How does spouse career support relate to employee turnover? Work interfering with family and job...

How does spouse career support relate to employeeturnover? Work interfering with family and jobsatisfaction as mediators†

ANN H. HUFFMAN1*, WENDY J. CASPER2 AND STEPHANIE C. PAYNE3

1Department of Psychology and W. A. Franke College of Business, Northern Arizona University, Flagstaff, Arizona, U.S.A.2College of Business Administration, Department of Management, The University of Texas at Arlington, Arlington, Texas, U.S.A.3Department of Psychology, Texas A&M University, College Station, Texas, U.S.A.

Summary Employee turnover is a major concern because of its cost to organizations. Although theory supports theinfluence of nonwork factors on turnover, our understanding of the degree to which nonwork factors relateto actual turnover behavior is not well developed. Using a sample of 5505 U.S. Army officers, we assessedthe extent to which spouse career support related to reduced turnover four years later through work interferingwith family (WIF) and job satisfaction as mechanisms. Results revealed that spouse career support decreasedthe odds of turnover, and WIF and job satisfaction sequentially mediated this relationship, with lowerWIF and higher job satisfaction reducing the odds of turnover. Practical implications of using family supportsystems as retention interventions are discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Keywords: work–family conflict; turnover behavior; spouse support; career support; job satisfaction

Turnover has long been a focus of organizational researchers because of the high costs of recruitment andselection to replace employees who have left the organization (Kacmar, Andrews, Van Rooy, Steilberg, & Cerrone,2006). Turnover also has less overt but nonetheless important negative effects such as declining morale (Abbasi& Hollman, 2000), disruption of social and communication patterns (Mobley, 1982), and reductions in organizationalperformance (Shaw, Gupta, & Delery, 2005). In fact, Abbasi and Hollman (2000) argued that turnover is “one of themost significant causes of declining productivity and sagging morale in both the public and private sectors” (p. 333).Because of this, turnover is a critical concern for organizations (Boswell, Ren, & Hinrichs, 2008) and, not surprisingly,is an extensively researched topic in organizational behavior (Hom, Mitchell, Lee, & Griffeth, 2012).Early theoretical models that aimed to explain and predict turnover behavior (e.g., March & Simon, 1958; Price, 1977;

Sheridan & Abelson, 1983) depicted declining job attitudes, which were associated with an increased likelihood ofturnover. These models included characteristics of the work environment as antecedents of turnover but excludedcharacteristics of the nonwork environment. However, the nonwork demands of U.S. workers have changed a great dealsince early turnover models were developedwhenmost members of the workforce weremen in single-earner families. In2011, 58.5 percent of married employees were in dual-earner couples, and 70.6 percent of mothers with children underthe age of 18 were employed (Bureau of Labor Statistics, 2012). Such changes are reflected in more recent turnovermodels that acknowledge the important role that nonwork factors can play in turnover (e.g., Hom & Kinicki, 2001).Despite the recognition that nonwork factors contribute to turnover decisions, nonwork and cross-domain

variables such as spouse support (a nonwork factor) and work–family conflict (a cross-domain variable) havereceived minimal attention in turnover research (e.g., Hom & Kinicki, 2001). This is an important oversight giventhat past research has found that nonwork variables such as spouse support relate to a number of work outcomes and

*Correpondence to: Ann H. Huffman, Department of Psychology and W. A. Franke College of Business, Northern Arizona University,Box 15106, Flagstaff, Arizona 86011, U.S.A. E-mail: [email protected]†Correction added on 10May 2013 after first publication online on 15April 2013. Due to an error, the ordering of the author names originally appeared in thisarticle as Huffman, Payne and Casper, instead of the correct order of Huffman, Casper and Payne. This error has been corrected in this version of the article.

Copyright © 2013 John Wiley & Sons, Ltd.Received 29 August 2011

Revised 26 February 2013, Accepted 06 March 2013

Journal of Organizational Behavior, J. Organiz. Behav. (2013)Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/job.1862

Research

Article

the cross-domain variable of work–family conflict (Eby, Casper, Lockwood, Bordeaux, & Brinley, 2005). In thecurrent study, we integrate the traditional turnover theory (e.g., Price, 1977) with the resources and demands model(Voydanoff, 2005) and conservation of resources (COR) theory (Hobfoll, 2001) to examine how spouse careersupport relates to turnover, through its effects on work-to-family conflict and job satisfaction as mechanisms.We contribute to the turnover and work–family literatures in several ways. First, we examine the role of the spousein work decisions, building on recent findings that spouse attitudes matter for employee attitudes (Wayne, Casper,Matthews, & Allen, in press). Second, we examine an “emergent construct in turnover theory,” interrole conflict(Steel & Lounsbury, 2009), drawing on both work (i.e., job satisfaction) and nonwork (i.e., spouse career support)factors to predict turnover. Third, we respond to calls from work–family researchers to study work behavior ratherthan attitudes or behavioral intentions (Allen, Herst, Bruck, & Sutton, 2000; Casper, Eby, Bordeaux, Lockwood, &Burnett, 2007; Eby et al., 2005) and to examine the effects of work–family conflict over time (e.g., Adams, King, &King, 1996; Allen et al., 2000; Bruck, Allen, & Specter, 2002; Casper et al., 2007; Greenhaus, Collins, & Shaw,2003). From a theoretical perspective, we shed light on why spouse career support and work-to-family conflictrelate to employee turnover.The current study examines the processes through which spouse career support relates to actual turnover

behavior. Although work–family studies have found that support for family from the work domain relates to workoutcomes (e.g., Kossek, Pichler, Bodner, & Hammer, 2011), less research has explored how support from homeaffects work outcomes. From the resources and demands model (Voydanoff, 2005) and COR theory (Hobfoll,1989, 2001), we propose that spouse support for an employee’s career reduces the odds of employee turnover.Moreover, we argue that this relationship occurs through sequential mediator variables: work interfering withfamily (WIF) and job satisfaction. We test these relationships (depicted in Figure 1) with a sample of U.S. Armyofficers over an extended period.

Spouse Career Support and Employee Turnover

Employees make career decisions considering a multitude of influences. The systems perspective (Bronfenbrenner,1989) suggests that employee career decisions are influenced by the attitudes, beliefs, and values not only of theemployee but also of members of his or her family unit. Thus, a spouse can have an important influence on anemployee’s decision about whether to continue employment with her or his organization by encouraging ordiscouraging the employee to remain. In fact, recent studies have found that spouse attitudes toward a worker’semployer are related to that employee’s organizational commitment (Wayne et al., in press).A military career can make extensive demands on an employee’s family. Military families often face frequent

relocations or long periods of separation from each other due to deployments (Britt & Dawson, 2005). This may

Figure 1. Model of hypothesized relationships

A. H. HUFFMAN ET AL.

Copyright © 2013 John Wiley & Sons, Ltd. J. Organiz. Behav. (2013)DOI: 10.1002/job

make it difficult for a spouse to develop his or her own career (Castaneda & Harrell, 2008) and result in adjustmentdifficulties for children (Lincoln, Swift, & Shorteno-Fraser, 2008). Spouses are often extensively involved in themilitary organization themselves, working as volunteers on the base or providing support to other military familiesduring periods of deployment (Blaisure, Saathoff-Wells, Pereira, Wadsworth, & Dombro, 2012). Because of thedemands a military career places on the family, the degree to which an employee’s spouse is able and willing tosupport her or his career is a critical issue. In fact, job-related problems due to poor family adaptation have beenwell documented in the military (Burnam, Meredith, Sherbourne, Valdez, & Vernez, 1992). Thus, a spouse’swillingness to support an employee’s military career is likely a key influence on retention. Research on militaryreserve employees found higher retention among reservists whose spouses’ had more positive attitudes toward theirmilitary career (Lakhani & Fugita, 1993). Thus, we expect that the more supportive an officer’s spouse is of his orher military career, the less likely the officer will leave the military.

Hypothesis 1: Spouse career support is negatively related to turnover.

Spouse Career Support and Work Interfering with Family

There continues to be a need to understand the role of social support in employee experiences of the work–familyinterface (Eby et al., 2005). At home, a spouse is a key person who can provide support (Adams et al., 1996).Although research suggests that social support generally relates to positive work outcomes (Baruch-Feldman,Brondolo, Ben-Dayan, & Schwartz, 2002), spouse support has not been studied extensively. The resources anddemands model (Voydanoff, 2005) suggests that positive outcomes result from having adequate resources to meetthe demands of work and family. Although resources can be drawn from both work and family domains, our focusis on resources drawn from the family. Spouse career support is a boundary-spanning resource that originates in thefamily domain but can improve attitudes and behavior at work. This study explores the mechanism through whichspouse career support relates to reduced employee turnover through reductions in work interference with familyand increased job satisfaction.Although researchers recognize the prevalence and importance of positive work–family spillover (e.g., Wayne,

Grzywacz, Carlson, & Kacmar, 2007), work–family research has primarily focused on conflict between the twodomains (Eby et al., 2005; Greenhaus & Beutell, 1985; Kossek & Ozeki, 1998). The resources and demands modelsuggests that demands and resources conjointly determine if a person can meet environmental demands(Voydanoff, 2005). Work–family conflict can result when the available resources are inadequate to meet demands.Although work–family conflict can involve both WIF and family interfering with work (FIW; Frone, Russell, &Cooper, 1992), in this study, we focus on WIF for three reasons. First, our study examines spouse career support,which specifically focuses on how a spouse supports an employee’s career rather than more general spouse support.Spouse career support is a boundary-spanning resource that originates in the family domain but reduces demands atwork (Voydanoff, 2005). Support from a spouse that benefits an employee’s career specifically should reducedemands at work and, thus, relate to lower WIF (as opposed to FIW). Second, drawing on the matching hypothesisthat suggests that employees react to the domain that is the source of the interference (Amstad, Meier, Fasel,Elfering, & Semmer, 2011), we argue that, when work interferes with family, negative feelings and behavior shouldbe directed toward the job (reduced job satisfaction and increased turnover) rather than the family. Third, fororganizations, understanding WIF may be more practically important than understanding FIW given the higher levelsof WIF that workers experience (Frone et al., 1992) and the fact that WIF is more strongly related to work outcomesthan FIW (Carr, Boyar, & Gregory, 2008; Grandey, Cordeiro, & Crouter, 2005; MacDermid & Harvey, 2005).Drawing from the resources and demands model (Voydanoff, 2005), we argue that boundary-spanning resources

drawn from family can help reduce the degree to which work interferes with family. Spouse career support is a

WORK–FAMILY CONFLICT AND TURNOVER


boundary-spanning resource that, although generated in the family domain, enables one to more effectively managethe boundary between work and family and can reduce demands associated with work (Voydanoff, 2005). Asupportive spouse can provide emotional and instrumental resources to support an employee’s career (Ostberg &Lennartsson, 2007). For instance, a spouse may provide advice about problems at work or just listen when theemployee needs to vent about work-related frustrations. Spouses can also provide instrumental career support byproofreading work-related correspondence or reports, running work-related errands, and/or entertaining colleaguesto facilitate relationship building. Employees should manage work and family relationships more successfully andhave less WIF when family resources are adequate to meet the demands of work (Voydanoff, 2005). Accordingly,we expect that higher levels of spouse career support will be associated with lower WIF.

Hypothesis 2: Spouse career support is negatively related to WIF.

Work interfering with family and turnover behavior

In recent years, scholars (e.g., Halbesleben, Zellars, Carlson, Perrewé, & Rotondo, 2010; Hoobler, Hu, & Wilson,2010) have begun to examine the relationship between work–family conflict and its outcomes through the lens ofCOR theory (Hobfoll, 2001). COR theory posits that the threat of losing resources is stress provoking. Resourcesare defined as “objects, personal characteristics, conditions, or energies that are valued by the individual or thatserve as a means for attainment of these objects, personal characteristics, conditions, or energies” (Hobfoll,2001, p. 4). According to COR theory, people seek to acquire and maintain resources. When work interferes withfamily, the stress of managing this conflict results in resource loss and attempts to conserve resources, resulting inreduced role quality (i.e., job satisfaction) and an increased likelihood of role withdrawal (i.e., turnover) (Grandey& Cropanzano, 1999). People who continuously experience WIF may experience resource “loss spirals,” resultingin inadequate resources to cope with environmental demands (Demerouti, Bakker, & Bulters, 2004). As resourcesare lost, people tap into other resources or leave the situation that drains them of their resources.Hobfoll (1989) argued that people possess a long-term outlook with regard to maintaining adequate resources.

Thus, one way that people may respond to resource loss spirals associated with WIF is to withdraw from thework role over time (i.e., turnover). The COR framework has been used previously to understand how stress-relatedvariables lead to turnover behavior (e.g., Wright & Cropanzano, 1998). In addition to COR theory, the “matchinghypothesis” proposes that people have negative affect toward and make changes in the domain in which the interferenceoriginates, and empirical research supports this by finding that WIF has a deleterious effect on work-relatedoutcomes (Amstad et al., 2011).Most studies linking WIF to turnover have assessed turnover intentions rather than actual turnover, finding

that workers with higher WIF have greater turnover intentions (Allen et al., 2000; Kossek & Ozeki, 1999).However, only a few studies have examined whether WIF relates to actual turnover. In a study of occupationalturnover, Greenhaus, Parasuraman, and Collins (2001) found that those with higher WIF (but not FIW) were morelikely to leave public accounting as a profession. Only two studies we are aware of provide even tentative evidencefor the idea that work interference with family relates to turnover from the organization. Good, Page, and Young(1996) found some evidence for an indirect relationship between general work–life conflict and turnover throughturnover intentions. However, researchers studied more general work–life conflict rather than WIF, and therelationship was found only among entry-level, but not upper-level, managers. A more pertinent study byCarr et al. (2008) found that those with more WIF were more likely to turn over 12months later. This providessome evidence that workers who experience higher WIF are more likely to leave their organizations over time.One challenge to predicting turnover is the low base rate (ratio of number of people who leave to total number in

sample), which results in less variance in the criterion. Steel and Griffeth (1989) suggested that one solution to thelow base rate problem was to extend the time lag between the predictor and the criterion. For example, in a four-yearturnover study, Dalton and Todor (1987) found that every additional year increased the base rate by about 11 percent,



which increased criterion variance. Researchers have yet to reach consensus about the ideal time lag betweenmeasuring predictor variables and assessing turnover (Boswell et al., 2008; Steel & Griffeth, 1989), but there isagreement that criterion variance is important (Steel & Griffeth, 1989). In the current study, we examine turnoverbehavior among U.S. Army officers four years after survey data were collected for two reasons. First, because ofobligations to remain through the completion of their current commitment, we wanted to ensure that officers hadthe opportunity to turn over in the time frame examined. Given that military commitments are typically made infour-year increments, assessing turnover four years later ensured that most, if not all, study participants had theopportunity to leave the military without the high penalty and stigma associated with breaking a commitment.Second, Boswell et al. (2008) stressed that turnover is a “process that develops over time” (p. 207). We proposethat four years not only allows for the officer to complete the average military obligation but also provides anopportunity for the turnover process to unfold.

Hypothesis 3: Work interfering with family is positively related to turnover.

Spouse Career Support,Work Interfering with Family, and Turnover Behavior

Thus far, we have suggested that spouse career support relates to reduced WIF, and reduced WIF relates tolower odds of turnover. In short, WIF is expected to mediate the relationship between spouse career supportand turnover. Scholars have proposed (Odle-Dusseau, Britt, & Greene-Shortridge, 2012), and there has beensome empirical support for, the idea that aspects of the work–family interface, including conflict, may ex-plain the relationships between family variables and work outcomes. For example, Graves, Ohlott, andRuderman (2007) found that FIW mediated the relationship between commitment to marriage and jobperformance.The notion that spouse career support relates to reduced turnover indirectly through reductions in WIF is

consistent with the resources and demands model. This model suggests that both work and family domains provideresources but also place demands on a person, and for positive outcomes to occur, resources must be adequate tomeet demands (Voydanoff, 2005). When employees face work demands, they often draw on resources from home,such as the support of a spouse. Without spouse support, employees may lack the resources to keep work–familyconflicts at bay. Over time, one way employees may cope with WIF is to leave their job in favor of alternativeemployment that allows more time and energy for family. When spouses support employees’ careers, employeesshould experience less WIF, reducing the chance of turnover. Although we expect an indirect relationship betweenspouse career support and turnover through WIF, spouse career support is also likely to have a direct effect onturnover. In addition to providing career support, which reduces WIF, a spouse may also encourage or discouragethe employee to leave or stay, directly influencing turnover.

Hypothesis 4: Work interfering with family partially mediates the relationship between spouse career support andturnover.

Job satisfaction as a mediator of the work interfering with family–turnover relationship

Theory and research suggest that WIF may relate to turnover indirectly as well as directly; thus, we also propose jobsatisfaction as a key mediator. Job satisfaction is defined as a psychological response to one’s job (Hulin & Judge,2003) or “a pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences”(Locke, 1976, p. 1300). Researchers have argued that one factor that influences a person’s job satisfaction iswhether that job is compatible with other important life roles such as family (Grandey et al., 2005; Mobley,



1982). When a job is not compatible with a stable family life (i.e., excessive work hours and frequent work-relatedtravel that causes family separation), an employee can experience the aversive state of WIF. Because WIF originates inthe work domain, the negative psychological responses that it provokes should be directed toward work (i.e., job dis-satisfaction). Consistent with this, the matching hypothesis suggests that people have negative affect directed towardthe domain that is the source of the interference (Amstad et al., 2011). Theoretically, when work interferes with family,people should become less satisfied with their jobs. Empirically, researchers have found that WIF is indeed negativelyrelated to job satisfaction (Allen et al., 2000; Kossek & Ozeki, 1998), as theory would suggest.Turnover theory has long recognized the centrality of job satisfaction to the turnover process (Hom & Kinicki,

2001; March & Simon, 1958; Steel & Lounsbury, 2009). Seminal turnover theory (Mobley, 1982) long agosuggested that low job satisfaction is a key reason for organizational turnover, and substantial empirical worksupports this relationship (Griffeth, Hom, & Gaertner, 2000). When employees become dissatisfied with their jobs,they are likely to seek ways to resolve this negative affect. If low job satisfaction results from aspects of the job ororganization that cannot be changed, leaving the organization may be the only viable option for becoming moresatisfied. Turnover theory suggests that employees who are dissatisfied develop intentions to leave their jobs, whichresult in actual turnover if and when the opportunity to leave becomes available (Mobley, 1982). In the currentstudy, because officers were fulfilling obligations to the military, the effect of job satisfaction on actual turnovermay not be evident until this obligation is complete. However, in the four years after officers completed the jobsatisfaction measure, their commitments should have been fulfilled, giving them the opportunity to leave.In short, we propose, consistent with seminal turnover theory (Mobley, 1982), that WIF relates to turnover both

directly and indirectly through job satisfaction. The indirect effect occurs because incompatibility between one’sjob and family life, assessed by WIF, is associated with less job satisfaction, which relates to higher turnover.A direct effect of WIF on turnover is still expected given that WIF is also related to various other work-relatedattitudinal responses (i.e., organizational commitment), which also relate to turnover.

Hypothesis 5: Job satisfaction partially mediates the WIF–turnover relationship.

Method

Research context

We examined our hypotheses using a sample of U.S. Army officers. Officers experience many work stressors suchas long hours, unpredictable schedules, frequent deployments, and high workload (Castro & Adler, 1999), makingit an ideal organization in which to studyWIF and turnover. In addition, a military career places many demands on aspouse and family (Blaisure et al., 2012), making it a well-suited organization in which to study spouse careersupport. Although there are many unique aspects to a military career, the military has been described by someresearchers as a reflection of civilian society (Martin, Rosen, & Sparacino, 2000). Work–family issues are animportant concern to both military and civilian employees. Moreover, turnover is an important problem for bothmilitary and nonmilitary work organizations.

Participants and procedure

Participants were officers who responded to the 1996 Survey on Officer’s Careers conducted by the U.S. ArmyResearch Institute (ARI) for the Behavioral and Social Sciences (N = 9146). The primary purpose of the surveywas to identify factors that relate to officers’ career decisions and examine the implications for personnel policy(Jones, 1999). Data were collected via a mail survey distributed to a random sample of officers worldwide, and



the overall response rate was 47 percent. Participation was voluntary, and survey instructions indicated that the datawould be safeguarded to protect privacy. Surveys and research protocol were approved by the ARI InstitutionalReview Board.Survey data collected at one period were matched by social security number with archival turnover data

four years after the survey was administered. Data were available for 8142 (89 percent) respondents. Officerswho involuntarily left the Army (n = 333), those who left and information about whether turnover was voluntarywas not available (n = 5), and unmarried officers including officers in unmarried long-term romantic relationships(n = 2299) were excluded, resulting in a final sample of 5505 officers (4461 men and 1044 women). At the time ofthe survey, participants had served in the Army up to 29 years (M = 12.36, SD = 5.44) and were on average 33 yearsold (SD = 5.8 years), and 81 percent were men. More respondents (42 percent) held the rank of captain than anyother rank. Approximately, 32 percent of the respondents had been deployed at least once either prior to or at thetime of the survey.

Measures

Measures were created from items in the survey described earlier. Although items did not come from standardmeasures, they were developed from theoretical conceptualizations of the constructs of interest. To provideevidence of convergent validity, we collected data from both military (n = 24) and nonmilitary participants(n = 90) on key constructs using the items from this study along with previously validated measures of the sameconstructs.1 Correlations between each measure used in this study and the validated measure for the militaryrespondents are presented with each measure’s description.

CovariatesRespondents were asked to report their sex (0 = female and 1 =male), number of children, and whether their spousewas employed (0 = spouse not employed and 1 = spouse employed). Additionally, respondents were provided a listof locations to which the Army had deployed troops in the 34 years directly before the survey and asked to indicateif they were deployed to any of them. Those who checked at least one location were coded as 1 (deployment experience),and those who reported that they had not been deployed to any of these locations were coded as 0 (no deploymentexperience). Organizational tenure was calculated using organizational records by subtracting year of entry into theArmy from 2000, the year for which we had turnover data.

Work interfering with familyWe used two items to measure WIF, which are consistent with Netemeyer, Boles, and McMurrian’s (1996)conceptualization of WIF. The items were “An Army career would/does create a lot of conflict between my workand my family life” and “The demands of an Army career would/does make it difficult to have the kind of familylife I would like.” Participants responded on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree).Items were written in both future and present tenses to ensure relevance to all respondents regardless of whetherthey had decided to make the Army a career. Scale scores were calculated as the mean of the items. Correlationsbetween the items were r = 58. This two-item WIF scale correlated strongly with the WIF measure of Netemeyeret al., r = .62, p< .05.

1An online survey was administered to civilian and military employees. The sample was recruited through snowball sampling. Sample descriptivestatistics were as follows: 55.8 percent men, 22.8 percent military, 63.2 percent married/in a long-term relationship, age (M= 29.6, SD= 29.6), andnumber of children (M= 1.4, SD= 97).



Job satisfactionWe measured job satisfaction with the following six items: How satisfied are you with your current job; howsatisfied are you with your current assignment; how satisfied are you with life as an officer; how satisfied areyou with the quality of supervision you receive in your current assignment; all in all, how satisfied are you withyour job; and all in all, how satisfied are you with your career prospects in the Army? Responses were based ona 5-point Likert scale (1 = very/extremely dissatisfied to 5 = very/extremely satisfied). Coefficient alpha was .84.This job satisfaction scale correlated strongly with Quinn and Shepard’s established measure of job satisfaction,r = .81, p< .05.

Spouse career supportSpouse career support was measured with the following question: “How supportive is your spouse of your making acareer of the Army?” Response options range from 1 = very unsupportive to 5 = very supportive. This item had astrong correlation with King, Mattimore, King, and Adams’ (1995) Family Support Inventory for Workers(r = .57, p< .05), although this scale focused on a broader construct of family support rather than a specific focuson spouse career support.

TurnoverTurnover information was collected from the Officer Longitudinal Research Database, an organizationalarchive maintained for research purposes (Hunter, Rachford, Kelly, & Duncan, 1987). Turnover was codeddichotomously (0 = stayed and 1 = voluntarily left) on the basis of whether the officer had voluntarily stayedin the Army up to four years after the survey administration (2000). As noted earlier, officers who invol-untarily left the Army were omitted. A total of 1641 (20 percent) officers voluntarily left the Army in thefour years after the survey. We examined turnover after four years, because this ensured that the majorityof respondents would have completed their obligation (which are on average four years long) and have hadthe opportunity to leave. In fact, survey responses about current obligations indicated that 96 percent of thesample would have the opportunity to leave in the four years following the survey, as 2675 (49 percent)officers indicated that they had completed their current obligation and 93 percent of those with a currentobligation (51 percent) indicated they had 48 or less months remaining in their current obligation at thetime of the survey.

Results

Table 1 depicts descriptive statistics, correlations, and reliabilities for all study variables. Sex, organizationaltenure, number of children, spouse employment, and deployment experience were included as control variablesbecause of their theoretical relevance and relationships with turnover (Griffeth et al., 2000), family demands(Friedman & Greenhaus, 2000), and WIF (Byron, 2005). To fully examine the relationship of sex with the variablesof interest, we provide descriptive statistics for men and women separately in Table 2. Sex (r =�.10, p< .05; 27.8percent women vs 17.6 percent men, w2 = 56.37; Cramer’s V = 0.10), number of children (r = .21,Mstayers = 1.31 vs.Mleavers = 1.01; t = 6.64, p< .05, Cohen’s d = 0.23), and deployment experience (r = .12) were significantly relatedto turnover. Specifically, women officers with more children and those who had been deployed were more likely toturn over than men officers with fewer children and those who had not been deployed. An examination of thebivariate relationships reveals patterns consistent with our hypotheses as spouse career support (r =�.24, p< .05),WIF (r = .15, p< .05), and job satisfaction (r =�.21, p< .05) were significantly related to turnover, in thedirection hypothesized.Hypotheses 1 and 3 were assessed using logistic regression because of the dichotomous outcome, and

Hypothesis 2 was assessed using linear regression because of the continuous nature of the outcome. To providea more conservative estimate, Bonferroni adjustments were made for all main effects given our large sample size



(p = .05/number of tests: 5; adjusted p= .01). Consistent with Hypothesis 1, spouse career support was negativelyrelated to turnover (B =�.55; Nagelkerke R2 = .11; Wald = 259.04; p< .01). Consistent with Hypothesis 2, spousecareer support was negatively related to WIF (b=�.35; p< .01; R2 = .12). In Hypothesis 3, we predicted thatemployees with more WIF would be more likely to turn over. As shown in Table 3, the odds ratio indicated thatfor every one unit increase in WIF, the odds of leaving the Army within four years increased significantly by1.52 times (B = .42; Nagelkerke R2 = .07; Wald = 118.06; p< .01), supporting Hypothesis 3.For Hypotheses 4 and 5, we conducted mediated regression analyses using Baron and Kenny’s (1986) procedure

(using Bonferroni adjustment). Additionally, we used the more conservative formal test of the indirect effect using

Table 1. Correlations between the variables of interest.

M SD 1 2 3 4 5 6 7 8

1. Sexa 0.81 0.39 —2. Organizational tenure 12.36 5.43 .06** —3. Number of children 1.27 1.09 .21** .34** —4. Spouse employmentb 0.54 0.50 �.30** �.06** �.31** —5. Deployment

experiencec0.32 0.47 .12** .13** .06** �.06** —

6. Spouse careersupport

4.35 0.96 �.00 .14* .11** �.01 .04** —

7. Work interfering withfamily

3.39 0.97 �.05** �.01 �.03* .01 .04** �.34** (.73)

8. Job satisfaction 3.80 0.75 .01 .03* .05** �.01 .02 .24** �.29** —9. Turnoverd 0.19 0.40 �.10** �.02 �.09** �.09** �.08** �.24** .15** �.21**

Note. N= 5505. Correlations between dichotomous variables and continuous variables are point-biserial correlations. All other correlations areproduct moment correlations. Coefficient alphas on the diagonal.a0 = female, 1 =male.b0 = spouse not employed, 1 = spouse employed.c0 = no deployment experience, 1 = at least one deployment in career.d0 = stay and 1 = leave.*p< .05; **p< .01.

Table 2. Demographic variables by sex.

Women Men

M SD M SD

Tenure 8.54 5.44 9.82 5.35Age 32.77 6.18 33.51 5.72Number of children 1.80 2.36 2.36 1.17Rank (1 = second lieutenant, 6 = colonel) 3.02 1.11 3.12 1.10Spouse employmenta 0.85 0.35 0.47 0.50Deployment experienceb 0.21 0.41 0.35 0.48Spouse career support 4.35 1.02 4.35 0.94Work interfering with family 3.49 1.02 3.37 0.96Job satisfaction 3.79 0.76 3.81 0.75Turnoverc 0.28 0.45 0.18 0.38

Note. All means are significantly different from one another except spouse career support and job satisfaction.a0 = spouse not employed, 1 = spouse employed.b0 = no deployments, 1 = at least one deployment in career.c0 = stay, 1 = leave.



the Sobel (1982) test. Because the Sobel test is already known to be a conservative test with low power (Preacher &Hayes, 2008), we did not use Bonferroni adjustment with it. It should be noted that the B coefficients are based ondifferent scales for logistic and multiple regression. Therefore, we needed to ensure that the coefficients werecomparable across equations. To do this, we standardized the B coefficients and standard errors by multiplyingeach coefficient by the standard deviation of the predictor variable and dividing by standard deviation of the outcomevariable (MacKinnon & Dwyer, 1993). Once the coefficients were standardized, we conducted the Sobel (1982) test.For Hypothesis 4, we predicted that WIF would partially mediate the relationship between spouse career support

and turnover. Spouse career support was significantly related toWIF (b=�.35, p< .01; Step 2). Both spouse careersupport (Step 1) and WIF (Step 3) were significantly related to turnover (B =�.55, p< .01 and B = .42, p< .01,respectively). Finally, the B for spouse career support dropped to �.47 (p< .01) but remained significant whenWIF was added to the equation (B = .24, p< .01) to predict turnover (Step 4), suggesting partial mediation. Table 4displays Steps 3 and 4 of the mediation analysis. The Sobel test provided support for the idea of mediation byrevealing an indirect effect (z =�2.15, p = .03). We also tested the indirect and direct effects with bootstrapping,which is a more powerful technique and preferred for sequential mediation as in the current study (Preacher &Hayes, 2008). Preacher and Hayes’s (2008) bootstrapping approach (estimated 10 000 bootstrap samples;Mallinckrodt, Abraham, Wei, & Russell, 2006) revealed that the indirect effect (mean effect =�0.01, SE = 0.00,95 percent CI [�0.017, �0.008]) and the direct effect (B =�.09, SE = 0.01, p< .000) were both significant. Thissupports Hypothesis 4 that the effects of spouse career support on turnover were partially mediated by WIF.Finally, in Hypothesis 5, we predicted that job satisfaction would partially mediate the relationship between WIF

and turnover. WIF was significantly related to job satisfaction (b=�.23, p< .01; Step 2). Both WIF (Step 1) and jobsatisfaction (Step 3) were significantly related to turnover (B= .42 and .01 and B =�.67 and .01, respectively).Finally, the B for WIF dropped to .28 (p< .01) but remained significant when job satisfaction was added to theequation (B =�.57, p< .01) to predict turnover (Step 4), suggesting partial mediation (Table 5). The Sobel testprovided further evidence of mediation by revealing a significant indirect effect (z = 11.46, p = .00). Again, wealso tested the indirect and direct effects following Preacher and Hayes (2008). Similarly, the indirect (mean

Table 3. Logistic regression analyses for spouse career support (Hypothesis 1) and work interfering with family (Hypothesis 3)predicting turnover.

Variable B SE Wald statistic Exp (B)

Hypothesis 1a (criterion: turnover)Sexb �.44** 0.09 23.62 0.65Number of children �.10* 0.04 6.39 0.91Organizational tenure .02** 0.01 8.18 1.02Deployment experiencec �.39** 0.08 21.48 0.68Spouse employmentd .30** 0.08 13.96 1.35Spouse career support �.55** 0.03 259.04 0.58

Hypothesis 3e (criterion: turnover)Sexb �.33** 0.09 13.46 0.72Number of children �.14** 0.04 13.07 0.87Organizational tenure .01 0.01 1.31 1.01Deployment experiencec �.45** 0.08 29.85 0.64Spouse employmentd .27** 0.08 11.79 1.32Work interfering with family .42** 0.04 118.06 1.52

Note.a�2 log likelihood= 4802.33, w2 = 110.73 (p= .000), Nagelkerke R2 = .11.b0 = female, 1 =male.c0 = no deployments, 1 = one or more deployments.d0 = spouse not employed, 1 = spouse employed.e�2 log likelihood= 4935.10; w2 = 235.99 (p= .000), Nagelkerke R2 = .07.*p< .05; **p< .01.



effect =�0.02, SE=0.00, 95 percent CI [0.017, 0.025]) and direct effects (B= .06, SE= 0.01, p< .000) were bothsignificant. This supports Hypothesis 5 that the effects of WIF on turnover were partially mediated by job satisfaction.

Discussion

Although turnover researchers have theorized that nonwork factors have an impact on turnover decisions (e.g., Hom &Kinicki, 2001; March & Simon, 1958; Mobley, 1977, 1982; Price, 1977), few studies have examined the relationshipbetween family variables and employee turnover. In the current study, we examined spouse career support as an

Table 4. Logistic regression analyses for WIF mediate the spouse career support–turnover relationship (Hypothesis 4).


Step 1: DV: turnover (logistic regression)Sexa �.44** 0.09 23.62 0.65Number of children �.01 0.04 6.39 0.91Organizational tenure .02* 0.01 8.18 1.02Deployment experienceb �.39** 0.08 21.48 0.68Spouse employmentc .30** 0.08 13.96 1.35Spouse career support �.55** 0.03 259.04 0.58

Step 2: DV: WIF (linear regression)Sexa �.15** 0.03Number of children .01 0.01Organizational tenure .01 0.03Deployment experienceb .11** 0.03Spouse employmentc �.01 0.03Spouse career support �.35** 0.01

Step 3: DV: turnover (logistic regression)Sexa �.33** 0.09 13.46 0.72Number of children �.14** 0.04 13.07 0.83Organizational tenure .01 0.01 1.31 1.01Deployment experienceb �.45** 0.08 29.85 0.64Spouse employmentc .27** 0.08 11.79 1.32Work interfering with family .42** 0.04 118.06 1.52

Step 4 DV: turnover (logistic regression)Sexa �.40** 0.09 19.88 0.67Number of children �.10** 0.04 6.73 0.91Organizational tenure .02** 0.01 7.36 1.02Deployment experienceb �.42** 0.08 25.27 0.66Spouse employmentc .31** 0.08 13.98 1.36Spouse career support �.48** 0.04 168.67 0.62Work interfering with family .24** 0.04 35.09 1.28

Notes. Step 1: �2 log likelihood= 4802.33, w2 = 370.70, p= .00, Nagelkerke R2 = 0.11; Step 3: �2 log likelihood= 4935.31, w2 = 235.97, p= .00,Nagelkerke R2 = 0.07; Step 4: �2 log likelihood = 4756.02, w2 = 406.13, p= .00, Nagelkerke R2 = 0.12. Coefficients are B coefficients in theirunstandardized form.DV, dependent variable.a0 = female, 1 =male.b0 = no deployments, 1 = one or more deployments.c0 = spouse not employed, 1 = spouse employed.*p< .05; **p< .01.



antecedent of employee turnover. Spouse career support was expected to reduce the odds of turnover both directlyand indirectly through its effect on reduced WIF and increased job satisfaction.Findings confirmed the important role of spouse career support in employee turnover decisions, extending

past findings that a spouse’s attitudes are important to employee attitudes (Wayne et al., in press). We found thatofficers who reported higher levels of spouse career support were less likely to have left the military up to four yearslater. Some of the relationship of spouse career support with turnover was indirect through a reduction of WIF andan enhancement of job satisfaction. However, even after these variables were considered, a direct effect of spousecareer support on officer turnover remained. Thus, in addition to reducingWIF by providing resources to support anemployee’s career, a supportive spouse might also enhance employee retention by facilitating harmony in thefamily unit. For example, spouses who are career supportive may be more willing to accommodate career demands

Table 5. Logistic regression analyses for job satisfaction mediate the work interfering with family–turnover relationship (Hypothesis 5).


Step 1: DV: turnover (logistic regression)Sexa �.33** 0.09 13.46 0.72Number of children �.14** 0.04 13.07 0.83Organizational tenure .01 0.01 1.31 1.01Deployment experienceb �.45** 0.08 29.85 0.64Spouse employmentc .27** 0.08 11.79 1.32Work interfering with family .42** 0.04 118.06 1.52

Step 2: DV: job satisfaction (linear regression)Sexa �.03 0.03Number of children .03** 0.01Organizational tenure .00 0.00Deployment experienceb .04 0.02Spouse employmentc .01 0.02Work interfering with family �.23** 0.01

Step 3: DV: turnover (logistic regression)Sexa �.39** 0.09 19.17 0.67Number of children �.12** 0.04 9.70 0.89Organizational tenure .01 0.01 1.19 1.01Deployment experienceb �.40** 0.08 23.49 0.67Spouse employmentc .28** 0.08 12.05 1.32Job satisfaction �.67** 0.05 218.19 0.51

Step 4 DV: turnover (logistic regression)Sexa �.36** 0.09 15.57 0.70Number of children �.12** 0.04 9.85 0.89Organizational tenure .01 0.01 1.69 1.01Deployment experienceb �.44** 0.08 27.82 0.64Spouse employmentc .28** 0.08 12.23 1.33Work interfering with family .29** 0.04 0.01 1.33Job satisfaction �.57** 0.05 147.24 0.56

Notes. Step 1: �2 log likelihood= 4935.31, w2 = 235.97, p= .00, Nagelkerke R2 = 0.07; Step 3: �2 log likelihood= 4851.44, w2 = 330.72.97,p= .00, Nagelkerke R2 = 0.10; Step 4: �2 log likelihood= 4787.73, w2 = 383.55, p= .00, Nagelkerke R2 = 0.11. Coefficients are B coefficientsin their unstandardized form.DV, dependent variable.a0 = female, 1 =male.b0 = no deployments, 1 = one or more deployments.c0 = spouse not employed, 1 = spouse employed.*p< .05; **p< .01.



such as the need for family relocation or frequent deployments. In contrast, when spouses resist making accommo-dations that are necessary for a career (i.e., refuse to relocate and become upset over deployments), turmoil in thefamily unit is likely to erupt. An employee facing such turmoil due to a spouse that is not career supportive wouldbe more likely to leave the organization in an attempt to restore family harmony.The relationship between spouse career support and reduced WIF is consistent with the demands and resources

model’s (Voydanoff, 2005) assertion that a match between demands and resources is important. The modelsuggests that maintaining a balance between work and family roles is about having adequate family resources tomeet the demands of work. Although spouse career support is a resource that emerges from the family domain,because it is a boundary-spanning resource, it enables one to more effectively meet work (as well as family)demands, keeping work demands in check and WIF manageable.Our findings are also consistent with the COR framework (Hobfoll, 1989), which suggests the importance of

adequate resources to promoting positive work behavior. For instance, COR theory suggests that when peoplelose resources because of managing work and family conflicts, one way to conserve resources is through reducinginvestment in one or both roles (Grandey & Cropanzano, 1999). When resource loss is excessive, as may be the casewhen WIF is extremely high, one way to conserve resources is to withdraw from that work role in favor of alternativeemployment that promotes less WIF. The military officers in our study were in work roles that often require long andirregular hours, family separation due to deployment, and family relocation, likely resulting in substantial WIF.Consistent with COR, employees who experienced WIF were more likely to leave the organization up to four years later.Exploratory analyses prompted by an anonymous reviewer revealed that parental status moderated theWIF–turnover

relationship such that those without children were more likely to leave than those with children (B=�.15, p= .000).Officers without children (relative to those with children) may have a greater propensity to turn over underconditions of high WIF because turnover is a more viable option for people with fewer financial responsibilitiesto others. Given that women often have greater responsibility for family domain activities than do men(Halpern, 2005), sex differences might also be expected. However, in our sample, men and women were equallylikely to leave as a function of WIF (B=�.46, p> .05). This finding should be generalized with caution, of course,given that our sample was overwhelmingly male. Moreover, because the military is such a male-dominatedorganization, women who are military officers may differ in some systematic ways from women who work infemale-dominated industries.Our results also suggest that WIF relates to turnover both directly and indirectly through reduced job satisfaction.

Consistent with the focal role of job satisfaction in early turnover models (March & Simon, 1958, Price, 1977), jobsatisfaction was a key mechanism through which WIF relates to turnover. Work–family researchers have alsoalluded to the important role of job satisfaction in explaining how WIF might relate to turnover (Allen et al.,2000). That said, because job satisfaction partially rather than fully mediated the relationship between WIF andturnover, job satisfaction does not fully explain why WIF relates to turnover, suggesting that other mechanisms are alsopossible. For instance, becauseWIF also relates to attitudes toward the organization more generally such as organizationalcommitment (Allen et al., 2000), this may be another mechanism through whichWIF relates to turnover, which wasnot examined in the current study. Regardless, given that the effects of WIF on turnover are both direct and indirect,interventions designed to reduce WIF or enhance job satisfaction (or ideally both) are likely to improve retention.

Theoretical and practical implications

Theoretically, the present study reveals the relevance of nonwork variables to turnover, providing further evidencefor their importance in the turnover process (Hom & Kinicki, 2001). Our study suggests that both spouse careersupport and WIF should be included in models of turnover. The important role of spouse career support in reducingWIF and actual turnover behavior is consistent, with several theories emphasizing the importance of resources indealing with demands (Hobfoll, 1989, 2001; Voydanoff, 2005). Given that spouse career support is an importantvariable that helps explain why people quit their jobs, findings are practically important for organizations who wish



to enhance employee retention. Specifically, they suggest that organizations might benefit from retention effortsthat include spouses as well as employees.To enlist the career support of spouses, organizations can invest in policies that benefit families andmarket them directly

to spouses. For instance, organizations could create websites with information on family-supportive policies to provideone-stop access for employees and spouses. Employers might also include spouses in new employee orientation programsto make sure they are well-informed about what the organization does for employees’ families. In addition to policies,organizations could impress upon spouses that they recognize the importance of and care about a family-supportive workenvironment by hosting family-friendly social events (e.g., company picnic and day at a ballgame). Finally, employerswho seek to identify problems through employee satisfaction surveys to proactively prevent turnover could survey spousesas well employees.Theoretically, this study also provides evidence that job satisfaction was an explanatorymechanism throughwhichWIF

related to turnover, consistent with the key psychological processes that have been proposed to underlie turnoverrelationships (March & Simon, 1958, Price, 1977; Steel & Lounsbury, 2009). The relationship between WIF and jobsatisfaction is also consistent with the matching hypothesis, which proposes that people make changes in the domainsin which interference originates (Amstad et al., 2011). Practically, it is also important to know that WIF relates to greaterturnover. When resources are tight, organizations may consider devoting fewer resources to family-support programs, andresults of our study suggest this would be ill-advised. Given the high cost of turnover in recruitment and selection efforts(Kacmar et al., 2006), morale (Abbasi &Hollman, 2000), and performance (Shaw et al., 2005), organizations that invest instrategies to reduce WIF, even when times are tight, might benefit in the long run by retaining their top talent.

Strengths, limitations, and future directions

Although this study has several strengths including longitudinal multisource data, there are limitations to consideras well as strengths. Because our sample was comprised entirely of U.S. military officers, the extent to which resultsgeneralize beyond military officers is unknown. That said, our hypotheses are theoretically based, not specific to themilitary, and the patterns of correlations among variables examined by other researchers are consistent withresearch on nonmilitary samples (e.g., Carr et al., 2008; Good et al., 1996). Therefore, we expect that our resultsshould generalize beyond the current sample.However, some of the relationships we found may be conservative estimates of these relationships in other contexts.

The generosity of the military retirement package is relatively unique, and such generous benefits may reduce the degreeto which nonwork factors relate to turnover by providing a strong incentive to remain until retirement. Despite this,spouse career support and WIF related significantly to turnover in this military sample. Thus, nonwork factors mighthave an even stronger effect on turnover in other types of organizations without such strong incentives to remain.To test the degree to which relationships identified in our military sample might generalize to civilian workers, we used

the data collected for our measure validation study to compare military and civilian employees on correlations betweenvariables relevant to the current study. Correlations were in the same direction and not statistically different in magnitudefor several relationships including the correlation between spouse career support and WIF (rmilitary =�.15, rcivilian =�.21;z=0.82), spouse career support and turnover intentions (rmilitary =�.43; rcivilian =�.34; z=0.38), and WIF and turnoverintentions (rmilitary = .32, rcivilian = .24; z=0.38). Although a direct replication of our results is the best way to assess thegeneralizability of findings, these data tentatively suggest that results may generalize to workers in some nonmilitaryorganizations. Generalizability may be higher to organizations that share some similarities with the military such as large,male-dominated organizations with highly structured career paths (i.e., law enforcement agencies) as opposed to thosethat share fewer commonalities with the military (e.g., small startup firms and female-dominated industries).It should be noted that using archival data required us to use some measures of psychological constructs not

commonly used in this literature and, in the case of spouse career support, a single-item measure. Althoughsingle-item measures have been criticized, research has found that single-item measures are strongly correlatedwith multi-item measures (Nagy, 2002; Wanous & Hudy, 2001; Wanous & Reichers, 1996; Wanous, Reichers,



& Hudy, 1997), suggesting they can be valid. However, because single-item measures are likely to attenuate relation-ships, the relationships we found may underestimate the true effect sizes. To overcome concerns about nonstandardscales, we collected additional data to provide some convergent validity evidence for our measures. Resultsrevealed strong positive relationships between the measures used in our study and previously validated measuresof family support (King et al., 1995), WIF (Netemeyer et al., 1996), and job satisfaction (Quinn & Shepard, 1974).Our study examined turnover over a longer period (up to four years) than had been examined in past

studies. Although there is little consensus about the ideal measurement time lag for turnover studies (Boswellet al., 2008), Steel and Griffeth (1989) posited that criterion variance increases by extending the time betweenmeasuring the predictors and turnover. Future research might explore whether WIF affects turnover at bothshorter and longer time lags tomore fully understand how the influence of nonwork variables on turnover unfolds overtime. Boswell et al. (2008) suggested that turnover research capture multiple time lags to assess how withdrawal playsout over time.Although the goal of our research was to develop a parsimonious model of how spouse career support relates

to turnover, future research could provide additional insight into how variables from the family domain relate toemployee turnover. For example, relocation with or without the family is a significant stressor and can haveprofound effects on family members, and therefore, turnover decisions. Researchers (Casper & Swanberg, 2009)have also suggested that work–life stressors can have negative effects on single employees without children,suggesting that research should examine how work–life conflict affects turnover decisions of single employees.Studies might also examine how spouse support and WIF relate to other forms of work withdrawal behaviors(e.g., absenteeism, tardiness, and lowered effort; Hulin, 1991). As yet, our understanding of the relationship ofWIFwithwork behaviors is less well developed than our understanding of how WIF relates to with attitudes (Eby et al., 2005).Although this study makes an important step by providing evidence that WIF relates to increased odds of turnover, thereis much more to be learned about how WIF relates to other important work behaviors.Finally, this study focused on conflict between work and family, omitting variables that tap into positive forms of

work–family spillover such as work–family enrichment (Greenhaus & Powell, 2006). Thus, future research mightexamine whether work-to-family enrichment can reduce the odds of turnover. Still, given the link between spousecareer support and reduced WIF and turnover, our study clearly suggests that family can benefit work. Futurestudies might extend our model to explore the degree to which spouse career support is associated with experiencesof family-to-work enrichment.

Conclusion

Understanding why people leave their jobs is key to developing effective retention strategies (Hom, Roberson, &Ellis, 2008). The current study demonstrated that WIF was related to greater odds of turnover measured fouryears later, demonstrating support for the long-term importance of nonwork factors on turnover decisions. More-over, employees whose spouses supported their careers had lower WIF, and in turn, lower turnover. The impactof WIF on turnover was partially explained by a reduction in job satisfaction such that those with higher levelsof WIF reported lower job satisfaction and were, in turn, more likely to leave. Future research should continueto include nonwork factors in turnover research to more fully capture the reasons why employees stay at anorganization or leave.

Acknowledgements

The data for this paper were supplied by the U.S. Army Research Institute for the Behavioral and Social Sciences(ARI). The views, opinions, and/or findings contained in this article are solely those of the authors and should notbe construed as an official Department of the Army or Department of Defense position, policy, or decision, unless



so designated by other documentation. We wish to express our appreciation to ARI for allowing us to access thedata, to Trueman Tremble, Jr., for facilitating this process, and to Kevin Williams and Trueman Tremble, Jr., fortheir helpful comments and suggestions. A previous version of this paper was presented at the 19th AnnualConference of the Society for Industrial and Organizational Psychology, Chicago, IL, in April 2004.

Author biographies

Ann H. Huffman is an associate professor of Psychology and Management (W. A. Franke College of Business) atNorthern Arizona University. Ann received her PhD in Industrial-Organizational Psychology from Texas A&MUniversity in 2004. Ann’s primary research interests include environmental sustainability issues, the work-life inter-face, high stress occupations (e.g., police, military), and diversity in the workplace.Stephanie C. Payne is an associate professor in the Psychology Department at Texas A&M University. Her re-search interests include individual differences, performance appraisal and management, safety climate, and work-lifebalance initiatives.Dr. Wendy J. Casper is an associate professor of Management at the University of Texas at Arlington, and VisitingFaculty at Cyprus International Institute of Management. She received her PhD in Industrial/Organizational Psy-chology from George Mason University. Her research examines family-friendly policies, as well as the work-lifeexperiences of under-studied groups.

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