uggerslev et al., 2012). Given the importance of PO fit, I know turn to reviewing previous research examining the determinants of PO fit. A first impression of this work suggests there has not been nearly of an emphasis examining the determinants of PO fit. This makes sense given the extant literature on PO fit treats the construct as exogenous and static in nature (e.g., Cable & Edwards, 2004; Edwards & Cable, 2009). Of the research examining predictors of PO fit during recruitment, overwhelmingly, the research has focused on (a) early parts of the recruitment process (i.e., 11 application stage), and (b) organizational factors that can predict PO fit perceptions. For instance, Roberson, Collins, and Oreg (2005) examined the effects that detailed recruitment advertisements would have on PO fit. Using an experimental design, their findings suggested that individual’s who received a detailed recruitment message had higher PO fit perceptions and were subsequently more likely to apply to the organization. Saks and Ashforth (1997) also considered factors that influence job seeker’s perception of PO fit. Using a sample of new labor market entrants, they found that information provided by recruiters and university placement centers about the organization positively predicted PO fit perceptions. The existing literature on the causes of PO fit can be parsimoniously summarized as being organization-centric in that it is focused on organizational characteristics. According to Lievens and Highhouse (2003), organizational characteristics provide signals to potential candidates. These signals can be instrumental (e.g., providing job and organizational information) or symbolic (i.e., general impressions) in nature, which in turn directly influence candidates’ level of organizational attraction (Lievens & Highhouse, 2003). The findings reviewed suggest these signals do indeed influence individual’s PO fit perception and generate initial attraction to the organization. Moving forward, because recruitment is a multiphase process, job seeker’s receive additional opportunities to interact with the recruiting organization that serves as source for information to update PO fit. This dynamic perspective and factors contributing to the evolution of PO fit is currently omitted from the recruitment literature. The few studies examining changes in PO fit have largely utilized cognitively oriented theoretical frameworks to understanding how job candidates integrate new recruitment information with previous content. For instance, Swider and colleagues (2015) used differentiation-consolidation theory to argue that candidates consider and consolidate all relevant 12 incoming information, which in turn helps them to inform their perceptions of PO fit and update their preferences among the different organizations they are pursuing. However, as argued in this study, another potential route to shaping PO fit perceptions is a candidates’ affective experiences during recruitment. Existing fit theories (e.g., Yu, 2009) and affective science literature (e.g., Clore, Schwarz, & Conway, 1994; Forgas, 2001) point to affective experiences as an important antecedent leading to the formation and updating of attitudes and beliefs. Applied to the recruitment setting, because candidates rarely possess complete information about the recruiting organization, their affective experiences originating from recruiting events may also provide information about to what extent they fit with the recruiting organization. Thus, candidates’ affective experiences take on significance in that they provide additional information that may feed into PO fit perceptions. Review of Core Affect Dimensional approach. According to Barsade and Gibson (2007; p. 38) affect is an “umbrella term encompassing a broad range of feelings that individuals experience, including states, such as moods and discrete emotions, and traits, such as trait positive and negative affectivity.” More specifically, core affect can be best summarized using a two dimensional structure: valence (the degree of pleasantness) and activation (the degree of arousal) (Barrett & Russell, 1999). Crossing these two categories yields four emotion quadrants. For instance, the emotion “anger” is considered a high-activated unpleasant emotion whereas “sadness” is considered a low-activated unpleasant emotion. Similarly, “excitement” is considered a highactivated pleasant emotion whereas “calm” is considered a low-activated pleasant emotion. An advantage to this conceptualization is, when theorizing about affect, it allows for the flexibility to forward broad differential predictions regarding how affect is related to key constructs as a 13 function of valence and activation. As can be seen, however, a disadvantage of using the dimensional approach to affect is that it does not allow for the differentiation between emotions that occupy the same quadrant. For example, although anger and fear share the same valence and activation, they are differentiated on their appraisal dimensions of certainty and control, thereby leading to different kinds of behavior (e.g., anger increases approach behavior whereas fear leads to avoidance behavior) and decision-making (e.g., anger leads to optimistic risk assessment whereas fear leads to pessimistic risk assessment) (Lerner & Keltner, 2000, 2001). Categorical approach. The categorical approach, emphasizes the importance of examining the effects of discrete emotions (e.g., anger, disgust, guilt). In reviewing the basic emotions literature, Keltner and Lerner (2010; p. 320) concluded that individuals use eight criteria when evaluating an event: attention, certainty, controllability, pleasantness, perceived obstacle, responsibility, legitimacy, and anticipated effort. Attention is defined as the “degree to which you focus on and think about the stimulus.” The external environment is dynamic, and when situations arise that deviate from normal, expected patterns, it forces the individual to allocate attention to this novel event and determine whether there is a need to change or continue ongoing behavior. Certainty is defined as the “degree to which you are certain about what is going to happen.” In the course of goal pursuit, events are evaluated in relation to their consequences, thus certainty predictions pertaining to future affairs are influential in directing which emotion or set of emotions are going to be experienced. Controllability is defined as the “extent to which you have control over outcomes in the environment.” When an individual experiences an event, a core appraisal focuses on the extent to which the individual has a sense agency to affect subsequent outcomes; this in turn not only elicits certain emotions but also the strategies employed to deal with the event. Pleasantness is defined as “degree that the event is 14 positive or negative.” Perceived obstacle, defined as the “extent to which the pursuit of your goals is blocked.” During goal pursuit, experienced events can either aid or block/delay the current goal that is activated, which could lead to different emotions (e.g., anger or frustration in the case of goal blockage). Responsibility is defined as “extent to which other people, you, and situational factors are responsible for events. Legitimacy is defined as the “extent to which the event is fair and deserved or unfair and undeserved.” Legitimacy takes into consideration notions of justice and norm violation regarding social affairs with clear differentiation in felt emotions when individuals appraise situations as fair and deserved (e.g., guilt) or unfair and underserved (e.g., anger). The final dimension is anticipated effort, defined as the “extent to which you must expend energy to respond to the event” (Keltner & Lerner, 2010, p. 320). Anticipated effort is very much similar to arousal or activation. Some of the event features are used during primary appraisal process (attention, perceived obstacle, pleasantness) while others are used during secondary appraisal process (controllability, legitimacy, certainty, responsibility). An advantage to the categorical approach is specific emotions lead to specific consequences thus yielding greater predictive power. For example, emotions differ in their action tendencies—that is, their ability to organize internal resources to execute a particular action— thus leading to different behavior as is the case with fear and fleeing danger (Zeelenberg, Nelissen, Breugelmans, & Pieters, 2008). However, focusing on discrete emotions and their consequences presents some challenges. For instance, given the myriad emotions individuals experience, emotion scholars have only studied a small set of emotions (i.e., basic emotions such fear, anger, joy), thus leading researchers to clearly differentiate only some emotions as a function of their underlying appraisals (Keltner & Lerner, 2010). 15 When considering the two approaches presented above, both have their strengths and weaknesses. Within the recruitment context, candidates are exposed to numerous events that have the potential to elicit core affect—that is, experiencing pleasure and displeasure to varying degrees. At other times, through attributional and appraisal processes, the same affective reactions may manifest in their discrete form, as is the case when a candidate feels frustrated because the recruiting organization is slow with their communication processes. This dual perspective to emotions (Russell, 2003) is useful in theorizing about the affective events because candidates may not be able to engage in specific interpretation and categorization of all signals to arrive at specific emotion states. One reason for this could be that not all information is available to them—that is to say, recruitment signals may at times be imperfect on many dimensions. Another reason is, because individuals are cognitive misers, they may satisfice and rely on their core affective state to guide their inferences and subsequent decisions. As such, for the purpose of this study, I focus my discussion of core affect at the dimension level (i.e., valence and activation). The Role of Core Affect in Predicting PO Fit The importance of affective experiences in providing us with information to inform our judgments is foundational to many emotion theories (see Keltner & Lerner, 2010 for a recent review). The affect infusion model forwarded by Forgas (1995) states that core affect influences judgment through two information-processing systems: heuristic and substantive. Under heuristic processing, individuals exert minimal cognitive effort, employ cognitive shortcuts (i.e., heuristics), and process information automatically resulting in affective experiences directly having a main effect on judgment. Under substantive processing, however, individuals engage in controlled, systematic processing, and integrate new information with existing knowledge 16 structure. Under this processing strategy, affect has influence on judgment indirectly through activating emotion-congruent memories that in turn reshape existing beliefs and knowledge about the focal object (e.g., PO fit). According to Forgas (2000), these two informationprocessing systems have each been linked with separate affective infused mechanisms, namely the affect-as-information mechanism with heuristic processing and affective-priming with substantive processing. Affect-as-information. According to the affect-as-information mechanism (Schwarz & Clore, 1983, 1988, 2003), individuals form a judgment about a focal object by asking themselves, “How do I feel about it?” Pleasant affect is expected to lead to positive evaluations whereas unpleasant affect is expected to lead to negative evaluations. The idea that affective experiences are used to inform judgments has garnered much empirical support (see Schwarz, 1990; 2012; Schwarz & Clore, 2003 for a review). Recently, Yu (2009; p.1213) described the affective consequences for person-environment (PE) fit and noted, “the mood-as-information hypothesis suggests that judgments by individuals of their P-E fit status are based on the informative qualities of how they feel about their current work situation (i.e., experienced workbased affect).” Therefore, experiencing positive or negative affect originating from the recruitment context may lead candidates to appraise their PO fit in ways that are consistent with their affective states. Thus, our affective experiences are useful in that they simplify the judgment task at hand by decreasing the need to engage in effortful thinking (Schwarz, 2001). This complements the more effortful information processing that is already taking place as candidates receive valuable information about the organization that in turn shapes PO fit. Affective-priming. According to the affective-priming mechanism, when individuals experience core affect, these emotional experiences spread activation throughout a memory 17 network of affect-related content (Bower, 1981). Consequently, when people are asked to make a judgment about an object, the affective-priming mechanism suggests that memories that are associatively linked with the current affective state will be activated. As Forgas and Vargas (2000) point to, “the affect-priming principle suggests that affect can indirectly inform social judgments through facilitating access to related cognitive categories” (p. 353). To illustrate the concept, Forgas and Vargas go on to say that, “just as the activation of a construct like “bread” facilitates the activation of concepts like “jam,” “butter,” “crust,” and so on, the activation of mood states may facilitate the activation of other mood-linked memory structures” (p. 353). For example, Wright and Bower (1992) found participants’ subjective probabilities of positive or negative events occurring in the future (i.e., forecasting the future) were contingent on whether they were in a sad versus a happy mood. Specifically, people who were in a happy mood increased their subjective probability estimates about future events such as world peace and finding a cure for cancer, while lowering their estimates for future disasters such as a meltdown at a nearby power plant. In contrast, subjects in a sad condition had the reverse assessment when considering future events. These findings are consistent with the affective-priming mechanism in that different affective states make it easier to access certain facts that are consistent with the affective state under consideration (e.g., positive emotions activate thoughts and facts that are more optimistic in nature versus negative emotions) (Bower & Forgas, 2000). Applied to the recruitment context, the affective-priming mechanism suggests that affective experiences shape the type of information that becomes active and available in memory, consequently influencing candidates’ PO fit judgment. The affective-priming mechanism further suggests that these affective experiences—depending on their level of significance—also get stored in memory resulting in the expansion of memories associated with the judgment under consideration. For 18 example, when evaluating PO fit, a candidate who experienced unpleasant affect (e.g., anger, upset, nervous) may recall more negative information about the company, resulting in negative thoughts about PO fit. Thus, the affect-priming mechanism interacts more with the information the candidate already has about the company such that certain information receives more attention as a function of the underlying affective state (Forgas, 1995). As recognized by Simon (1967), emotions play a coordinating function in that they allow for the communication between the conscious and unconscious system. Specifically, emotions serve as a signal as to whether things are going well or not (Simon, 1967). When individuals experience pleasant affect, the situation is appraised as benign compared to the experience of negative affect, which suggest that something is wrong and needs further attention (Forgas, 2000, 2001; Simon, 1967). For example, a candidate feeling excited versus upset as a function of interacting with a recruiter will appraise that experience differently resulting in divergent reactions which may have downstream consequences for PO fit perception. As previously discussed, core affect can be coherently placed along a two-dimensional space that categorizes affect in terms of whether they are experienced as pleasant versus unpleasant, and active versus inactive (Russell, 2003). Further, these two dimensions convey information that is particularly useful for candidates. The pleasantness dimension conveys the positivity or negativity of an event, whereas activation conveys information regarding importance, with high arousal affective states (e.g., elation, anger) deemed more important than calm affective states (e.g., relief, bored) (Clore, & Storbeck, 2006; Frijda, Ortony, Sonnemons & Clore, 1992; Simon, 1967). When considering deactivated affective states (e.g., calm, satisfied, contented, sad), it is suspected that this would result in the transference of those affective states directly to perceptions of PO fit, as predicted by affect-as-information. For example, a job 19 candidate feeling contentment during the recruitment experience may conclude by saying statements such as “I feel good about this company, therefore I must fit” As such, it is predicted that candidates who experiences deactivated core affect with respect to the recruiting organization (e.g., gloomy, discouraged, calm) may think of their fit perceptions in ways that are consistent with their affect (i.e., the mood-congruency effect). Similar to the effects for deactivated core affect, it is suspect that activated core affect will also have a direct effect on PO fit perceptions. However, because of the additional information value associated with the energizing aspect of arousal, it is suspected that it will activate other concepts that are important to the candidate. First, it is expected that events that elicit high arousing affective states should capture the individual’s attention resulting in the use of substantive processing. For instance, when a candidate experiences a recruitment event that elicits unpleasant affect, this experience is remembered more because it is construed as more threatening to well-being (Ito, Larsen, Smith, & Cacioppo, 1998). Take for example this quote from a candidate in Rynes and colleagues’ (1991) study: “The guy at the interview made a joke about how nice my nails were and how they were going to ruin them there due to all the tough work (female engineering undergraduate)” (p. 500). Initially, it is suspected that such an interpersonal event would result in the experience of activated negative affect. The candidate may perceive that the event violates interpersonal justice norms (respect, courtesy) and the entity involved (interviewer) was to blame, resulting in negative emotions (e.g., anger, irritation). The negative affective states may prompt the candidate to think more about the event and what it may signal about future workplace relationships. The affective priming mechanism suggests that under such state of activated unpleasant affect, the 20 candidate would negatively evaluate such future workplace relationships. Based on the preceding theoretical perspectives, I forward the following hypotheses. H1: At the within-person level, activated (H1a) and deactivated (H1b) positive affect will be positively related to PO fit. H2: At the within-person level, activated (H2a) and deactivated (H2b) negative affect will be negatively related to PO fit. Recruitment Events as Signals for Needs Fulfillment According to Morgeson, Mitchell, and Liu (2015) events are defined as “external, bounded in time and space, and involve the intersection of different entities” (p. 520). This definition is useful because it explicitly focuses on occurrences that are external to the entity (e.g., an individual) and highlights the importance of interaction between entities, such as those between the organization and candidate. Applied to the recruitment context, recruitment events are those that are initiated by the organization or the individual and have the potential to influence the candidates’ decisions throughout the recruitment stages (applying, maintaining, job choice). Examples of recruitment events that are within the organizations’ control include recruitment fairs and site visits, whereas recruitment events that are initiated by the candidates include communicating with a personal source such as a family member or a friend that has direct relationship with the focal organization (Slaughter et al., 2014). Throughout the recruitment process, candidates are exposed to numerous events that may elicit a reaction. During the job application phase, these events include interactions with organizational agents (e.g., recruiter), recruitment activities (e.g., website recruitment), and communicating with personal sources (e.g., friends and families who are connected with the organization); during the maintaining or interaction phase, these events include additional interactions with organizational agents (e.g., interviewer), recruitment process characteristics 21 (e.g., timeliness), and selection processes; during the job acceptance phase, these events include a site visit and interacting with additional organizational members such as future coworkers (Table 1 provides a list of typical recruitment events throughout the recruitment stages). In all of these events, signaling theory suggests that candidates will use the incoming information to update their perceived understanding of what it would be like to work for the recruiting organization (Rynes, 1991; Turban, Forret, & Hendrickson, 1998). For example, Turban et al. (1998) found that recruiter behaviors (e.g., friendliness) indirectly influenced organizational attraction via effects on perceptions of job and organizational attributes. Signaling theory provides a general perspective to understanding how applicants make sense of their experiences during the different recruitment events. Because applicants are continuously thinking about what life will be like working for the recruiting organization, the various recruitment events experienced may signal to the candidate something about the extent to which the company may be able to provide an environment that satisfies their basic psychological needs for autonomy, relatedness, and competence. According to self-determination theory (SDT; Deci & Ryan, 2000), the fulfillment of these three psychological needs are important because they lead to growth and healthy functioning of all individuals. Autonomy refers to the desire for independence and to experience volition in one’s behavior. Relatedness refers to “developing secure and satisfying connections with others in one’s social milieu” (Deci, Vallerand, Pelletier, & Ryan, 1991, p. 327). Competence refers to the need to feel effective at interacting with the social environment and achieving valued outcomes (Deci & Ryan, 1991, 2002). Existing literature on psychological needs satisfaction finds that fulfillment of these needs are related to enhanced well-being (Sheldon, Ryan, & Reis, 1996), and within organizational settings, has been shown to relate to organizational commitment and job performance (Greguras 22 & Diefendorff, 2009). Thus, it is suspected that recruitment events where the candidate experiences needs fulfillment should be associated with increases in positive affect and decreases in negative affect. Job candidates may be more attentive to recruitment events where they experience needs fulfillment because this has direct consequences for personal well-being (Deci & Ryan, 2002). For example, a job candidate may interact with a personable recruiter or organizational members which makes the candidate feels connected to others in the organization thereby satisfying the individuals’ relatedness needs. According to SDT, when needs are satisfied individuals experience more positive and less negative affect (Sheldon, Elliot, Kim, & Kasser, 2001). Conversely, job candidates may experience recruitment events that thwart their basic psychological needs. Take for example this quote from a job seeker in Ali, Ryan, Lyons, Ehrhart, and Wessel’s (2016) study: “In my job search and in the interview process I have experienced rude treatment from the interviewer. I was talked down to in a demeaning tone which didn’t make me feel very confident in the interview. I also felt that they had their nose up to me and made me feel very underneath them” (p. 339). In this example, the job candidate may feel that his/her feelings of belonging, as well as worthiness (i.e., competence) are undermined, which may result in the experience of both activated (e.g., anger) and deactivated negative affect (e.g., dejection). Based on SDT, I thus suspect that job candidates, who experience increased needs fulfillment (i.e., autonomy, competence, and relatedness), will experience increases in their activated and deactivated positive affect and decreases in their activated and deactivated negative affect. H3: At the within-person level, needs fulfillment will be positively related to activated (H3a) and deactivated (H3b) positive affect. H4: At the within-person level, needs fulfillment will be negatively related to activated (H4a) and deactivated (H4b) negative affect. 23 Table 1: A List of Recruitment Events as a Function of Recruitment Stage. • • • • • • • • • • • • • • • Application Stage Word-of-mouth Employee referrals Referrals by friends and relatives Organization advertisement o In print or online Organization webpage Recruiters Placement agencies and search firms Professional conferences o Conference placement center Campus recruiting College placement offices Online recruiting o Social media sites owned by the organization o Websites not directly affiliated with the organization (e.g., Glassdoor, online forums Job fairs Virtual job tryout Job boards Interacting/Maintenance Stage • Online assessments • o Situational judgment tests o Biographical data • Realistic job preview • Email communication • Virtual interview • • Phone screening • Assessment center • In-person interview • • • • Secondary interview Interaction with organizational representatives Site visit 24 • Job Offer Stage Additional interaction with organizational representatives Site visit Verbal or written offer Job offer negotiation Consequences of PO Fit Though the focus up to this point has been on determinants of PO fit, I expect that changes in PO fit should also influence recruitment outcomes including acceptance intentions and withdrawal cognitions. Acceptance intentions captures applicant cognition about taking a job offer if extended by the recruiting organization, whereas withdrawal cognition captures the extent to which the applicants wants to disengage from the hiring process (Highhouse et al., 2003). These outcomes are expected to be proximal determinants of job choice decisions (Chapman et al., 2005). For example, Chapman and colleagues (2005) meta-analysis found that acceptance intentions was positively related to job choice (ρ = .33). The theoretical linkage between PO fit and recruitment outcomes is derived from social identity theory (Tajfel & Turner, 1985) and the similarity-attraction paradigm. According to social identity theory, individuals will seek to join organizations where they see themselves as compatible (e.g., similarity in values) because this reinforces their self-concept. As such, when job candidates experience increases in PO fit, this enhances attraction because these experiences affirm one’s own beliefs. Such a situation represents an ideal for the candidate and as such it is suspected when job candidates experience an enhanced PO fit, they are more likely to report higher job acceptance intentions and less likely to withdrawal from the hiring process. H5: At the within-person level, PO fit will be positively related to job acceptance intentions (H5a), and negatively related to withdrawal cognitions (H5b). Finally, it is predicted that job candidate’s experiences of needs fulfillment as a function of the recruitment events is indirectly related to acceptance intentions and withdrawal behavior through its influence on core affect and PO fit. H6: At the within-person level, core affect (positive and negative) and PO fit will serially mediate the association between need fulfillment and job acceptance intentions (H6a) and withdrawal cognitions (H6b). 25 Moderating Role of Regulatory Focus Although the affect-as-information and affective-priming perspective provide a useful framework to understanding how individuals use their feeling as a source of information when thinking about their PO fit perception, it largely fails to distinguish how specific affective experiences with the same valence (i.e., activated versus deactivated positive affect) are more or less predictive of PO fit. In this study, I utilize regulatory focus theory (Higgins, 1997) to contribute to our understanding of how job candidates make sense of their affective experiences in relation to their PO fit perceptions. In the following paragraphs, I review the basic elements of regulatory focus theory, and hypothesize about the cross-level relationship between regulatory focus, core affect, and PO fit. Regulatory focus theory (Higgins, 1997) extended the hedonic principle—which suggests people are motivated to approach pleasure and avoid pain—in that the theory states people differ in the way they approach pleasure and avoid pain. According to regulatory focus theory, individuals can have a promotion focus or a prevention focus. Individuals who are promotion focused are concerned with satisfying their growth and development needs, whereas individuals who are prevention focused are concerned with satisfying their security and safety needs. Those with a promotion focus are attentive to their wishes and aspiration (i.e., an ideal-self) and because of it engage in behaviors that move them closer to the desired end-states. Those with a prevention focus are attentive to their duties and responsibilities (i.e., an ought-self) and this prompts behaviors intended to avoid conditions that pulls them away from desired end-states. Finally, promotion and prevention focus also differ in the affective experiences associated with them. Individuals with a promotion focused are concerned with the presence and absence of positive outcomes. The presence of a positive outcome represents a match or congruency 26 between the current situation and the ideal self, which results in the experience of activated positive affect (e.g., happiness, satisfied). The absence of a positive outcome represents a mismatch, and as a result, individuals who are promotion focused are more likely to experience deactivated negative affect (e.g., sad, disappointed). Conversely, individuals with a prevention focus are concerned with presence and absence of negative outcomes. The absence of a negative outcome represents a match or congruency between the current situation and their ought self, which results in the experience of deactivated positive affect (e.g., calm, relaxed). The presence of a negative outcome, however, represents a mismatch, and as a result, individuals who are prevention focused are more likely to experience activated negative affect (e.g., tense, restlessness) (Brockner & Higgins, 2001; Higgins, Shah, & Friedman, 1997). Given job candidate’s affective experience during recruitment, regulatory focus theory suggest those affective experiences are informative to individuals because it signals the extent to which the current situations sustains regulatory effectiveness. Therefore, I suspect that individual differences in promotion and prevention focus will moderate the relationship between core affect and PO fit. Applicants who are higher on promotion focus are sensitive to the presence and absence of positive outcomes. As such, experiencing activated positive affect such as excitement during the recruitment process signals that there is congruency between the current situation and their ideal self, which facilitates the psychological experience of fit. Conversely, experiencing deactivated negative affect such as sadness signals that the current situation represents an absence of positive outcomes and the individual may not be able to fulfill his/her ideals, which in turn is suspected to diminish perception of PO fit. Applicants who are higher on prevention focus are sensitive to the presence and absence of negative outcomes. Therefore, experiencing deactivated positive affect such as relief signals that there is congruency between the current 27 situation and their ought self, which is expected to enhance perceived fit. Experiencing activated negative affect such as anxiety, however, signals to the individual that current situation represents the presence of negative outcomes and that the individual may not be able to fulfill his/her ought goal; experiencing such a discrepancy is expected to diminish perceptions of fit. The proposed hypotheses are displayed graphically in Figures 2-5. H7: The positive relation between activated positive affect (e.g., excitement) and PO fit is stronger when promotion focus is high versus low. H8: The negative relation between deactivated negative affect (e.g., dejected) and PO fit is stronger when promotion focus is high versus low. H9: The positive relation between deactivated positive affect (e.g., quiescence) and PO fit is stronger when prevention focus is high versus low. H10: The negative relation between activated negative affect (e.g., anxious) and PO fit is stronger when prevention focus is high versus low. 28 Figure 2: Hypothesized Cross-Level Interaction between Promotion Focus, Activated Positive Affect, and PO Fit. Figure 3: Hypothesized Cross-Level Interaction Between Promotion Focus, Deactivated Negative Affect, and PO Fit. 29 Figure 4: Hypothesized Cross-Level Interaction between Prevention Focus, Deactivated Positive Affect, and PO Fit. Figure 5: Hypothesized Cross-Level Interaction between Prevention Focus, Activated Negative Affect, and PO Fit. 30 PO Fit Predicting Core Affect and Needs Fulfillment Though the preceding discussion positioned needs fulfillment and core affect as antecedents to PO fit, previous research suggest PO fit may indeed cause core affect and needs fulfillment (Gabriel, Diefendorff, Chandler, Moran, & Greguras, 2014; Greguras & Diefendorff, 2009). When considering the relationship between PO fit and needs fulfillment, Greguras & Diefendorff (2009) hypothesized that needs fulfillment serves as a partial mediator between different types of person-environment fit (i.e., PO fit, person-group fit, demands-abilities fit) and employee outcomes (i.e., job performance and affective commitment). Using a time-lagged design, their findings were consistent with their hypotheses such that PO fit positively related to all three needs fulfillment, and indirectly related to outcomes through the needs fulfillment mediator. The authors concluded that, “PO fit likely relates to need satisfaction because when employees’ and organizations’ values are aligned, organizations are more likely to provide circumstances enabling employees to satisfy their basic psychological needs” (Greguras & Diefendorff, 2009; 473). Applied to the recruitment context, as job candidates learn more about the organizations and update their PO fit, positive changes in PO fit may signal to candidates the recruiting organization may fulfill their basic psychological needs. H11: At the within-person level, PO fit will be positively related to needs fulfillment. Gabriel and colleagues (2004) recently conducted a direct test of the relationship between PO fit and affect. Using a self-regulatory perspective (Johnson, Taing, Chang, & Kawamoto, 2013) the authors argued that when employees’ experience misfit this is similar to perceiving a discrepancy, which should result in negative affect; however, the experience of fit should result in no discrepancy resulting in the experiences of positive affect. The authors utilized an experience sampling approach where they surveyed administrative assistants for five times a day 31 for 10 consecutive working days. There results were partially supported such that PO fit was positively related to positive affect but PO fit was not significantly related to negative affect. Though the result for negative affect was not significant, the authors explained that this could be due to their study design of examining variations in affect and PO fit within a two-week period. It is suspected that under a longer time frame such as those associated with the recruitment context, candidates who experience increases in perceived PO fit are expected to show favorable affect (e.g., activated positive affect) toward the organization and less likely to experience negative affect. H12: At the within-person level, PO fit will be positively related to both activated (H12a) and deactivated (H12b) positive affect and negatively related to activated (H12c) and deactivated (H12d) negative affect. 32 CHAPTER 3: METHODS Pilot Study Job candidate’s recruitment experiences can vary drastically such that some candidates may experience several recruitment events within a short period of time whereas others may find their recruitment expands over several weeks or months. As such, the first aim of the Pilot Study was to get a reasonable estimate of the typical recruitment timeframe once a person has submitted an application. The second aim of the Pilot Study was to examine the measurement properties of the two adapted measures for needs fulfillment. Testing the measurement properties (i.e., reliability and confirmatory factor analysis) of these two measures provides an empirical basis for the measure used in the main study. Sample and Procedure The sample consisted of Amazon’s Mechanical Turk (MTurk) workers. A request was posted that asked workers to share their recent job search experiences in a short (less than one minute) survey. Worker requirement criteria was set for the survey such that only workers who lived in the United States and have an approval rating greater than 95 percent were eligible. In addition to the screening criteria from MTurk, the survey also included several other questions to ensure quality data. Specifically, to minimize the magnitude of retrospective bias and to select participants with recent recruitment experiences, one question in the survey asked whether participants engaged in job search within the last three months where they were looking for fulltime employment. Additionally, the survey contained two attention check items that asked participants to answer, “neither agree nor disagree” to the question. Based on the MTurk criteria, 133 participants were recruited as the initial sample. Though it is quite possible that multiple MTurk workers could share the same computer, 12 responses that were from same IP address 33 were deleted as to ensure there were no duplicates. An additional 13 responses were deleted because they failed either one or both attention check items. Finally, an additional 14 responses were deleted because those participants reported they were not actively searching for a full-time job within the past three months. The final sample (n = 93) was majority White (67.7%), female (54.8%), college educated (46.2% with bachelor’s degree), and with an average age of 33.68 (SD = 9.37). Participants were paid $.35 for taking the survey. Summary of Findings Recruitment time course. To capture the typical recruitment process for a given organization, participants were asked to report how long (in weeks) the recruitment process took from the time of submitting their application to when they either accepted an offer or received a rejection letter. Each participant was given the opportunity to provide the recruitment time course associated with four organizations. The averages for the four organizations across all the participants are as follows: organization one (M = 2.60, SD = 2.89); organization two (M = 3.18, SD = 3.06); organization three (M = 4.24, SD = 4.44); organization four (M = 5.31, SD = 5.87). These findings suggests, on average, the recruitment experiences for job candidates can take anywhere between two to six weeks. As such, the eight weeks time period selected for the main study is appropriate in length to capture the majority of recruitment experiences. Needs fulfillment. The first measure of needs fulfillment I adapted for the recruitment context was developed by La Guardia, Ryan, Couchman, and Deci (2000). A total of six items were used to assess the three dimensions of needs fulfillment. Sample items include: “The recruiting organization makes me feel free to be who I am” (autonomy), and “The recruiting organization makes me feel loved and cared about” (relatedness). The complete list of items can be found in Appendix C. The second measure of needs fulfillment I adapted was developed by 34 Van den Broeck, Vansteenkiste, De Witte, Soenens, and Lens (2010). For this measure, a total of nine items were used to assess the three dimensions (see Appendix C for complete list). In the Pilot Study, participants were instructed to respond to the questions in regards to only one organization they recently had recruitment experiences with. In addition, participants were told to answer the questions from the perspective of when they were going through the hiring process. Both measures were on 5-point Likert-scale ranging from 1 = strongly disagree to 5 = strongly agree. To examine the measurement properties of the two measures of needs fulfillment, as a first step, I ran internal reliability (i.e., Cronbach's alpha) analyses. La Guardia et al’s (2000) adapted measure yielded acceptable internal consistency across the three dimensions (autonomy = .81; competence = .88; relatedness = .83), and when considering the items as a single measure of needs fulfillment (α = .87). For Van den Broeck et al’s (2010) adapted measure, at the dimension level, only competence displayed adequate internal consistency (autonomy = .47; competence = .80; relatedness = .49); in addition, the overall measure approached an acceptable internal reliability cutoff (α = .67). As a next step, I analyzed the data using confirmatory factor analysis (CFA) to establish the construct validity of the two measures. Similar to the reliability analysis, for each measure, I ran two separate models—a single factor model and a multiple factors model capturing the needs fulfillment dimensions. For the La Guardia et al’s (2000) measure, the three-factor model separating the dimensions (χ2 = 21.35, df = 6; RMSEA = .16; CFI = .95; SRMR= .04) significantly fit the data better (Δχ2 = 72.08, Δdf = 3, p < .01) than a single-factor model (χ2 = 93.43, df = 9; RMSEA = .32; CFI = .74; SRMR = .10). The findings from the three-factor model also suggested the dimensions are strongly and significantly correlated (see Table 2). Similarly, for Van den Broeck et al’s (2010) measure, a three-factor 35 model separating the dimensions (χ2 = 48.17, df = 24; RMSEA = .10; CFI = .87; SRMR= .09) significantly fit the data better (Δχ2 = 13.82, Δdf = 3, p < .01) than a single-factor model (χ2 = 61.99, df = 27; RMSEA = .12; CFI = .82; SRMR = .09). Further, the standardized correlations between the dimensions were similar to the findings from La Guardia et al’s (2000) measure with the exception of the non-significant relationship between autonomy and competence. As a final step, I ran a model where both measures were included so that I can examine the factor correlations between the same dimensions of each measure. This model fit the data reasonably well: (χ2 = 114.21, df = 75; RMSEA = .08; CFI = .94; SRMR= .07). The factor correlations indicated that the two measures are highly correlated: autonomy (r = .63, p < .01), competence (r = .75, p < .01), and relatedness (r = .75, p < .01). In sum, the findings from the reliability and CFA analyses suggests that the adapted measure from La Guardia et al. (2000) seems to slightly work better than the measure from Van den Broeck et al (2010). Further, the high correlations between the two measures provide evidence of their similarity and redundancy. As such, given that La Guardia et al’s (2000) measure also has fewer items—which is more practically ideal in intensive longitudinal designs—I use this as my measure of needs fulfillment in the main study. 36 Table 2: Standardized Latent Correlations Between Needs Fulfillment Dimensions. Variable 1. Autonomy 2. Competence 3. Relatedness 1 1 .66* .86* 2 .57* 1 .50 3 .52 .63* 1 Note. Correlations below the diagonal represent La Guardia et al’s (2000) measure, whereas correlations above the diagonal represent Van den Broeck et al’s (2010) measure. 37 Main Study Sample and Procedure The sample for this study consisted of graduating students currently on the job market as well as adults that were recruited through various methods including social media (Facebook, LinkedIn) and job search listservs. In each of these recruitment methods, individuals responded to a recruitment ad asking them to participate in a study about their job searching and recruitment experiences. The recruitment ad covered information about the study such as the purpose of the study and compensation for participating (see Appendix A). Interested participants were instructed to complete a short survey (see Appendix B) focused on questions regarding their current job search status. Participants who met the main qualifications of the study (i.e., they are early in their recruitment experiences and are actively pursing career relevant jobs) were sent a follow-up email notifying them that they were selected to participate in the study. All data collection took place online using the Qualtrics platform. Eligible participants were emailed the survey links they needed to complete along with information about the purpose of the study and expectations surrounding the participation process. In the baseline survey (see Appendix C), participants were instructed to provide additional information about the organizations they provided in the initial screening survey. For example, participants were instructed to provide their overall attraction levels to each of the three organizations they were considering for employment. They were further instructed that in the weekly survey, they would be asked to provide recruitment information for the same three specific organizations. One week after the completion of the initial survey, participants were sent the weekly surveys (see Appendix C) that captured participant’s recruitment experiences with each of the three organizations. Participants received eight weekly surveys over a period of two months. 38 Sample Characteristics A total of 397 participants completed the initial short survey. Of these potential participants, only 194 participants met the study inclusion criteria. The baseline survey was sent to 194 participants, of which 102 participants provided complete data. Of the 102 participants who completed the initial baseline survey, 32 participants were removed from the study because they failed to complete at least one weekly survey. As such the final sample consisted of 70 participants (36% of those initially contacted). The response rates for the weekly survey were good, once two valid non-response patterns were taken into consideration. The first was if participants accepted an offer from an organization and terminated their job search. In the sample, there were nine participants who were in this category. In each of the weekly surveys, all participants were asked whether they were still considering the focal organization for employment. Participants were no longer surveyed if they reported that they no longer are pursuing employment with the three organizations they listed. Further, if they also received a rejection notification from all three organizations, they were no longer surveyed. A total of 35 participants were in this category. I explored whether these 35 participants differed in their weekly responses to the focal variables compared to participants who remained in the sample, and I found that there were no statistically significant differences. The response rates for each week across the eight weeks once the two non-response patterns are taken into consideration are as follows: week 1 (64%); week 2 (97%); week 3 (52%); week 4 (86%); week 5 (76%); week 6 (76%); week 7 (76%); week 8 (60%). The final sample of 70 participants were majority female (67%), White (55.7%), adult job seekers (80% vs. 20% new labor market entrants), with an average age of 39.47 years (SD = 13.70). In addition, the majority of participants (77%) had a bachelor or a master’s degree, and 39 were mostly applying to organizations in the private, for-profit sector (69%). Finally, this sample (n=70) was compared to those who only completed the baseline survey (n= 32) on the variables measured in the baseline survey. Findings from a one-way ANOVA suggested there were no significant differences in age, gender, work experience, regulatory focus, PO fit, perceived job alternatives, and job seeker type (i.e., adult vs. new labor market entrant). The only significant difference found was for race/ethnicity such that those who only completed the baseline survey were more likely to be White (78%) compared to the final sample (56%). Finally, in the weekly survey, I examined whether applicants had enough opportunities to interact with the organization. The average number of contact episodes per job seeker (across the threeorganizations) during the 8-week period was 5.41 (SD = 4.25). Measures Initial Survey Perceived alternatives. Perceived alternatives was measured using an adapted single item from Griffeth and Hom (1988). Specifically, the item asked, “What is the probability that you can find acceptable job alternatives to the organizations you are applying to?” Scale for this item ranged from (1=no chance to 5=100 percent chance). Trait affect. Trait affect was measured using 20 items from the Positivity and Negativity Affect Scale (PANAS; Watson, Clark, & Tellegen, 1988). Participants were asked, to what extent they generally feel, for example, “excited,” “content,” “active,” “proud,” and “happy” (positive affectivity), as well as “jittery,” “nervous,” “dejected,” “ashamed,” and “angry” (negative affectivity). Scale ranged from 1 (very slightly or not at all) to 5 (extremely). The coefficient alpha was .85 for positive affect and .79 for negative affect. 40 Organizational attraction. Organizational attraction was measured with 4 items from Highhouse et al. (2003). Participants were asked to report their organizational attraction to the three organizations they listed in the initial screening survey. A sample item is, “This organization is attractive to me as a place for employment.” The scale for organizational attraction ranges from 1=strongly disagree to 5=strongly agree. The coefficient alpha was .79 for organization one, .74 for organization two, and .73 for organization three. Organizational prestige. This measure was assessed using 5 items from Highhouse et al. (2003). Participants reported on their organizational prestige perceptions for each of the three organizations, using a response scale ranging from 1=strongly disagree to 5=strongly agree. A sample item is, “This is a reputable company to work for.” The coefficient alpha was .92 for organization one, .90 for organization two, and .93 for organization three. Organizational reputation. This measure was assessed using 3 items from Highhouse, Brooks, and Gregarus, (2009). Participants reported on their organizational reputation perceptions for each of the three organizations, using a response scale ranging from 1=strongly disagree to 5=strongly agree. A sample item is, “This company is widely admired and respected.” The coefficient alpha was .92 for organization one, .93 for organization two, and .95 for organization three. PO fit. PO fit was measured using 3-items from Cable and DeRue (2002). Participants were asked to respond on scales ranging from 1 = strongly disagree 5 = strongly agree, for each of the three organizations. A sample item is “The things that I value in life are very similar to the things that the organization values?” The coefficient alpha was .96 for organization one, .97 for organization two, and .96 for organization three. 41 Regulatory focus. Participants’ regulatory focus was measured using 16 items from Lockwood, Jordan, and Kunda, (2002). Participants were instructed to “indicate their agreement with the following statements” using a 5-point scale ranging from 1 = strongly disagree 5 = strongly agree. Sample item include: “I frequently imagine how I will achieve my hopes and aspirations through my job search” for promotion focus and “In general, I am focused on preventing negative events in my job search” for prevention focus. The coefficient alpha was .85 for promotion focus and .72 for prevention focus. Weekly Survey Affect. The affect items are from Russell (2003) to capture both activated and deactivated dimensions of positive and negative affect. Participants were asked how intensely they feel the following adjectives described their recruitment experiences this past week with organization X. Participants rated the adjectives on 1-5 Likert-type scale (very slightly to extremely). For activated positive affect the four adjectives were: enthusiastic, excited, inspired, and cheerful. For deactivated positive affect the four adjectives were: relaxed, contented, relieved, and calm. For activated negative affect the four adjectives were: anxious, angry, frustrated, and distressed. For deactivated negative affect the four adjectives were: sad, gloomy, depressed, and miserable. The mean coefficient alpha (across weeks and organizations) was .95 for activated positive affect, .89 for deactivated positive affect, .80 for activated negative affect, and .91 for deactivated negative affect. Psychological needs fulfillment. Need fulfillment was adapted using 6-items from La Guardia et al. (2000). Participants indicated the extent to which each statement characterized their experiences with the recruiting organization, using a 5-point scale ranging from 1 = very slightly or not at all to 5 = very frequently. Sample items include: “The recruiting organization 42 makes me feel free to be who I am” (autonomy), and “The recruiting organization makes me feel loved and cared about” (relatedness). The mean coefficient alpha (across weeks and organizations) was .92 for autonomy, .95 for competence, and .92 for relatedness. PO fit. PO fit was assessed with an identical measure from the baseline survey. The mean coefficient alpha (across weeks and organizations) was .97. Acceptance intentions. This measure was assessed using 2 items from Harris and Fink (1987). Participants were instructed to “indicate their agreement with the following statements” using a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). A sample item is, “If I was offered a job at this organization, I would accept it immediately. The mean coefficient alpha (across weeks and organizations) was .86. Withdrawal cognition. Because previous measures capturing withdrawal from the recruitment process do not exist, commitment to the recruiting organization will be used as a proxy. As such, 3 items were adapted from the commitment scale from Hollenbeck, Klein, O’Leary, and Wright (1989). Participants were instructed to “indicate their agreement with the following statements” using a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). A sample item is, “It wouldn’t take much to make me abandon my employment pursuit with this organization.” The mean coefficient alpha (across weeks and organizations) was .89. Analytical Strategy and Data Analysis Confirmatory Factor Analysis Before formal hypotheses testing, I conducted CFAs to examine whether several of the constructs in the baseline survey were distinct from each other. Specifically, I assessed the distinctiveness between organizational attraction, organizational reputation, organizational prestige, and PO fit. For the weekly survey, I examined whether needs fulfillment, core affect, 43 PO fit, acceptance intentions, and withdrawal cognitions were separate distinct constructs. All analyses were done using the Mplus 7 statistical software (Muthén & Muthén, 2012) using full information maximum likelihood (FIML) as the estimation method. CFA for the baseline survey. For the measures in the baseline survey, I specifically tested and compared four different models: (1) a single-factor model with all items loading onto one factor; (2) a two-factor model with organizational attraction, organizational reputation, and organizational prestige loading onto one factor and PO fit loading onto a separate factor; (3) a three-factor model with organizational attraction and PO fit loading onto separate factors and organizational reputation, and organizational prestige loading onto one factor; and (4) a fourfactor model including organizational attraction, organizational reputation, organizational prestige, and PO fit as separate constructs. Because each participant provided information from three organizations, there was nesting in the data structure such that organizational ratings were nested within applicants. Because of the multilevel nature of the data, I ran multilevel CFA to examine the factor structure of the proposed constructs once the dependency in the data was taken into account. As can be seen from Table 3, the four-factor model fit the data significantly better than the alternative factor structures. 44 Table 3: Comparison of Alternative Factor Structures for Baseline Measures. Model 1 One factor: organizational attraction, organizational prestige, organizational reputation, and PO fit. 2 Two factors: Factor 1(organizational attraction, organizational prestige, organizational reputation), Factor 2 (PO fit). 3 Three factors: Factor 1(organizational attraction), Factor 2 (PO fit), Factor 3 (organizational prestige, organizational reputation) 4 Four factors: All constructs as theorized χ2/df Δχ2/Δdf (Model comparison) RMSEA CFI SRMR 511.84/90 -- .12 .74 .09 344.27/89 167.57/1* (Model 1) .10 .84 .07 222.75/87 121.52/2* (Model 2) .07 .92 .07 192.59/84 30.16/3* (Model 3) .07 .93 .06 Note. n = 70. RMSEA = root-mean-square error of approximation; CFI = comparative fit index; SRMR = standardized root-mean-square residual; PO = person-organization. * p < .01. 45 CFA for the weekly survey. In the weekly measures, I examined four different CFA models to ascertain the distinctiveness of the constructs. Specifically, the four models I considered were: (1) a single-factor consisting of all the measures; (2) a five-factor model that combines the core affect dimensions, and the needs fulfillment dimensions, PO fit, acceptance intentions, and withdrawal cognitions; (3) a six-factor model that breaks down core affect into positive affect and negative affect, along with the other factors from the previous model (Model 2); and (4) a final model that separates all the measures into their respective constructs. As can be seen from Table 4, overall, the ten-factor model fit the data significantly better than alternative factor structures. Observing the within-person correlations, however, the dimensions of needs fulfillment displayed strong correlations amongst each other in that they all exceeded .60. The strong relationships between the dimensions are not surprising because, in naturalistic settings, the three dimensions overlap considerably (Baard, Deci, & Ryan, 2004; Sheldon & Niemiec, 2006). For example, in Baard et al’s study, they found intercorrelations between the three dimensions ranging from .54-.57; across four studies, Sheldon and Niemiec also found similar results where intercorrelations ranged between .29-.82. Because of the high correlations between the dimensions, in testing of the hypotheses, I will use the overall measure of needs fulfillment. 46 Table 4: Comparison of Alternative Factor Structures for Weekly Measures. χ2/df Models Model 1 Model 2 Model 3 Model 4 One factor: HAPA, LAPA, HANA, LANA, autonomy, competence, relatedness, PO fit, acceptance intentions, withdrawal cognitions Five factors: Factor 1(HAPA, LAPA, HANA, LANA), Factor 2 (autonomy, competence, relatedness), Factor 3 (PO fit), Factor 4 (acceptance intentions), Factor 5 (withdrawal cognition) Six factors: Factor 1(HAPA, LAPA), Factor 2 (HANA, LANA), Factor 3 (autonomy, competence, relatedness), Factor 4 (PO fit), Factor 5 (acceptance intentions), Factor 6 (withdrawal cognition) Ten factors: All constructs as theorized Δχ2/Δdf (Model comparison) 7878.79/405 ---- RMSEA CFI SRMR .19 .08 .21 3216.78/395 4662.01/10* (Model 1) .12 .65 .18 1119.65/390 2097.13/5* (Model 2) .06 .91 .07 715.46/360 404.19/30* (Model 3) .05 .96 .07 Note. n = 489 (level-1: within-organizations over time). RMSEA = root-mean-square error of approximation; CFI = comparative fit index; SRMR = standardized root-mean-square residual; HAPA = high-activated positive affect; LAPA = low-activated positive affect; HANA = highactivated negative affect; LANA = low activated negative affect; PO = person-organization. * p < .01. 47 Random Coefficients Modeling All hypotheses were tested using random coefficients modeling (RCM) using the statistical software Mplus 7 (Muthén & Muthén, 2012). RCM allows for the examination of variance across multiple levels and is able to effectively account for the dependencies within the data. Because some of the hypotheses for this study are at level-1 (i.e., within-organizations over time), RCM is an appropriate statistical model. Before testing the study hypotheses, I initially ran an intercepts only (null) model to partition the variance into its respective levels: level-1 (withinorganizations over time), level-2 (between-organizations within-person), and level-3 (betweenperson). Support for utilizing a multilevel approach and testing of the hypotheses is determined if significant portion of the variance—as indicated by the intraclass correlation coefficient—is at level-1. Once that is established, I group-mean centered all level-1 predictor variables (Hofmann & Gavin, 1998). Group-mean centering essentially removes any level-2 (between-organizations within-person) and level-3 (between-person) variance from the repeated measures thereby allowing for better interpretation of level-1 relationships. For hypotheses concerning cross-level moderators, all level-3 (between-person) variables were grand-mean centered; doing so allows for better interpretation of the intercepts and reduces any multicollinearity from the predictor variables (Raudenbush & Bryk, 2002). Hypotheses concerning mediation were tested by calculating the indirect effect (e.g., product of paths a1 and a2 and b coefficients) and examining whether the 95% bootstrapped confidence interval (CI) for the indirect effect contains zero. CIs that do not contain zero suggest the indirect effect is significant. Table 5 provides the proportion of level-1 variance for each variable. The findings suggest there is considerable level-1 variance, with values ranging from 26 to 81 percent. As 48 such, these results demonstrate that there is sufficient level-1 variance and the use of RCM is justified in testing the study hypotheses. 49 Table 5: Random Coefficients Modeling Estimates of Null Models. Note. Level-1 = within-organization over time; Level-2 = between-organization, withinapplicant; Level-3 = between-applicant. PO = person-organization. 50 CHAPTER 4: RESULTS Tables 6 presents the means, standard deviations, and correlations among variables. The demographic variables (gender, race/ethnicity, employment status, work experience) were minimally related to the focal variables. Of the significant relationships, findings suggested job candidates with more work experience reported less activated positive affect (e.g., excited) and lower levels of needs fulfillment, whereas job candidates who perceived to have more job alternatives reported more deactivated positive affect (e.g., calm). Finally, job candidates who were currently employed (compared to those who were not) reported experiencing lower levels of deactivated negative affect (e.g., dejection). 51 Table 6: Means, Standard Deviations, Reliabilities, and Correlations Among Variables. Note. Level-1 n = 489; Level-2 n = 164; Level-3 n = 70. The level-1 variables were first aggregated before correlating with the Level3 variables, and are represented above the diagonal. The level-1 correlations are presented below the diagonal. Gender was coded as 1= male, 0 = female; race/ethnicity was coded as 1 = White, 0 = non=White. Employment status was coded as 1 = employed, 0 = unemployed. Job seeker type was coded as 1 = student job seeker, 0 = adult job seeker. *p < .05. **p < .01. 52 Main Effect Hypotheses Given that Hypotheses 1 through 6, and Hypotheses 11 and 12 focused on examining the within-person direct, reciprocal, and indirect effects of the focal variables, I present those findings first. Hypothesis 1 stated that activated (H1a) and deactivated (H1b) positive affect would be positively related to PO fit. As shown in Table 7, activated positive affect was positively related to PO fit (b = .14, p < .05), but deactivated positive affect was not related to PO fit (-.02, ns). As such, Hypothesis 1 was partially supported. Hypothesis 2 stated both activated (H2a) and deactivated (H2b) negative affect would negatively relate to PO fit. This hypothesis was not supported since activated (b = -.08, ns) and deactivated negative affect (b = .09, ns) was not related to PO fit. Also shown in Table 7 (Model 2) are the results for when core affect and PO fit are separated temporally such that the PO fit variable is lagged to represent values from the following week. Results for the lagged effects suggest activated positive affect is still positively related to PO fit the following week (.24, p < .01), whereas deactivated positive affect is not related to PO fit (-.13, ns). For negative affect, the findings suggest activated negative affect is negatively related to PO fit the following week (-.41, p < .01), whereas deactivated negative affect is positively related to PO fit (.32, p < .01)—a finding that runs contrary to prediction. Further, the pattern of relationships were similar even when I ran a model that included prior PO fit as a predictor of lagged PO fit (.13, ns). Specifically, the findings were: activated positive affect (.15, p < .01); deactivated positive affect (-.06, ns); activated negative affect (-.38, p < .01); and deactivated negative affect (.28, p < .01). Thus, Hypothesis 1 and 2 were partially supported. Hypothesis 3 stated that needs fulfillment would be positively related to both activated (H3a) and deactivated (H3b) positive affect. As shown in Table 6 (Model 1), this hypothesis was 53 supported such that needs fulfillment was positively related to both activated (b = .44, p < .01) and deactivated positive affect (b = .22, p < .05). Hypothesis 4 predicted that needs fulfillment would be negatively related to both activated (H4a) and deactivated (H4b) negative affect. Results from Table 7 show that needs fulfillment was negatively related to both activated (b = .28, p < .01) and deactivated negative affect (b = -.32, p < .05). These findings suggest when job candidates perceive their needs will be fulfilled by the recruiting organization, they are more likely to experience positive affect and less likely to experience negative affect. In a separate model, I examined whether needs fulfillment related to core affect when both positive and negative affect were lagged to the following week. Results still supported the proposed relationships, such that needs fulfillment was positively related to both activated (b = .33, p < .01) and deactivated (b = .14, p < .05) positive affect, and negatively related to activated (b = .25, p < .05) and deactivated (b = -.27, p < .05) negative affect. Similar to Hypotheses 1 and 2, I ran an additional model whereby I included the autoregressive term associated with each of the affect dimensions. The results are consistent with prior findings such that needs fulfillment is positively related to changes in both lagged activated (b = .29, p < .01) and deactivated (b = .14, p < .05) positive affect, controlling for previous levels of activated (b = .19, p < .01) and deactivated (b = .10, p < .05) positive affect. Similarly, needs fulfillment is negatively related to lagged activated (b = -.23, p < .05) and deactivated (b = -.25, p < .05) negative affect, controlling for previous levels of activated (b = .16, p < .01) and deactivated (b = .11, ns) negative affect. Therefore, Hypotheses 3 and 4 were both fully supported. Hypothesis 5 proposed that PO fit would be positively related to acceptance intentions (H5a) and negatively related to withdrawal cognitions (H5b). As shown in Table 6 (Model 1), this hypothesis was fully supported since PO fit was positively related to acceptance intentions 54 (.15, p < .05), and negatively related to withdrawal cognition (-.31, p < .01). In a separate model, I examined whether PO fit relates to acceptance intentions and withdrawal cognition when both outcomes are lagged to the following week. Findings suggest PO fit is still positively related to changes in acceptance intentions (.38, p < .01) and negatively related to changes in withdrawal cognition (-.29, p < .01) during the following week, controlling for previous levels of acceptance intentions (b = .38, p < .01) and withdrawal cognitions (b = .24, p < .01). The results therefore fully support Hypothesis 5. 55 Table 7: Random Coefficients Modeling Results for the Relationships between Focal Study Variables. Note. Level-1 n = 489-599; Level-2 n = 164; Level-3 n = 70. *p < .05. **p < .01. 56 Hypothesis 6 predicted that needs fulfillment has a positive indirect effect on acceptance intentions (H6a) and a negative indirect effect on withdrawal cognitions (H6b) via the serial mediating pathway of core affect and PO fit. Following the recommendation outlined by Preacher, Zyphur, and Zhang (2010) for testing serial within-level mediation (e.g., 1-1-1-1 model), I estimated the indirect effect and used Monte Carlo simulation with 20,000 replications to obtain a robust confidence interval (CI) around the indirect effect. As shown in Table 8, the majority of the indirect effects were not supported across two separate models (a simultaneous model versus one where the outcomes of PO fit, acceptance intentions, withdrawal cognitions are lagged to the following week). As such, Hypothesis 10 received minimal support. 57 Table 8: Results from Random Coefficients Modeling Serial Mediation. Model Model 1 (simultaneous): Needs fulfillmentt0 ! core affectt0 ! PO fitt0 ! outcomest0 1. Needs fulfillment ! activated positive affect ! PO fit ! acceptance intentions 2. Needs fulfillment ! deactivated positive affect ! PO fit ! acceptance intentions 3. Needs fulfillment ! activated negative affect ! PO fit ! acceptance intentions 4. Needs fulfillment ! deactivated negative affect ! PO fit ! acceptance intentions 5. Needs fulfillment ! activated positive affect ! PO fit ! withdrawal cognition 6. Needs fulfillment ! deactivated positive affect ! PO fit ! withdrawal cognition 7. Needs fulfillment ! activated negative affect ! PO fit ! withdrawal cognition 8. Needs fulfillment ! deactivated negative affect ! PO fit ! withdrawal cognition Model 2 (lagged): Needs fulfillmentt0 ! core affectt0 ! PO fitt + 1 ! outcomest + 1 1. Needs fulfillment ! activated positive affect ! PO fit ! acceptance intentions 2. Needs fulfillment ! deactivated positive affect ! PO fit ! acceptance intentions 3. Needs fulfillment ! activated negative affect ! PO fit ! acceptance intentions 4. Needs fulfillment ! deactivated negative affect ! PO fit ! acceptance intentions 5. Needs fulfillment ! activated positive affect ! PO fit ! withdrawal cognition 6. Needs fulfillment ! deactivated positive affect ! PO fit ! withdrawal cognition 7. Needs fulfillment ! activated negative affect ! PO fit ! withdrawal cognition 8. Needs fulfillment ! deactivated negative affect ! PO fit ! withdrawal cognition Indirect effect 95 % CI .010 [-.002, .021] -.001 [-.004, .002] .003 [-.003, .009] -.005 [-.012, .003] -.019* [-.036, -.003] .002 [-.004, .007] -.007 [-.021, .008] .009 [-.008, .027] .031* [.001, .062] -.006 [-.015, .003] .033 [-.001, .067] -.023* [-.046, -.001] -.042 [-.088, .005] .008 [-.006, .022] -.045 [-.103, .014] .032 [-.008, .071] Note. Level-1 n = 489-599; Level-2 n = 164; Level-3 n = 70. *p < .05. **p < .01. 58 Hypotheses 11 and 12 pertained to examining the causal direction between PO fit and needs fulfillment and PO fit and core affect. In testing these hypotheses, I ran two separate models, one more restrictive than the other. The first model examined the relationship between the focal variables when a one-week lag is introduced. The second and more restrictive model consisted of incorporating the autoregressive associated with the dependent variable (i.e., the dependent variable regressed on itself over time). In running the first model, Hypothesis 11 proposed that PO fit would be positively related to lagged changes in needs fulfillment. The results indicated that PO fit was not related to lagged needs fulfillment (b = .10, ns). Further, the more restrictive model was not supported, in that once I controlled for previous needs fulfillment (b = .29, p < .01), PO fit was not related to changes in needs fulfillment (b = .03, ns). Therefore, Hypothesis 11 was not supported. Hypothesis 12 predicted that PO fit is positively related to both activated and deactivated positive affect (12a), and negatively related to both activated and deactivated negative affect (12b). The same approach to testing Hypothesis 11 was taken to test Hypothesis 12. When not taking into account the autoregressive component, PO fit was not significantly related to both activated (b = .12, ns) and deactivated (b = -.01, ns) positive affect, as well as activated (b = .17, ns) and deactivated (b = -.002, ns) negative affect. For the more restrictive model, controlling for previous levels of activated positive affect (b = .27, p < .01), PO fit was not related to changes in activated positive affect (b = -.06, ns). The findings are similar for deactivated positive affect such that once previous levels of deactivated positive affect are controlled for (b = .14, p < .05), PO fit was not related to changes in deactivated positive affect (b = -.5, ns). For activated negative affect, the results ran counter to prediction such that, controlling for previous levels of activated negative affect (b = .38, p < .01), PO fit was positively related to changes in activated 59 negative affect (b = .12, p < .05). Lastly, PO fit did not significantly predict changes in deactivated negative affect (b = -.02, ns) once I controlled for previous levels of deactivated negative affect (b = .25, p < .01). Thus, Hypothesis 12 was not supported. Cross-Level Moderation Hypotheses Hypotheses 7 through 10 proposed that promotion and prevention focus would moderate the within-person relationship between core affect and PO fit. As a first step, I examined whether there was significant slope variation among participants—that is, do the within-person relationships between core affect and PO fit significantly vary across participants in order to examine cross-level moderators (Bliese & Ployhart, 2002). The results indicated the variance associated with the within-person slopes did not significantly vary at the applicant level (level3); therefore, this did not satisfy the precondition needed prior to incorporating the cross-level moderators of prevention and promotion focused. As such, Hypotheses 7 through 10 were not supported. Though not formally hypothesized in my model, I examined the cross-level main effects of promotion and prevention focus on core affect. Promotion and prevention focus were first grand-mean centered before testing the cross-level effects. Findings suggested promotion focus was significantly related to both activated (b = .58, p < .01) and deactivated positive affect (b = .69, p < .01), whereas prevention focus was only significantly related to activated negative affect (b = .25, p < .05). As a supplementary analysis, I also examined the pattern of trajectories associated with the dynamic variables. Specifically, I examined whether the dynamic variables increased over time (a linear effect) as well as examined the rate of change over time (a quadratic effect). Table 9 provides the coefficients associated with each variable. The only significant relationship was found for needs fulfillment in that job candidates experienced an increase in needs fulfillment 60 over time, though their rate of increase decelerated over the eight week period. These findings mostly suggest that the focal variables are mostly fluctuating within-person (i.e., there is no systematic change) rather than increasing or decreasing over time. 61 Table 9: Linear and Quadratic Effects for Focal Variables. Linear growth Quadratic growth HAPA LAPA HANA -.058 -.004 -.075 .000 -.002 .005 LANA Needs PO fulfillment fit -.004 .118* .028 -.001 -.018* -.008 Acceptance intentions .005 Withdrawal cognition .011 -.005 .004 Note. Level-1 n = 489; Level-2 n = 164; Level-3 n = 70. *p < .05. HAPA = high activated positive affect; LAPA = low activated negative affect; HANA = high activated negative affect; LANA = low activated negative affect. 62 CHAPTER 5: DISCUSSION The purpose of this study was to better understand the psychological factors that influence PO fit dynamics during organizational recruitment. A review of previous literature on PO fit found that research has largely treated the construct as static and exogenous. In addition, the few studies examining the determinants of fit have largely focused on organizational factors such as advertisement and recruiters that shape applicants fit perceptions (Dineen & Noe; 2009; Jones et al., 2014; Roberson et al., 2005; Saks & Ashforth, 1997). The view taken in this study complements previous research on applicant reactions to PO fit by explicitly examining psychological factors that affect PO fit perceptions. Specifically, using the affect-infusion model, I proposed core affect as an important determinant of changes in PO fit. To this end, I stated that candidates’ PO fit would be infused with affective reactions originating from their experiences with the recruiting organization. As such, this study extends existing cognitively oriented theoretical frameworks (e.g., signaling theory, ELM) within the recruitment literature (Uggerslev et al., 2012) by suggesting that recruitment experiences can activate core affect that, in turn, job candidates can use when thinking about their PO fit. An additional part of the model focuses on determinants of affective experiences during the recruitment process. Drawing from SDT, I proposed applicant’s psychological needs fulfillment may, in part, contribute to core affect experienced during recruitment, and influence recruitment outcomes indirectly through core affect and PO fit perceptions. Finally, the current study examined a motivational boundary condition by stating that job seeker’s regulatory focus (i.e., promotion versus prevention) should differentially shape how applicants interpret their affective experiences in relation to PO fit. Thus, the current study highlights the potential organizational implications of affective 63 experiences during recruitment, and further provides an initial theoretical framework to extend our understanding of the determinants of PO fit dynamics. Summary of Findings and Contributions Affect as predictor of PO fit. In Hypotheses 1 and 2, it was expected that job candidate’s affective experiences originating from the recruitment context would shape PO fit in ways consistent with the underlying affective quality. In the current study, activated positive affect was positively related to changes in PO fit, whereas activated negative affect was negatively related to changes in lagged PO fit. These findings are consistent with the affect-asinformation perspective in that job candidates utilize their affective experiences to make sense of their PO fit perceptions with the recruiting organization. In addition, a pattern that emerged from the findings suggests affective experiences higher on arousal (i.e., high activation) seem to be particularly important when making evaluative judgments such as PO fit perceptions. These findings make sense given that an event that elicits arousal signals importance to the job candidate, and is more likely to facilitate substantive processing (Forgas, 1995). As such, affective experiences higher on arousal are more meaningful to job candidates and more impactful in shaping their PO fit perceptions. Although the findings were not supportive for the relationship between deactivated positive and negative affect on PO fit, the results revealed a positive relationship between deactivated negative affect and PO fit. This finding runs counter to prediction and suggests that job candidates who experience deactivated negative affect such as sadness or dejection in connection to the recruiting organization are more likely to report they fit with the organization. This finding is surprising given that according to appraisal theories of emotions, dejected-related affect such as sadness are characterized by a feeling of loss and helplessness—a feeling of not 64 being able to cope with or modify situations—which should activate withdrawal cognition (Lazarus, 1991). In addition, individuals in such dejected state should process these feelings as “something rewarding is missing” which leads to the eagerness to change to circumstances where the reward probabilities are higher (Raghunathan & Pham, 1999; p. 59). Since job candidates experiencing deactivated negative affect reported increased PO fit perceptions, it would be important to examine the cause behind such a counter-intuitive relationship. One likely possibility is job seeker’s desire to reduce cognitive inconsistency. According to cognitive dissonance theory (Festinger, 1957), job seeker’s may interpret their deactivated negative affect as a signal of caring about the organization which may result in them adjusting their PO fit perceptions in order to reduce internal discrepancy. A more speculative perspective is that given the relationship emerged for when PO fit was lagged to the following week, this suggest that they have yet to receive a rejection from the organization which may signal to the candidate that he/she is still in the running for the position thereby reestablishing PO fit. Needs fulfillment as predictor of core affect. Hypotheses 3 and 4 suggested that changes in needs fulfillment would be positively related to changes in activated and deactivated positive affect, and negatively related to changes in activated and deactivated negative affect. Findings were supportive of the prediction in that job candidates who perceived their needs were fulfilled experienced greater activated (e.g., excitement) and deactivated (e.g., relaxed) positive affect, and were less likely to experience activated (e.g., anxious) and deactivated (e.g., sadness) negative affect associated with the recruiting organization. These findings extend SDT literature in that previous work has shown needs fulfillment’s relationship with positive and negative affect (see Van den Broeck, Ferris, Chang, & Rosen [2016] for a recent meta-analytic review). The findings also suggest that in a recruitment context, when job candidates experience needs 65 fulfillment this ultimately affects their affective experiences associated with the recruiting organization in that they are more positive and less negative. Such findings underscore the importance of job candidates experiencing needs fulfillment in that it affects the affective quality of the recruitment experience. PO fit as predictor of acceptance intentions and withdrawal cognition. Hypothesis 5 was concerned with linking PO fit to recruitment outcomes including acceptance intentions and withdrawal cognition. Consistent with previous recruitment literature (Chapman et al., 2005; Uggerslev et al., 2012), findings suggested that changes in job candidates perception of PO fit is positively related to their willingness to accept an offer from the recruiting organization if an offer is extended, and is negatively related to their likelihood of withdrawing from the recruitment process. Though previous research linking PO fit to acceptance intentions and withdrawal cognition has been at the between-person level of analysis (Kristoff-Brown et al., 2005), the findings in this study suggest those relationships extend to the within-person level of analysis and therefore are homologous at multiple levels of analysis (Chen, Bliese, Mathieu, 2005). Reciprocal relationship between PO fit, needs fulfillment, and core affect. Although the model states a directional relationship whereby needs fulfillment and core affect are antecedents to PO fit, Hypotheses 11 and 12 empirically tested a competing model whereby PO fit is an antecedent to needs fulfillment and core affect. Findings from this study indicate that changes in PO fit do not subsequently influence changes in needs fulfillment as well as core affect over time. At first glace, these findings appear inconsistent with the few studies within the work context that have examined these relationships. For instance, when considering the relationship between PO fit and needs fulfillment, Gregurus and Diefendorff (2009) argued that 66 employees experiencing PO fit at work should allow them to fulfill their psychological needs. Using a time-lag design, their study found a positive relationship between PO fit and psychological need fulfillment. Given that their model is at the between-person level of analysis, I conducted a multilevel SEM model to examine whether I was able to replicate their betweenperson (i.e., level-3, between-applicant) finding. The results suggested, indeed at the betweenperson level-of-analysis, PO fit positively predicts subsequent changes in needs fulfillment. However, as presented in the results section, the findings do not hold when considering the relationships at level-1. These findings suggest a more complex pattern of relationship between PO fit and needs fulfillment in that when considering average levels across individuals, the findings seem to be consistent with previous research, but not when considering the dynamics of PO fit and needs fulfillment over time. Previous research examining the dynamic relationship between PO fit and core affect has come to the conclusion that PO fit precedes core affect. For example, Gabriel et al. (2014) tested a reciprocal relationship between work-based affect and PO fit. Their findings suggested PO fit perceptions exhibited causal precedence over positive affect but not negative affect. The findings in their study are inconsistent with the results from this study in that the current study found core affect to be an antecedent to PO fit. The pattern of relationship could be partly explained by differences in context—with the current study examining PO fit during pre-organizational entry and Gabriel et al. examining PO fit of job incumbents. When considering PO fit during preorganizational entry, job seekers have less established knowledge and experience with the recruiting organization to base their PO fit perception, so affective experiences may provide additional information as predicted by the affect-as-information theory. However, as individuals become organizational members and have longer tenure with the organization, they are more 67 likely to have well established PO fit perceptions that may be less susceptible to change as a function of momentary affective experiences. As theorized by Yu (2009), it is suspected that affect (positive or negative) experienced over a longer period of time should illicit more change in PO fit compared to affect experienced in a short duration. As such, future research would benefit from building on the findings in this study as well findings from Gabriel et al.’s study to further explore the temporal dynamics between core affect and PO fit. Regulatory focus as a moderator. It was expected that job candidate’s regulatory orientation of promotion versus prevention focus would shape how core affect is experienced in relation to PO fit. The cross-level moderation effects were not supported since there was no significant inter-individual variability in intra-individual change. However, as a supplementary analysis, I did examine the cross-level effects of promotion and prevention focus on core affect. The results indicated that promotion focus was positively related to both activated and deactivated positive affect, whereas prevention focus was negatively related to activated negative affect. These findings—consistent with regulatory focus theory (Higgins, 1997)—suggest asymmetrical effects in that promotion-focused individuals are more susceptible to experiencing affect derived from current-ideal self-congruency (e.g., excitement, elation), whereas preventionfocused individuals are more susceptible to experiencing affect derived from current-ought selfdiscrepancy (e.g., anxiety, worry). The current findings therefore establish the relevance of regulatory focus theory within the job search and recruitment context. Practical Implications The results of this study have several practical implications for how organizations can manage the recruitment process. As the study highlighted, job seeker’s PO fit perceptions are variable and fluctuate throughout the recruitment process. Since organizations care about 68 maintaining job seeker’s interest during recruitment (Barber, 1998), effectively managing their PO fit perceptions matter since this influences their continual engagement. Findings from this study suggest organizations can potentially manage PO fit perceptions through managing job candidate’s need fulfillment and subsequent affective experience and needs fulfillment. Organizations can utilize several strategies to effectively shape job candidate’s affective experiences. In particular, organizations can manage recruitment activities in ways that minimize negative affect. For instance, given candidates’ fairness concerns (e.g., Harold et al., 2015), this might entail managing recruitment activities in ways that ensure they do not lead to negative fairness reactions. Fairness reactions are important when discussing the effects of recruitment events because the appraisal dimension “legitimacy” attends to whether the situation eliciting the affect violates or adheres to justice norms (e.g., respect, information adequacy). In general, applicants expect the information provided to them during recruitment will be adequate and timely, their interactions with organizational representatives will be appropriate, and the selection tools used for evaluation will be valid and fair (Bell, Ryan, & Weichmann, 2004; Gilliland, 1993; Ryan & Huth, 2008; Ryan & Ployhart, 2000). The results of the current study suggest managing recruitment activities to minimize negative affect would be beneficial given its relationship to PO fit. Beyond attending to ways to minimize negative affect, given the importance of positive affect on PO fit, organizations can be more strategic in their efforts to elicit positive affect. For instance, organizational representatives such as recruiters may use impression management (IM) tactics to induce liking—a positive affective state—resulting in increased PO fit perception (Wilhelmy, Kleinmann, König, Melchers, & Truxillo, 2016). Previous research has shown that candidate’s use of IM tactics or “the conscious or unconscious attempt to control images that are 69 projected in real or imaged social interaction” (Schlenker, 1980, p. 6) influences interviewer evaluations (Barrick, Shaffer, & DeGrassi, 2009). Using an inductive behavioral design, Tsai, Hunag, and Lieu (2009) found that interviewers used a variety of IM tactics including job enhancement (highlighting positive aspects of the job such as autonomy) and other-focused enhancements (e.g., praising) influenced candidates organizational attraction and job pursuit intentions. These IM tactics could influence the candidates’ perceptions of organizational attributes (e.g., a friendly workplace). In addition, in a recent semi-structured qualitative study, Wilhelmy and colleagues (2016) found that interviewers do engage in IM behaviors to influence fit perceptions. Interviewers accomplished this by highlighting similarities between themselves and the candidate. Findings from these studies suggest that such IM tactics may also elicit momentary positive affect, which according to the current findings would lead job candidates to favorably evaluate the match between their self and the organization. Organizations can also be more thoughtful in the content and delivery of material pertaining to the job and organization. Specifically, findings from this study suggest job candidate’s needs fulfillment affects how they experience the recruitment context. To the extent possible, organizations can highlight features of the work environment that facilitates needs fulfillment. For instance, organizational members can discuss with job candidates the extent to which they are able to express their ideas and opinions in their job thereby signaling information regarding autonomy. Further, job candidates are in continuous contact with organizational members, which provides organizations with numerous opportunities to make job candidates feel as though they are connected, which may contribute to their satisfaction of the need for relatedness. In addition to engaging in activities that signal to needs fulfillment or satisfaction, organization needs to be mindful to not engage in activities that may signal need thwarting or 70 frustration. For example, job seeker’s experience of rude or discourteous behaviors may activate momentary experience of need thwarting that results in job candidates making negative inferences about organizational reality and potentially PO fit perceptions. As such, the fulfillment of needs while minimizing needs thwarting may enhance the candidate’s experience and positively influence recruitment outcomes. Limitations Though the current study has several strengths including utilizing a dynamic approach to examining PO fit during recruitment, examining psychological factors contributing to PO fit change, and doing so with a sample of largely adult job seekers, it is not without limitations. First, the study conceptualized PO fit as the perceived value congruence between the individual and organization. Thus, this study took a molar approach to studying PO fit (Edwards et al., 2006). Though this conceptualization is consistent with previous recruitment literature on PO fit (Kristoff-Brown et al., 2014), the current study does not shed light on exactly what part of the PO fit (i.e., the person or the organization) component is changing in relation to core affect and needs fulfillment. Therefore, future research could build on the findings from this study by delineating on what aspect of PO fit is changing as function of experiencing core affect and needs fulfillment. Answering these questions would require utilizing an atomistic approach where job candidates rate their own perceived value as well as the perceived value of the organization. A second limitation stems from the lack of time-varying controls in the model. Though survey fatigue is a real concern when engaging in intensive longitudinal surveying, future research may particularly incorporate theoretically relevant controls to isolate the effects of needs fulfillment. For instance, although conceptually distinct, needs fulfillment from SDT 71 perspective may empirically correlate with perceived needs-supply fit—or “perceived congruence between employees’ needs and the rewards they receive in return for their service and contributions from the job (e.g., pay, benefits, training; Cable & DeRue, 2002; p. 875).” As such, it is unclear the relative importance of general needs (i.e., needs fulfillment) versus domain specific needs (i.e., needs-supply fit) and its relation to PO fit. A final limitation is regarding the use of survey design to draw causal inferences among focal variables. Given that the study did not manipulate needs fulfillment and core affect, this limits the extent to which causal inferences can be made. Given this limitation, however, I did examine different ordering of the focal variables using a temporally lagged design to examine the directional nature of the relationships. Further, separating the variables temporally minimized the effects of common method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). In order to enhance the findings from this study, future studies can utilize a laboratory design whereby they manipulate needs fulfillment and core affect to examine their effects on PO fit. Directions for Future Research The current study examined how needs fulfillment and core affect influenced PO fit within a single organization over time. It is the case, however, that candidates pursue multiple organizations simultaneously (Breaugh, 2008), resulting in them holding unique PO fit perceptions with each recruiting organization. The hierarchical nature of the recruitment context—that is, PO fit over time and across multiple organizations—presents interesting crossover effects on the dynamic processes unfolding with each organization. Investigating these relationships may be possible since some of the proportion of variability in core affect, needs fulfillment, and PO fit was at level-2 (see Table 5). Previous research has shown that job candidates increasingly differentiate their PO fit perceptions throughout organizational 72 recruitment (Swider et al., 2015). Taking the level of analysis to the between-organization, within-applicant, it would be important to examine whether job candidate’s affective experiences and perception of needs fulfillment may contribute to the extent of PO fit differentiation. Also, since job candidates may enter the recruitment process with an initial favorite organization, future research could examine at what point job candidates are willing to reevaluate their preferences in response to their affective and needs fulfillment experiences. Regulatory focus theory was expected to operate as a cross-level moderator between core affect and PO fit. Future studies can consider additional personal characteristics that place boundary conditions on the dynamic process. Existing research suggest that individuals differ to the extent that they generally experience positive and negative emotions. Specifically, affective traits or “enduring predispositions to experience certain types of feelings across time and contexts” should influence how the environment is evaluated (i.e., benign or hostile) as well as the affective reactions that follow (Yeo, Frederiks, Kiewitz, & Neal, 2014; p. 430). For instance, when a candidate has a general tendency to experience positive emotions, he or she may tolerate events that create minor setbacks to goal progress, resulting in a less intense affective reaction. In addition, the trait-state interaction can be further extended to considering job candidate’s general levels of needs satisfaction. For instance, candidates with lower levels of autonomy across context may react more positively (e.g., greater increase in positive affect and PO fit) to autonomy fulfillment with the recruiting organization compared to candidates with higher levels of trait autonomy. Taken together, studies examining these personal characteristics would provide nuanced understanding on the dynamic relationship between core affect, needs fulfillment, and PO fit. 73 Future research can also extend the dynamic aspect of the current study. Although core affect and needs fulfillment are important predictors of PO fit, the results from the current study indicated there is significant residual variance suggesting room to examine additional timevarying predictors. Given its importance in explaining applicant behavior, future research can consider the role of justice perceptions in shaping PO fit. In particular, researchers can examine the extent to which information received during recruitment adheres to or violates justice perceptions (Colquitt, Long, Rodell, & Halvorsen-Ganepola, 2015). Previous research examining justice dynamics during recruitment have highlighted cognitive mechanisms such as positive certainty as partially mediating the link between justice adherence and recruitment outcomes (Walker, Bauer, et al., 2013). Findings from the current study would further suggest core affect as an additional mechanism, and as such, future research would benefit from incorporating these variables into a single model. Finally, though the current study examined how core affect influences PO fit, future research can consider the behavioral consequences of experiencing affect. According to affective events theory (Weiss & Cropanzano, 1996), affective experiences could also lead to emotiondriven behavior. Emotion-driven behaviors happen in part due to the action-tendencies (inherent behavioral responses) associated with emotions. Therefore, emotions not only make us think a certain way (e.g., evaluation of PO fit), but they also impel us to act a certain way. Recently, Matta, Erol-Korkmaz, Johnson, and Bicaksiz (2014) found that employees’ negative emotional reactions stemming from daily work events resulted in counterproductive workplace behavior. Applied to the recruitment context, when candidates experience positive or negative emotions, they may be impelled to act in ways that are consistent with their emotions. For example, following a negative recruitment event that results in anger, a candidate may decide to voice his 74 or her frustration by posting a complaint on their social media account (e.g., Twitter) as well as giving the target company a bad rating on sites like Glassdoor. Studies that focus on emotiondriven behavior would complement existing research focused on cognitive-driven behavior such as when candidate withdraw from recruitment or selection process (e.g., Ployhart, McFarland, & Ryan, 2002; Ryan, Sacco, McFarland, & Kriska, 2000). Conclusion The current study examined the dynamic antecedents of PO fit during recruitment using a sample of adult job seekers. The study incorporated several theoretical perspectives (e.g., affectas-information theory, SDT) to shed light on the factors that shape PO fit. The results highlighted job candidates affective experiences matter since changes in activated positive affect positively influenced PO fit whereas changes in activated negative affect negatively influenced PO fit— findings that are consistent with affect-as-information. Findings further indicated job candidate’s experience of needs fulfillment influenced their affective reactions in such a way that job candidates experienced more positive and less negative affect towards the recruiting organization. Overall, this research suggests predictions derived from SDT and affect-asinformation theory could be used when considering the within-person dynamics of PO fit. 75 APPENDICES 76 APPENDIX A: Recruitment Information Dear job seeker, We are a group of researchers from Michigan State University, and we are conducting a study aimed at understanding the candidate experience during the hiring process. The purpose of the study is to better understand the role of emotional experiences when looking for work and how that might affect your recruitment experiences. From this study, the researchers hope to identify what key experiences are important to job candidates in the hiring process so that organizations can better manage the recruitment process better. We see our research also informing how career counselors and educators can better serve job seekers. If you choose to participate, you will complete 8 surveys over a period of 2 months (i.e., one survey each week). We will email the surveys to an email address you provide us. The surveys will be completed online. The study will begin when you complete the first survey. The surveys will be confidential and will not affect you in anyway. You will be paid $3 for each of the 8 weekly surveys you complete, which means you can possibly earn up to $24 dollars. After you sign up to participate, we will email you with the dates and times when you can expect to receive the surveys in your email. Please watch your email carefully. If you are interested in participating, please click on the link below to complete some basic information about yourself. [Add the screening Qualtrics survey link here] We have a limit on the number of study participants that we can compensate, so we will contact you to let you know if you have been selected to participate. Thank you, Abdifatah Ali Department of Psychology Michigan State University Email: aliabdi1@msu.edu 77 APPENDIX B: Screening Questionnaire 1. What is your first and last name? (Note: we will only use your name to track your responses to the survey). 2. What is your email address (this should be the email you check most frequently)? 3. What is your phone number? 4. Are you a college student? 5. If yes is selected: a. What is your college major? (only applies to student sample) b. How many weeks have you been actively seeking a job that you would like to begin after college? 6. How many jobs have you applied to so far? 7. If question 6 is more than zero, then show question 8 a. Please provide the names of the organizations with which you have already applied to. i. For organization X, which stage mostly reflects where you are? 1. Initial stage (e.g., heard back from the organization on the status of my application, but nothing else) 2. Early middle stage (e.g., scheduled for an interview) 3. Late middle stage (e.g., interviews completed or attended an organization site visit) 4. Early final stage (e.g., waiting to hear back on job offer decision) 5. Late final stage (e.g., in the process of negotiating the job offer) b. On a scale from 1 to 5 with 1 being the lowest, please rate your overall interest with this organization. c. This question will be asked for each of the organizations the participant lists. 8. What is your race/ethnicity? 9. What is your gender? 10. What is your age? 11. What type of job are you looking for? 78 APPENDIX C: Measures One-Time Measures 1. What is your gender? Male ____ Female ____ 2. What is your age (in years)? _______ 3. Please select your race/ethnicity (choose one or more) from the following groups. a. American Indian or Alaska Native b. East Asian c. South Asian d. Middle Eastern or Arab e. Hispanic f. Black or African American g. Native Hawaiian or Other Pacific Islander h. White/Caucasian/Not of Hispanic origins i. Other (please specify) 4. Are you currently employed? a. Yes b. No 5. If yes to question 4, how many hours do you work each week? 6. If yes to question 4, how would you rate your satisfaction with your current job? a. Not at all satisfied b. Slightly satisfied c. Somewhat satisfied d. Moderately satisfied e. Extremely satisfied 7. If you are unemployed, please indicate (in months) how long you have been unemployed. 8. Are you currently a college student? 9. If yes to question 7, what is your overall grade point average (GPA)? 10. Please indicate your combined work experience in months. 11. Job search expectations: During your whole job search, how many total: a. First interviews do you expect to be invited to? b. Second interviews or site visits do you expect to be invited to? c. Job offers do you expect to receive? 12. How many weeks have you been actively searching for a new job? 13. Job search intensity: Since starting my job search, how many times have you done the following activities? (Response scale from 1 [0 times] to 5 [at least 10 times]). a. Prepare/revised your resume b. Listed yourself as a job applicant online or through a professional association (e.g. career services) c. Spoke with others (friends, relatives, faculty, previous employers, etc.) about possible job leads d. Used the Internet to locate job openings e. Contacted an employment agency, executive search firm, or state employment service. 79 14. Perceived alternatives will be measured using an adapted single item from Griffeth and Hom (1988). What is the probability that you can find acceptable job alternatives to the organizations you are applying to? Scale for this item ranges from (1=no chance to 5=100 percent chance) 15. Trait affect scale a. Instructions: Please indicate how much you feel this way in general i. Cheerful, happy, excited, enthusiastic, content, relaxed, proud, active, jittery, ashamed, nervous, hostile, guilty, angry, dejected, and sad. 16. Participants will be asked to report on their organizational attraction to the three organizations which they have indicated they will be reporting on during the weekly survey. a. The scale for organizational attraction will range from 1=strongly disagree to 5=strongly agree. i. For me, this organization would be a good place to work ii. I would not be interested in this organization except as a last resort iii. This organization is attractive to me as a place for employment iv. A job at this organization is very appealing to me 17. Organizational prestige will be measured using the 5 items from Highhouse et al. (2003). The scale for organizational prestige will range from 1=strongly disagree to 5=strongly agree. a. Employees are probably proud to say they work at this company. b. This is a reputable company to work for. c. This company probably has a reputation as being an excellent employer. d. I would find this company a prestigious place to work. e. There are probably many who would like to work at this company. 18. Organizational reputation was assessed using the three-items from Highhouse et al. (2009). The scale for organizational reputation will range from 1=strongly disagree to 5=strongly agree. a. This company has an excellent reputation. b. This company is widely admired and respected. c. This company is among the best. 19. Participant’s baseline PO fit perceptions toward each of the three organizations will also be assessed. 20. Regulatory focus will be measured using an adapted measure specific to the job search context from Lockwood et al. (2002). a. In general, I am focused on preventing negative events in my job search b. I am anxious that I will fall short of my responsibilities and obligations while searching for a job c. I frequently imagine how I will achieve my hopes and aspirations through my job search d. I often think about the job I am afraid I might obtain through my job search e. I often think about the job I would ideally like to obtain in the future f. I typically focus on the success I hope to achieve in my job search g. I often worry that I will fail to accomplish my job search goals h. I often think about how I will achieve success during my job search i. I often imagine myself experiencing bad things that I fear might happen to me in my job search 80 j. I frequently think about how I can prevent failures in my job search k. In my job search, I am more oriented toward preventing losses than I am toward achieving gains l. My major goal in my job search now is to achieve my ambitions m. My major goal in my job search right now is to avoid becoming a failure n. I see myself as someone who is primarily striving to obtain my “ideal” job—to fulfill my hopes, wishes, and aspirations o. I see myself as someone who is primarily striving to obtain the job I “ought” to have—to fulfill my duties, responsibilities, and obligations p. In general, I am focused on achieving positive outcomes in my job search q. I often imagine myself experiencing good things that I hope will happen to me in my job search r. Overall, I am more oriented toward achieving success than preventing failure in my job search Repeated Measures Recruitment Events In the initial survey, you listed the organizations that you have already submitted an application to. For each of the organization, please answer the following questions. [The entire survey will be repeated for each of the organizations the job candidate applied to.] Instructions: Listed below are examples of recruitment events that you may or may have not experienced so far since submitting your application. Select all the events that you have experienced since the last time you took the survey. [Note to committee: the table below looks different from Table 1 because recruitment events at stage 1 (e.g., web advertisement, job fair) do not apply to this group because they have already submitted an application to the organizations which they will be reporting their recruitment experiences] Recruitment Event • No recruitment event has occurred • Communication with the organization (e.g., email, phone, in-person) • Was able to get a preview of what your job would be like • Took an assessment (online or in-person) • Initial phone interview • Initial virtual interview • First interview • Second interview • Third interview • Interacted with organizational members (e.g., coworkers, managers) • Went on a site visit to the organization 81 Check all that applies • Received an offer There may be additional interactions or events you have experienced with organization [1,2,3] that are not listed above. If this is true, please provide them here. For those job candidates who indicate they experienced a recruitment event, the following follow-up questions will be asked to gather additional information about the event. 1. Please describe the recruitment event or episode using the following questions as a guide. a. What occurred or took place? b. When did it occur (please provide the date)? c. Did the recruitment event involve any person from the organization? 2. Please describe any other reaction you may have had with the recruitment event. In this survey, I will also assess events that are initiated by the job candidate. 1. Have you tried to get in contact with organization X? a. Yes b. No 2. If yes, what was the form of contact (e.g., email, phone call, in-person)? 3. Have you received a response from organization X since your contact? 4. If there was a response from the organization, what was your reaction? (Here, I can similar questions as above regarding the event. State affect: Please indicate the extent to which each of the emotions below capture how you have felt about organization X this past week. 1. Positive affect: enthusiastic, excited, inspired, cheerful, relaxed, contented, relieved, and calm. 2. Negative affect: anxious, angry, frustrated, distressed, sad, gloomy, depressed, and miserable. Adapted need fulfillment measure from Van den Broeck et al., (2010). With this organization, I feel like… Need for autonomy 1. I will have to follow other people’s commands (R). 2. The tasks I will have to do are in line with what I really want to do. 3. I will be free to do the job the way I think it could best be done. Need for competence 1. I will be competent at my job. 2. I will be good at the things I will do in my job. 3. I will be able to accomplish the most difficult tasks associated with this job. Need for relatedness 1. I will not really connect with other people (R). 2. I will be part of a group. 3. I will be able to talk with people about things that really matter to me. Adapted need fulfillment from La Guardia et al., (2000). 82 The recruiting organization makes me feel… Need for autonomy 1. Free to be who I am 2. I have a say in what happens and can voice my opinion Need for competence 3. Like a competent person 4. Very capable and effective Need for relatedness 5. A lot of closeness and personally connected. Loved and cared about PO fit 1. The things that I value in life are very similar to the things that the organization values. 2. My personal values match the organization’s values and culture. 3. The organization’s values and culture provide a good fit with the things that I value in life. Acceptance intentions will be measured using two items from Harris and Fink, 1987. This measure is on a scale from 1 (strongly disagree) to 5 (strongly agree). 1. If I was offered a job by the organization, I would accept it. 2. If I was offered a job at this organization, I would accept it immediately. Because previous measures capturing withdrawal from recruitment do not exist, commitment to the recruiting organization will be used as a proxy. Thus, I will assess withdrawal cognitions using an adapted measure from Hollenbeck et al. (1989). This measure is on a scale from 1 (strongly disagree) to 5 (strongly agree). 1. I am no longer committed to pursuing employment with this organization. 2. I am unwilling to put forth effort beyond what I’d normally did to achieve employment with this organization. 3. It wouldn’t take much to make me abandon my employment pursuit with this organization. 83 REFERENCES 84 REFERENCES Ali, A. A., Ryan, A. M., Lyons, B. J., Ehrhart, M. G., & Wessel, J. L. (2016). The long road to employment: Incivility experienced by job seekers. Journal of Applied Psychology, 101(3), 333-349. Baard, P. P., Deci, E. L., & Ryan, R. M. (2004). Intrinsic need satisfaction: A motivational basis of performance and well-being in two work settings. Journal of Applied Social Psychology, 75, 2045-2068. Barber, A. E. (1998). Recruiting employees: Individual and organizational perspectives. Thousand Oaks, CA: Sage. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99-120. Barrett, L. F., & Russell, J. A. (1999). The structure of current affect: Controversies and emerging consensus. Current Directions in Psychological Science, 8, 10-14. Barrick, M. R., Shaffer, J. A., & DeGrassi, S. W. (2009). What you see may not be what you get: Relationships among self-presentation tactics and ratings of interview and job performance. Journal of Applied Psychology, 94(6), 1394-1411. Barsade, S. G., & Gibson, D. E. (2007). Why does affect matter in organizations? Academy of Management Perspectives, 21, 36-59. Bell, B. S., Ryan, A. M., & Wiechmann, D. (2004). Justice expectations and applicant perceptions. International Journal of Selection and Assessment,12, 24-38. Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models: Model building, testing, and illustrations. Organizational Research Methods, 5, 362-387. Bower, G. H. (1981). Mood and memory. American Psychologist, 36, 129-148. Bower, G. H., & Forgas, J. P. (2000). Affect, memory, and social cognition. In E. Eich, J. F. Kihlstrom, G. H. Bower, J. P. Forgas, & P. M. Niedenthal (Eds.), Cognition and Emotion (pp. 87-168). London: Oxford University Press. Breaugh, J. A. (2008). Employee recruitment: Current knowledge and important areas for future research. Human Resource Management Review, 18(3), 103-118. Breaugh, J. A. (2013). Employee Recruitment. Annual Review of Psychology, 64, 389-416. 85 Brockner, J., & Higgins, E. T. (2001). Regulatory focus theory: Its implications for the study of emotions in the workplace. Organizational Behavior and Human Decision Processes, 86, 35-66. Cable, D. M., Aiman-Smith, L., Mulvey, P. W., & Edwards, J. R. (2000). The sources and accuracy of job applicants' beliefs about organizational culture. Academy of Management journal, 43(6), 1076-1085. Cable, D. M., & Judge, T. A. (1996). Person-organization fit, job choice decisions, and organizational entry. Organizational Behavior and Human Decision Processes, 67, 294-311. Cable, D. M., & DeRue, D. S. (2002). The convergent and discriminant validity of subjective fit perceptions. Journal of Applied Psychology, 87(5), 875-884. Cable, D. M., & Edwards, J. R. (2004). Complementary and supplementary fit: a theoretical and empirical integration. Journal of Applied Psychology, 89(5), 822-834. Cable, D. M., & Kay, V. S. (2012). Striving for self-verification during organizational entry. Academy of Management Journal, 55, 360-380. Chapman, D. S., Uggerslev, K. L., Carroll, S. A., Piasentin, K. A., & Jones, D. A. (2005). Applicant attraction to organizations and job choice: A meta-analytic review of the correlates of recruiting outcomes. Journal of Applied Psychology, 90, 928-944. Chatman, J. A. (1989). Improving interactional organizational research: A model of person– organization fit. Academy of Management Review, 14, 333-349. Chen, G., Bliese, P. D., & Mathieu, J. E. (2005). Conceptual framework and statistical procedures for delineating and testing multilevel theories of homology. Organizational Research Methods, 8(4), 375-409. Clore, G. L., Schwarz, N., & Conway, M. (1994). Affective causes and consequences of social information processing. In R. S. Wyer & T. K. Srull (Eds.), Handbook of social cognition (2nd ed., Vol. 1, pp. 323-417). Hillsdale, NJ: Erlbaum. Clore, G.L. & Storbeck, J. (2006). Affect as information about liking, efficacy, and importance. In J. Forgas (Ed). Hearts and Minds: Affective influences on social cognition and behaviour. New York: Psychology Press. Colquitt, J. A., Long, D. M., Rodell, J. B., & Halvorsen-Ganepola, M. D. (2015). Adding the “in” to justice: A qualitative and quantitative investigation of the differential effects of justice rule adherence and violation. Journal of Applied Psychology, 100(2), 278-297. da Motta Veiga, S. P., & Turban, D. B. (in press). Who is searching for whom? Integrating applicant attraction and job search research. In U.-C. Klehe and E. A. J Van Hooft (Eds.), 86 The Oxford Handbook of Job Search and Job Loss. New York, NY: Oxford University Press. da Motta Veiga, S. P., & Turban, D. B. (2014). Are affect and perceived stress detrimental or beneficial to job seekers? The role of learning goal orientation in job search selfregulation. Organizational Behavior and Human Decision Processes, 125, 193-203. Darnold, T.C., & Rynes, S. (2012). Recruitment and job choice research: Same as it ever was? Handbook of psychology. Industrial and Organizational Psychology, Vol. 12, (pp. 04142). Hoboken, NJ: Wiley. Deci, E. L., & Ryan, R.M. (1991). A motivational approach to self: Integration in personality. In R. Dienstbier (Ed.), Nebraska symposium on motivation. Vol. 38. Perspectives on motivation (pp. 237-288). Lincoln, N.E.: University of Nebraska. Deci, E. L, & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227-268. Deci, E. L., Vallerand, R.J., Pelletier, L.G., & Ryan, R.M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26, 325-346. Dineen, B. R., & Noe, R. A. (2009). Effects of customization on application decisions and applicant pool characteristics in a web-based recruitment context. Journal of Applied Psychology, 94(1), 224-234. Edwards, J. R., & Cable, D. M. (2009). The value of value congruence. Journal of Applied Psychology, 94(3), 654-677. Edwards, J. R., Cable, D. M., Williamson, I. O., Lambert, L. S., & Shipp, A. J. (2006). The phenomenology of fit: Linking the person and environment to the subjective experience of person-environment fit. Journal of Applied Psychology, 91(4), 802-827. Festinger, L. (1957). A theory of cognitive dissonance. Evanston, IL: Row & Peterson. Forgas, J. P. (1995). Mood and judgment: The affect infusion model (AIM). Psychological Bulletin, 117, 39-66. Forgas, J. P. (2001). The Affect Infusion Model (AIM): An integrative theory of mood effects on cognition and judgments. In: L.L. Martin & G.L. Clore (Eds.). Theories of mood and cognition (pp. 99-134). Mahwah, N.J.: Erlbaum. Forgas, J. P. (2000). Feeling is believing? The role of processing strategies in mediating affective influences on beliefs. In: N.H. Frijda & A.S.R. Manstead & S. Bem (Eds.) Emotions and beliefs: How feelings influence thoughts. (pp. 108-144). Cambridge: Cambridge University Press. 87 Forgas, J. P. & Vargas, P. T. (2000). The effects of mood on social judgment and reasoning. In M. Lewis, & J. M. Haviland-Jones (2nd Eds), Handbook of Emotions (pp.350-367). New York, NY: The Guilford Press. Frijda, N., Ortony, A., Sonnemans, J., & Clore, G. (1992) The complexity of intensity: Issues concerning the structure of emotion intensity. In M. Clark (Ed.), Emotion. Review of personality and social psychology, Vol. 13 (pp. 60-89). Newbury Park, CA: Sage. Gabriel, A. S., Diefendorff, J. M., Chandler, M. M., Moran, C. M., & Greguras, G. J. (2014). The dynamic relationships of work affect and job satisfaction with perceptions of fit. Personnel Psychology, 67, 389-420. Gilliland, S. W. (1993). The perceived fairness of selection systems: An organizational justice perspective. The Academy of Management Review, 18, 694-734. Greguras, G. J., & Diefendorff, J. M. (2009). Different fits satisfy different needs: linking person-environment fit to employee commitment and performance using selfdetermination theory. Journal of Applied Psychology, 94(2), 465-477. Griffeth, R. W., & Hom, P. W. (1988). A comparison of different conceptualizations of perceived alternatives in turnover research. Journal of Organizational Behavior, 9(2), 103-111. Harris, M. M., & Fink, L. S. (1987). A field study of employment opportunities: Does the recruiter make a difference? Personnel Psychology, 40, 765-784. Harold, C. M., & Ployhart, R. E. (2008). What do applicants want? Examining changes in attribute judgments over time. Journal of Occupational and Organizational Psychology, 8, 191-218. Harold, C. M., Holtz, B. C., Griepentrog, B. K., Brewer, L. M., & Marsh, S. M. (2015). Investigating the effects of applicant justice perceptions on job offer acceptance. Personnel Psychology, 1-29. Highhouse, S., Brooks, M. E., & Gregarus, G. (2009). An organizational impression management perspective on the formation of corporate reputations. Journal of Management, 35(6), 1481-1493. Highhouse, S., Lievens, F., & Sinar, E. F. (2003). Measuring attraction to organizations. Educational and Psychological Measurement, 63, 986-1001. Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52, 1280-1300. Higgins, E. T., Shah, J., & Friedman, R. (1997). Emotional responses to goal attainment: Strength of regulatory focus as moderator. Journal of Personality and Social Psychology, 72, 515-525. 88 Hofmann, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 23, 723-744. Hogarth, R. M., & Einhorn, H. J. (1992). Order effects in belief updating: The belief-adjustment model. Cognitive Psychology, 24(1), 1-55. Hollenbeck, J. R., Klein, H. J., O'leary, A. M., & Wright, P. M. (1989). Investigation of the construct validity of a self-report measure of goal commitment. Journal of Applied Psychology, 74(6), 951-956. Ito, T. A., Larsen, J. T., Smith, N. K., & Cacioppo, J. T. (1998). Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations. Journal of Personality and Social Psychology, 75, 887-900. Jansen, K. J., & Shipp, A. J. (2013). A review and agenda for incorporating time in fit research. In A. L. Kristof-Brown and J. Billsberry (Eds.), Organizational Fit: Key Issues and New Directions. Chichester, UK: Wiley-Blackwell. Johnson, R. E., Taing, M. U., Chang, C., & Kawamoto, C. K. (2013). A self-regulation approach to person-environment fit. In A. L. Kristof-Brown & J. Billsberry (Eds.), Organizational fit: Key issues and new directions. Chichester, UK: Wiley-Blackwell. Jones, D. A., Willness, C. R., & Madey, S. (2014). Why are job seekers attracted by corporate social performance? Experimental and field tests of three signal-based mechanisms. Academy of Management Journal, 57, 383-404. Judge, T. A., & Cable, D. M. (1997). Applicant personality, organizational culture, and organization attraction. Personnel Psychology, 50, 359-394. Kausel, E. E., & Slaughter, J. E. (2011). Narrow personality traits and organizational attraction: Evidence for the complementary hypothesis. Organizational Behavior and Human Decision Processes, 114, 3-14. Keltner, D. T., & Lerner, J. S. (2010). Emotion. In The Handbook of Social Psychology, Vol. 1, ed. DT Gilbert, S. T Fiske. G Lindzey, (pp. 317-52). New York: Wiley. Kristof-Brown, A. L., Reeves, C. J., & Follmer, E. H. (2014). The Goldilocks pursuit during organizational entry: Applicants’ and recruiters’ search for the “perfect fit.” In K. Y. T. Yu, D. M. Cable (Eds), The Oxford Handbook of Recruitment (pp. 437-453). New York: Oxford University Press. Kristof-Brown, A. L., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of individuals’ fit at work: A meta-analysis of person-job, person-organization, persongroup, and person-supervisor fit. Personnel Psychology, 58, 281-342. 89 La Guardia, J. G., Ryan, R. M., Couchman, C. E., & Deci, E. L. (2000). Within-person variation in security of attachment: A self-determination theory perspective on attachment, need fulfillment, and well-being. Journal of Personality and Social Psychology, 79, 367-384. Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press on Demand. Lerner, J. S., & Keltner, D. (2000). Beyond valence: Toward a model of emotion-specific influences on judgment and choice. Cognition and Emotion, 14, 473-493. Lerner, J. S., & Keltner, D. (2001). Fear, anger, and risk. Journal of Personality and Social Psychology, 81, 146-159. Lievens, F., Decaesteker, C., Coetsier, P., & Geirnaert, J. (2001). Organizational attractiveness for prospective applicants: A person–organisation fit perspective. Applied Psychology, 50(1), 30-51. Lievens, F., & Highhouse, S. (2003). The relation of instrumental and symbolic attributes to a company's attractiveness as an employer. Personnel Psychology, 56, 75-102. Lockwood, P., Jordan, C. H., & Kunda, Z. (2002). Motivation by positive or negative role models: Regulatory focus determines who will best inspire us. Journal of Personality and Social Psychology, 83, 854-864. Matta, F. K., Erol‐Korkmaz, H. T., Johnson, R. E., & Bicaksiz, P. (2014). Significant work events and counterproductive work behavior: The role of fairness, emotions, and emotion regulation. Journal of Organizational Behavior, 35(7), 920-944. Morgeson, F., Mitchell, T., & Liu, D. (2015). Event system theory: An event-oriented approach to organizational sciences. Academy of Management Review, 40, 515-537. Muthén, B., & Muthén, B. O. (2012). Mplus user’s guide. 7th edition. Los Angeles, CA: Wiley. Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123-205. Ployhart, R. E., McFarland, L. A., & Ryan, A. M. (2002). Examining applicants’ attributions for withdrawal from a selection procedure. Journal of Applied Social Psychology, 32(11), 2228-2252. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15(3), 209-233. 90 Raghunathan, R., & Pham, M. T. (1999). All negative moods are not equal: Motivational influences of anxiety and sadness on decision making. Organizational Behavior and Human Decision Processes, 79(1), 56-77. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). Sage. Roberson, Q. M., Collins, C. J., & Oreg, S. (2005). The effects of recruitment message specificity on applicant attraction to organizations. Journal of Business and Psychology, 19(3), 319-339. Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110, 145-172. Ryan, A. M., Horvath, M., & Kriska, S. D. (2005). The role of recruiting source informativeness and organizational perceptions in decisions to apply. International Journal of Selection and Assessment, 13(4), 235-249. Ryan, A. M., & Huth, M. (2008). Not much more than platitudes? A critical look at the utility of applicant reactions research. Human Resource Management Review, 18, 119-132. Ryan, A. M., & Ployhart, R. E. (2000). Applicants’ perceptions of selection procedures and decisions: A critical review and agenda for the future. Journal of Management, 26, 565-606. Ryan, A. M., Sacco, J. M., McFarland, L. A., & Kriska, S. D. (2000). Applicant self-selection: correlates of withdrawal from a multiple hurdle process. Journal of Applied Psychology, 85(2), 163-179. Rynes, S. L. (1991). Recruitment, job choice, and post-hire consequences: A call for new research directions. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (2nd ed.), vol. 2, (pp. 399-444). Palo Alto: Consulting Psychologists Press. Rynes, S. L., Bretz, R. D., Jr., & Gerhart, B. (1991). The importance of recruitment in job choice: A different way of looking. Personnel Psychology, 44, 487-521. Saks, A. M., & Ashforth, B. E. (1997). A longitudinal investigation of the relationships between job information sources, applicant perceptions of fit, and work outcomes. Personnel Psychology, 50(2), 395-426. Saks, A. M., & Uggerslev, K. L. (2010). Sequential and combined effects of recruitment information on applicant reactions. Journal of Business and Psychology, 25(3), 351-365. Schlenker, B. R. (1980). Impression management: The self-concept, social identity, and interpersonal relations. Monterey: Brooks/Cole. 91 Schneider, B. (1987). The people make the place. Personnel Psychology, 40, 437-453. Schwarz, N. (1990). Feelings as information: Informational and motivational functions of affective states. In E.T. Higgins & R. Sorrentino (Eds.), Handbook of motivation and cognition: Foundations of social behavior (Vol. 2, pp. 527-561). New York: Guilford Press. Schwarz, N. (2001). Feelings as information: Implications for affective influences on information processing. In L. L. Martin & G. L. Clore (Eds.), Theories of mood and cognition: A user's handbook (pp. 159-176). Mahwah, NJ: Erlbaum. Schwarz, N. (2012). Feelings-as-information theory. In P. A. M. Van Lange, A. Kruglanski, & E. T. Higgins (eds.), Handbook of theories of social psychology (pp. 289-308). Thousand Oaks, CA: Sage. Schwarz, N., & Clore, G. L. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45, 513-523. Schwarz, N., & Clore, G. L. (1988). How do I feel about it? Informative functions of affective states. In K. Fiedler & J. Forgas (Eds.), affect, cognition, and social behavior (pp. 4462). Toronto: Hogrefe International. Schwarz, N., & Clore, G. L. (2003). Mood as information: Twenty years later. Psychological Inquiry, 14, 296-303. Sheldon, K. M., Elliot, A. J., Kim, Y., & Kasser, T. (2001). What is satisfying about satisfying events? Testing 10 candidate psychological needs. Journal of Personality and Social Psychology, 80(2), 325-339. Sheldon, K., & Niemiec, C. (2006). It’s not just the amount that counts: Balanced need satisfaction also affects well-being. Journal of Personality and Social Psychology, 91, 331-341. Sheldon, K. M., Ryan, R., & Reis, H. T. (1996). What makes for a good day? Competence and autonomy in the day and in the person. Personality and Social Psychology Bulletin, 22(12), 1270-1279. Shipp, A. J., & Jansen, K. J. (2011). Reinterpreting time in fit theory: Crafting and recrafting narratives of fit in medias res. Academy of Management Review, 36, 76-101. Simon, H. A. (1967). Motivational and emotional controls of cognition. Psychological Review, 74, 29-39. 92 Slaughter, J. E., Cable, D. M., & Turban, D. B. (2014). Changing job seekers’ image perceptions during recruitment visits: The moderating role of belief confidence. Journal of Applied Psychology, 99(6), 1146-1158. Song, Z., Uy, M. A., Zhang, S., & Shi, K. (2009). Daily job search and psychological distress Evidence from China. Human Relations, 62, 1171-1197. Swider, B. W., Zimmerman, R. D., & Barrick, M. R. (2015). Searching for the right fit: Development of applicant person-organization fit perceptions during the recruitment process. Journal of Applied Psychology, 100(3), 880-893. Tajfel, H. and Turner, J.C. (1985) The social identity theory of group behavior. In S. Worchel and W.G. Austin (Eds.), Psychology of intergroup relations. Chicago: Nelson-Hall. Tsai, W. C., Huang, T. C., & Liu, P. T. (2009). Is being informative enough? The effect of interviewer impression management on applicant attraction. Paper presented at the Academy of Management Annual Meeting, Chicago, USA. Turban, D. B., Forret, M. L., & Hendrickson, C. L. (1998). Applicant attraction to firms: Influences of organization reputation, job and organizational attributes, and recruiter behaviors. Journal of Vocational Behavior, 52(1), 24-44. Turban, D. B., & Jones, A. P. (1988). Supervisor-subordinate similarity: types, effects, and mechanisms. Journal of Applied Psychology, 73(2), 228-234. Uggerslev, K. L., Fassina, N. E., & Kraichy, D. (2012). Recruiting through the stages: A metaanalytic test of predictors of applicant attraction at different stages of the recruiting process. Personnel Psychology, 65, 597-660. Vancouver, J. B., Millsap, R. E., & Peters, P. A. (1994). Multilevel analysis of organizational goal congruence. Journal of Applied Psychology, 79(5), 666-678. Vancouver, J. B., & Schmitt, N. W. (1991). An exploratory examination of person‐organization fit: Organizational goal congruence. Personnel Psychology, 44(2), 333-352. Van den Broeck, A., Ferris, D. L., Chang, C. H., & Rosen, C. C. (2016). A review of selfdetermination theory’s basic psychological needs at work. Journal of Management, 42(5), 1195-1229. Van den Broeck, A., Vansteenkiste, M., Witte, H., Soenens, B., & Lens, W. (2010). Capturing autonomy, competence, and relatedness at work: Construction and initial validation of the Work‐related Basic Need Satisfaction scale. Journal of Occupational and Organizational Psychology, 83(4), 981-1002. Walker, H. J., Bauer, T. N., Cole, M. S., Bernerth, J. B., Feild, H. S., & Short, J. C. (2013). Is this how I will be treated? Reducing uncertainty through recruitment interactions. 93 Academy of Management Journal, 56, 1325-1347. Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063-1070. Weiss, H. M., & Cropanzano, R. (1996). Affective events theory: A theoretical discussion of the structure, causes and consequences of affective experiences at work. Research in Organizational Behavior, 18, 1-74. Wilhelmy, A., Kleinmann, M., König, C. J., Melchers, K. G., & Truxillo, D. M. (2016). How and Why Do Interviewers Try to Make Impressions on Applicants? A Qualitative Study. Journal of Applied Psychology, 313-332. Wright, W.F., & Bower, G.H. (1992). Mood effects on subjective probability assessment. Organizational Behavior and Human Decision Processes, 52, 276-291. Yeo, G. B., Frederiks, E. R., Kiewitz, C., & Neal, A. (2014). A dynamic, self-regulatory model of affect and performance: Interactions between states, traits and task demands. Motivation and Emotion, 38(3), 429-443. Yu, K. Y. T. (2009). Affective influences in person-environment fit theory: Exploring the role of affect as both cause and outcome of P-E fit. Journal of Applied Psychology, 94, 12101226. Yu, K. Y. T. (2014). Person–organization fit effects on organizational attraction: A test of an expectations-based model. Organizational Behavior and Human Decision Processes, 124, 75-94. Zeelenberg, M., Nelissen, R. M. A., Breugelmans, S. M., & Pieters, R. (2008). On emotion specificity in decision making: Why feeling is for doing. Judgment and Decision Making, 3, 18-27. 94