Developing and Validating Measures of Leaving Preference And

Home , Job embeddedness

You Can’t Always Get What You Want: Developing and Validating Measures of

Leaving Preference and Perceived Control

A dissertation presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Doctor of Philosophy

Eric E. Brasher

August 2016

This dissertation titled

You Can’t Always Get What You Want: Developing and Validating Measures of

Leaving Preference and Perceived Control

ERIC E. BRASHER

has been approved for

the Department of Psychology

and the College of Arts and Sciences by

Rodger W. Griffeth

Professor of Psychology

Robert Frank

Dean, College of Arts and Sciences

Abstract

BRASHER, ERIC E., Ph.D., August 2016, Industrial/Organizational Psychology

You Can’t Always Get What You Want: Developing and Validating Measures of

Leaving Preference and Perceived Control (164 pp.)

Director of Dissertation: Rodger W. Griffeth

Employee turnover has drawn strong interest from both researchers and practitioners for many decades. Although turnover can have some positive consequences, much more attention has focused on the costs incurred by organizations.

Advances in turnover theory over time have helped improve our understanding of why employees leave their jobs. However, few turnover models encompass the full scope and complexity of organizational withdrawal and participation. The proximal withdrawal states and destinations (PWSD) model (Hom, Mitchell, Lee, & Griffeth, 2012) takes a more comprehensive approach by expanding the turnover criterion to include multiple types of staying and leaving, as well as considering the destinations of those who leave.

This model introduced the concept of proximal withdrawal states to explain how preference and perceived control shape employee mindsets about participating or withdrawing from an organization. The current research makes two key empirical contributions in testing the PWSD model: (1) it develops reliable and efficient measures of preference and perceived control focused on organizational participation and withdrawal, and (2) it collects initial validation evidence for these new measures by evaluating their relationships with employee attitudes, cognitions, intentions, and behaviors. In Study 1, an exploratory factor analysis with a sample of 152 working adults identified a 2-factor structure. A scale refinement process including consideration 4 of factor loadings, communalities, redundancy, and internal consistency resulted in 5- item scales for leaving preference and perceived control. In Study 2, a confirmatory factor analysis with 525 working adults supported the initial two-factor structure.

Correlation and regression analyses provided validation evidence to support most, but not all, of the hypotheses made about leaving preference and perceived control. Finally, I discuss the implications of this research and potential future directions.

Acknowledgments

I deeply appreciate all the friends and family whose support helped me to finally bring this long and difficult journey to a successful conclusion. First of all, I am grateful for the constant love and support of my parents. Special thanks to my advisor, Dr.

Rodger Griffeth for giving me this opportunity after over a decade away from the program and for never losing faith along the way, even when I had my doubts. I thank my other dissertation committee members (Dr. Jeff Vancouver, Dr. Bruce Carlson, Dr.

Keith Markman, and Dr. Diana Schwerha) for their valuable insight that made the final product better and for their patience. I also acknowledge the contributions of my fellow

Ohio I/O graduate students, particularly Sean Robinson, Alli Tenbrink, Mike Warren,

Justin Purl, Kathleen Hall, and Leah Halper. They welcomed me with friendship and encouragement despite the distance in years and miles, so it never felt like I was going through this alone. I also thank Jeremy Jokinen, whose example showed me that a second chance was possible. Most of all, I must express a debt of gratitude to my wife,

Meredith. I would never have even tried to take on this daunting challenge, much less succeeded, without her encouragement, support, and patience!

Table of Contents

Page

Abstract...... 3

Acknowledgements...... 5

List of Tables...... 9

List of Figures...... 10

Introduction...... 11

Turnover Classification...... 15

Voluntary/Involuntary……….…..………………………………………..……..15

Avoidable/Unavoidable…….....…..…………...………………………………...16

Classification Challenges………………………………………………………...16

Turnover Theory Components…………………………………………………………...18

Job Attitudes……………………………………………………………………..18

Behavioral Intentions…………………………………………………………….21

Employment Opportunity………………………………………………………..23

Personal and Organizational Factors....………………………………………….24

Summary….……………………………………………………………………...25

Turnover Models………………………………………………………………………...28

Foundational Models……..……………………………………………………...28

Unfolding Model………………………………………………………………....31

Job Embeddedness……………………………………………………………….32

General Withdrawal Models……….…………………………………………….34

Summary….…………………………..………………………………………….35 7

Proximal Withdrawal States and Destinations Model………………………………..…37

Preference to Stay or Leave….………………………………………………….39

Constraints and Perceived Control………………………………………………42

Proximal Withdrawal States and Consequent Reactions….…..…………………45

Current Study and Hypotheses……………….……………….…………………………49

Job Attitudes…...…………....…...………………………………………………50

Engagement……...….………...………………………………...…………...... 53

Job Embeddedness.….………...………………………………...…………...... 55

Quit Intentions……………………………………………………………...……56

Search Behaviors…………………………………………………………...……58

Withdrawal Behaviors……………………………………………………...……60

Summary….…....…………....…...………………………………………………62

Study 1: Scale Development….………………………………………………………….63

Item Generation………………………………………………………………….63

Sample and Procedure…….……………..……………………………………….65

Measures…………………………………………………………………………66

Results…………...……………………………………………………………….67

Study 2: Scale Validation….….………………………………………………………….73

Sample and Procedure…………….……..……………………………………….73

Measures…………………………………………………………………………75

Results…...……………………………………………………………………….78

Discussion…………………………………………………………………………...…...89

Scale Development………...…………………………………………………….89 8

Scale Validation……….…...…………………………………………………….90

Limitations and Future Research………………………………………………...98

Conclusions……………...... ……..………………………….………………….100

References………………………………………………………………………………101

Appendix A: Leaving Preference Scale………………………….....……………..……154

Appendix B: Perceived Control Scale……………..…………….....……………..……155

Appendix C: Informed Consent Form……………………………...……………..……156

Appendix D: Demographic Questions…..….………....………………………………..157

Appendix E: Job Satisfaction Scale……..….………....………………………………..158

Appendix F: Organizational Commitment Scales.……………………………………..159

Appendix G: Engagement Scale....………..……..………………………...………...…160

Appendix H: Job Embeddedness Scale.………………………….……………………..161

Appendix I: Quit Intentions Scale....……..…..…..………………………...………...…162

Appendix J: Job Search Behaviors Scale…..……...………..……...….……………..…163

Appendix K: Withdrawal Behaviors Scale….………...…………………………..……164

List of Tables

Table 1. Meta-Analysis of Antecedents of Employee Turnover……………………...…27

Table 2. Correlation Matrix for Exploratory Factor Analysis…………………..…….…68

Table 3. Factor Loadings and Communalities for Final Exploratory Factor Analysis...... 72

Table 4. Item Pairs Used in Careless Response Analysis…………………………….….76

Table 5. Confirmatory Factor Analysis Model Fit Indices…………...……………...…..79

Table 6. Parameter Estimates for Two-Factor Confirmatory Factor Analysis…………..80

Table 7. Descriptive Statistics and Correlation Matrix for Study 2 Scales……..……….82

Table 8. Hierarchical Regression Analyses for Quit Intentions .………………………..85

Table 9. Hierarchical Regression Analyses for Search Behaviors .……………………..87

Table 10. Hierarchical Regression Analyses for Withdrawal Behaviors .……..………..88

Table 11. Summary of Results for All Hypotheses……………………….……………..91

List of Figures

Figure 1. Theory of Planned Behavior………………………………………………...…22

Figure 2. Antecedents and Consequences of Withdrawal States in the PWSD model…..38

Figure 3. Leaving Preference and Perceived Control Interaction on Quit Intentions……86

Introduction

The phenomenon of employee turnover has fascinated researchers and practitioners since the early twentieth century (Barrick & Zimmerman, 2005; Hom,

2011). The unwavering scholarly pursuit to understand how and why employees leave their jobs has inspired over 1,500 published turnover studies (Holtom, Mitchell, Lee, &

Eberly, 2008), as well as many comprehensive literature reviews (e.g., Hom & Griffeth,

1995; Maertz & Campion, 1998; Muchinsky & Tuttle, 1979; Schuh, 1967) and meta- analyses (e.g., Cotton & Tuttle, 1986; Griffeth, Hom, & Gaertner, 2000; Tett & Meyer,

1993; Zimmerman & Darnold, 2009). Most organizations regularly track turnover metrics and devote extensive resources to employee retention strategies for managing and reducing turnover (Allen, Bryant, & Vardaman, 2010; Hausknecht & Trevor, 2011; Hom,

Roberson, & Ellis, 2008; Steel, Griffeth, & Hom, 2002; Trevor & Nyberg, 2008).

The enduring focus on employee turnover mainly stems from the prevailing assumption that it has negative consequences for organizational performance

(Hausknecht & Trevor, 2011; Hom & Griffeth, 1995; Maertz & Campion, 1998). Most directly, organizations suffer the high costs associated with recruiting, selecting and training new employees to replace those who have left (Allen et al., 2010; Cascio, 2006;

O’Brien-Pallas et al., 2006; Sturman, Trevor, Boudreau, & Gerhart, 2003). From a human or social capital perspective, employee turnover can hurt organizational performance through the loss of valuable knowledge and skills, organizational memory, and resources embedded in social relationships (Dess & Shaw, 2001; Shaw, Duffy,

Johnson, & Lockhart, 2005). Other possible negative consequences include decreased satisfaction among stayers and negative publicity from leavers (Mobley, 1982). 12

Research has linked higher organizational turnover rates with undesirable financial outcomes such as lower sales (McElroy, Morrow, & Rude, 2001; Shaw, Duffy et al., 2005), weaker revenue growth (Baron, Hannan, & Burton, 2001; Batt, 2002), and reduced profits (McElroy et al., 2001; Morrow & McElroy, 2007; Peterson & Luthans,

2006; Riordan, Vandenberg, & Richardson, 2005). Furthermore, organizations may suffer detrimental effects from employee turnover in the areas of customer service

(Hausknecht, Trevor, & Howard, 2009; Kacmar, Andrews, Rooy, Steilberg, & Cerrone,

2006; Subramony & Holtom, 2012), instrumental communication (Bluedorn, 1982;

Mueller & Price, 1989), accident rates (Shaw, Gupta, & Delery, 2005), employee attitudes (Morrell, Loan-Clarke, & Wilkinson, 2004; Mueller & Price, 1989), and counterproductive behaviors (Gelade & Ivery, 2003; Kacmar et al., 2006). Retaining talent, particularly high performers and those with hard-to-replace skills, can provide crucial competitive advantages for organizations (O’Brien-Pallas et al., 2006; Reitz &

Anderson, 2011; Steel et al., 2002).

On the other hand, employee turnover can lead to some positive outcomes such as higher performing replacements (Dalton, Krackhardt, & Porter, 1981; Dalton & Todor,

1979), greater innovation (Abelson & Baysinger, 1984; Schneider, Goldstein, & Smith,

1995), and cost reduction through restructuring (Alexander, Bloom, & Nuchols, 1994;

Mobley, 1982). However, meta-analytic results suggest that turnover costs far outweigh any benefits for organizations (Hancock, Allen, Bosco, McDaniel, & Pierce, 2013). The pervasive concerns about negative consequences of turnover naturally raise questions about why employees leave and what organizations can do to prevent it. 13

Most turnover theory and research aligns with a goal of helping organizations identify actions to minimize the cost of unwanted quits (Price, 2004). Therefore, it has primarily focused on voluntary turnover decisions attributed to individual employees rather than termination or layoff decisions made by organizations and therefore categorized as involuntary turnover (Barrick & Zimmerman, 2005; Holtom et al., 2008).

Traditional turnover approaches rarely acknowledge reasons for leaving beyond dissatisfaction and alternative employment (Hom, Mitchell, Lee, & Griffeth, 2012;

Maertz & Campion, 1998). They also often skip the intervening steps between initial formation of a desire or intent to leave and the final act of leaving an organization (Lee &

Mitchell, 1994).

In their proximal withdrawal states and destinations (PWSD) model, Hom et al.

(2012) departed from traditional turnover models by expanding the conceptual domain to include the full scope of organizational participation and withdrawal. They observed that although everyone eventually leaves their organization, turnover varies considerably across individuals in its type, speed, and destination. With the aim of explaining these differences, Hom et al. (2012) introduced the concept of “proximal withdrawal states,” which hypothetically derive from employee preferences to stay or leave a job, as well as their perceived control over acting on that preference. These cognitive states motivate individuals to either participate or withdraw from organizations over time, as demonstrated through their attitudes and behaviors. The PWSD model includes a broad range of potential antecedents for preference and perceived control, as well as individual difference factors that may predispose people toward certain mindsets about organizational participation and withdrawal. 14

The current study builds on Hom et al.’s (2012) PWSD model with the intent of making two valuable contributions to the turnover literature. First, I develop reliable and efficient measures of the preference to stay or leave and perceived control over acting on this preference. In the PWSD model, these two core constructs determine the proximal withdrawal states, but Hom et al. (2012) did not specify how to measure them.

Therefore, developing these measures represents a logical first step toward empirically testing the PWSD model’s propositions. Second, I demonstrate the construct validity of these new measures by testing their relationships with employee attitudes, intentions, and behaviors. Not only would compelling validation evidence support the measurement endeavor, but also it would provide support for the PWSD model’s fundamental assumption that stay/leave preference and perceived control play crucial roles in the context of organizational participation and withdrawal.

The sections below cover approaches to classifying turnover, then an overview of core and secondary components featured in turnover theory, followed by a review of selected influential models that concludes with the PWSD model. Next, I introduce the current research, including hypotheses derived from the PWSD model and other relevant literature. After that, I present the methodology and results from a measurement development study followed by a measurement validation study. Finally, I discuss implications of these studies for the PWSD model and future research. 15

Turnover Classification

Historical approaches to classifying turnover reflect prevailing assumptions about this event, in particular the perceptions of control. Researchers and organizations often classify turnover as either voluntary or involuntary (Maertz & Campion, 1998). Some also classify turnover outcomes as avoidable or unavoidable. This section reviews the underlying assumptions and challenges related to these popular classification approaches.

Voluntary/Involuntary

Historically, both research and practice have focused on voluntary turnover decisions attributed to individual employees, while paying less attention to the termination or layoff decisions made by organizations (Allen et al., 2010; Hom, 2011;

Maertz & Campion, 1998). This voluntary/involuntary distinction rests on a core assumption that organizations already hold control over the latter type of turnover decision, which implies that they no longer want and/or need the employee (Campion,

1991; Holtom et al., 2008). However, turnover due to other reasons typically classified as involuntary, such as death or illness, does not fall under the control of organizations or employees (Abelson & Baysinger, 1984; Price, 1975). In situations where employees quit in favor of another job, they do appear to have much more control over that decision than the organization does (Bluedorn, 1978; Muchinsky & Tuttle, 1979). The elimination of mandatory policies has shifted control over retirement decisions toward the individual as well (Hanisch & Hulin, 1990). Therefore, theory and research typically focuses on turnover classified as voluntary, treating it as a motivated individual choice behavior

(Campion, 1991).

Avoidable/Unavoidable

Dalton et al. (1981) proposed an alternative dichotomy based on whether the organization held control over the outcome and therefore could have avoided that employee’s exit. In particular, organizations lack control over employee departures for reasons such as health, family commitments, and education. Such reasons seem less avoidable than other quit decisions classified as voluntary turnover, at least from the organizational perspective (Dalton et al., 1981). Most organizations view losing employees through seemingly uncontrollable factors as less acceptable than situations where the organization could presumably have prevented employees from leaving, but chose to let them go (Campion, 1991). Focusing on dysfunctional turnover (i.e., organization preferred to retain the individual), Dalton et al. (1981) found that approximately half of these cases fit into one of the reasons classified as unavoidable.

Other research suggests that those who leave for avoidable reasons are less satisfied and committed than those who leave for unavoidable reasons (Abelson, 1987).

Classification Challenges

Most organizations record and categorize reasons for turnover when employees leave, but ambiguity and error plague this data (Mobley, Griffeth, Hand, & Meglino,

1979; Muchinsky & Tuttle, 1979). Poor and/or incomplete record keeping, subjective interpretation, post-decision justification, and even falsification seriously weaken attempts to classify turnover decisions (Abelson, 1987; Dalton, Todor, & Krackhardt,

1982). For example, employers may code involuntary quits as voluntary to protect the reputation of leavers and avoid any potential retaliation (Campion, 1991; Hom et al.,

2012). Furthermore, employers typically code turnover avoidability based on their own 17 perceptions, sometimes only somewhat supplemented by the alleged reasons shared by leavers in exit interviews (Griffeth, Lee, Mitchell, & Hom, 2012). Another challenge stems from the complex reasons behind turnover decisions, for which the organization and individual may share responsibility (Bluedorn, 1978; Maertz & Campion, 1998).

Inaccurate classifications can result when employees hide some of their true motivations, such as when they feel coerced into leaving due to pressure from family, another employer, or some other authority figure (Campion, 1991).

Given these significant challenges with classification, treating voluntariness and avoidability as continuous variables might prove more helpful in explaining complex turnover decisions (Campion, 1991). For example, some turnover reasons (e.g., quitting due to relocation of a spouse, pregnancy, or to avoid expected involuntary termination) seem to have involuntary aspects despite ultimately resulting in an individual employee choice (Maertz & Campion, 1998). Furthermore, the typical classification of retirement as involuntary turnover conflicts with recent employment trends. Most organizations have moved from defined benefit plans awarding pensions to defined contribution or

401K plans, thereby allowing participants to retire on their own timelines (Zurlo, 2012).

Finally, even if an organization could prevent the majority of employee turnover with sufficient intervention, its decision makers would often choose not to act after considering the costs (Campion, 1991). Acknowledging the inherent complexity of turnover reasons, Hom et al. (2012) advocated moving away from the traditional focus on voluntary turnover to an expanded criterion including all types and destinations.

Turnover Theory Components

In order to explain and predict turnover, researchers have developed and tested many conceptual models, incorporating a multitude of psychological and environmental antecedents (Holtom et al., 2008; Schwab, 1991). Although these models differ notably in their interpretations of how the turnover process works and what factors to consider, they have consistently featured three core components: job attitudes, behavioral intentions, and employment opportunity (Steel & Lounsbury, 2009). In this section, I introduce these core components, as well as some secondary ones that have drawn significant attention in turnover theory and research.

Job Attitudes

Attitudes represent relatively stable evaluative dispositions toward a specific object (i.e., person, situation, or other entity) that vary in intensity and tend to guide an individual’s responses to that object (Eagly & Chaiken, 1999). Therefore, job attitudes consist of evaluations that express feelings toward, beliefs about, and attachment toward one’s job (Judge & Kammeyer-Mueller, 2012). Specific job attitudes vary in their evaluation target (e.g., occupation, organization, supervisor, aspects of the job itself) and in what factors most influence that evaluation (Schleicher, Hansen, & Fox, 2011). Two of the most popular job attitude constructs, job satisfaction and organizational commitment, have long and influential histories in turnover research (Hom, 2011).

Job satisfaction. Job satisfaction reflects an individual’s attitude toward their job overall, as well as various specific aspects (e.g., pay, supervision, co-workers) of the job

(Spector, 1997). Empirical research, including meta-analyses (e.g., Faragher, Cass, &

Cooper, 2005; Griffeth et al., 2000; Hackett, 1989; Hom & Griffeth, 1995; Judge, 19

Thoresen, Bono, & Patton, 2001; Koslowsky, Sagie, Krausz, & Singer, 1997; Tett &

Meyer, 1993) has consistently found evidence linking job satisfaction with many important organizational (e.g., turnover, absenteeism, lateness, performance) and individual (e.g., burnout, depression, subjective physical illness) outcomes. Since the earliest studies of turnover (e.g., Giese & Ruter, 1949; Hulin, 1966; Waters & Roach,

1971; Wickert, 1951), researchers have identified job dissatisfaction as a primary reason why people leave their jobs (Brayfield & Crockett, 1955; Maertz & Campion, 1998;

Schuh, 1967). These feelings of discontent with one’s job can arise from beliefs that successful performance will not lead to valued outcomes (Vroom, 1964), inequitable comparisons between inputs and job rewards (Adams, 1965), and/or discrepancies between job experiences and expectations (Porter & Steers, 1973).

Organizational commitment. Organizational commitment represents the degree to which an employee feels linked or attached with his or her organization (Schleicher et al., 2011). Therefore, organizational commitment differs from job satisfaction in both substance and referent. Most early commitment research (e.g., Becker, 1960; Grusky,

1966; Kanter, 1968; Porter, Steers, Mowday, & Boulian, 1974) focused on explaining turnover and it featured many different conceptual definitions. However, the three- component model (Allen & Meyer, 1990; Meyer & Allen, 1991) has dominated commitment research since its debut (Klein, Molloy, & Cooper, 2009; Solinger, van

Olffen, & Roe, 2008), despite lingering questions about the meaning, structure, and measurement of commitment (e.g., Jaros, 1997; Klein, Malloy, & Brinsfield, 2012).

In the three-component model, psychological linkage between employees and their organizations takes three distinct forms: affective, continuance, and normative 20 commitment (Allen & Meyer, 1996). Affective commitment refers to an employee’s emotional bond to their organization that inspires a voluntary connection (Allen &

Meyer, 1990). Continuance commitment arises from an employee’s comparison of perceived costs and benefits associated with organizational membership (Allen & Meyer,

1996). Finally, normative commitment refers to feelings of obligation toward the organization (Meyer & Allen, 1991). The model treats these three components as related, but distinct (Meyer, Stanley, Herscovitch, & Topolnytsky, 2002).

Several meta-analytic studies (e.g., Mathieu & Zajac, 1990; Meyer et al., 2002;

Tett & Meyer, 1993) have helped establish organizational commitment as a valuable predictor of organizational (e.g., turnover, attendance, performance, organizational citizenship behavior) and individual (e.g., stress, work-family conflict) outcomes.

However, affective commitment has received far more empirical support than the other two components (Meyer et al., 2002; Solinger et al., 2008). Research has shown strong relationships between organizational commitment and other job attitudes, particularly job satisfaction and job involvement (Meyer et al., 2002; Tett & Meyer, 1993).

Summary. Extensive research activity has bolstered the importance of job attitudes in predicting turnover (Holtom et al., 2008; Hom, 2011). Other job attitude variables that have drawn significant attention in turnover research include job involvement (e.g., Huselid & Day, 1991; Lee & Mowday, 1987) and perceived organizational support (e.g., Edwards & Peccei, 2010; Rhoades, Eisenberger, & Armeli,

2001). Therefore, almost all theoretical models feature job attitudes, primarily job satisfaction and/or organizational commitment, as key turnover antecedents (Steel &

Lounsbury, 2009). 21

Behavioral Intentions

The robust research evidence linking attitude and behavior across situations naturally raises process questions about how attitudes influence behaviors (Schleicher et al., 2011). One of the most influential models to address this link, Ajzen’s (1985, 1991) theory of planned behavior, evolved from the theory of reasoned action (Ajzen &

Fishbein, 1980; Fishbein & Ajzen, 1975). In both of these theories, the most proximal cause of any given behavior is an individual’s intention to perform that behavior. In general, the stronger the intention to engage in a behavior, the more likely the individual will perform it (Fishbein & Ajzen, 1975). However, performance of most behaviors depends at least partly on the degree of volitional control, expressed through non- motivational factors such as availability of opportunities and resources (Ajzen, 1985).

The theory of planned behavior extended the theory of reasoned action by stressing the crucial importance of both actual and perceived behavioral control (Ajzen,

1991). This refers to perceptions about ease or difficulty of performing the behavior. It presumably reflects experience as well as anticipated obstacles. As shown in Figure 1, perceived behavioral control can influence behavior both directly as a proxy for actual control and indirectly through intentions. Behavioral intentions also depend on the individual’s attitude toward the behavior and subjective norm associated with performing the behavior (Ajzen, 1985). The strongest intentions to perform a behavior should occur when someone has a favorable attitude about performing the behavior, believes that important others support performing it, and perceives that they have control over performing the behavior (Ajzen, 1991).

Attitude Toward the Behavior

Behavioral Subjective Norm Behavior Intention

Perceived Behavioral Control

Figure 1: The theory of planned behavior (adapted from Ajzen, 1991).

Fishbein and Ajzen’s theoretical work on attitudes and behavior has greatly influenced turnover theory and research (Maertz & Campion, 1998; Steel & Lounsbury,

2009). In many models of the turnover process (e.g., Aquino, Griffeth, Allen, & Hom,

1997; Jackofsky, 1984; Mobley et al., 1979; Steers & Mowday, 1981), behavioral intentions serve as the crucial intermediate step between attitudes and turnover behavior.

Some models (e.g., Hom, Griffeth, & Sellaro, 1984; Maertz & Griffeth, 2004) have also included normative beliefs or pressures to quit or stay. However, perceived behavioral control has received relatively little attention in turnover research (Hom et al., 2012).

In a turnover context, researchers typically express behavioral intentions as a specific intent to quit or stay (Steel & Lounsbury, 2009). Some (e.g., Crossley, Bennett,

Jex, & Burnfield, 2007; Hom & Griffeth, 1991; Mobley et al., 1979) have also considered intentions toward job search as a potential antecedent of turnover. Extensive empirical evidence confirms the recognized status of quit intentions as the strongest predictor of individual turnover (Griffeth et al., 2000; Maertz & Campion, 1998; Tett & Meyer,

1993). 23

Employment Opportunity

Historically, research focused on labor-market statistics (e.g., unemployment rates) has demonstrated that the state of the economy strongly predicts aggregate turnover rates across a wide range of employee populations (Hausknecht & Trevor, 2011; Hulin,

Roznowski, & Hachiya, 1985; March & Simon, 1958; Steel & Griffeth, 1989).

Therefore, most turnover models include some consideration of employment opportunity or alternatives (Steel & Lounsbury, 2009). In general, these models suggest that individuals who have greater opportunity for obtaining alternative employment will more likely leave their jobs.

Researchers have represented employment opportunity with both perceptual measures (e.g., Crossley et al., 2007; Gerhart, 1990; Hom & Griffeth, 1991) and more objective job-market indicators (e.g., Hulin et al., 1985; Kammeyer-Mueller, Wanberg,

Glomb, & Ahlburg, 2005; Steel, 2004). Macro-level studies of unemployment rates typically suggest stronger links to turnover than individual-level results using either job market or perceptual measures (Griffeth & Hom, 1988; Steel, 1996; Steel & Griffeth,

1989). However, national and local labor market data may poorly reflect the job opportunities available to an individual with his/her own specific mix of skills and experiences (Hulin et al., 2005). On the other hand, job market perceptions must rely on the extent and accuracy of job information available to the individual.

Steel and Griffeth (1989) contended that several methodological issues have contributed to the relatively weak empirical support for perceived employment alternatives in shaping turnover decisions. For example, most studies relied on single- item measures that fail to capture the multi-dimensional nature of job perceptions. 24

Griffeth and his colleagues (e.g., Griffeth, Steel, Allen, & Bryan, 2005) addressed this issue by developing an employment opportunity index (EOI) with five dimensions: ease of movement, desirability of movement, networking, crystallization of alternatives, and mobility. Their multi-dimensional approach to measuring perceived employment opportunity has helped reinforce its value in predicting and understanding turnover.

Personal and Organizational Factors

Although job attitudes, behavioral intentions, and employment opportunity have dominated turnover theory and research for over 60 years, other personal and organizational factors have drawn attention as well. Researchers have advanced our understanding and prediction of turnover by incorporating a wide range of personal factors. Some of them draw from the person’s work background, such as skill (e.g.,

Hulin et al., 1985; Mobley et al., 1979), training (e.g., Hulin et al., 1985; Price &

Mueller, 1986), and tenure (e.g., Barrick & Zimmerman, 2005; Mowday, Porter, & Stone,

1978). For example, more tenured employees have a lower likelihood of leaving their organizations (Griffeth et al., 2000) than newer employees.

Other factors cover what individual employees bring to the work environment.

They include personality traits (e.g., Allen, Weeks, & Moffit, 2005; Muchinsky &

Morrow, 1980), cognitive ability (e.g., Gerhart, 1990; Maltarich, Nyberg, & Reilly,

2010), values (e.g., Steers & Mowday, 1981; Takase, Yamashita, & Oba, 2008), family responsibilities (e.g., Kim, Price, Mueller, & Watson, 1996; Mobley et al., 1979), age

(Bluedorn, 1982; Jackofsky, 1984), and education (e.g., Mowday et al., 1978; Spencer &

Steers, 1980). For example, several studies have shown that employees with higher self- 25 confidence are less likely to quit (e.g., Barrick & Zimmerman, 2005; Hom & Griffeth,

1995; Lee et al., 1992).

Many job and organizational factors have played prominent roles in turnover research. They include job characteristics (e.g., Price & Mueller, 1981; Thompson &

Prottas, 2005), job expectations (e.g., Michaels & Spector, 1982; Steers & Mowday,

1981), role conflict (e.g., Bedeian & Armenakis, 1981; March & Simon, 1958), job rewards (e.g., Price, 1975; Rusbult & Farrell, 1983), costs of quitting (e.g., Hulin et al.,

1985; Kammeyer-Mueller et al., 2005), and job stress (e.g., Hom & Kinicki, 2001;

Sheridan & Abelson, 1983). For example, individuals are more likely to remain in their jobs when the costs of quitting are higher (Hulin et al., 1985; Kammeyer-Mueller et al.,

2005; Rusbult & Farrell, 1983).

Several turnover models (e.g., Allen & Griffeth, 1999; Steers & Mowday, 1981) feature job performance and most research suggests that lower performers are more likely to leave (McEvoy & Cascio, 1987), with some evidence of a curvilinear relationship

(e.g., Jackofsky, 1984; Williams & Livingstone, 1994). Finally, some turnover research includes behaviors that may represent alternative forms of withdrawal from the organization. In general, individuals who ultimately leave their jobs tend to engage in higher levels of absenteeism (Lyons, 1972; Mitra, Jenkins, & Gupta, 1992), lateness

(Koslowsky et al., 1997; Rosse, 1988), and job search behavior (Blau, 1993; Hom &

Griffeth, 1991).

Summary

The continued interest in identifying turnover predictors has led to a high level of diversity among turnover models, despite their reliance on similar core mechanisms. 26

However, some of these potential predictors have received relatively weak empirical support and even complex models rarely explain more than 25% of the variance in turnover (Hom, 2011; Maertz & Campion, 1998; Mitchell & Lee, 2001). A comprehensive meta-analysis (Griffeth et al., 2000) identified proximal antecedents in the withdrawal process (e.g., job attitudes, withdrawal cognitions, and job search) as the strongest turnover predictors (see Table 1). On the other hand, distal antecedents such as compensation, alternative job opportunities, and characteristics of the work environment had weaker relationships with turnover (Griffeth et al., 2000). This meta-analytic study also demonstrated that for some turnover predictors, the effect sizes and even directionality vary widely across situations and populations. The moderate findings for even its strongest predictors reinforce the prevailing view of turnover as a very complex phenomenon. The next section covers some of the most influential theoretical models developed to help explain turnover. 27

Table 1

Meta-Analysis of Antecedents of Employee Turnover

Corrected Corrected Predictor validity Predictor validity coefficient coefficient Job attitudes Compensation Organizational commitment -.27 Distributive justice -.11 Overall job satisfaction -.22 Pay levels -.11 Pay satisfaction -.08 Withdrawal cognitions Job Content Quit intentions .45 Work satisfaction -.19 Withdrawal cognitions .36 Job scope -.14 Thoughts of quitting .29 Job involvement -.12 Expected withdrawal utility .28 Routinization .11 Job search Stress Job search methods .50 Role clarity -.24 Search intentions .34 Role conflict .22 Job search behaviors .31 Overall stress .16 General job search .29 Role overload .12 Other withdrawal actions Demographics Absenteeism .21 Tenure -.23 Performance -.17 Children -.16 Lateness .06 Age -.11 Alternative employment Kinship obligation -.10 Comparison of alternatives .19 Training -.08 Alternative job opportunities .15 Education .06 Other job aspects Marital status .05 Promotional chances -.16 Sex -.03 Instrumental communication .14 Race -.02 Participation -.13 Cognitive ability .02 Leadership Leader-Member exchange -.25 Supervisory satisfaction -.13 Co-worker Co-worker satisfaction -.13 Work group cohesion -.13 Note. Adapted from Griffeth, Hom, & Gaertner (2000, Tables 1-4, p. 465-469).

Turnover Models

The vast body of literature devoted to employee turnover has regularly featured papers proposing new turnover models or revisions of existing models. Using relatively conservative criteria, Steel and Lounsbury (2009) identified 24 distinct theoretical models of the turnover process in publication, not including their many revisions. Turnover theory has grown in complexity over time, expanding its focus from immediate causes and consequences to more distal ones (Holtom et al., 2008). Despite the fact that most models share the key components discussed above, they differ greatly in their interpretation of how this complex process works and which factors matter most. In this section, I review some of the most influential models of the turnover process, with a particular emphasis on those that directly inspired the PWSD model (Hom et al., 2012).

Foundational Models

Organizational equilibrium. In March and Simon’s (1958) seminal theory of organizational equilibrium, perceived desirability of movement (i.e., job attitudes) combines with perceived ease of movement (i.e., employment opportunity) to influence an employee’s motivation to leave the organization. They proposed that the decision to stay or leave depends on the balance of organizational inducements with employee contributions. Individuals who believe that their contributions exceed the inducements provided by the organization would have higher desire to leave (March & Simon, 1958).

According to March and Simon (1958), job satisfaction influences perceived desirability of movement, such that more satisfied employees would have less desire to leave the organization. In contract, perceived ease of movement primarily depends on perceived alternatives outside the organization, as employees with more alternatives 29 would more likely leave the organization. However, other factors such as perceived possibilities for transfer within the organization may decrease an employee’s perceived desirability for leaving the organization (March & Simon, 1958). Although only a few published studies (e.g., Anderson & Milkovich, 1980; Jackofsky & Peters, 1983) have directly tested March and Simon’s model, the fact that almost all subsequent models include conceptual variations of movement ease and desirability (Steel & Lounsbury,

2009) demonstrates its profound impact on turnover theory. Most notably, March and

Simon inspired the prolific research associated with Price (1975) and Mobley (1977), as described below.

Model of employee turnover. James Price strongly influenced turnover research and theory by introducing concepts from other fields and pioneering causal model testing

(Hom, 2011). Price (1975) built his initial model on March and Simon’s (1958) foundation of movement ease and desirability. One of its earliest refinements (Price &

Mueller, 1981) incorporates behavioral intentions. Price and his colleagues developed and refined a complex model through over 30 studies, mostly involving hospital employees (e.g., Kim et al., 1996; Mueller & Price, 1990; Price, 1975; Price & Bluedorn,

1980). Their research found support for a variety of environmental (e.g., opportunity, kinship responsibility), individual (e.g., training, job involvement, positive affectivity, negative affectivity), and structural (e.g., autonomy, justice, stress, pay, promotional chances, routinization, social support) variables that can influence turnover, primarily through job satisfaction and/or organizational commitment (Price, 2001, 2004). Price’s work led other researchers to focus on explaining the predictors of job satisfaction and 30 organizational commitment as a means to the end of understanding turnover (Holtom et al., 2008).

Intermediate linkages. In his intermediate linkages model, Mobley (1977) focused on explaining how the turnover process works by introducing a set of withdrawal cognitions and job search behaviors that link job dissatisfaction to actual turnover behavior. As in March and Simon’s (1958) model, turnover results from job dissatisfaction and evaluation of alternative job opportunities. However, Mobley (1977) added seven cognitive steps to explain how employees progress from dissatisfaction to the actual quit decision, with behavioral intent to quit as the immediate precursor to turnover. In his framework, quit intentions only develop after dissatisfied employees progress through a series of withdrawal cognitions (e.g., thoughts of quitting, estimating benefits and costs) and job search behaviors (e.g., job search, evaluate alternatives).

Over the next decade, his work inspired a series of empirical studies that tested predictions of the original model and its many variations (e.g., Arnold & Feldman, 1982;

Bannister & Griffeth, 1986; Michaels & Spector, 1982; Miller, Katerberg, & Hulin, 1979;

Mobley, Horner, & Hollingsworth, 1978; Mowday, Koberg, & McArthur, 1984;

Youngblood, Mobley, & Meglino, 1983). The intense research activity focused on this model has significantly advanced our understanding of the withdrawal process (Hom &

Griffeth, 1995). Since their debut in Mobley’s (1977) intermediate linkages model, behavioral intentions have played a prominent role in almost all subsequent turnover models (Steel & Lounsbury, 2009).

Summary. The foundational models described above all made significant contributions to the study of turnover. They have inspired many other theoretical models 31 and a vast body of turnover research over the past 40 years. Several key concepts introduced by these models remain core components of turnover theory today. The next few sections cover alternative perspectives that have also proven influential.

Unfolding Model

Lee and Mitchell (1991) challenged the traditional approach to turnover by contending that it has an overly restrictive focus on a rational decision process that starts with dissatisfaction and concludes with voluntary exit from the organization. They developed the unfolding model with four paths to help explain why many employees leave their jobs for reasons other than dissatisfaction or alternative employment. Only one of these paths resembles the rational cognitive process described by the traditional turnover approach (e.g., March & Simon, 1958; Mobley et al., 1979; Price & Mueller,

1981) where gradual job misfit leads to job dissatisfaction and ultimately, turnover (Lee

& Mitchell, 1991).

The unfolding model explains other decision paths through an innovative concept called shocks to the system, inspired by image theory (Beach & Mitchell, 1987). A variety of shock events may prompt further evaluation of the job and thoughts of quitting

(Lee & Mitchell, 1994). In these three decision paths, a personal event triggers enactment of a pre-existing plan or script, a negative shock triggers re-evaluation of attachment to the organization, or an unsolicited job offer triggers comparison of alternatives (Mitchell & Lee, 2001).

Empirical findings have linked shocks to turnover in accounting (e.g., Lee,

Mitchell, Holtom, McDaniel, & Hill, 1999), banking (e.g., Holtom, Mitchell, Lee, &

Inderrieden, 2005), information technology (e.g., Niederman, Sumner, & Maertz, 2007), 32 and nursing (e.g., Lee, Mitchell, Wise, & Fireman, 1996; Morrell, 2005). However, research on the unfolding model relies heavily on accounts of quit decisions after turnover has occurred (Hom, 2011), and only takes into account people who leave. This approach has questionable accuracy due to retrospective rationalization, faulty attributions, memory lapses, and oversimplification (Miller, Cardinal, & Glick, 1997).

Furthermore, it does not address employees who want to leave, but cannot because of other demands in their lives (Hom, 2011).

Despite these methodological challenges, the unfolding model has profoundly influenced thinking on turnover, leading others to explore beyond the traditional job dissatisfaction path. Several subsequent turnover models have incorporated shocks to help explain why and how people leave (e.g., Allen et al., 2009; Kammeyer-Mueller, et al., 2005; Maertz & Campion, 2004; Pleskac, Keeney, Merritt, Schmitt, & Oswald,

2011). Finally, the unfolding model’s revolutionary view of turnover helped inspire the concept of job embeddedness, shifting the focus from why people leave to why they stay.

Job Embeddedness

Mitchell and his colleagues introduced job embeddedness to help explain why some people stay on the job, even under circumstances that would lead others to leave

(Mitchell, Holtom, Lee, Sablynski, & Erez, 2001). It considers the extent to which individuals feel like they are part of an intricate web of attachments to the organization and/or community. A highly embedded individual can feel stuck, which suggests inertia and makes that individual less likely to leave a job (Mitchell & Lee, 2001).

The key aspects of job embeddedness are the links that an employee has to other people or groups, how he or she fits into the organization or community, and sacrifices 33 associated with leaving (Burton, Holtom, Sablynski, Mitchell, & Lee, 2010). Work- related sources (e.g., positive relationships with supervisor and/or coworkers, good health benefits) and non-work related sources (e.g., spouse works in the same area, parents live in the community) can contribute to job embeddedness (Ramesh & Gelfand, 2010).

Therefore, job embeddedness includes six dimensions: organization links, organization fit, organization sacrifice, community links, community fit, and community sacrifice

(Lee, Mitchell, Sablynski, Burton, & Holtom, 2004).

A recent meta-analysis of 65 independent samples found that on-the-job and off- the job embeddedness had inverse relationships with turnover intentions and actual turnover (Jiang, Liu, McKay, Lee, & Mitchell, 2012). These relationships held even after controlling for job satisfaction, affective commitment, and job alternatives. Other studies have linked job embeddedness to performance (e.g., Halbesleben & Wheeler, 2008; Lee et al., 2004; Sekiguchi, Burton, & Sablynski, 2008), organizational citizenship behaviors

(e.g., Lee et al., 2004; Sekiguchi et al., 2008), and absences (e.g., Lee et al., 2004).

Job embeddedness research has delivered promising early returns, but has some issues that merit further consideration (Zhang, Fried, & Griffeth, 2012). For example, research has generated limited support for the link between community embeddedness and turnover (e.g., Lee et al., 2004; Mitchell et al., 2001), with recent studies failing to find such a relationship (e.g., Allen, 2006; Crossley et al., 2007, Mallol, Holtom, & Lee,

2007; Ramesh & Gelfand, 2010). Another challenge relates to the formative measurement model used by Lee et al. (2004), which assumes that causality flows from the items to the latent construct of embeddedness. This makes it more difficult to evaluate the psychometric properties of these scales and to incorporate them into 34 theoretical models (MacKenzie, Podsakoff, & Jarvis, 2005). Measurement concerns have led other researchers to either use shortened versions of the original scales (e.g., Allen &

Shanock, 2013; Felps et al., 2009) or create alternative scales with a reflexive measurement model (e.g., Crossley et al., 2007; Ng & Feldman, 2012). Despite these challenges, job embeddedness theory has greatly enhanced our understanding of why people stay and stimulated new avenues of research (Hom, 2011).

General Withdrawal Models

The term “withdrawal” links turnover with other behaviors such as absence and lateness that are very costly and visible for most organizations (Roznowski & Hanisch,

1990). Hanisch and Hulin (1991) defined organizational withdrawal as “a general construct composed of a variety of acts, or surrogate intentions, that reflect both the negativity of the precipitating job attitudes and the target of these negative job attitudes”

(p. 111). It includes employee efforts to remove themselves from their jobs, as well as avoidance of specific aspects of their jobs while maintaining organizational membership

(Hanisch & Hulin, 1990).

In models of general employee withdrawal (e.g., Beehr & Gupta, 1978; Bluedorn,

1982; Hanisch & Hulin, 1991; Herzberg, Mausner, Peterson, & Capwell, 1957; Lyons,

1972; Rosse & Miller, 1984), affective responses to the job can trigger a variety of behaviors that serve to place physical and/or psychological distance between employees and their aversive work environments. Withdrawal cognitions such as perceptions and thoughts about the work environment, alternatives, equity, fairness, and adaptive strategy can help determine the specific behavioral reaction (Colquitt, Noe, & Jackson, 2002;

Koslowsky, 2009; Rosse & Hulin, 1985; Rosse & Miller, 1984). 35

Some advocates of this approach suggest using measures of general attitudes to predict general behavioral constructs or behavioral families, rather than focusing on individual withdrawal behaviors (Hanisch, 1995; Hanisch & Hulin, 1991). The research evidence suggests that dissatisfied individuals are more likely to engage in a wide range of organizational withdrawal behaviors (Blau, 1994; Bowling, Burns, & Beehr, 2010;

Kammeyer-Mueller, et al., 2005; Koslowsky et al., 1997; Laczo & Hanisch, 1999).

However, the positive attraction of alternative jobs or activities can motivate these behaviors for some employees (Mobley, 1982; Porter & Steers, 1973; Sablynski, Lee,

Mitchell, Burton, & Holtom, 2002).

Despite the theoretical breadth of organizational withdrawal, most research has focused on three main behaviors: turnover, absence and lateness. Meta-analytic studies

(e.g., Koslowsky et al., 1997; Mitra et al., 1992) have reported modest positive relationships among these three withdrawal behaviors. Other relatively minor behavioral manifestations of withdrawal (e.g., social loafing, free riding, long lunch breaks, daydreaming, skipping meetings, shirking responsibilities, excessive socializing) have less impact on organizations, but may lead to more overt withdrawal behaviors in the future (Hanisch, 1995; Koslowksy, 2009). Consideration of the full range of organizational withdrawal behaviors can expand our understanding of how and why people leave their organizations.

Summary

The complex phenomenon of employee turnover continues to fascinate practitioners and scholars after many decades of intense research activity. Building upon the strong foundation laid by March and Simon (1958), the turnover literature has 36 generated a wide variety of theories to help explain how and why employees leave their jobs. Despite their many differences, virtually all turnover approaches share core assumptions about the process (Hom et al., 2012). Specifically, they assume that distal antecedents (e.g., individual and job characteristics) lead to attitudinal antecedents (e.g., job satisfaction, organizational commitment), which in turn influence the turnover criterion space (e.g., quit intentions and voluntary quits). The vast majority of research activity has focused on distal and attitudinal antecedents. In the next section, I discuss the PWSD model (Hom et al., 2012), which departs from traditional views by highlighting and elaborating turnover criteria and proximal antecedents.

Proximal Withdrawal States and Destinations Model

Hom et al. (2012) challenged traditional views by depicting turnover as a time- based process wherein distal influences (e.g., job characteristics) lead to intermediate antecedents (e.g., job attitudes, job embeddedness), which lead to direct or proximal antecedents (e.g., intentions), and ultimately to actual leaving destinations. Their approach explicitly classifies intentions and other withdrawal cognitions as proximal antecedents, not as part of the turnover criterion space. It also moves beyond the typical focus on voluntary turnover to include all types of leaving and staying, as well as turnover destinations. They introduced “proximal withdrawal states” to encompass cognitive mind-sets dependent on perceived purposes (staying vs. leaving) and reasons

(locus of causality). These cognitions evolve over time and precede actual departures.

Proximal withdrawal states arise from whether an employee wants to stay or leave a job, as well as their perceived control over acting on that preference (Hom et al., 2012).

These two continuous dimensions are crossed to form four primary states: enthusiastic stayers, reluctant stayers, enthusiastic leavers, and reluctant leavers. Enthusiastic stayers remain because they want to stay and feel like they can stay, whereas reluctant stayers feel like they cannot leave although they would prefer to do so. Enthusiastic leavers want to leave and feel like they can, whereas reluctant leavers feel like they must go despite preferring to stay. Consideration of these proximal withdrawal states can enrich our understanding of why people stay and leave, as well as help explain a wide range of other job attitudes and behaviors. Figure 2 shows the PWSD model’s theorized antecedents and consequences of the proximal withdrawal states (Hom et al., 2012).

Preference Antecedents Constraint Antecedents  Affective Forces  Legal Forces o Job Fit o Employer Pressure to Leave o Initial Job Attitudes o Employment Contracts o Negative Workplace Shocks  Normative Forces  Calculative Forces o External Pressure  Constituent Forces o Workplace Pressure o On- and Off-the-Job Links  Behavioral Forces o Turnover Contagion o Job Sacrifices o Bullying Co-Workers o Community & Family Sacrifices o Abusive Supervisors  Alternative Forces o Racial or Gender Harassment o Few or Undesirable Job Alternatives  Moral/Ethical Forces  Job Protection Systems  Embedding HRM Practices o Unionization o High Involvement Work Structures o Tenure/Seniority Protections o Workforce Inducements  Performance-Enhancing HRM o Workforce Investments o Contingent Rewards  Alternative Forces o Performance Monitoring o Desirable Work or Extrawork  Just Termination Practices Alternatives . Personal Shocks . Spontaneous Job Offers

Individual Differences Stay/Leave Perceived o Core Self-Evaluation Preference Control o Collectivism o Gender Role Ideology Proximal Withdrawal States o Family Power o Corporate Power

o Demographic Background

Consequent Reactions o Job Attitudes o Job Performance o Organizational Citizenship o Counterproductive Behaviors o Work Withdrawal o Job Search

Turnover Types, Destinations, & Speed

Figure 2. Antecedents and consequences of withdrawal states in the PWSD model (Hom et al., 2012, p. 838).

Hom et al. (2012) adopted the motivational forces framework (Maertz & Griffeth,

2004) to describe the main antecedents of stay/leave preference and perceived control. In this comprehensive framework, events and other cognitions can trigger conscious deliberations about organizational membership that involve self-questioning and responding (Maertz & Campion, 2004). Cognitive and emotional responses to these deliberations create motivational forces to either stay or quit the current organization

(Maertz & Griffeth, 2004). The framework includes eight motive categories or forces: affective, calculative, contractual, behavioral, alternative, normative, constituent, and moral/ethical forces. People often experience multiple motivational forces simultaneously, which can interact to exacerbate, mitigate, or even offset the effects of each other (Maertz & Boyar, 2012; Maertz & Griffeth, 2004).

In the next two sections, I discuss the PWSD model’s core dimensions of stay/leave preference and perceived control. That includes reviewing the theoretical background and proposed antecedents for each dimension. In addition to the rather comprehensive sets of antecedents listed, Hom et al. (2012) also acknowledged the potential role of individual differences (e.g., collectivism, core self-evaluations, corporate power, family power, gender role ideology, and demographic background) that may predispose people toward certain proximal withdrawal states.

Preference to Stay or Leave

The PWSD model departs from traditional turnover models by clearly separating desire from perceived freedom to act on that desire (Hom et al., 2012). Some employees essentially quit their jobs psychologically before they physically leave (Burris, Detert, &

Chiaburu, 2008; Greenhalgh, 1980). These desires or preferences differ from behavioral 40 intentions in that they have less specificity about timing and circumstances. Although narrow intention measures targeted at specific actions can serve as potent predictors of those actions, they lack scientific utility for predicting related actions and explaining the underlying behavioral constructs (Graen & Ginsburgh, 1977; Hanisch, Hulin, &

Roznowski, 1998). Therefore, the PWSD model focuses on general preferences toward staying or leaving to help determine the proximal withdrawal states, which have implications for a broad range of attitudes and behaviors.

According to Hom et al. (2012), these stay/leave preferences result from embedding human resource management (HRM) practices and five of the motivational forces (i.e., affective, alternative, calculative, constituent, moral/ethical). Embedding

HRM practices include high involvement work structures, workforce inducements, and workforce investments. These practices can strengthen staying preferences by providing intrinsically satisfying work or developmental opportunities (Batt & Colvin, 2011; Trevor

& Nyberg, 2008).

Affective forces involve emotions aroused by thinking about the organization and membership in it (Maertz & Griffeth, 2004). They can strengthen preferences to stay when an employee feels like a good fit with the organization and/or job (Meyer, Becker,

& Vandenberghe, 2004; Mitchell & Lee, 2001). Affective forces can increase leaving preferences through perceived poor fit (Meyer et al., 2004), negative work shocks (Lee et al., 1996) and work stressors (Chen & Spector, 1992; Rosse & Hulin, 1985).

Calculative forces involve a cognitive evaluation about meeting goals and values through future membership in the current organization (Maertz & Griffeth, 2004).

Motivation to stay will result from a calculation that one can achieve valued goals 41 through continued membership (Lee & Mitchell, 1994). On the other hand, if these valued goals seem unachievable at the current organization, motivation to leave will result (Maertz & Boyar, 2012; Mobley et al., 1979).

Constituent forces stem from employee relationships with individuals and/or groups within the organization (Maertz & Griffeth, 2004). Attachments to constituents would typically motivate employees to stay with the organization, but may become a force to withdraw if a highly valued constituent leaves (Feeley & Barnett, 1997; Felps et al., 2009). Employees may also want to withdraw from people or groups responsible for bullying or other forms of mistreatment in the workplace (Hershcovis & Barling, 2010;

Houshmand, O’Reilly, Robinson, & Wolff, 2012; Olson-Buchanan & Boswell, 2008).

Moral or ethical forces arise from an employee’s values related to turnover in general (Maertz & Griffeth, 2004). Some people believe they should persevere and work hard for the organization that hired them regardless of the circumstances, perhaps due to religious or moral traditions (Blau & Ryan, 1997; Niles, 1999). Others view changing jobs more positively, based on openness to experience, careerism, or an instinctive impulse toward movement (Dougherty, Dreher & Whitely, 1993; Judge & Watanabe,

1995). Employees who hold strong positive or negative values about turnover would feel motivated toward behaving consistently with those values (Festinger, 1957).

Alternative forces result from the employee’s beliefs about obtaining a valued alternative to continuing work at the current organization (Maertz & Griffeth, 2004).

Employees may feel motivated to leave their organization by the attractiveness of work or non-work options (Bretz, Boudreau, & Judge, 1994; March & Simon, 1958; Steel,

2002). This psychological pull of more attractive options can overwhelm other positive 42 feelings about the current job and organization. On the other hand, desire to minimize employment uncertainty and risk can motivate people to stay, particularly if they lack attractive alternatives (Hulin et al., 1985; Vardaman, Allen, Renn, & Moffitt, 2008).

Constraints and Perceived Control

In the PWSD model (Hom et al., 2012) perceived control over acting on a preference to stay or leave the organization will partially determine an individual’s proximal withdrawal state. Employees can feel restricted from staying or leaving by a wide range of circumstances, including conditions based on the occurrence of a future event (Maertz & Campion, 2004). Some turnover research has incorporated constraints such as available job opportunities (e.g., Greenhalgh, 1980; Hulin et al., 1985; Mobley,

1977), job markets (e.g., Steel, 1996; Trevor, 2001), family or kinship responsibilities

(e.g., Kim et al., 1996; Mobley et al., 1979), and costs of quitting (e.g., Hom & Kinicki,

2001; Rusbult & Farrell, 1983). However, most turnover models still implicitly assume that employees have full discretion to either stay or leave at any time (Hom et al., 2012).

Despite the severe limitations of this dichotomy, both science and practice have focused on so-called voluntary turnover (Hom, 2011; Maertz & Campion, 1998). Similarly, the standard approach of treating all stayers equally excludes people who may prefer to leave, but stay due to constraints (Allen et al., 2005).

Ajzen’s (1985) theory of planned behavior represents an expansion of the theory of reasoned action (Fishbein & Ajzen, 1975) that applies to behaviors where at least some people have only limited control. Given the many constraints on turnover behavior, it would certainly fit this description. Attitudes toward a behavior, subjective norms, and perceived behavioral control can predict intentions, which combine with perceived 43 behavioral control to predict actual behavior (Ajzen, 1991). These subjective perceptions of control can influence intentions and attempts to perform a behavior, regardless of their accuracy (Ajzen, 1985).

According to Hom et al. (2012), the antecedents of perceived control include job protection systems, just termination practices, performance-enhancing HRM practices, and four of the motivational forces (i.e., alternative, behavioral, legal/contractual, normative). Job protection systems (e.g., unionization, tenure/seniority protections) can act as an overt constraint on organizational authority to fire employees (Batt & Colvin,

2011), thereby increasing the control perceptions of employees. Just termination practices (e.g., outplacement services, generous severance) can serve to ease the negative consequences of leaving and potentially reframe it as a positive opportunity for employees (Fugate, Kinicki, & Prussia, 2008). Performance-enhancing HRM practices

(e.g., contingent rewards, performance monitoring) can also influence perceived control over the turnover decision, particularly by signaling to sub-standard performers that they have dim prospects for future success with the organization (Becker & Cropanzano,

2011; Shaw, Dineen, Fang, & Vellella, 2009).

Alternative forces result from the employee’s beliefs about obtaining a valued alternative to continuing work at the current organization (Maertz & Griffeth, 2004).

Poor job markets tend to restrict quitting behavior (Hom & Kinicki, 2001; Trevor, 2001), which may lead people to feeling trapped in their current jobs. Similarly, scarce job opportunities can weaken control beliefs about the turnover decision (Maertz & Campion,

2004). On the other hand, receiving a job offer that seems too lucrative to justify refusing can also weaken one’s sense of control over whether to stay or leave (Hom et al., 2012). 44

Behavioral forces involve perceived costs associated with leaving the current organization (Maertz & Griffeth, 2004). Employees may consider economic, adjustment- related, and psychological costs when deciding whether to leave an organization

(Salancik, 1977; Shore, Tetrick, Shore, & Barksdale, 2000). Community sacrifices and location amenities may also influence the cost evaluation (Lee et al., 2004; Mitchell et al.,

2001; Ramesh & Gelfand, 2010). Turnover decisions carry inherent risk with significant consequences coupled with uncertainty regarding outcomes and their probability (Allen,

Renn, Moffitt, & Vardaman, 2007).

Contractual forces arise from perceptions about employee obligations to the organization and organizational obligations to the employee (Maertz & Griffeth, 2004).

These obligations comprise a psychological contract in which each party maintains its side of the bargain only as long as the other party does (Robinson, Kraatz, & Rousseau,

1994; Robinson & Morrison, 2000). Contract violations or breaches can lead employees to question whether to stay or leave (Clinton & Guest, 2013; Lee & Mitchell, 1991).

Some employers (e.g., military) can also exert direct pressure on employees to stay or leave (Burrell, Adams, Durand, & Castro, 2006; Iverson & Zatzick, 2011).

Normative forces result from the employee’s thoughts about whether family and friends want him/her to leave or stay with the organization (Maertz & Griffeth, 2004). In some cases, these normative expectations even have more influence than an employee’s own attitudes about the job and organization (Prestholdt, Lane & Mathews, 1987;

Ramesh & Gelfand, 2010). For example, an individual may feel forced into leaving a job to relocate with a spouse or care for dependents (Shauman, 2010; Steel, 2002). Others 45 may feel stuck or embedded in a job and/or organization by their links to co-workers

(Hom & Xiao, 2011; Mitchell et al., 2001).

Proximal Withdrawal States and Consequent Reactions

The proximal withdrawal states come from the combination of an employee’s preference to stay or leave with his/her perceived control over acting on that preference

(Hom et al., 2012). Individual differences can also influence these mind-sets, which lead to a wide range of consequent attitudinal and behavioral reactions (e.g., job attitudes, job performance, organizational citizenship, counterproductive behaviors, work withdrawal, job search). These attitudes and behaviors ultimately determine turnover types, destinations, and speed for individual employees (Hom et al., 2012).

Hom et al. (2012) crossed the two continuous dimensions of stay/leave preference and perceived control to derive four main proximal withdrawal states. They also described 13 possible subtypes within these four prime states by distinguishing between employer control and other extrinsic forms of control. The categorical approach inevitably oversimplifies a complex phenomenon, but can help identify and describe groups of people who share similar attributes. In this section, I discuss the consequent reactions associated with each of the four main proximal withdrawal states: enthusiastic stayers, enthusiastic leavers, reluctant stayers, and reluctant leavers.

Enthusiastic stayers. The group described as enthusiastic stayers includes those who have both desire and freedom to stay (Hom et al., 2012). They may arrive at this mind-set through different reasons (e.g., person-job fit, strong workplace links; Mitchell

& Lee, 2001) or forces (e.g., affective, calculative, constituent, moral; Maertz & Griffeth,

2004). Regardless of the reasons for their strong desire to stay, most enthusiastic stayers 46 fit the ideal profile of highly engaged and loyal employees with a low risk of leaving the organization (Hom et al., 2012).

These individuals should demonstrate a wide range of organizationally desirable outcomes such as higher performance (Harrison, Newman, & Roth, 2006; Meyer et al.,

2002), more organizational citizenship behaviors (Lee et al., 2004; Ng & Feldman, 2011), and fewer counterproductive work behaviors (Koslowsky et al., 1997; Spector & Fox,

2002). However, enthusiastic stayers motivated primarily by preserving good corporate benefits may lack favorable job attitudes other than satisfaction with pay and security

(Hom et al., 2012). Employee perceptions of control can also facilitate positive responses such as openness to change (Wanberg & Banas, 2000), effective coping (Bowman &

Stern, 1995), and employee adjustment over time (Paulsen et al., 2005).

Enthusiastic leavers. Hom et al. (2012) categorized individuals who have both desire and freedom to leave as enthusiastic leavers. Actual quit intentions only emerge once they have completed intermediate steps and have alternative options to evaluate

(Maertz & Campion, 2004; Steel, 2002). In the interim, they may exhibit a broad set of behaviors indicative of withdrawal (e.g., lateness, absence, social loafing; Koslowsky,

2009). Some of these leavers exit without another job, either due to a shock event (Lee &

Mitchell, 1994) or because they choose an alternative outside the workforce (e.g., full- time parent, student; Hulin et al., 1985; Lee et al., 2008).

Enthusiastic leavers motivated by dissatisfaction or a negative job shock would best fit the empirical evidence about voluntary leavers by displaying lower performance

(Harrison et al., 2006), more job search behaviors (Boswell, Boudreau, & Dunford,

2004); more counterproductive work behaviors (Dalal, Lam, Weiss, Welch, & Hulin, 47

2009), and fewer organizational citizenship behaviors (Chen, Hui, & Sego, 1998). On the other hand, enthusiastic leavers motivated purely by strong alternative forces (e.g., plan or job offer) may not display such negative attitudes and behaviors.

Reluctant stayers. Hom et al. (2012) categorized reluctant stayers as employees who wish to leave, but feel compelled to stay. They will remain as long as necessary, or until their barriers to leaving are removed. Reluctant stayers may wish to quit due to poor fit in terms of interests, abilities, and/or values (Kristof-Brown, Zimmerman, &

Johnson, 2005; Maltarich et al., 2010). However, they feel constrained by factors such as turnover costs (Mobley, 1977; Rusbult & Farrell, 1983), job scarcity (Steel, 2002), employment contracts (Mobley et al., 1979), normative pressures (Ramesh & Gelfand,

2010), and/or community sacrifices (Mitchell & Lee, 1981). Reluctant stayers may respond to their lack of control over the desired outcome with negative work attitudes

(Tetrick & LaRocco, 1987), lower performance (Greenhalgh, 1980) and fewer organizational citizenship behaviors (Gellatly, Meyer, & Luchak, 2006). Employees blocked from leaving may also engage in more work avoidance behaviors (Mobley et al.,

1979; Sagie, Birati, & Tziner, 2002) and counterproductive behaviors (Hulin et al., 1985) that damage the company.

Reluctant leavers. Hom et al. (2012) identified reluctant leavers as individuals who want to stay but expect that they must leave. This mind-set may develop as a result of imminent downsizing (Trevor & Nyberg, 2008), business struggles (Datta, Guthrie,

Basuil, & Pandey, 2010), performance-enhancing HRM (Shaw et al., 2009), and/or pay- for performance plans signaling sub-standard performers that they have dim prospects for future success (Becker & Cropanzano, 2011; Nyberg, 2010). Other reluctant leavers 48 experience pressure to leave not from the company, but from family or other influential authorities (Meyer et al., 2004; Wasti, 2003). Employees who expect the company to force them out may respond with a variety of negative work attitudes and behaviors

(Maertz, Wiley, LeRouge, & Campion, 2010). Similar to the reluctant stayers who also feel unable to attain their desired outcome, this group of employees may respond with reduced productivity (Becker & Cropanzano, 2011), less discretionary effort on behalf of the company (LePine & Van Dyne, 1998), fewer organizational citizenship behaviors

(Chen et al., 1998; Holtom, Burton, & Crossley, 2012), and more counterproductive work behaviors (Dalal et al., 2009).

Summary. The Hom et al. (2012) concept of proximal withdrawal states can help explain many different workplace attitudes and behaviors beyond turnover.

Although there are many more possible combinations of perceived control and stay/leave preference than the four primary states suggest, this simplified approach illustrates the importance of cognitive mind-sets based on these dimensions. In the PWSD model, distal influences (e.g., job characteristics) lead to intermediate antecedents (e.g., job attitudes, on-the-job embeddedness), which lead to direct or proximal antecedents (e.g., intentions), and ultimately to actual leaving destinations. This approach more comprehensively explains organizational participation than prior models by expanding the conceptual domain to include all types of leaving and staying, as well as destinations.

Although it holds great promise for growth in our understanding of how and why people stay and leave, we need more research to refine and test the model’s core components.

The current study described below represents a critical early step in that process.

Current Study and Hypotheses

The current study focuses on two main contributions related to the PWSD model

(Hom et al., 2012). The first involves developing reliable and efficient measures of preference and perceived control focused on organizational participation (i.e., staying and leaving). In the PWSD model, stay/leave preference and perceived control serve as the core constructs that determine the proximal withdrawal states. As suggested by Hom et al. (2012), I adapted measurement approaches used in research on the theory of planned behavior (Ajzen, 1985). The scale development process includes evaluating the dimensionality and reliability of these measures.

The current study’s second contribution involves collecting and evaluating initial validation evidence for the new measures of stay/leave preference and perceived control.

Validity refers to the degree to which empirical evidence and theoretical rationale support the inferences proposed about the target of assessment (Putka & Sackett, 2010). The validation process can include a variety of different approaches and procedures that contribute to an integrated evaluative judgment (Guion, 2002; Kane, 2006; Messick,

1989). Some authors (e.g., Cronbach & Meehl, 1955; Messick, 1975; Schwab, 2005) have split validity evidence into three main types (i.e., construct, content, and criterion- related) while others (e.g., Binning & Barrett, 1989; Guion, 1980, 2002; Schmitt &

Landy, 1993) favor a unified perspective.

One of the core validation strategies for new measures consists of generating and testing hypotheses linking the focal construct to other theoretically related constructs, thereby building a nomological network (Cronbach & Meehl, 1955; Schmitt & Landy,

1993). Finding evidence to support hypothesized relationships with other constructs 50 serves to increase confidence about our interpretation of what a measure represents

(Putka & Sackett, 2010; Schwab, 2005). In their PWSD model, Hom et al. (2012) contended that stay/leave preference and perceived control play crucial roles in influencing organizational participation and withdrawal. Building upon the rich history of turnover theory and research, the PWSD model features an extensive network of proposed relationships with other constructs such as motivational forces, embeddedness, behaviors, attitudes, performance, individual differences, and turnover (see Figure 2).

Therefore, I begin the validation process for this study’s new stay/leave preference and perceived control measures by evaluating hypothesized relationships with some of the main components of turnover theory, as indicated by the PWSD model.

Job Attitudes

As previously discussed, the attitudinal constructs of job satisfaction and organizational commitment have played prominent roles in the history of turnover theory and research. The PWSD model (Hom et al., 2012) features job attitudes among many potential consequent reactions of the proximal withdrawal states. In presenting their configural approach to motivational states, Hom and his colleagues crossed stay/leave preference and perceived control to create four prime states. Their descriptions of these states include references to job attitudes that suggest links to the core PWSD dimensions.

We can infer other potential relationships from how the PWSD model (Hom et al., 2012) incorporates the motivational forces framework (Maertz & Griffeth, 2004) into its proposed antecedents for stay/leave preference and perceived control.

Hom et al. (2012) indicated that most enthusiastic stayers (i.e., want to stay and can stay) have positive job attitudes due to factors such as person-job fit (Mitchell & Lee, 51

2001), strong workplace links (Grant, 2007), calculative or moral forces (Maertz &

Griffeth, 2004), and shared values with the organization (Cable & Edwards, 2004; Meyer et al., 2004). Most reluctant leavers (i.e., want to stay, but expect that they must leave) also have relatively positive job attitudes for similar reasons to those described for enthusiastic stayers (Hom et al., 2012). However, their attitudes would likely worsen over time as the forces to leave increase before eventual termination (Becker &

Cropanzano, 2011; Trevor & Nyberg, 2008).

On the other hand, Hom et al. (2012) predicted more negative job attitudes from reluctant stayers (i.e., want to leave, but feel that they must stay). They feel trapped in their jobs despite reasons for leaving such as unsatisfied vocational interests (Maltarich et al., 2010), underutilized skills and training (Benson, Finegold, & Mohrman, 2005), and conflicts with company values (Kristof-Brown et al., 2005). Their attitudes may continue to worsen over time until removal of the barriers to leaving (Meyer et al., 2004; Mobley et al., 1979).

The enthusiastic leavers (i.e., want to leave and can leave) most closely correspond to the theoretical models of turnover (e.g., Hulin et al., 1985; March &

Simon, 1958; Mobley, 1977; Price, 1975; Steers & Mowday, 1981) that present negative job attitudes as core factors that determine why employees choose to leave their jobs.

However, unfolding model research suggests that only those who leave through the negative job shock or dissatisfaction paths would display negative job attitudes (Lee et al., 1996, 1999). Job attitude may not play a significant role in quits prompted by a pre- existing plan or unsolicited job offers (Mitchell & Lee, 2001). Hom et al. (2012) acknowledged these exceptions, but suggested that most enthusiastic leavers have 52 relatively negative job attitudes. In conclusion, leaving preference should have inverse relationships with job satisfaction and affective commitment.

Hypothesis 1A: Leaving preference will relate inversely to job satisfaction, such

that individuals who are less satisfied with their jobs will have a greater

preference toward leaving.

Hypothesis 1B: Leaving preference will relate inversely to affective commitment,

such that individuals with weaker affective commitment will have a greater

preference toward leaving.

The other two forms of commitment, continuance and normative, seem to align more closely with leaving constraints, rather than preferences. Continuance commitment results from recognition of perceived costs associated with termination, including availability of job alternatives (Meyer & Allen, 1991; Meyer & Herscovitch, 2001). Due to these constraints, employees with high continuance commitment may feel unable to leave, even if they want to go. Normative commitment represents a feeling of obligation to stay with the organization due to internalized company norms through socialization, perceived need to reciprocate for rewards, or a psychological contract (Meyer & Allen,

1991; Meyer & Herscovitch, 2001). Employees with strong feelings of obligation may view leaving as impossible, despite their personal preferences. In the PWSD model

(Hom et al., 2012), the proposed antecedents for perceived control include alternative and normative forces. Therefore, perceived control should have inverse relationships with these two commitment variables. 53

Hypothesis 2A: Perceived control will relate inversely to continuance

commitment, such that individuals with stronger continuance commitment will

perceive less control over leaving.

Hypothesis 2B: Perceived control will relate inversely to normative commitment,

such that individuals with stronger normative commitment will perceive less

control over leaving.

Engagement

Over the past 15 years, the concept of employee engagement has dramatically grown in popularity, driven by practitioner claims that it contributes to employee retention and other positive outcomes for organizations (Bakker, Schaufeli, Leiter, &

Taris, 2008; Macey & Schneider, 2008; Saks, 2006). Scientific interest in engagement lagged behind practitioner usage, but has increased recently (Britt, McKibben, Green-

Shortridge, Odle-Dusseau, & Herleman, 2012; Christian, Garza, & Slaughter, 2011).

Engagement research has suffered from inconsistent definition and measurement

(Bakker, Albrecht, & Leiter, 2011; Wefald, Mills, Smith, & Downey, 2012), leading some to challenge its value (e.g., Griffin, Parker, & Neal, 2008; Newman & Harrison,

2008; Wefald & Downey, 2009).

Despite their differences, most of these approaches built upon the work of Kahn

(1990, 1992). He proposed that personal engagement represents a state in which employees invest personal energy into their work roles, experiencing an emotional connection. Kahn’s engagement is a multi-dimensional motivational concept that helps determine how intensely and persistently individuals allocate personal resources to work role performance (Rich, LePine, & Crawford, 2010). Similarly, Schaufeli and his 54 colleagues (e.g., Schaufeli & Bakker, 2004; Schaufeli, Bakker, & Salanova, 2006;

Schaufeli, Salanova, Gonzalez-Roma, & Bakker, 2002) define engagement as a positive, fulfilling, and work-related state of mind characterized by absorption, dedication, and vigor. This cognitive-affective state shows some stability, but may fluctuate over time

(Dalal, Brummel, Wee, & Thomas, 2008; Mauno, Kinnunen, & Ruokolainen, 2007;

Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009).

Despite the conceptual differences, engagement does share antecedents (e.g., job characteristics, leadership, dispositional characteristics) with the most popular job attitude variables (Hirschfeld & Thomas, 2008; Wollard & Shuck, 2011). A recent meta- analysis (Christian et al., 2011) on engagement reported moderately positive relationships with job attitudes such as organizational commitment, job satisfaction, and job involvement. Research has linked engagement to individual outcomes such as employee performance (e.g., Christian et al., 2011; Rich et al., 2010), organizational citizenship behaviors (e.g., Britt et al., 2012; Rich et al., 2010), proactive behavior (e.g., Salanova &

Schaufeli, 2008), and turnover intention (e.g., Saks, 2006; Shuck, Reio, & Rocco, 2011).

Studies have also found empirical relationships with organization-level outcomes such as customer loyalty (e.g., Salanova, Agut, & Peiro, 2005) and financial returns (e.g.,

Xanthopoulou et al., 2009).

The PWSD model (Hom et al., 2012) does not directly include engagement as an antecedent or consequent reaction of the proximal withdrawal states. However, their discussion of subtypes within the main four states covers “engaged stayers” and

“embedded engaged stayers” as subtypes of enthusiastic stayers who want to remain as long as possible and believe that they can. As previously noted, engagement theory and 55 research indicates that it should have a strong link with employee desires to stay or go.

The PWSD model and engagement research also imply that more engaged employees would generally have greater perceived control over staying or going. Therefore, I expect engagement to have an inverse relationship with leaving preference, but a positive one with perceived control.

Hypothesis 3A: Leaving preference will relate inversely to engagement, such that

individuals with greater preference toward leaving will be less engaged.

Hypothesis 3B: Perceived control will relate positively to engagement, such that

individuals who perceive less control over leaving will be less engaged.

Job Embeddedness

As described in the review of turnover models, job embeddedness reflects the extent to which employees experience a web of attachments to their organization and/or community (Mitchell et al., 2001). The sources of these embedding attachments can include perceived fit, links with people or groups, and sacrifices associated with leaving

(Burton et al., 2010). Highly embedded individuals tend to stay with their organizations, even under negative circumstances that would inspire others to leave (Mitchell & Lee,

2001). Although job embeddedness has positive implications for employees and organizations, some people may feel trapped by their attachments (Mitchell et al., 2001).

The PWSD model (Hom et al., 2012), includes dimensions of job embeddedness as antecedents of stay/leave preference and perceived control. Job fit and links (on and off the job) help determine stay/leave preference, whereas sacrifices (job, community, and family) contribute to perceived control. This aligns with past theory and research on job embeddedness (e.g., Burton et al., 2010; Jiang et al., 2012; Mitchell et al., 2001). 56

Therefore, job embeddedness should have inverse relationships with both leaving preference and perceived control.

Hypothesis 4A: Leaving preference will relate inversely to job embeddedness,

such that individuals who feel less embedded in their jobs will have a greater

preference to leave.

Hypothesis 4B: Perceived control will relate inversely to job embeddedness, such

that individuals who feel less embedded in their jobs will perceive greater control

over leaving.

Quit Intentions

As discussed, quit intentions have long played a central role in turnover theory and research. Intentions about future organizational attachment range from shorter to longer term, sometimes tied to specific events, such as shocks (Lee & Mitchell, 1994).

Furthermore, some people act on their quit intentions quickly, while others indefinitely delay taking action (Allen et al., 2005; Vardaman et al., 2008). However, most turnover models treat quit intentions as focused on carrying out an immediate desire to leave, rather than expressing an overall preference that may involve leaving at some indefinite future time when circumstances allow (Hom et al., 2012).

Higher risks and/or costs weaken the predictive link from intentions to behaviors

(Ajzen, 1991; Ajzen, Brown, & Carvajal, 2004). Risk perceptions and comfort level with risk vary across individuals, helping to explain the gaps between intended and actual quitting behavior (Vardaman et al., 2008). Furthermore, individual differences in personality (e.g., locus of control, self-monitoring, risk aversion) can make people more or less likely to actively pursue their intentions (Allen et al., 2005; Blau, 1987). 57

In the PWSD model, individual perceptions of control over staying or leaving the organization shape their mind-sets about withdrawal (Hom et al., 2012). A wide range of real and imagined constraints can serve to frustrate preferences and/or undermine intentions (Ajzen, 1991). These constraints can range from short-term to long-term in their effects (Allen et al., 2005). Therefore, employee perceptions about control over staying or leaving should influence not only their actual participation and withdrawal behavior, but also the formation of quit intentions.

Hom et al. (2012) contend that enthusiastic leavers (i.e., individuals who want to leave and think they can) have stronger quit intentions than the other three groups. The enthusiastic stayers (i.e., individuals who want to stay and think they can) have the weakest quit intentions. Quit intentions for the reluctant stayers and reluctant leavers should fall between the other two groups due to their perceptions of insufficient control over the desired outcome of staying or leaving. Therefore, leaving preference should have a strong positive relationship with quit intentions. Based on the PWSD model

(Hom et al., 2012) and the theory of planned behavior (Ajzen, 1985), perceived control should have a positive relationship with quit intentions and also moderate its relationship with leaving preference.

Hypothesis 5A: Leaving preference will positively relate to quit intentions, such

that individuals with greater preference toward leaving will have stronger quit

intentions.

Hypothesis 5B: Perceived control will positively relate to quit intentions, such that

individuals who perceive more control over leaving will have stronger quit

intentions. 58

Hypothesis 5C: Perceived control will moderate the positive relationship between

leaving preference and quit intentions. It will be stronger for individuals who

perceive greater control over leaving.

Search Behaviors

In many theoretical models, job search behaviors play an important role as the most proximal behavioral antecedent to actual turnover (Boswell, Zimmerman, &

Swider, 2012). For example, Mobley’s (1977) intermediate linkages model illustrates a process wherein a negative job evaluation fosters negative job attitudes that trigger a series of withdrawal cognitions, searching for and evaluating alternatives, and ultimately result in a decision to either quit or stay. Some turnover models (e.g., Mobley et al.,

1979; Price & Mueller, 1981; Steers & Mowday, 1981) suggest that job search activity reinforces or even facilitates withdrawal cognitions by shaping the individual’s perceptions about leaving. However, Blau (1994) made an important distinction between two types of job search behaviors. He contended that preparatory search behaviors (e.g., reading job listings, revising resume) do not always lead to the active search behaviors

(e.g., sending out resume, interviewing with a prospective employer) that link more strongly to turnover.

Research on the unfolding model (e.g., Lee & Mitchell, 1991; Lee et al., 1999;

Morrell et al., 2004) has shown that some turnover occurs more quickly due to shock events, skipping the search and evaluation of job opportunities. In these cases, individuals who want to find another job may not start engaging in search behaviors until after quitting. However, greater search activity typically indicates that those employees 59 are more likely to leave their organizations in the near future (Blau, 1993; Bretz et al.,

1994; Lee, Gerhart, Weller, & Trevor, 2008).

The theory of planned behavior (Ajzen, 1985) also suggests that perceived control should have positive relationships with search intentions and behaviors. Empirical studies have generally found stronger support for perceived control’s predicted link to intentions than directly to search behaviors (Jaidi, Van Hooft, & Arends, 2011; Song,

Wanberg, Niu, & Xie, 2006; Van Hooft, Born, Taris, Van Der Flier, & Blonk, 2004; Van

Hooft & De Jong, 2009). Other applications of the theory of planned behavior have reported a similar pattern of results (Armitage & Conner, 2001).

In the PWSD model (Hom et al., 2012), enthusiastic leavers display the most search behaviors, whereas enthusiastic stayers display the fewest search behaviors. As with quit intentions, the reluctant stayers and reluctant leavers should fall between the other two groups in search behaviors due to their low perceived control. Therefore, leaving preference should also have a stronger relationship with search behaviors than perceived control. I also expect perceived control to moderate the relationship between leaving preference and search behaviors.

Hypothesis 6A: Leaving preference will positively relate to search behaviors, such

that individuals with greater preference toward leaving will engage in more search

behaviors.

Hypothesis 6B: Perceived control will positively relate to search behaviors, such

that individuals who perceive greater control over leaving will engage in more

search behaviors. 60

Hypothesis 6C: Perceived control will moderate the positive relationship between

leaving preference and search behaviors. It will be stronger for individuals who

perceive greater control over leaving.

Withdrawal Behaviors

As noted in the review of general withdrawal models, researchers have identified a wide range of behaviors (e.g., absence, tardiness, turnover) that may reflect an employee’s effort to move away from his or her organization. An even broader term,

“counterproductive work behaviors” (CWB) covers a variety of distinct volitional (i.e., not accidental or mandated) behaviors that harm or intend to harm organizations and/or organization stakeholders (Spector & Fox, 2005). Researchers have studied these behaviors from a variety of theoretical perspectives, such as deviance (e.g., Bennett &

Robinson, 2000; Hollinger, 1986), aggression (e.g., Fox & Spector, 1999; Neuman &

Bennett, 1995), revenge (e.g., Bies & Tripp, 2005), and retaliation (e.g., Skarlicki &

Folger, 1997). Most of this work suggests that CWB stem from negative emotions such as frustration and/or anger responses to conditions of the work environment (Hershcovis et al., 2007; Meier & Spector, 2013).

Spector and his colleagues (Spector et al., 2006) categorized the broad range of

CWB into five dimensions: abuse against others, production deviance, sabotage, theft, and withdrawal. Although the proximal withdrawal states should have some influence on other CWB as well (Hom et al., 2012), the current study focuses on the dimension of withdrawal behaviors. These behaviors (e.g., absence, arriving late or leaving early, and taking longer breaks than authorized) restrict the amount of time working to less than required by the organization (Spector et al., 2006). Most of the general employee 61 withdrawal models discussed earlier (e.g., Beehr & Gupta, 1978; Hanisch & Hulin, 1991;

Rosse & Miller, 1984) also include turnover as a withdrawal behavior. However, turnover represents a single act of final and complete withdrawal from the organization, whereas the others can potentially continue for a long time while the individual remains with the organization (Spector et al., 2006).

In the PWSD model, reluctant stayers display the most withdrawal behaviors, followed by the reluctant leavers (Hom et al., 2012). In other words, people may find other ways to withdraw from the organization if they either feel trapped in the organization or expect that they will be forced out in the near future (Harrison et al.,

2006; Mobley et al., 1979). The enthusiastic stayers should engage in the fewest withdrawal behaviors, followed by the enthusiastic leavers. These groups feel in control of their actual withdrawal from the organization, so they should be less motivated to act out in other ways (Hom et al., 2012). Therefore, withdrawal behaviors should have a positive relationship with leaving preference, but a negative one with perceived control.

The moderator effect would increase withdrawal behaviors for employees with lower perceived control and higher preference to leave.

Hypothesis 7A: Perceived control will relate inversely to withdrawal behaviors,

such that individuals who perceive less control over leaving will report more

withdrawal behaviors.

Hypothesis 7B: Leaving preference will positively relate to withdrawal behaviors,

such that individuals with greater preference toward leaving will report more

withdrawal behaviors. 62

Hypothesis 7C: Leaving preference will moderate the negative relationship

between perceived control and withdrawal behaviors. It will be stronger for

individuals with greater leaving preference and weaker for those with lower

leaving preference.

Summary

Gathering and evaluating the hypotheses presented above will help validate the new measures of leaving preference and perceived control. This study will also contribute to the PWSD model by evaluating some important relationships involving its core constructs. It represents a crucial early step toward understanding the role of proximal withdrawal states in organizational participation and withdrawal.

Study 1: Scale Development

Study 1 focused on developing and refining new measures for the PWSD model constructs of leaving preference and perceived control. The scale development process followed best practice recommendations (e.g., DeVellis, 2011; Floyd & Widaman, 1995;

Hinkin, 1995; Worthington & Whittaker, 2006) by including multiple steps to address item generation, exploratory factor analysis (EFA), scale refinement, and internal consistency reliability. These steps are described below.

Item Generation

The Hom et al. (2012) discussion of stay/leave preference and perceived control in the context of their PWSD model served as my main source when generating items. I also considered research on Ajzen’s (1985) theory of planned behavior, which helped inspire the PWSD constructs. As recommended by Hom et al. (2012), I developed this study’s measures based on review of similar scales from this body of research (e.g.,

Ajzen, 2002; Ajzen, Joyce, Sheikh, & Cote, 2011; Gagne & Godin, 2007; Griepentrog,

Harold, Holtz, Klimoski, & Marsh, 2012; Kidwell & Jewell, 2003; Povey, Conner,

Sparks, James, & Shepherd, 2000).

Leaving preference. I developed ten items (Appendix A) to measure preference toward staying or leaving the organization. The items featured content adapted from similar measures found in research on the theory of planned behavior (e.g., Ajzen et al.,

2004; Ajzen et al., 2011; Griepentrog et al., 2012; Povey et al., 2000) and turnover (e.g.,

Kim et al., 1996; Price, 2001; Steers, 1977). These brief statements addressed affective and evaluative beliefs about the specific act of staying or leaving the organization, without referencing any particular turnover destination. To minimize confounds with 64 measures of job attitudes (e.g., job satisfaction, organizational commitment), the items did not specify any particular motivation driving preferences. I also tried to avoid measurement overlap with conceptually similar constructs such as quit intentions and withdrawal cognitions.

Perceived control. Twelve items (Appendix B) were developed to measure perceived control over staying or leaving the organization. I adapted the items from various perceived control measures used in research on the theory of planned behavior

(e.g., Ajzen, 2002; Ajzen et al., 2011; Gagne & Godin, 2007; Kidwell & Jewell, 2003;

Povey et al., 2000). These brief statements addressed perceptions of control about the specific act of staying or leaving the organization, without specifying a particular turnover destination. Some items focused on leaving, whereas others focused on staying.

As with the leaving preference items, I tried to avoid overlap with similar constructs such as job attitudes, quit intentions, and withdrawal cognitions.

I expected to retain approximately half of the developed items, leaving four to six for each scale, typically sufficient for measuring most constructs (Clark & Watson, 1995;

Cortina, 1993; Hinkin, 1998). Relatively short scales help satisfy the needs of organizational researchers who gather data on multiple constructs through online administration and contend with survey fatigue among employees (Stanton, Sinar, Balzer,

& Smith, 2002). General advantages of shorter scales include reduced administration time, less participant fatigue, and increased response rate (Burisch, 1984; Porter,

Whitcomb, & Weitzer, 2004; Schmitt & Shults, 1985). Low response rates can weaken organizational research by decreasing statistical power, limiting generalizability of results, producing misleading conclusions, and undermining credibility with stakeholders 65

(Luong & Rogelberg, 1998; Rogelberg & Stanton, 2007). These considerations helped inspire the Study 1 goal of developing parsimonious scales that offer the practical advantages of efficiency without sacrificing psychometric value.

Sample and Procedure

The study participants came from a sample of working adults provided by

Qualtrics, a global supplier of enterprise data collection and analysis. Potential participants received an e-mail message from Qualtrics with instructions for completing the survey electronically through an embedded link. Individuals first reviewed an informed consent form (Appendix C) to help them decide whether to participate in the study. All participants received some small monetary compensation, larger for those who successfully completed the entire survey. Most successful participants completed it in less than ten minutes. Qualtrics managed the survey administration and data collection process, ensuring that participant responses remain confidential.

A total of 363 individuals started the survey. Of these, 67 individuals (19%) did not meet the study criteria (i.e., United States residents with either full-time or part-time employment). As recommend by Meade and Craig (2012), the survey included three attention check items that asked participants to select a specific response. Of the 363 individuals who started the survey, 126 (34.7%) failed to to select the requested response.

Of the remaining 170 individuals, I excluded 18 more who excessively (i.e., over 50%) used one of the seven response options across 77 items used to measure the ten scales.

Therefore, the final Study 1 sample consisted of 152 (41.9% of the original 363 cases).

Because the sampling strategy included quotas to ensure an even split on gender, 50% of these respondents were male. Moreover, 71.1% were employed full-time, 50.7% were 66 married, and 59.9% completed at least a 2-year college degree. Their average age was

43.8 years and their average organizational tenure was 8.5 years.

Measures

Study 1 included measures of perceived control, leaving preference, and quit intentions. Each scale represented an average of its item scores. The item responses ranged from strongly disagree (1) to strongly agree (7). It also included demographic questions (Appendix D) regarding gender, ethnicity, age, marital status, number of children, education, employment status, industry, and organizational tenure.

Leaving Preference. Leaving preference (Appendix A) was measured with ten items (α = .96), as described in the item generation section above. Five items used reverse scoring so higher values would correspond to a higher preference to leave. A sample item is “I want to stay with my current organization as long as possible.”

Perceived Control. Perceived control (Appendix B) was measured with 12 items

(α = .80), as described in the item generation section above. Five items used reverse scoring so higher values would indicate higher perceptions of control. A sample item is

“I can stay with my organization for as long as I want to be there.”

Quit Intentions. Quit intentions (Appendix I) was measured with four items (α =

.89) used by Crossley et al. (2007). They designed this scale to avoid content overlap with measures of job search and job attitudes that may confound other intent to quit measures (Tett & Meyer, 1993). Higher values indicate a higher intention to quit. A sample item is “I intend to leave this organization soon.”

Results

Study 1 generated data for evaluation of the 22 items developed to measure the

PWSD dimensions of leaving preference and perceived control. Following best practice recommendations for scale development (e.g., Costello & Osbourne, 2005; DeVellis,

2011; Floyd & Widaman, 1995; Hinkin, 1995; Worthington & Whittaker, 2006), I conducted an exploratory factor analysis (EFA) to identify the major factors. The EFA process also involved eliminating items that had low communalities, failed to load strongly on any factor, or demonstrated excessive cross-loading on multiple factors.

Removing items often changes these indices, so I reanalyzed the data after each deletion, following a similar iterative process to other scale development studies (e.g., Arseneau,

Grzanka, Miles, & Fassinger, 2013; Gill & Hodgkinson, 2007; Pasuraman, Zeithaml, &

Malhotra, 2005; Slaughter, Zickar, Highhouse, & Mohr, 2004; Sliter, 2013).

Table 2 displays the correlation matrix for all 22 PWSD items included in the initial EFA. Support for using factor analysis on this data set was provided by a .904 value on the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (Kaiser, 1970) and a statistically significant result (χ2 = 2578.75, p < .001) on Bartlett’s Test of Sphericity

(Bartlett, 1950). I also checked the items for extreme non-normality based on the standards of + 2.0 for skewness and + 7.0 for kurtosis (Curran, West, & Finch, 1996).

Although they showed some evidence of skewness (-1.50 to 1.05) and kurtosis (-1.17 to to 2.86), all items fell within the acceptable ranges and were retained. 68

Table 2

Correlation Matrix for Exploratory Factor Analysis

Item PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 LP1 LP2 LP3 LP4 LP5 LP6 LP7 LP8 LP9 LP10 PC1 - PC2 .48 - PC3 -.15 -.06 - PC4 .26 .17 -.13 - PC5 .20 .24 .24 .17 - PC6 .34 .26 .07 .27 .10 - PC7 .35 .37 .13 .22 .46 .19 - PC8 .13 .21 .08 .22 .18 .48 .21 - PC9 .38 .43 -.27 .46 .09 .23 .22 .27 - PC10 .45 .37 .19 .34 .30 .59 .39 .31 .30 - PC11 .48 .58 .05 .17 .28 .33 .40 .24 .38 .51 - PC12 .52 .42 .03 .25 .37 .33 .39 .29 .37 .51 .66 - LP1 -.33 -.19 .43 -.53 -.04 -.15 -.01 -.05 -.52 -.15 -.14 -.14 - LP2 -.32 -.07 .37 -.63 -.02 -.25 -.05 -.13 -.56 -.25 -.11 -.23 .74 - LP3 -.28 -.15 .40 -.64 -.04 -.24 .01 -.11 -.58 -.25 -.13 -.21 .79 .91 - LP4 -.25 -.12 .42 -.47 .04 -.07 -.04 .04 -.52 -.06 -.06 -.13 .77 .77 .76 - LP5 -.27 -.13 .28 -.62 -.01 -.27 -.03 -.21 -.58 -.27 -.12 -.24 .67 .84 .84 .67 - LP6 -.06 .01 .53 -.27 .19 -.02 .14 -.03 -.31 .04 -.03 .04 .51 .51 .49 .50 .48 - LP7 -.23 -.18 .28 -.55 -.05 -.27 -.10 -.22 -.45 -.25 -.18 -.20 .63 .62 .67 .54 .59 .42 - LP8 -.22 -.14 .39 -.51 -.02 -.15 -.01 -.08 -.56 -.14 -.12 -.18 .76 .69 .73 .75 .64 .56 .61 - LP9 -20 -.12 .46 -.61 -.03 -.11 -.03 -.02 -.57 -.11 -.08 -.12 .84 .83 -.86 .86 -.76 .56 -.66 .80 - LP10 -.27 -.12 .37 -.62 -.04 -.21 -.03 -.10 -.58 -.25 -.12 -.16 -.80 .85 .90 -.79 .79 -.51 .70 -.74 -.89 - Notes: N = 152. Full item text shown in Appendices A and B. The following items were eliminated from the final Study 1 EFA model: PC3, PC4, PC5, PC6, PC7, PC8, PC9, LP2, LP3, LP6, LP9, LP10. 69

I conducted the EFA with principal axis factoring and promax (oblique) rotation in SPSS 17.0. Before considering factor retention, I examined item communality coefficients, which reflect the portion of each item’s variance accounted for by the common factors (MacCallum, Widaman, Zhang, & Hong, 1999). Therefore, items with low communalities have relatively weak correlations with the factors and the other items, making them candidates for deletion (DeVellis, 2011; Worthington & Whittaker, 2006).

Using the recommended threshold of .40 (Costello & Osbourne, 2005; Kahn, 2006;

Worthington &Whittaker, 2006), I deleted the item with the lowest communality and then ran the EFA again to check the remaining items. Following this iterative process, I eliminated four items developed to measure perceived control (as shown in Appendix B), leaving 18 items with acceptable communalities for the next step.

I examined the results of an EFA with the remaining 18 items to determine which factors and items to retain. Factor retention was based on the scree test and parallel analysis, two of the most accurate methods (Costello & Osborne, 2005; Ford,

MacCallum, & Tait, 1986; Reise, Waller, & Comrey, 2000; Zwick & Velicer, 1986).

Cattell’s (1966) scree test involves looking for a natural bend or break point where data flattens out in the graph of eigen values. Parallel analysis (Horn, 1965) involves generating random sample data using the same number of respondents and variables as the sample to produce eigen values that correspond to factors. The retained factors have eigen values exceeding those of the randomly generated data (Hayton, Allen, &

Scarpello, 2004; Ledesma & Valero-Mora, 2007). Both of these methods supported retaining two factors from this data set. 70

The next step involved eliminating any items that failed to load cleanly on one of the two factors. Following best practice recommendations (Costello & Osborne, 2005;

Tabachnick & Fidell, 2001; Worthington & Whittaker, 2006), I considered deleting items with less than .32 on their highest factor loading or cross-loading at .15 or higher. All items met the minimum threshold for highest factor loading, so I started by deleting the item with the most extreme cross-loading. After each item deletion, I ran the EFA again and checked the factor loadings. This iterative process resulted in deleting three more items developed to measure perceived control (as shown in Appendix B).

At this stage, the model included only five of twelve items developed to measure perceived control items, but all ten items developed to measure leaving preference. As shown in Table 2, 12 of the 45 intercorrelations among leaving preference items were at least +/- .80, indicating potential redundancy (DeVellis, 2011; Hinkin, 1995). Redundant items with strong intercorrelations contribute virtually no incremental information after including one of them in a scale (Boyle, 1991; Clark & Watson, 1995). Scales with a large number of redundant items typically demonstrate very high levels of internal consistency (Boyle, 1991; Cortina, 1993), but may suffer negative consequences such as narrow measurement of the construct (Cattell, 1973; Smith & Stanton, 1998), reduced validity (Briggs & Cheek, 1986; Loevinger, 1954), and factor structure issues (Cattell,

1978; Cortina, 1993; Landis, Beal, & Tesluk, 2000).

The next step of scale development addressed the redundancy among leaving preference items, based on the approaches described in several studies (e.g., Hansen,

Byrne, & Kiersch, 2013; Kacmar, Crawford, Carlson, Ferguson, & Whitson, 2014;

Russell et al., 2004; Stanton et al., 2002). Specifically, I regressed the ten leaving 71 preference items on a relevant criterion (i.e., quit intentions) and examined their variance inflation factors (VIF), using an acceptable threshold of 5.0. Several items did exceed this threshold, so I eliminated the most extreme item and then ran the analysis again to check those that remained. This iterative process eliminated four of the leaving preference items and retained six for the next step (as shown in Appendix A).

To estimate reliability for the two scales, I calculated coefficient alpha (Cronbach,

1951). This analysis indicated that the 5-item perceived control scale (α = .83) had a very good level of internal consistency (DeVellis, 2011; Nunnally & Bernstein, 1994), which could not be improved by deleting additional items. Deleting one item did improve internal consistency for leaving preference, resulting in a 5-item leaving preference scale

(α = .91). Scores ranged from 1.00 to 7.00 on both scales. However, the study participants generally scored much higher on perceived control (M = 5.57, SD = 0.99) than leaving preference (M = 3.37, SD = 1.60).

Next, I ran another EFA to ensure that these item deletions did not change the factor structure and that items still met all established criteria (Worthington & Whittaker,

2006). The final EFA of the remaining ten items converged in seven iterations and accounted for 67.0% of variance. The first factor accounted for 42.9% of variance and included the five leaving preference items. The second factor accounted for 24.2% of variance and included the five perceived control items. These two factors were significantly intercorrelated (r = -.29, p < .001). The remaining ten items all loaded cleanly on one of the two factors, satisfying the best practice criteria described above.

Table 3 shows the final EFA results, including factor loadings and communality 72 coefficients of all items retained for Study 2, which featured confirmatory factor analysis and a scale validation process.

Table 3

Factor Loadings and Communalities for Final Exploratory Factor Analysis

Leaving Perceived Preference Control Communality Item Factor Factor Loading Loading PC1 - Whether I stay or leave my organization is entirely .137 .625 .461 up to me. PC2 - I can stay with my organization for as long as I want .000 .632 .402 to be there. PC10 - I do not have much control over being able to leave -.019 -.638 .415 my organization. PC11 - It is mostly up to me whether I can stay in my -.111 .861 .697 current organization. PC12 - It is mostly up to me whether I can leave my .011 .769 .586 current organization. LP1 - My current organization is where I really want to be. .890 -.006 .789 LP4 - I want to stay with my current organization as long .881 -.093 .737 as possible. LP5 - Leaving my organization would be a good thing. -.765 -.061 .617 LP7 - I would not be comfortable staying with this -.672 -.094 .498 organization for many years. LP8 - Thinking about a long career with this organization .863 -.034 .729 makes me feel good. Notes: The highest factor loadings for each item are in bold. Factor loadings shown are from the pattern matrix and communality coefficients are from after extraction.

Study 2: Scale Validation

The main purpose of Study 2 entailed validating the new measures of perceived control and leaving preference by demonstrating that they can help explain employee attitudes, intentions, and behaviors. I conducted correlational and regression analyses to test the hypothesized relationships. Study 2 also included a confirmatory factor analysis

(CFA) to assess whether the factor structure identified in Study 1 would adequately describe data collected in a separate sample. CFA and validation represent crucial steps when developing new scales (DeVellis, 2011; Floyd & Widaman, 1995; Hinkin, 1995;

Worthington & Whittaker, 2006). The following paragraphs describe the methodology and results associated with these analyses.

Sample and Procedure

I conducted a priori power analyses to determine a desired sample size for the regression analyses with the G*Power 3.1.3 program (Faul, Erdfelder, Buchner, & Lang,

2009). It indicated a minimum of 127 cases for an effect size of .15 with power of .80 and alpha of .05. Factor analysis literature includes many conflicting recommendations regarding sample size requirements, primarily because appropriate sample size relies on multiple aspects of the study (Schmitt, 2011). These include the level of communality among the variables and level of overdetermination of the factors, as well as the precision and power of model parameter estimates (MacCallum et al., 1999). The relatively large communalities and factor loadings for the final EFA model in Study 1 suggest that the

CFA should only require 100 to 200 cases (MacCallum et al., 1999). For Study 2, I targeted a minimum sample size of 500 participants, ensuring more than adequate power. 74

Study 2 used a Qualtrics online sampling approach focused on working adults in the United States. As described for Study 1, potential participants were contacted electronically with instructions for completing the survey. Individuals first reviewed an informed consent form (Appendix C) to help them determine whether to participate in the study. All participants received some small monetary compensation. It increased slightly for those who successfully completed the entire survey, which generally took less than ten minutes. Qualtrics managed the survey administration and data collection process, thereby helping to maintain participant confidentiality.

A total of 1,200 people started the survey, which began with two questions designed to restrict the sample to United States residents with either full-time or part-time employment. The 240 individuals (20.0%) who did not meet these requirements were immediately exited from the survey. The survey design used quotas to achieve approximately even splits on two variables: gender and intent to leave within 90 days.

The survey randomized items within each section to minimize any order effects.

I employed several methods to address careless or insufficient effort responding, which can seriously threaten data quality (Huang, Curran, Keeney, Poposki, & DeShon,

2012; Huang, Liu, & Bowling, 2015; Meade & Craig, 2012). Four attention check items spread throughout the survey asked participants to select a specific response and triggered an early exit for 360 people (30%) who failed to do so. As recommended by Meade and

Craig (2012), the survey closed by asking participants whether to use their data in this study, which led to dropping the ten cases (0.8%) with negative responses. Next, I ran analyses on the remaining 590 cases to identify careless responses to seven pairs of items with opposite meanings, as shown in Table 4. All of these items used a 7-point response 75 scale that ranged from strongly disagree to strongly agree. The checking process flagged cases where individuals agreed or strongly agreed with both items in each pair. It also flagged those who disagreed or strongly disagreed with both items in each pair. Similar responses to these dissimilar item pairs provided evidence of careless responding (Meade

& Craig, 2012).

The seven item pairs showed similar frequencies of careless responses, ranging from 6.1% to 8.5%. This analysis led to eliminating 65 of 590 cases (11.0%) due to flags on two or more of the seven careless response pairs. Consequently, I retained 525

(43.8%) of the original 1,200 cases for the Study 2 sample. Its demographic make-up was: 51.4% female, 81.5% employed full-time, 55.2% married, 52.4% completed at least a 2-year college degree, average age of 42.4 years, and average organizational tenure of

7.5 years.

Measures

Study 2 included measures of perceived control, leaving preference, job satisfaction, organizational commitment, engagement, job embeddedness, quit intentions, job search behaviors, and withdrawal behaviors. Each scale represented an average of its item scores, where higher values indicated higher levels of that construct. Unless otherwise noted, item responses ranged from strongly disagree (1) to strongly agree (7).

The demographic questions (Appendix D) covered gender, ethnicity, age, marital status, number of children, education, employment status, industry, and organizational tenure.

Table 4

Item Pairs Used in Careless Response Analysis

% # Scale Item 1 Item 2 Careless Response 1 Leaving I would not be comfortable Thinking about a long 8.5% Preference staying with this career with this organization for many years. organization makes me feel good. 2 Leaving I want to stay with my Leaving my organization 6.6% Preference current organization as long would be a good thing. as possible. 3 Perceived I do not have much control It is mostly up to me 6.1% Control over being able to leave my whether I can leave my organization. current organization. 4 Job It would be difficult for me It would be easy for me to 6.9% Embeddedness to leave this organization. leave this organization. 5 Quit Intentions I intend to leave this I do not plan on leaving this 7.1% organization soon. organization soon. 6 Job I find real enjoyment in my I consider my job to be 6.6% Satisfaction work. rather unpleasant. 7 Normative I do not feel any obligation I owe a great deal to my 6.9% Commitment to remain with my current organization. employer.

Leaving preference. Leaving preference (Appendix A) was measured with the 5- item scale (α = .91) developed in Study 1. A sample item is “I want to stay with my current organization as long as possible.” This scale is coded so that higher values correspond to a higher preference to leave.

Perceived control. Perceived control (Appendix B) was measured with the 5- item scale (α = .83) developed in Study 1. A sample item is “I can stay with my organization for as long as I want to be there.” 77

Job satisfaction. Job satisfaction (Appendix E) was measured with the 5-item shortened version (α = .89; Judge, Bono, & Locke, 2000) of Brayfield and Rothe’s (1951) scale. A sample item is “I feel fairly satisfied with my present job.”

Affective commitment. Affective commitment (Appendix F) was measured with the 6-item scale (α = .79) from Meyer and Allen (1997). A sample item is “I would be very happy to spend the rest of my career in this organization.”

Continuance commitment. Continuance commitment (Appendix F) was measured with the 6-item scale (α = .83) from Meyer and Allen (1997). A sample item is

“If I had not already put so much of myself into this organization, I might consider working elsewhere.”

Normative commitment. Normative commitment (Appendix F) was measured with the 6-item scale (α = .85) from Meyer and Allen (1997). A sample item is “I would feel guilty if I left my organization now.”

Engagement. Engagement (Appendix G) was measured with the 9-item Work &

Well-being survey (UWES; α = .85) from Schaufeli et al.(2006). A sample item is “I am enthusiastic about my job.” The item responses ranged from Never (0) to Always (6).

Job embeddedness. Job embeddedness (Appendix H) was measured with the 7- item scale (α = .88) from Crossley et al. (2007). A sample item is “I feel attached to this organization.”

Quit intentions. Quit intentions (Appendix I) was measured with four items (α =

.89) used by Crossley et al. (2007). Higher values indicate a higher intention to quit. A sample item is “I intend to leave this organization soon.” 78

Job search behaviors. Job search behaviors (Appendix J) was measured with the

6-item Active Search Behavior scale (Blau, 1993), as modernized by Hancock (2012, α =

.90). A sample item is “Listed yourself as job applicant in a newspaper, journal, professional association or website.” The item responses ranged from Never - 0 times (1) to Very frequently - at least 10 times (5).

Withdrawal behaviors. Withdrawal behaviors (Appendix J) was measured with the 4-item subscale (α = .64) of the Counterproductive Work Behavior Checklist (Spector et al., 2006). A sample item is “Stayed home from work and said you were sick when you weren’t”. The item responses ranged from Never (1) to Every day (5).

Results

Confirmatory factor analysis. The CFA evaluated the two-factor structure identified in Study 1 for fit using the Study 2 sample to provide evidence of construct validity (DiStefano & Hess, 2005; Gerbing & Anderson, 1988; Smith & McCarthy, 1995;

Thompson & Daniel, 2005). I also tested an a priori competing model to reduce the risk of needing post hoc model modifications that capitalize on chance variations in the sample (Jackson, Gillaspy, & Purc-Stephenson, 1999). In the competing model, all ten items loaded on a single factor. I used a maximum likelihood estimation procedure in

Mplus 5.2 (Muthén & Muthén, 2007) to conduct a CFA on the item covariance matrix.

Following best practice recommendations (e.g., Browne & Cudeck, 1993; Byrne,

2010; Hu & Bentler, 1999), I selected a set of five criteria to evaluate model fit. The model fit criteria included chi square (2) degrees of freedom ratio, comparative fit index

(CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), 79 and standardized root mean square residual (SRMR). The 2 degrees of freedom ratio decreases and approaches zero as model fit improves, with values between 2.0 and 5.0 generally indicating an acceptable fit (Byrne, 2010). Values greater than .95 on CFI and

TLI indicate good fit, while SRMR and RMESA should have values less than .08

(Browne & Cudeck, 1993; Byrne, 2010; Hu & Bentler, 1999).

As shown in Table 5, the two-factor model fit the data based on all five criteria

(2/df = 4.27, CFI = .972, TFI = .964, RMSEA = .078, SRMR = .049). Furthermore, the competing one-factor model did not come close to meeting any of the standards for acceptable fit. Therefore, these CFA results provide support for the two-factor model identified through EFA in Study 1.

Table 5

Confirmatory Factor Analysis Model Fit Indices

Model  df df CFI TFI RMSEA SRMR Two-Factor 145.25 34 4.27 .972 .964 .078 .049 One-Factor 1212.48 35 34.64 .702 .616 .256 .213 Independence 3990.11 45 88.67 .000 .000 .409 .382

Examining the modification indices revealed that allowing certain item residuals to intercorrelate could improve the model’s fit. However, changing the model based on these indices would capitalize on sample-specific characteristics that lack theoretical justification and may not generalize to the population (MacCallum, Wegener, Uchino, &

Fabrigar, 1992). Allowing such modifications may also mask the underlying structure of the data (Gerbing & Anderson, 1984) or lead to untenable conclusions regarding model 80 fit (Landis, Edwards, & Cortina, 2009). Therefore, I retained the original model instead of allowing intercorrelations between the item residuals without theoretical justification.

Table 6 shows the completely standardized parameter estimates for the two-factor

CFA model. All ten of the item-to-factor parameters were significant at p < .001. Unlike in the Study 1 EFA, these CFA results did not show a significant relationship between the leaving preference and perceived control factors ( = -.038, p = .429).

Table 6

Parameter Estimates for Two-Factor Confirmatory Factor Analysis

Completely Standard P- Parameter Standardized Error Value Estimate PC to PC1: Whether I stay or leave my organization is entirely .820 .019 < .001 up to me. PC to PC2: I can stay with my organization for as long as I .620 .030 < .001 want to be there. PC to PC3: I do not have much control over being able to leave -.597 .032 < .001 my organization. PC to PC4: It is mostly up to me whether I can stay in my -.791 .021 < .001 current organization. PC to PC5: It is mostly up to me whether I can leave my .817 .020 < .001 current organization. LP to LP1: My current organization is where I really want to .920 .008 < .001 be. LP to LP2: I want to stay with my current organization as long .949 .006 < .001 as possible. LP to LP3: Leaving my organization would be a good thing. -.894 .010 < .001 LP to LP4: I would not be comfortable staying with this -.826 .015 < .001 organization for many years. LP to LP5: Thinking about a long career with this organization .890 .010 < .001 makes me feel good. PC to LP -.038 .048 .429 Notes: PC = Perceived Control. LP = Leaving Preference. 81

Correlational analyses. Table 7 displays the means, standard deviations and reliability estimates for all Study 2 scales, as well as the correlations among them. All scales had acceptable (α > .80) levels of internal consistency. Most importantly, the new leaving preference (α = .95) and perceived control (α = .85) scales had even higher coefficient alpha values than reported in Study 1. As described below, I examined correlation coefficients to evaluate all the hypotheses except for the three involving potential moderators.

The results provided support for three of the four hypotheses about job attitudes.

As predicted by Hypotheses 1A and 1B, individuals with greater leaving preference had lower job satisfaction (r = -.79, p < .001) and lower affective commitment (r = -.86, p <

.001). Furthermore, people with higher perceived control over leaving did have lower continuance commitment (r = -.27, p < .001). However, perceived control did not have a significant relationship with normative commitment (r = -.01, p = .777). Therefore, the results supported Hypothesis 2A, but not 2B.

This study’s findings supported both Hypotheses 3A and 3B about employee engagement. As predicted, individuals with greater leaving preference had lower engagement (r = -.59, p < .001) and so did those with lower perceived control over leaving (r = .20, p < .001). Regarding job embededdness, Hypothesis 4A received support, but 4B did not. People with greater leaving preference had lower job embeddedness (r = -.78, p < .001) as predicted, but those with higher perceived control did not (r = -.06, p = .196). 82

Table 7

Descriptive Statistics and Correlation Matrix for Study 2 Scales

Scale M SD LP PC JS AC CC NC E JE QI SB WB G A T ES

Leaving Preference 3.98 1.94 .95 Perceived Control 5.62 1.09 .02 .85 Job Satisfaction 4.37 1.61 -.79 .10 .92 Affective Commitment 4.06 1.71 -.86 .06 .82 .93 Continuance Commitment 3.97 1.18 -.34 -.27 .22 .31 .74 Normative Commitment 3.70 1.54 -.78 -.01 .71 .86 .42 .90 Engagement 4.41 1.34 -.59 .20 .79 .68 .11 .59 .95 Job Embeddedness 3.59 1.53 -.78 -.06 .73 .83 .44 .85 .60 .92 Quit Intentions 3.95 2.03 .89 .01 -.74 -.79 -.39 -.73 -.51 -.74 .96 Search Behaviors 1.84 .97 .48 -.06 -.38 -.40 -.28 -.37 -.18 -.37 .55 .93 Withdrawal Behaviors 1.59 .67 .15 -.11 -.18 -.15 .02 -.09 -.16 -.10 .13 .16 .80 Gender (M=1, F=2) 1.51 .50 -.10 .04 .05 .09 .11 .10 .03 .06 -.12 -.14 -.12 - Age 42.43 11.83 -.22 -.01 .21 .13 .16 .10 .16 .14 -.26 -.22 -.08 -.04 - Tenure 7.53 7.47 -.27 .09 .17 .21 .11 .17 .14 .24 -.27 -.24 .05 -.06 .40 - Employment Status 1.82 .39 .07 .16 -.08 -.06 -.08 -.09 -.01 -.07 .06 -.02 .08 -.15 .05 .18 - (PT=1, FT=2) Notes: N = 525. Scale reliabilities shown in italics on diagonals except for single item demographics (last 4 variables). Correlations of +/- .12 are significant at p < .01. Correlations of +/- .09 are significant at p < .05. 83

The correlational analyses yielded mixed results with respect to intentions and behaviors. They supported Hypothesis 5A about quit intentions, but not Hypothesis 5B.

As predicted, individuals with stronger quit intentions did have greater preferences for leaving (r = .89, p < .001). However, they did not have higher perceived control (r = .01, p = .777). Similarly, the results supported Hypothesis 6A about search behaviors, but not

Hypothesis 6B. People who engaged in more search behaviors had greater preferences for leaving (r = .48, p < .001), but not have higher perceived control (r = .06, p = .211).

Finally, the findings supported Hypotheses 7A and 7B about withdrawal behaviors.

Individuals with greater preferences for leaving reported more withdrawal behaviors (r =

.15, p = . 001), as did those with lower perceived control (r = -.11, p = .012).

Overall, this study found support for 10 of the 14 hypotheses tested with correlational analyses. This total included all seven hypotheses focused on leaving preference. However, only three of the seven hypotheses focused on perceived control received support.

The correlational analyses revealed some other notable relationships for leaving preference and perceived control beyond those hypothesized. For example, individuals with greater leaving preference had lower continuance commitment (r = -.34, p < .001) and normative commitment (r = -.78, p < .001). Furthermore, people with higher perceived control over leaving had higher job satisfaction (r = .10, p = .025). Examining relationships with the demographic variables indicated that male (r = -.10, p = .025), younger (r = -.22, p < .001), and less tenured employees (r = -.27, p < .001) had greater leaving preference. Finally, the more tenured (r = .09, p = .039) and full-time employees

(r = .16, p < .001) had higher perceived control over leaving. 84

Regression analyses. I conducted a set of two-step regression analyses to test the moderation hypotheses regarding quit intentions (5C), job search behaviors (6C), and withdrawal behaviors (7C). For each one, I entered leaving preference and perceived control together in the first step, then the interaction term in the second step. All predictor variables were mean centered to clarify interpretation of results (Dalal &

Zickar, 2012). I did not include any statistical control variables, due to a lack of theoretical justification for their use with these new PWSD model constructs.

The misuse of statistical control variables has drawn strong criticism as it can lead to serious issues such as flawed inferences, conceptually ambiguous relationships, and inappropriate interpretation of results (e.g., Attinc, Simmering, & Kroll, 2012; Becker,

2005; Breaugh, 2008; Carlson & Wu, 2012; Spector & Brannick, 2011). I included several potentially relevant demographic variables in this study, but instead of treating them as control variables, I only reported their observed relationships with the variables involved in my hypotheses. This approach can help rule out alternative explanations and suggest future research questions (Spector & Brannick, 2011).

I evaluated any significant interactions by probing with the Johnson-Neyman technique (Johnson & Fay, 1950; Johnson & Neyman, 1936; Pothoff, 1964), which identifies regions in the range of the moderator variable where the effect of the focal predictor is significant and nonsignificant. This avoids the potential arbitrariness of pick- a-point approaches by mathematically deriving the point or points along the moderator’s continuum where the predictor’s effect transitions between statistically significant and nonsignificant (Hayes & Matthes, 2009). These points provide information about the range of values where predictors have a statistically significant effect. Although the 85 primary application of this technique has involved evaluating categorical by continuous interactions, it can generalize to test for a variety of interactions that arise in both fixed and random effects regression (Bauer & Curran, 2005; Hayes, 2012).

Table 8 shows the results of regressing quit intentions on leaving preference, perceived control, and their interaction term. The omnibus test of the full regression model was significant, F (3, 524) = 658.75, p < .001, R2 = .791. In the first step, there was a significant main effect for leaving preference ( = .887, p < .001), but not for perceived control ( = .008, p = .704). In the second step, there were significant effects for leaving preference ( = .547, p < .001), perceived control ( = -.117, p = .009), and the interaction term ( = .368, p = .002). Therefore, this analysis provided support for

Hypothesis 5C, indicating that perceived control moderates the relationship between leaving preference and quit intentions.

Table 8

Hierarchical Regression Analyses for Quit Intentions

Predictor  p-Value R2 Step 1 .788 Leaving Preference .887 < .001 Perceived Control .008 .380 Step 2 .003 Leaving Preference .547 < .001 Perceived Control -.117 .009 Leaving Preference X Perceived Control .368 .002 Notes: N = 525. Total R2 = .791 (p < .001).

Next, I used the Johnson-Neyman technique to probe this interaction and found that its region of significance covered the full range of the moderator variable (i.e., perceived control). Figure 3 graphs this significant interaction for one standard deviation above and below the mean of perceived control (x = 5.62 on a 7-point scale). As shown, leaving preference had a significantly positive effect on quit intentions for both groups.

However, this analysis revealed an even stronger effect for individuals with higher levels of perceived control (b = .977, p < .001) than for those with lower levels of perceived control (b = .845, p < .001).

Figure 3. Leaving preference and perceived control interaction on quit intentions.

Table 9 shows the results of regressing search behaviors on leaving preference, perceived control, and their interaction term. The omnibus test of the full regression model had a significant result, F (3, 524) = 54.09, p < .001, R2 = .237. In the first step, leaving preference ( = .483, p < .001) had a significant main effect, but perceived control ( = -.056, p = .146) did not. In the second step, leaving preference still had a significant effect ( = .697, p < .001), while perceived control ( = .023, p = .792) and the interaction term ( = -.232, p = .308) did not. Therefore, this analysis did not support

Hypothesis 6C that perceived control would moderate the relationship between leaving preference and search behaviors.

Table 9

Hierarchical Regression Analyses for Search Behaviors

Predictor  p-Value R2 Step 1 .236 Leaving Preference .483 < .001 Perceived Control - .056 .146 Step 2 .001 Leaving Preference .697 < .001 Perceived Control .023 .792 Leaving Preference X Perceived Control -.232 .308 Notes: N = 525. Total R2 = .237 (p < .001).

Table 10 shows the results of regressing withdrawal behaviors on leaving preference, perceived control, and their interaction term. The omnibus test of the full regression model was significant, F (3, 524) = 6.04, p < .001, R2 = .034. In the first step, leaving preference ( = .18, p = .001) and perceived control ( = -.110, p = .011) had 88 significant main effects. In the second step, the effects for leaving preference ( = .189, p

= .432), perceived control ( = -.095, p = .327), and the interaction term ( = -.046, p =

.857) all fell short of statistical significance. Therefore, these findings did not support

Hypothesis 7C that leaving preference would moderate the relationship between perceived control and withdrawal behaviors.

Table 10

Hierarchical Regression Analyses for Withdrawal Behaviors

Predictor  p-Value R2 Step 1 .034 Leaving Preference .147 .001 Perceived Control -.110 .011 Step 2 .000 Leaving Preference .189 .432 Perceived Control -.095 .327 Leaving Preference X Perceived Control -.046 .857 Notes: N = 525. Total R2 = .034 (p < .001).

Discussion

The complex phenomenon of employee turnover has deeply interested both researchers and practitioners for decades. Prevalent concerns about its negative consequences for organizations have helped motivate a sustained effort to better understand why and how employees leave. Recently, Hom et al.’s (2012) PWSD model expanded the conceptual domain to include multiple types of staying and leaving, as well as consideration of turnover destinations. It also introduced the concept of proximal withdrawal states to help explain why employees participate and withdraw from organizations. These motivational states stem from whether an employee wants to stay or leave a job, as well as their perceived control over acting on that preference.

The current research makes two important contributions toward applying the

PWSD model to the study of employee participation and withdrawal. First, it addresses the need for reliable and efficient measures of leaving preference and perceived control, the two core determinants of proximal withdrawal states. Second, it evaluates these new measures through empirical tests of theorized relationships with employee attitudes, cognitions, intentions, and behaviors. This section features a discussion of the research findings and limitations, as well as their implications for future study.

Scale Development

The goal of Study 1 was to develop parsimonious leaving preference and perceived control scales that offer the practical advantages of efficiency without sacrificing psychometric value. I developed the initial set of 22 items based on Hom et al.’s (2012) description of their PWSD model constructs, as well as a review of similar measures used in research on turnover and the theory of planned behavior (Ajzen, 1985). 90

Following best practice guidelines for scale development (e.g., DeVellis, 2011; Hinkin,

1995; Worthington & Whittaker, 2006), I applied several analytical techniques to item data collected through an online survey.

The exploratory factor analysis retained two factors corresponding to leaving preference and perceived control. I eliminated 12 items through an iterative process that included consideration of communality coefficients, minimum factor loading, excessive cross loading, redundancy, and internal consistency reliability. The remaining ten items split evenly into new leaving preference and perceived control scales that met all applicable psychometric standards.

In Study 2, I evaluated the factor structure identified in Study 1 with a separate and larger sample collected through a similar online survey methodology. Conducting a confirmatory factor analysis represents an essential step when developing new scales

(DeVellis, 2011; Floyd & Widaman, 1995; Worthington & Whittaker, 2006) that provides evidence of construct validity (DiStefano & Hess, 2005; Gerbing & Anderson,

1988; Smith & McCarthy, 1995; Thompson & Daniel, 2005). The two-factor model fit the data well based on all five criteria examined, whereas the competing one-factor model failed to meet any of these fit criteria. Overall, the results of fit indices, factor loadings, model comparisions, and internal consistency converged in support of the final model, which has separate unrelated factors that correspond to the PWSD model constructs of leaving preference and perceived control.

Scale Validation

In addition to confirming the initial factor structure, Study 2 tested hypotheses that link leaving preference and perceived control to other theoretically related constructs. 91

I made these predictions based on the PWSD model (Hom et al., 2012) as well as other relevant theory and research. This core validation strategy for new measures helps build a nomological network (Cronbach & Meehl, 1955; Schmitt & Landy, 1993) and increase confidence in how we interpret measures (Putka & Sackett, 2010; Schwab, 2005). To validate the new leaving preference and perceived control scales, I evaluated hypothesized relationships with some of the most prominent components of turnover theory: job attitudes, engagement, job embeddedness, quit intentions, search behaviors, and withdrawal behaviors. Table 11 summarizes the results for all 17 hypotheses tested in Study 2.

Table 11

Summary of Results for All Hypotheses

Hypothesis Prediction Supported 1A LP inversely related to Job Satisfaction Yes 1B LP inversely related to Affective Commitment Yes 2A PC inversely related to Continuance Commitment Yes 2B PC inversely related to Normative Commitment No 3A LP inversely related to Engagement Yes 3B PC positively related to Engagement Yes 4A LP inversely related to Job Embeddedness Yes 4B PC inversely related to Job Embeddedness No 5A LP positively related to Quit Intentions Yes 5B PC positively related to Quit Intentions No 5C PC moderates relationship between LP and Quit Intentions Yes 6A LP positively related to Search Behaviors Yes 6B PC positively related to Search Behaviors No 6C PC moderates relationship between LP and Search Behaviors No 7A PC inversely related to Withdrawal Behaviors Yes 7B LP positively related to Withdrawal Behaviors Yes 7C LP moderates relationship between PC and Withdrawal Behaviors No Notes: LP = Leaving Preference. PC = Perceived Control.

Leaving preference. The PWSD model (Hom et al., 2012) separates employee preferences and desires to stay or leave the organization from their intentions to act.

Their configural approach to the proximal withdrawal states forms four categorizes by crossing leaving preference and perceived control. They classify individuals with greater preferences to leave as either enthusiastic leavers (i.e., want to leave and think they can) or reluctant stayers (i.e., want to leave, but think they must stay). This approach identifies employees with lesser preferences to leave as either enthusiastic stayers (i.e., want to stay and think they can) or reluctant leavers (i.e., want to stay, but think they must leave).

As shown in Table 11, the results provided support for all seven hypotheses that focused on leaving preference. Beyond these hypothesized relationships, individuals with greater leaving preference also had lower continuance and normative commitment than other employees. The pattern of observed relationships with these constructs suggests general profiles for employees who share similar preferences about leaving their organizations. For example, individuals with greater preferences to leave (i.e., enthusiastic leavers and reluctant stayers) tended to be less satisfied, less committed, less engaged, and less embedded than those with lesser preferences to leave (i.e., enthusiastic stayers and reluctant leavers). Furthermore, they had stronger quit intentions while displaying more search and withdrawal behaviors than other employees. Demographic results suggest that employees with greater preferences to leave are more likely to be male, younger, and have shorter tenure than those with lesser preferences to leave.

This general profile of employees who prefer to leave aligns reasonably well with the accumulated theory and research about organizational participation and withdrawal 93

(e.g., Blau, 1994; Griffeth et al., 2000; Hanisch & Hulin, 1991; Lee & Mitchell, 1991;

March & Simon, 1958; Meyer & Allen, 1991; Mitchell et al., 2001; Mobley et al., 1979;

Price, 1975). Similarly, it appears to fit very well with the PWSD model’s (Hom et al.,

2012) description of enthusiastic leavers, but much less well with how they describe reluctant stayers. The model indicates that these groups should have some similarities, but their contrasting perspectives about control over leaving should result in different levels of intentions and behaviors. Although I address this topic more thoroughly in the next section, the results for leaving preference do yield some interesting implications.

Enthusiastic leavers and reluctant stayers should share similarly negative job attitudes and low engagement. These predictions received support from leaving preference’s strong inverse correlations with job satisfaction (r = -.79, p < .001), affective commitment (r = -.86, p < .001), and engagement (r = -.59, p < .001). Both enthusiastic leavers and reluctant stayers could also have high job embeddedness, although the PWSD model (Hom et al., 2012) splits its dimensions into preference and constraint antecedents.

Organization fit and links can strengthen employee desires to stay, whereas consideration of sacrifices can constrain leaving. The magnitude of the observed correlation between the global measure of job embeddedness and leaving preference (r = -.78, p < .001), particularly compared to its correlation with perceived control (r = -.06, p = .196), suggests that organization fit and links may have mattered more than sacrifices to the employees in this study.

Although all three observed correlations reached statistical significance, leaving preference had a much stronger relationship with quit intentions (r = .89, p < .001) than with search behaviors (r = .48, p < .001) and withdrawal behaviors (r = .15, p < .001). 94

This pattern of results aligns with previous research showing that preference and attitudes have stronger links with intentions than actual behavior (e.g., Ajzen et al., 2004; Allen et al., 2005; Griffeth et al., 2000; Vardamann et al., 2008). As previously discussed, an individual’s perceived constraints and control influence whether he or or she acts on a preferred behavior (Armitage & Conner, 2001; Maertz & Campion, 2004; Mobley et al.,

1979). According to the theory of planned behavior (Azjen, 1985, 1991), low perceived control also weakens intentions. Therefore, the magnitude of this relationship between leaving preference and quit intentions seems stronger than we would expect if perceived constraints on leaving played a prominent role with the participants. This conclusion receives further support from the rather modest results found for the perceived control scale, as discussed in the next section. Overall, the newly developed leaving preference scale has very encouraging initial validation results.

Perceived control. The PWSD model (Hom et al., 2012) introduced perceived control to the study of organizational participation and withdrawal, thereby challenging the implict assumption in most turnover models that employees have full discretion to stay or go at any time. Their configural approach to the proximal withdrawal states categorizes individuals with greater perceived control over leaving as either enthusiastic stayers (i.e., want to stay and think they can) or enthusiastic leavers (i.e., want to leave and think they can). On the other hand, their approach identifies employees with lesser perceived control over leaving as either reluctant stayers (i.e., want to leave, but think they must stay) or reluctant leavers (i.e., want to stay, but think they must leave).

As shown in Table 11, the current study provided mixed support for the hypotheses about perceived control. The pattern of observed relationships with these 95 constructs suggests general profiles for employees who share similar levels of perceived control over leaving their organizations. Individuals with lower perceived control over leaving (i.e., reluctant stayers and relucant leavers) tended to be more committed based on costs and benefits of leaving, less engaged, and display more withdrawal behaviors than those with higher perceived control (i.e., enthusiastic stayers and enthusiastic leavers). Beyond the hypothesized relationships, individuals with lower perceived control also tended to be less satisfied with their jobs, have shorter tenure, and were more likely to work part-time than other employees. Results did not support predictions that employees with lower perceived control would be more committed based on feelings of obligation, less embedded in their jobs, have weaker quit intentions, and engage in fewer search behaviors.

As described above, the current study supported perceived control’s predicted relationship with continuance commitment, but not normative commitment. This finding implies that the potential costs of quitting may have represented a stronger perceived constraint for employees than any feelings that the organization deserves their continued participation. This pattern of results aligns with prevailing research trends, as normative commitment has received weaker empirical support than the other forms of commitment

(Meyer et al., 2002; Solinger et al., 2008).

Results of the current study also failed to support the predicted inverse relationship between job embeddedness and perceived control, which seems surprising given the theoretical linkages between these constructs. In their PWSD model, Hom et al. (2012) identified the job embeddedness dimension of sacrifices as a constraint antecedent and noted that the reluctant stayer category includes highly embedded 96 employees who feel stuck (Mitchell & Lee, 2001). As discussed above, the highly embedded employees in this study had a very low preference to leave, suggesting that their embeddedness may depend more on organizational fit and links than sacrifices.

Future research could try using separate measures for the embeddedness dimensions to potentially reveal contrasting relationships with perceived control and leaving preference.

The PWSD model (Hom et al., 2012) predicts that enthusiastic leavers and reluctant stayers will differ on intentions and behaviors due to to their opposing perspectives about having control over leaving. For example, the enthusiastic leavers should have stronger quit intentions because they have higher perceived control over acting on their preference to leave. As noted above, leaving preference and quit intentions (r = .89, p < .001) had a very strong correlation in this study. However, the moderator analysis revealed an even stronger effect with higher levels of perceived control (b = .977, p < .001) for than with lower levels (b = .845, p < .001). Therefore, these results provide some support for the PWSD model’s contention that perceived control plays a key role in the link between preference and intentions. The PWSD model also predicts that enthusiastic leavers (i.e., want to leave and think they can leave) will engage in the most search behaviors and reluctant stayers (i.e., want to leave, but think they have to stay) will engage in the most withdrawal behaviors. However, the moderation hypotheses involving search behaviors and withdrawal behaviors did not receive support.

One potential explanation for finding so many non-significant results with the perceived control scale involves its distribution in this sample. As shown in Table 7, the respondents generally had very high levels of perceived control (M = 5.62 on a 7-point 97 scale, SD = 1.09). Furthermore, only 6.5% of respondents scored below the scale’s midpoint of 4.00, whereas 14.3% had the maximum value of 7.00. On the other hand, the leaving preference scale had more variability (SD = 1.98) with its median at the exact midpoint (i.e., 4.00) of the 7-point scale and its mean (M = 3.98) very close to that. As reported earlier, Study 1 participants had a similar pattern of results on these scales.

Applying the basic categorization approach from the PWSD model (Hom et al., 2012), these results indicate that the current study’s sample included far more enthusiastic stayers and enthusiastic leavers than reluctant stayers and reluctant leavers.

Therefore, the apparent restriction of range for perceived control may have weakened the observed relationships used to test hypotheses (Bobko, 1995; Tabachnick

& Fidell, 2001). However, the lack of prior research on perceived control over leaving the organization prevents any reasonable assumptions about the population distribution.

Therefore, these results raise the possibility that most employees perceive high levels of control over leaving the organization, perhaps due in part to the prevailing assumptions about voluntary turnover covered earlier (Allen et al., 2005; Hom et al., 2012; Maertz &

Campion, 1998). If true, this would continue to present a challenge for future validation efforts of perceived control over leaving.

Some studies on the theory of planned behavior (Ajzen, 1985) have reported similarly high levels of perceived control over behaviors such as initiating sexual activity

(Simms & Byars, 2013), complying with information security policies (Hu, Dinev, Hart,

& Cooke, 2012) and withdrawal from an organizational recruitment process (Griepentrog et al., 2012). More research can help uncover the true nature of how employees perceive their control over leaving an organization. One potentially interesting approach would 98 involve comparing scale scores to self-identification of the four main proximal withdrawal states. Future research could also compare groups of employees expected to differ in perceived control due to overt circumstances, such as a potential layoff or union rules. Overall, the validation results represent a promising early step toward understanding the role of these two core constructs from the PWSD model (Hom et al.,

2012) in organizational participation and withdrawal.

Limitations and Future Research

This section covers several potential limitations that merit further consideration for the current study and have implications for future research. The sampling approach represents one such potential limitation. I recruited the participants from an online sample provided by the vendor, who coordinated data collection and monetary compensation. If the recruitment or compensation strategy resulted in overly homogeneous or biased samples, the findings might not reflect the general population of working adults in the United States (Landers & Behrend, 2015; Paolacci & Chandler,

2014). However, other studies using similar sampling approaches have reported more demographic diversity than commonly used alternatives, such as typical college student and single organization samples (Goodman, Cryder, & Cheema, 2013; Paolacci,

Chandler, & Ipeirotis, 2010).

Consistent with previous research, the Study 1 and Study 2 samples appear sufficiently diverse in terms of industry, job tenure, education, and other demographics.

Furthermore, potential participants reviewed only a general description of the study that should not have differentially attracted people with more interest in the topic. Research also suggests that compensation rates do not adversely affect data quality, reliability, or 99 validity (Buhrmester, Kwang, & Gosling, 2011). Although not unique to online survey samples, insufficient effort responding can present serious challenges to data quality if not thorougly addressed by screening procedures (DiSimone, Harms, & DiSimone, 2015;

Fleischer, Mead, & Huang, 2015). The current research provides strong justification for including attention checks as they led to eliminating 36% of the initial Study 2 sample.

The online survey approach seems to have overcome its potential obstacles in this case, but I recommend employing a variety of sampling strategies in future validation efforts to advance our understanding of leaving preference and perceived control.

Another potential limitation involves the measurement approach, which may raise concerns about the biases often associated with using common methods, particularly self- report (Moorman & Podsakoff, 1992; Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).

However, self-report seems like the only reasonable way to assess the two core constructs of leaving preference and perceived control (Hom et al., 2012). Other than leaving and withdrawal behaviors, all of the constructs assessed lack directly observable evidence to use in an alternate measure. Respondents should have had little motivation to present themselves in a more socially desirable manner, given the confidentiality of data.

Furthermore, research indicates little if any inflation of correlations when employing only self-report measures (Moorman & Podsakoff, 1992; Spector, 2006; Spector & Brannick,

1995). Therefore, future research should employ similar measurement approaches that fit the constructs of interest.

The PWSD model (Hom et al., 2012) positions job attitudes, withdrawal behaviors, and search behaviors as consequent reactions of the proximal withdrawal states. However, the use of a cross-sectional design precludes any causal inferences 100 about the observed relationships with leaving preference and perceived control (Bobko,

1995; Spector, 1994). The scope of this initial research effort also did not permit inclusion of other key components of the PWSD model such as the theorized antecedents of preference and control, as well as turnover and destinations. However, future research could incorporate these newly developed and validated scales into an evaluation of other relationships shown in the PWSD model with a longitudinal design. Another interesting research approach would involve testing whether leaving preference can outperform quit intentions in predicting some of the attitudes and behaviors associated with employee withdrawal.

Conclusions

The current research represents an early attempt to develop and validate the two core constructs of the PWSD model (Hom et al., 2012). The new leaving preference and perceived control scales demonstrated strong psychometric properties with relatively few items. The initial validation evidence provided strong support for leaving preference and mixed support for perceived control based on testing their hypothesized relationships with attitudes, intentions, and behaviors. Much more research is needed to further evaluate these scales and the PWSD model’s many predictions. However, this study suggests that Hom et al’s (2012) proximal withdrawal states may prove helpful in understanding the complex phenomena involved in employee participation and withdrawal. This auspicious path of research could eventually lead to practical benefits for organizations that target their retention strategies based on diagnosing these mindsets within their workforce (Hom et al., 2012).

101

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Appendix A: Leaving Preference Scale

Responses: 1 = Strongly disagree 2 = Disagree 3 = Slightly disagree 4 = Neutral 5 = Slightly agree 6 = Agree 7 = Strongly agree

Items:

Study 1 Study 2 Item Code Code My current organization is where I really want to be. LP1 LP1 (reverse scored) I would like to leave my current organization as soon as LP2 possible. I would prefer to work somewhere other than my current LP3 organization. I want to stay with my current organization as long as LP4 LP2 possible. (reverse scored) LP5 LP3 Leaving my organization would be a good thing. It would be unpleasant to leave my organization. (reverse LP6 scored) I would not be comfortable staying with this organization for LP7 LP4 many years. Thinking about a long career with this organization makes LP8 LP5 me feel good. (reverse scored) LP9 I want to remain with this organization. (reverse scored) LP10 I want to leave this organization.

Notes: In Study 1, items LP3, LP9, LP10, and LP2 were eliminated in that order due to redundancy. Item LP6 was eliminated to increase coefficient alpha. Therefore, these five items were not included in Study 2.

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Appendix B: Perceived Control Scale

Responses: 1 = Strongly disagree 2 = Disagree 3 = Slightly disagree 4 = Neutral 5 = Slightly agree 6 = Agree 7 = Strongly agree

Items:

Study 1 Study 2 Item Code Code PC1 PC1 Whether I stay or leave my organization is entirely up to me. I can stay with my organization for as long as I want to be PC2 PC2 there. It would be difficult for me to leave my organization. PC3 (reverse scored) PC4 I feel trapped at my organization. (reverse scored) When I want to go, I should have no trouble leaving my PC5 organization. I have little influence over how long I will stay at my PC6 organization. (reverse scored) PC7 If I wanted to leave my organization, I could easily do so. I will have to leave this organization before I am ready. PC8 (reverse scored) PC9 This organization makes it easy for me to stay. I do not have much control over being able to leave my PC10 PC3 organization. (reverse scored) It is mostly up to me whether I can stay in my current PC11 PC4 organization. It is mostly up to me whether I can leave my current PC12 PC5 organization.

Notes: In Study 1, items PC5, PC7, PC8, and PC6 were eliminated in that order due to low communality coefficients. Items PC9, PC4, and PC6 were eliminated in that order due to excessive cross-loading on factors. Therefore, these seven items were not included in Study 2.

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Appendix C: Consent Form

You are invited to participate in a research project about employee attitudes and behaviors. This online survey should take about 10 minutes to complete. No risks or discomforts are anticipated. Participation is voluntary and no penalty will be imposed for failure to respond to the survey or any particular question. Individual responses will be kept completely confidential and your identity will never be shared with the researchers. You will be compensated for your participation according to your agreement with Qualtrics.

This is not a test. There are no right answers other than your honest and thoughtful replies. The information obtained will be used to better understand employee turnover and retention in organizations.

If you have any questions about the research, please contact the Principal Investigator, Eric Brasher, at [email protected] or the faculty advisor, Dr. Rodger Griffeth, at [email protected]. If you have any questions regarding your rights as a research participant, please contact Dr. Chris Hayhow, Director of Research Compliance, (740)593-0664 or [email protected].

Submission of the survey will be interpreted as your informed consent to participate. Before you proceed, please note this action signifies that:  you have read this consent form and are aware who to contact if you have any questions about this study or your participation;  you understand that Ohio University is conducting this study and your personal identifying information WILL NOT be shared with the researchers;  you are 18 years of age or older;  your participation in this research is completely voluntary;  you may withdraw from this study at any time with no penalty.

Sincerely yours,

Eric Brasher Doctoral Candidate Ohio University

Rodger W. Griffeth, Ph.D. Byham Chair of I/O Psychology Ohio University

157

Appendix D: Demographic Questions

1) Do you currently reside in the United States of America? Yes / No 2) What is your current employment status? Unemployed / Self-employed / Employed part-time / Employed full-time 3) What is your gender? Male / Female 4) What is your age (in years)? 5) What is your marital status? Single / Married / Separated / Divorced/ Widowed 6) What is the highest level of education you have completed? Less than High School High School / GED Some College 2-year College Degree 4-year College Degree Masters Degree Doctoral Degree Professional Degree (JD, MD) 7) In which industry are you currently employed? Forestry, fishing, hunting or agriculture support Mining Utilities Construction Manufacturing Wholesale trade Retail trade Transportation or warehousing Information Finance or insurance Real estate or rental and leasing Professional, scientific or technical services Management of companies or enterprises Admin, support, waste management or remediation services Educational services Health care or social assistance Arts, entertainment or recreation Accommodation or food services Other services (except public administration) Unclassified establishments 8) How long have you been employed by your current organization?

158

Appendix E: Job Satisfaction Scale

Source: Judge, Bono, and Locke (2000)

Responses: 1 = Strongly disagree 2 = Disagree 3 = Slightly disagree 4 = Neutral 5 = Slightly agree 6 = Agree 7 = Strongly agree

Items: 1) I feel fairly satisfied with my present job. 2) Most days I am enthusiastic about my work. 3) Each day at work seems like it will never end. (reverse scored) 4) I find real enjoyment in my work. 5) I consider my job to be rather unpleasant. (reverse scored) 159

Appendix F: Organizational Commitment Scales

Source: Meyer and Allen (1991, 1997)

Responses: 1 = Strongly disagree 2 = Disagree 3 = Slightly disagree 4 = Neutral 5 = Slightly agree 6 = Agree 7 = Strongly agree

Affective Commitment Items: 1) I do not feel “emotionally attached” to this organization. (reverse scored) 2) I do not feel a strong sense of “belonging” to my organization. (reverse scored) 3) I do not feel like “part of the family” at my organization. (reverse scored) 4) I would be very happy to spend the rest of my career in this organization. 5) This organization deserves my loyalty. 6) This organization has a great deal of personal meaning for me.

Continuance Commitment Items: 1) I feel that I have too few options to consider leaving this organization. 2) If I had not already put so much of myself into this organization, I might consider working elsewhere. 3) It would be very hard for me to leave my organization right now, even if I wanted to. 4) One of the few negative consequences of leaving this organization would be the scarcity of available alternatives. 5) Right now, staying with my organization is a matter of necessity as much as desire. 6) Too much of my life would be disrupted if I decided I wanted to leave my organization now.

Normative Commitment Items: 1) Even if it were to my advantage, I do not feel it would be right to leave my organization now. 2) I do not feel any obligation to remain with my current employer. (reverse scored) 3) I owe a great deal to my organization. 4) I really feel as if this organization’s problems are my own. 5) I would feel guilty if I left my organization now. 6) I would not leave my organization right now because I have a sense of obligation to the people in it.

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Appendix G: Engagement Scale

Source: Schaufeli, Bakker, and Salanova (2006)

Instructions: The following statements are about how you feel at work. Please read each statement carefully and select the option that best describes how frequently you feel that way.

Responses: 0 = Never 1 = Almost never 2 = Rarely 3 = Sometimes 4 = Often, 5 = Very often 6 = Always

Items: 1) At my work, I feel bursting with energy. 2) At my job, I feel strong and vigorous. 3) I am enthusiastic about my job. 4) My job inspires me. 5) When I get up in the morning, I feel like going to work. 6) I feel happy when I am working intensely. 7) I am proud of the work that I do. 8) I am immersed in my work. 9) I get carried away when I’m working.

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Appendix H: Job Embeddedness Scale

Source: Crossley, Bennett, Jex, and Burnfield (2007)

Responses: 1 = Strongly disagree 2 = Disagree 3 = Slightly disagree 4 = Neutral 5 = Slightly agree 6 = Agree 7 = Strongly agree

Items: 1) I feel attached to this organization. 2) It would be difficult for me to leave this organization. 3) I'm too caught up in this organization to leave. 4) I feel tied to this organization. 5) I simply could not leave the organization that I work for. 6) It would be easy for me to leave this organization. (reverse scored) 7) I am tightly connected to this organization.

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Appendix I: Quit Intentions Scale

Source: Crossley, Bennett, Jex, and Burnfield (2007)

Responses: 1 = Strongly disagree 2 = Disagree 3 = Slightly disagree 4 = Neutral 5 = Slightly agree 6 = Agree 7 = Strongly agree

Items: 1) I intend to leave this organization soon. 2) I will quit this organization as soon as possible. 3) I do not plan on leaving this organization soon. (reverse scored) 4) I may leave this organization before too long.

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Appendix J: Job Search Behavior Scale

Source: Adapted from Blau’s (1993) Active Job Search Behavior, as in Hancock (2012)

Instructions: Please indicate the number of times you have participated in the following behaviors in the last three months.

Responses: 1 = Never (0 times) 2 = Rarely (1 or 2 times) 3 = Occasionally (3-5 times) 4 = Frequently (6-9 times) 5 = Very frequently (at least 10 times)

Items: 1) Listed yourself as job applicant in a newspaper, journal, professional association or website. 2) Sent out resumes to potential employers. 3) Filled out a job application. 4) Had a job interview with a prospective employer. 5) Contacted an employment agency, executive search firm, or state employment service. 6) Contacted a prospective employer.

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Appendix K: Withdrawal Behavior Scale

Source: Spector, Fox, Penney, Bruursema, Goh, & Kessler (2006)

Instructions: How often have you done each of the following things on your present job?

Responses: 1 = Never 2 = Once or Twice 3 = Once or Twice per Month 4 = Once or Twice per Week 5 = Every Day

Items: 1) Came to work late without permission. 2) Stayed home from work and said you were sick when you weren’t. 3) Taken a longer break than you were allowed to take. 4) Left work earlier than you were allowed to.

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