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Article

Does Turnover IntentionMatter? Evaluating the

Usefulness of TurnoverIntention Rate as a Predictorof Actual Turnover Rate

Galia Cohen 1, Robert S. Blake 2, and Doug Goodman 1

AbstractTurnover research has traditionally examined intention to turnover rather thanactual turnover. Such studies assume that leave intent serves equally well as both aproxy for and predictor of employees’ actual turnover behavior. The purpose of thisstudy is to provide an agency-level evaluation of the usefulness of turnover intentionas a reliable proxy and predictor of actual turnover across 180 U.S. federal agencies,using hierarchical (stepwise) multiple regression. Our findings suggest that, at theorganizational level, turnover intention and actual turnover are distinct concepts,predicted by different sets of variables. Based on these findings, we conclude thatpublic managers tasked with retention might have better foresight concentrating ontheir agencies’ unique demographic characteristics and specific management practices,rather than on their employees’ self-reported aggregated turnover intention rate.

Keywordsturnover, federal government, human resource management

IntroductionHuman capital planning in the federal government mostly relies on measuring employ-ees’ future intention to leave (Broach & Dollar, 2006). However, studies that empiri-cally examined the relationship between intention to turnover and actual turnover are

1University of Texas at Dallas, USA2Hospital Corporation of America, Plano, TX, USA

Corresponding Author:Doug Goodman, Associate Professor of Public Affairs and MPA Director, Program in Public Affairs,School of Economic, Political, and Policy Sciences, University of Texas at Dallas, 800 W. Campbell, GR31,Richardson, TX 75080, USA.Email:[email protected]

ROPXXX10.1177/0734371X15581850Reviewof Public Personnel Administration Cohen etal.research-article 2015

mailto:[email protected]:[email protected]


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2 Review of Public Personnel Administration

scarce and demonstrate conflicting results in regard to the usefulness of intentions asa reliable proxy of behavior (Cho & Lewis, 2012; Jung, 2010; Kirschenbaum &Weisberg, 1990). In particular, some scholars have found that turnover intention is a

poor predictor of actual turnover (e.g., Jung, 2010; Kirschenbaum & Weisberg, 1990;T. W. Lee & Mowday, 1987). In this study, we explore the relationship between turn-over intention rates and actual turnover rates of U.S. federal agencies.

This research seeks to contribute to the emerging body of public employee turnoverresearch by addressing a heretofore-neglected aspect of empirical study (Meier &Hicklin, 2008; Selden & Moynihan, 2000). Moreover, the vast majority of the alreadysmall number of public administration studies on turnover have mostly been usingturnover intention as the dependent variable rather than actual turnover (Jung, 2010).

In addition, although previous turnover research has been mostly conducted at theindividual level, very little empirical research has examined turnover at the organiza-tional level. This lack of research is a matter of practical concern. For, although orga-nizational withdrawal is a personal decision affected by socio-psychologicalconsiderations and each individual’s own unique circumstances, employee retention,recruitment, and training are strategic human resource management functions neces-sarily administered at the organizational level (Ingraham & Rubaii-Barrett, 2007;Perry, Hondeghem, & Wise, 2010; Van Marrewijk & Timmers, 2003). As Hausknechtand Trevor (2011) pointed out, turnover analysis at the organizational level is muchmore consistent with the way HR managers and leaders prefer to learn about turnoverin their organizations (Gardner, Moynihan, & Wright, 2007). As a tenuous step towardthe advancement of understanding organizational turnover, this study uses agency-level data for the analysis.

In this study, we explore the relationship between the turnover intention rate andactual rate of federal government agencies in the following ways. First, and most fun-damentally, we assess whether agencies’ turnover intention rate and agencies’ actualturnover rate correlate within our sample. Second, we investigate the relative impactsof organizational-level perceptions toward HR practices on federal agencies’ actualturnover rates. The ultimate goals of HR practices are to increase organizational effec-tiveness and decrease actual employee turnover rate (Gardner et al., 2007; Gould-Williams, 2004), but limited number of studies have addressed this relationship in

public administration research (Farnham & Giles, 1996; Hays & Kearney, 2001;Gould-Williams, 2004; Cho & Lewis, 2012). Thus, this research evaluates the impactof perceptions toward HR practices on agency turnover.

Third, we explore whether the organizational-level determinants that best explainagencies’ actual turnover rates also explain turnover intention rates. If intention rate isa reliable proxy for actual turnover rate, then the same patterns should hold for bothdependent variables. Finally, we assess whether turnover intention rate actually pre-dicts agencies’ actual turnover rate.

Antecedents of Employee Turnover Rates and Hypotheses

The “Actual–intention” link. Analysis of leave intentions has been a mainstay of thegeneral turnover research since its advent (Cho & Lewis, 2012; Dalton, Johnson, &


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Daily, 1999; Kirschenbaum & Weisberg, 1990). The empirical turnover literature isreplete with examples of turnover behavior that is inferred based on analyses ofemployees’ leave intentions and its correlates. Even as turnover models becomeincreasingly sophisticated, this conceptual linkage between intent and actual turnoverhas remained cardinal. Such research is premised on the vital link between attitude and

behavior, and thus, on the assumption that intent is the best predictor of actual turnover(e.g., Bertelli, 2007; Dalton et al., 1999; S. Y. Lee & Whitford, 2007; Steel & Ovalle,1984; Tett & Meyer, 1993).

The rationale justifying intentions’ use as a turnover proxy is twofold. First, from atheoretical perspective, attitude theory generally supports the belief that intent is the

best predictor of behavior (Kraut, 1975; Mobley, Horner, & Hollingsworth, 1978;Price & Mueller, 1981). As Fishbein and Ajzen (1975) wrote, “The best single predic-tor of an individual’s behavior will be a measure of his intention to perform that behav-ior” (p. 369). According to this line of research, turnover intention is expected to be thestrongest predictor of actual turnover behavior (e.g., Currivan, 1999; Griffeth, Hom, &Gaertner, 2000; Hom, Griffeth, & Sellaro, 1984; S. Y. Lee & Whitford, 2007; Mobley,1977; Vandenberg & Nelson, 1999). This theoretical expectation has empirical sup-

port. For instance, in a meta-analysis of job attitudes and behavior, Harrison, Newman,and Roth (2006) concluded that job attitudes, such as turnover intentions, reliably

predict job behaviors, such as quitting.Second, turnover scholars also rely on intentions for pragmatic reasons. As a sur-

rogate, the intent construct is more amenable to research than actual turnover. It pos-sesses desirable statistical qualities (i.e., easily scaled) and is more economic (Daltonet al., 1999). Conversely, the actual turnover construct is a dichotomous variable thatgenerally requires costly longitudinal designs to fully assess. Most important, surveysare typically administered anonymously. Thus, connecting information gleaned fromthem to individuals’ actual behaviors is usually impossible and tends to be fraught byethical implications (Dalton et al., 1999).

For these reasons, scholars commonly use turnover intention as a proxy of actualturnover (e.g., Bertelli, 2007; Kim, 2005; S. Y. Lee & Whitford, 2007; Pitts, Marvel,& Fernandez, 2011). This is true of turnover studies in general (Griffeth et al., 2000)and especially true of public-sector studies (Jung, 2010; Tett & Meyer, 1993).

Generally speaking, scholars have found that employees turnover intentions and quit behaviors to be statistically correlated. Findings as to the strength of the relationship,however, are inconclusive. Some studies report finding the constructs strongly anddirectly correlated (e.g., Griffeth et al., 2000; Hom et al., 1984; S. Y. Lee & Whitford,2007; Mobley, 1977; Steel & Ovalle, 1984). For instance, based on a random sampledrawn from the U.S. Office of Personnel Management’s (OPM) Central Personnel DataFile, Cho and Lewis (2012) found a correlation of .80. Other studies, however, havefound the relationship to be much weaker and even insignificant. T. W. Lee and Mowday(1987) found employees’ intentions explained only about 6% of turnover variance.Also, Kirschenbaum and Weisberg (1990) found a poor and non-significant relation-ship between intention to leave and actual turnover behavior. According to them, sur-vey responses to whether one’s intent to leave his or her job or not, cannot actuallyattest to real future behavior. Finally, there is also research showing that the relationship


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between employees’ leave intentions and actual separation behaviors is incidental oreven non-existent (cf. Jung, 2010; Kirschenbaum & Weisberg, 1990).

These finding suggest that although there is a general consensus among scholarsabout the positive relationship between quit intention and actual quitting, with regardto the strength of this relationship, alarming discrepancies exist. Therefore, this studyexamines the relationship between the turnover intention rates and actual turnoverrates of U.S. federal agencies.

Hypothesis 1: An agency’s average turnover intention rate is positively associatedwith the average actual turnover rate of the agency.

Collective member perceptions. According to Hausknecht and Trevor (2011), a majorcategory of turnover antecedents includes employees’ aggregated attitudes and per-ceptions of organizational characteristics, such as the quality management, HR prac-tices, and organizational culture and climate. These predictors are well recognized in

both the individual-level and organizational-level literature, which is generally basedon turnover intention. In this study, we seek to explore whether these known predictorsof turnover intention also apply to actual turnover in the organizational level.

Considering these various kinds of perceptional determinants of turnover, the col-lective perceptions regarding six types of organizational practices were chosen asindependent variables for this research: telework, performance culture, pay satisfac-tion, advancement opportunities, workload, and flexible work schedule. These six

practices were selected because they have been frequently identified within the public personnel management literature as factors related to employees’ perceptions of anorganization’s HR management practices. The rationale underlying incorporation ofthese constructs is the theoretical expectation that effective HR management practicesincrease employees’ work motivation and thus reduce turnover (Wright & Boswell,2002).

Telecommuting and telework describe remote working arrangements, where moderninformation technologies allow employees to perform tasks and fulfill transactionalobligations away from centralized or physical organizational locations (Belanger &Collins, 1998). In recent years, due to enabling legislation and guidelines (e.g., OPM,2005; U.S. Congress House, 1999), adoption of telework has become increasingly com-mon across the U.S. federal government (Gajendran & Harrison, 2007). Organizational

benefits accrued from the arrangement include reduced real estate expenses and reducedcosts for compliance with regulations such as those associated with the Americans WithDisabilities Act of 1990. Telecommuting benefits employees by affording them somemeasure of flexibility and control over when and from where they may fulfill their jobrequirements (Cayer, 2003; S. Y. Lee & Hong, 2011). Studies evaluating telework pro-grams have generally found them to increase employees’ motivation and productivitywhile reducing absenteeism and turnover (Iscan & Naktiyok, 2005).

Hypothesis 2: An agency’s average level of satisfaction with telework practices isnegatively associated with the actual turnover rate of the agency.


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Performance management practices are implemented to connect employees’ per-formance to rewards through continuous feedback and ongoing evaluation (Kettl,2005). G. Lee and Jimenez (2011) describe adoption of performance-oriented manage-ment practices as one of the most important HR developments to have occurred in

public sector over the last two decades. Holding that employees value distributive justice, social exchange theory predicts that effectively administered management sys-tems emphasizing performance-based rewards should be associated with reducedorganizational turnover. Empirical studies supporting this posited linkage includeHuselid (1995), who found significantly reduced turnover in organizations with strong

performance practices, and Pitts et al. (2011) found enhanced job satisfaction, produc-tivity, and retention in organizations valuing and rewarding performance.

Hypothesis 3: An agency’s mean level of satisfaction performance culture prac-tices is negatively associated with the actual turnover rate of the agency.

A substantial body of individual-level empirical research indicates job satisfaction measures are among turnover’s strongest correlates (e.g., Bertelli, 2007; Bright, 2008;Carsten & Spector, 1987; Cotton & Tuttle, 1986; Lambert, Hogan, & Barton, 2001;Mobley, Griffeth, Hand, & Meglino, 1979; Porter & Steers, 1973). As increased jobsatisfaction is associated with individuals reduced propensity for withdrawal, the sameconceptual logic extends to organizations. That is, as employees’ collective job satis-faction increases, organizational turnover decreases (Hausknecht & Trevor, 2011).This theoretical expectation is based, in part, on Hackman and Oldham’s (1976) clas-sic model of work motivation. From this perspective, specific job elements (e.g., sal-ary, benefits, opportunities, certain duties, and tasks) positively alter employees’

psychological state, thereby increasing work motivation, performance, job satisfac-tion, and their likelihood to stay in an organization.

Among satisfaction constructs, satisfaction with pay is one consistently associatedwith reduced voluntary turnover (Blau & Kahn, 1981; Cotton & Tuttle, 1986; Lambertet al., 2001; Park, Ofori-Dankwa, & Bishop, 1994; Shaw, Delery, Jenkins, & Gupta,1998). Employees maximize self-interest through higher wages. Consequently,employees who perceive that their wages are highly satisfactory tend to remain inorganizations (Shaw et al., 1998). Satisfaction with pay also reduces individuals’financial anxieties (Lambert et al., 2001) and, thus, decreases job-search motivations(Blau & Kahn, 1981).

Hypothesis 4: An agency’s average level of pay satisfaction is negatively associ-ated with the actual turnover rate of the agency.

Satisfaction with career advancement opportunities is also negatively associatedwith voluntary turnover (Cotton & Tuttle, 1986; Griffeth et al., 2000; Porter & Steers,1973; Spector, 1985). Promotions are usually linked to salary increases, which, in turn,affect reduced exits (Johnston, Griffeth, Burton, & Carson, 1993). Observing rigidcriteria complicate public-sector employee promotions, Pitts et al. (2011) argued that


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satisfaction with advancement opportunities is a key factor affecting federal employ-ees’ overall job satisfaction and, consequently, turnover intentions.

Hypothesis 5: An agency’s average level of satisfaction with advancement oppor-tunities is negatively associated with the actual turnover rate of the agency.

Work schedule and workload satisfaction also theoretically correlates with turn-over. Where alternative work schedules enhance employees’ flexibility and sense ofcontrol, social exchange theory predicts increased commitment and reduced turnover(S. Y. Lee & Hong, 2011). Similarly, social exchange theory predicts that workloadsatisfaction is associated with higher moral and reduced turnover (cf. Anderson,Corazzini, & McDaniel, 2004; Banaszak-Holl & Hines, 1996).

Hypothesis 6: An agency’s average level of satisfaction with workload is nega-tively associated with the actual turnover rate of the agency.Hypothesis 7: An agency’s average level of satisfaction with flexible work sched-ule is negatively associated with the actual turnover rate of the agency.

Collective member characteristics. Organization-level demographic characteristicsstrongly associate with organizational behavior such as employee turnover (Pfeffer,1985). In some cases, demographic factors have been evaluated independently asexplanatory variables (e.g., Cho & Lewis, 2012; Jung, 2010; Pitts et al., 2011),whereas, in other cases, they are operationalized as controls (Hausknecht & Trevor,2011).

Age is inversely associated with turnover and is prominently featured within theempirical literature (e.g., Cho & Lewis, 2012; Jung, 2010; Kellough & Osuna, 1995;Pitts et al., 2011). Previous research has demonstrated that the relationship betweenage and turnover is curvilinear (e.g., Cho & Lewis, 2012). Turnover among youngemployees tends to be very high, but progressively decreases as employees age (Lewis,1991; Lewis & Park, 1989). At the organizational level, in government, Kellough andOsuna (1995) observed proportions of younger employees within an agency’s work-force (i.e., less the 32 years old) correlate positively with agency’s quit rates. A com-mon socio-psychological explanation for this relationship is that older employees tendto be more risk adverse, less mobile (Moynihan & Landuyt, 2008), and more con-strained by family and financial obligations (Ippolito, 1987; O’Reilly, Chatman, &Caldwell, 1991).

Studies have also found that job tenure is inversely associated with turnover both atthe individual level (e.g., Blau & Kahn, 1981; Lewis, 1991) and organization level(e.g., Bennett, Blum, Long, & Roman, 1993; Glebbeek & Bax, 2004; Hausknecht,Trevor, & Howard, 2009; Spell & Blum, 2005; Terborg & Lee, 1984; Trevor & Nyberg,2008; Wiersema & Bird, 1993; Yanadori & Kato, 2007). By way of explanation, Farber(1999) argued that less tenured employees quit more frequently because they areyounger and earn less. Conversely, Ippolito (1987) argued more tenured employeesquit less frequently because they tend to be more vested in pension plans that are


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forfeited on withdrawal. Another explanation is that as employers invest in trainingand workers’ skills become increasingly firm-specific, comparable job alternatives

become increasingly scarce.Workforce diversity is another important determinant of organizational turnover.

Theory holds that as demographic diversity increases, psychological attachment andgroup commitment decreases (cf. Greenhaus, Parasuraman, & Wormley, 1990; Sackett,DuBois, & Noe, 1991; Tsui, Egan, & O’Reilly, 1992). Turnover research evaluating

gender differences has generally found that women, compared with men, are absentmore frequently, have lower intentions to stay, and voluntarily terminate employmentrelationships more frequently (Bae & Goodman, 2014; Choi, 2009). Similarly, turn-over has been linked to racial and minority status . Although the relationship is a com-

plicated one, at the organizational level, the relationship between minority status andturnover has generally been found to be a positive one (Hausknecht & Trevor, 2011).

Data and Method

DataThis study combines and aggregates data from several sources, with the unit of analy-sis being the agency. Data for the independent variables are drawn from the 2010Federal Employee Viewpoint Survey and the 2010 FedScope Employment Cube. Thegovernment-wide response rate for the survey is 52% ( n = 263,475). Data for the

dependent variables are drawn from the 2010 and 2011 FedScope Separation Cubes(www.fedscope.opm.gov ) in the 12-month period immediately following the adminis-tration of the 2010 Federal Employee Viewpoint Survey. Overall, our sample includesdata from 180 different U.S. federal agencies. Table 1 reports summary statistics forthe dependent and independent variables of this study.

Dependent VariableActual turnover rates are calculated as the total number of “quits” divided by the total

number of full-time, permanent employees for each agency. The data have been log-transformed to create a normal distribution. In addition, actual turnover rate of anagency includes only voluntary withdrawals (i.e., “quits”). Involuntary terminationsand voluntary separations for other causes, such as interagency transfers and retire-ments, are excluded.

Main Independent VariableOur main independent variable, turnover intention rate , serves both as a proxy and

predictor of agencies’ actual turnover rate. The question that addresses these variablesin the survey is as follows: “Are you considering leaving your organization within thenext year, and if so, why?” The possible answers are “No”; “Yes, to retire”; “Yes, totake another job within the federal government”; “Yes, to take another job outside the

http://www.fedscope.opm.gov/http://www.fedscope.opm.gov/


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Table 1. Descriptive Statistics of the Variables ( N = 180).

Variables Explanations M SD Minimum Maximum

Actual turnover rate Proportion of workforce 0.017 0.011 0.002 0.065Turnover intention rate Weighted proportion

(Q88)0.020 0.012 0.002 0.106

Average tenure Years 16.01 2.71 7.06 24.00Population < 30 years old Proportion of workforce 0.079 0.053 0.016 0.458Population 30 to 49 years old Proportion of workforce 0.477 0.079 0.301 0.787Female Proportion of workforce 0.451 0.145 0.124 0.738Non-White minority Proportion of workforce 0.337 0.130 0.050 0.906Professional/administrative Proportion of workforce 0.766 0.205 0.153 0.995Telecommuters Weighted proportion

(Q72)

0.299 0.192 0.027 0.757

Performance culture Weighted compositeaverage

3.082 0.214 2.342 3.774

Workload satisfaction Weighted Average (Q10) 3.369 0.199 2.806 4.004Opportunity satisfaction Weighted average (Q67) 3.089 0.200 2.389 3.697Pay satisfaction Weighted average (Q70) 3.724 0.194 3.020 4.331Work schedule satisfaction Weighted average (Q74) 3.794 0.383 2.423 4.580

Federal Government”; and “Yes, other.” Agency’s turnover intention rate reflects the proportion of employees’ responding “Yes, to take another job outside the federal gov-ernment.” 1 To compensate for under- and over-represented populations within thesample, all variables’ aggregations were calculated using OPM’s (2011) weightingmethodologies.

Independent VariablesIn addition to our main independent variable, the relationship between six theoreticalconstructs related to management practices and employees’ shared perceptions andactual turnover rate is tested. To measure these variables, we also aggregate employeeresponses to the agency level by using survey items from the 2010 Federal EmployeeViewpoint Survey. Employee responses are gauged based on a Likert-style scale rang-ing from 1 ( strongly disagree, very dissatisfied, very poor ) to 5 ( strongly agree, very

satisfied, very good ). To compensate for varying group response rates, individual rat-ings are aggregated using OPM’s weighting methodology. This procedure involvesmultiplying each response by its corresponding weight, summing the products, anddividing the result by the sum of each agency’s weights (OPM, 2011).

The measure of performance culture perception is constructed from four items:“Promotions in my work unit are based on merit”; “Awards in my work unit depend onhow well employees perform their jobs”; “Employees are recognized for providinghigh quality products and services”; and “Pay raises depend on how well employees


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Cohen et al. 9

perform their jobs” (adopted from Fernandez & Moldogaziev, 2010). The Cronbach’salpha of the four items is .95.

For workload satisfaction, opportunity satisfaction, pay satisfaction , and work schedule satisfaction , the following single items are used: “My workload is reason-able” (1 = strongly disagree to 5 = strongly agree ); “How satisfied are you with youropportunity to get a better job in your organization”; “Considering everything, howsatisfied are you with your pay”; and “How satisfied are you with the following Work/Life programs in your agency . . . Alternative Work Schedules (AWS)” (1 = very dis-

satisfied to 5 = very satisfied). For all these variables, employee responses are aggre-gated to the agency level using the survey weights.

The telecommuters variable is measured using the following survey item: “Pleaseselect the response below that BEST describes your teleworking situation.” The pos-sible answers are as follows: “I telework on a regular basis (at least one entire workday a week),” “I telework infrequently (less than one entire work day a week),” “I donot telework because I have to be physically present on the job (e.g., Law EnforcementOfficers, Park Rangers, Security Personnel),” “I do not telework because I have tech-nical issues (e.g., connectivity, inadequate equipment) that prevent me from telework-ing,” “I do not telework because I am not allowed to, even though I have the kind of

job where I can telework,” and “I do not telework because I choose not to telework.”This variable reflects the proportion of agency respondents indicating the following:“I telework on a regular basis (at least one entire work day a week).”

Control VariablesThis study controls for four collective member characteristics that might be anteced-ents of employee turnover: age, tenure, gender, and minority status. All the demo-graphic controls, with the exception of minority status, are extrapolated from the rawindividual-level FedScope employment data for March 2010 ( www.opm.gov/data/Index.aspx ). Proportions of “non-White minority” workers are taken directly fromtables available on OPM’s FedScope website (see www.fedscope.opm.gov ).

Employees’ precise ages ( Age) are not available within the FedScope data; rather,they are specified by age group. Based on these data, we model two age cohorts: youngemployees , defined as those less than 30 years of age, and mid-career employees ,defined as employees aged 30 to 49. The study’s reference group is thus composed offull-time, permanent employees aged 50 and older. Our average tenure variable ismeasured in years and operationalized as employees’ total combined years of servicedivided by agencies’ respective workforce populations in 2010. Gender is operational-ized as the proportion of females in the workforce, and minority status is analyzed as

proportions of non-White employees.

Statistical ApproachThis study uses hierarchical (stepwise) multiple regression to model the variance oftwo continuous dependent variables, quit rate and quit intention rate, across 180

http://www.opm.gov/data/Index.aspxhttp://www.opm.gov/data/Index.aspxhttp://www.fedscope.opm.gov/http://www.fedscope.opm.gov/http://www.opm.gov/data/Index.aspxhttp://www.opm.gov/data/Index.aspx


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federal agencies. Hierarchical multiple regression is essentially a series of consecutiveordinary least squares (OLS) regression models, each adding a new set of predictors(Lewis, 2007). This statistical method allows us to assess the impact of each set of

predictors above and beyond the previous set that was entered into the regressionmodel (Lewis, 2007). Hierarchical regression is the practice of building successivelinear regression models, each adding more predictors and is different from hierarchi-cal linear modeling (HLM), which is the practice of using multi-level models.

To normalize distribution of residuals, log transformation was conducted for theturnover rates variables creating semi-elasticity (i.e., log–lin) models for this study.Because measurements of the study’s variables are mixed (i.e., years, proportions, andaggregated survey responses), for interpretive ease, we analyze the study’s variables aszero-centered measures. Meaning, the standardized regression coefficients (i.e.,“beta”) represent constant proportional changes given the single standard deviationchange of a respective regressor. 2

This study’s ultimate objective is to assess the usefulness of agencies’ turnoverintention rate as a proxy of agencies’ actual turnover rate. To accomplish this, a three-stage analytical procedure is performed. At the first stage, turnover intention rate oper-ates as a sole independent variable so as to assess its direct contribution to predictingactual turnover rate (see Table 2).

At the second stage, two hierarchical regression models are executed (see Table 3).The first model (Model 1), comprised of two blocks of predictors (i.e., collective mem-

ber characteristics; collective member perceptions), is regressed against actual turnoverrate to determine its best predictors. In the second model (Model 2), the turnover inten-tion variable is designated as the dependent variable with the purpose of evaluatingwhether the same predictors of actual turnover (i.e., the same collective member char-acteristics and collective member perceptions) also predict turnover intention rate.

At the third stage, we elaborate Model 1 (the “actual turnover model”) by adding aturnover intention as a third block of independent variable to determine the marginalcontribution of agencies’ turnover intention rate as a predicator of agencies’ futureactual turnover rate, when holding all other factors constant. The next section bringsthe comparative results from the two models. 3

Table 2. Bivariate Regression Results for Actual Turnover Rate (logged; N = 180).

β SE

Turnover intention rate .136* 0.046Constant −4.273 0.046R2 .047Adjusted R2 .042F (1, 178) 8.77Prob > F .004

Note . Standardized coefficient; SE = standard error.*p < .05.


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Table 3. Log–Lin Regression Results ( N = 180).

β SE β SE

A.Model 1a: Actual turnover

rateModel 2a: Turnover intention

rate

Block 1 Average tenure −.411*** 0.045 −.076 0.053 Population < 30 years old .034 0.040 .015 0.048 Population 30 to 49 years

old−.119*** 0.040 .040 0.047

Female .120** 0.044 .062 0.052 Non-White minority −.012 0.041 −.117** 0.048 Professional/

administrative−.112** 0.039 .071 0.047

Constant −4.273*** 0.035 −4.047 0.041R2 .465 .080Adjusted R2 .447 .049Prob > F 25.09*** 2.52**

β SE β SE

B.Model 1b: Actual turnover

rateModel 2b: Turnover intention

rate

Block 1 Average tenure −.318*** 0.044 −.040 0.058 Population < 30 years old .100** 0.039 .060 0.051 Population 30 to 49 years

old−.062* 0.037 .065 0.049

Female .096** 0.040 .021 0.053 Non-White minority −.019 0.036 −.130*** 0.048 Professional/

administrative−.149*** 0.045 .098 0.060

Block 2 Telecommuters .145*** 0.045 .101* 0.060 Performance culture .286*** 0.060 −.005 0.079 Workload satisfaction −.083** 0.038 −.117** 0.050 Advancement opportunity

sat−.150** 0.064 .009 0.085

Pay satisfaction −.170*** 0.052 −.127* 0.069 Flexible work schedule sat −.130*** 0.044 −.048 0.059Constant −4.273*** 0.030 −4.047*** 0.040

R2

.618 .178Adjusted R2 .591 .119Prob > F 22.51*** 3.02***

(continued)


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β SE β SE

C. Model 1c: Actual turnover rate

Block 1 Average tenure −0.317*** 0.044

Population < 30 years old 0.093** 0.039Population 30 to 49 years

old−0.068* 0.037

Female 0.094** 0.040Non-White minority −0.008 0.037Professional/

administrative−0.153*** 0.045

Block 2 Telecommuters 0.133*** 0.046

Performance culture 0.279*** 0.060Workload satisfaction −0.072* 0.038Advancement opportunity

sat−0.144** 0.064

Pay satisfaction −0.166*** 0.052Flexible work schedule sat −0.116** 0.045

Block 3

Turnover intention rate 0.047 0.034Constant −4.273*** 0.030R2 .622Adjusted R2 .593Prob > F 21.03***

Note . Beta represents standardized coefficients. SE = standard error.*p < .1. **p < .05. ***p < .01.

Table 3. (continued)

Organizational-Level Results

The Direct Effect of Turnover Intention Rate on Actual Turnover Rate

As expected, agencies’ turnover intention rate and actual turnover rate correlate positively within our sample. Each standard deviation increase of intention to quitrate corresponds to a constant proportional quit-rate increase of 13.6% (i.e., 1.136times). Table 2 provides the results from the bivariate regression between turnoverintention rates and actual turnover rate. Although the bivariate regression model is

statistically significant ( p < .001), supporting Hypothesis 1, theory posits that thelinkage between employees’ intent and actual withdrawals should be a strong one. Inour sample, this is not the case as agencies’ turnover intention rate explains only4.2% of quit rate variance across the federal government. This initial result does not


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Cohen et al. 13

bode well for intentions’ utility as a proxy, thus reinforcing this study’s approach toexamine these constructs separately.

Collective Member Characteristics and Turnover RateTable 3 presents regression results for Model 1 and Model 2. In Model 1, actual turn-over rate operates as the sole dependent variable, and in Model 2, turnover intentionrate is designated as the dependent variable with the ultimate purpose of evaluatingwhether these two variables represent the same construct.

Due to the nature of our hierarchical models, we commence our discussion with thecontrol variables (see Table 3, Part A). At the first stage, six collective demographicvariables are introduced. For Model 1a (i.e., the actual turnover rate model), four col-

lective demographic variables—average tenure, mid-career workforce, female, andoccupation type—had a significant effect on agency actual turnover rate at. 05 level.Average tenure is most strongly associated with actual turnover rate ( p < .000), and itseffect is the greatest. With each standard deviation increase of average tenure , agen-cies’ predicted quit rates decrease by 41.1%, or are 4.11 times less likely to quit.Jointly, the six demographic factors specified within Model 1a explain about 45% ofthe variance in actual turnover across federal government agencies.

Our preliminary argument suggests that if turnover intention is a reliable proxy foractual turnover, then a set of variables explaining actual quit rates will similarly

explain intention rates. However, when comparing Models 1a and 2a, the results dif-fer from that which is expected. In Model 2a, although the variables in the modelremain jointly significant ( p = .023), only one demographic characteristic, non-Whiteminority, is significant beyond a probability of .10 level. In fact, with the exceptionof non-White minority , all the variables’ effects are substantially diminished in Model2a. As a result, the demographic features that explained 44.7% of agencies’ actualturnover rate in Model 1a explain less than 5% of agencies’ turnover intention rate inModel 2a. Interestingly, the member characteristics that explained a substantial por-tion of actual turnover rate variance in Model 1a explain employees’ turnover inten-

tion differently and less thoroughly in Model 2a. One implication of this comparisonis that, at the organizational level, the linkage between employees’ aggregated inten-tions and their aggregated actual behaviors may be a dubious one. Also, researchersand practitioners using intention data in place of actual turnover data might errone-ously conclude that these collective member characteristics are statistically unim-

portant, when in fact they are.

Collective Member Perceptions and Turnover Rate

Thus far, we find that turnover intention rate is not strongly correlated with actualturnover rate, and that member demographic characteristics explain actual turnoverrate variance better than they explain turnover intention variance. Our next goal is tofurther evaluate the usefulness of turnover intention rate as a proxy of actual turnover


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rate by adding another set of turnover intention predictors—collective member per-ceptions—to the regression model. To do so, we add six collective member percep-tions toward HR practices to the baseline model: telecommuters, performance culture,workload satisfaction, advancement opportunity satisfaction, pay satisfaction, andflexible work schedule satisfaction. As previously explained, the models similarlyspecified with the only difference between them being the dependent variable (i.e.,actual turnover rate and turnover intention rate).

Part B of Table 3 presents the agency-level OLS regression results for actual turn-over (Model 1b) and turnover intention (Model 2b) rates using survey weights. Overall,Models 1b and 2b are significant ( p < .000 for actual turnover rate and p < .001 forturnover intention rate). Model 1b explains 59.1% of actual turnover variance acrossthe federal government, whereas Model 2b combines to explain 11.9% of agencies’turnover intention rate.

For Model 1b, as anticipated, agencies’ pay satisfaction, advancement opportunitysatisfaction, workload satisfaction, and work schedule satisfaction are negatively cor-related with the agencies’ actual turnover rate, supporting Hypotheses 4 through 7.Meaning, that agencies’ predicted quit rates decrease as agency employees’ satisfac-tion collectively increases.

Interestingly, although statistically significant, telecommuters and performanceculture satisfaction do not correlate at the expected direction; thus, Hypotheses 2 and3 are partially rejected. We expected actual turnover rate to decrease as telecommutingand performance culture perceptions increased. Instead, as Model 1b shows, both vari-ables correlate positively with actual turnover rate. In other words, holding all otherfactors constant, a standard deviation increase in the proportion of telecommutersyields a quit-rate increase of 14.5%. Similarly, a standard deviation increase of the

performance culture index corresponds to a predicted 28.6% (i.e., 1.286 times)increase in turnover. Although these results are unexpected, they are not necessarilysurprising because both findings have theoretical explanations and empirical prece-dence within the turnover literature (see Cropanzano, Bowen, & Gilliland, 2007;Gajendran & Harrison, 2007). Possible theoretical explanations for these findings arefurther explained below.

When comparing Model 1b (i.e., actual turnover rate model) with Model 2b (i.e.,turnover intention rate model), we notice several directional differences. The telecom-muter variable is positively related to both dependent variables. Although advance-ment opportunity satisfaction is negatively correlated with agencies’ actual turnoverrate (Model 1b), it is positively related to agencies’ turnover intention rate (Model 2b).Furthermore, the performance culture variable is positively correlated with actualturnover rate (Model 1b) but negatively related to turnover intention rate (Model 2b).

With regard to the collective demographic factors, in Model 1b, average tenure’seffect remains most substantial, whereas the influence of non-White minority repre-sentation is the weakest and remains statistically insignificant. In Model 2b, non-White minority representation is a significant demographic variable ( p = .008).

These observed differences in the models suggest that the antecedent organiza-tional perceptions that best explain agencies’ actual quit rates do not necessarily


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Cohen et al. 15

simultaneously explain agencies’ turnover intention rates. This validates the study’s preliminary assumption that turnover intention and actual turnover are distinctly dif-ferent constructs, at least at the organizational level.

Turnover Intention Rate and Actual Turnover RateLast, we add the turnover intention rate variable to the model to evaluate the fullyspecified unrestricted model of actual turnover rate (i.e., Model 1c). OLS regressionresults for Model 1c are summarized in Table 3, Part C. The full model explains 59%of the variance in turnover rate.

Interestingly, turnover intention rate is not significantly associated with actual turn-over rate once other job and personal characteristics are taken into account. Theseresults are surprising considering the significant relationship between turnover inten-tion rate and actual turnover rate in the bivariate regression model (see table 2). Inother words, where actual turnover rate is otherwise explained, we reject Hypothesis1, which states that agencies’ turnover intention rate is positively associated with theagencies’ actual turnover rate.

Taken together, our results provide strong evidence that turnover intention rate andactual turnover rate are indeed two distinct constructs, explained by two separate sets ofdeterminants: Youthful workforce, proportion of females, telecommuting and agency’ssatisfaction with performance culture practices, and advancement opportunities arefound to influence actual turnover rate, but not turnover intention rate. The only vari-ables to have a dual influence on both types of turnover rate are average tenure andagencies’ satisfaction with pay, telecommuting, and workload. In addition, we find thatturnover intention rate is of little practical usefulness as predicator of agencies’ actualfuture turnover rate.

DiscussionThe intention–actual turnover linkage has been a fixed assumption in most studies ofvoluntary turnover. This study has sought to evaluate the usefulness of turnover inten-tion rate as a proxy and a predictor of actual turnover rate among U.S. federal govern-ment agencies. Several results observed here deserve further discussion.

First and most fundamentally, we found that turnover intentions have a direct effecton actual turnover, but so do other perceptual measures. As a perceptual variable, theturnover intention construct seems to be a less reliable predictor than it is initiallyassumed to be at the organization level. Turnover research posits that the linkage betweenturnover intentions to actual turnover should be a strong one. Our analysis illustrates thatat the organizational level, this is not always the case. Within our sample, intentionsexplain less than 5% of turnover variance across the federal government. Althoughunanticipated, this result is not without precedent. Individual-level research reportedintention–actual turnover correlations from .31 to .52 (Dalton et al., 1999). At the orga-nizational level, Jung (2010) found no statistically significant correlation betweenagency-level turnover and sampled government employees’ weighted leave intentions.


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It is true that at the aggregate level correlations are more likely to have a diminishedstatistical power due to linearity assumptions. However, these limitations alone do notexplain why other same-source aggregate perceptual measures are more powerfullyassociated with actual turnover.

At a minimum, our initial finding thus serves as evidence that the level at which a phenomenon is observed matters. This observation is one of no small consequence to public management as strategic HR management is necessarily an organizational-levelfunction. After all, public managers are mostly concerned with turnover and retentionrates at their agencies rather than the individual reasons for employees’ turnover

behavior (Gardner et al., 2007).Second, if the conceptual construct of “turnover intention” is a reliable proxy of the

concept “actual turnover,” then we would expect that the factors explaining the firstone would similarly explain the latter. Within our sample, we found 12 organizationaldeterminants explain more than 59% of actual turnover rate variance across the federalgovernment. At the same time, however, these same factors simultaneously explainless than 12% of variance associated with turnover intention rate.

In addition to explaining intentions less thoroughly, the organizational determi-nants found to best explain actual turnover are not necessarily the ones that bestexplain turnover intention. Meaning, of the 11 variables that significantly explainactual turnover, only 3 are statistically associated with intentions (i.e., telecommuters,workload satisfaction, and pay satisfaction).

For example, average tenure and performance culture found to be most signifi-cantly and substantially associated with agency actual turnover are insignificant pre-dictors of employees’ turnover intentions. Furthermore, non-White minorityrepresentation that is a consistently insignificant predictor of actual turnover is equallyconsistently statistically associated with turnover intentions.

Noteworthy is the relational shift occurring in some of the variables. We find thatthe proportion of an agency’s employees who telecommute as well as higher satisfac-tion with agencies’ performance cultures are each positively and significantly associ-ated with agencies’ increased actual turnover. Both of these findings are contrary tothose widely reported within the individual-level intentions-based empirical literature(cf. Gajendran & Harrison, 2007; Huselid, 1995; Pitts et al., 2011). However, testablehypotheses to explain these observations are available in the literature. For example, itis possible that the positive relationship between telecommuting and turnover does notnecessarily reflect the attitudes of the telecommuters, but rather those of the non-tele-commuters in agencies where telecommuting is practiced. In other words, it could bethat quit rates are higher in agencies with higher proportions of telecommuters becauseof the negative impact they have on non-telecommuters employees who work in thatagency. This idea that telework might have adverse consequences on non-teleworkerswas also introduced by Golden (2007).

In addition, Baruch and Nicholson (1997) argued that telecommuting mighthave negative organizational consequences due to employees’ social isolation, per-ceived career stagnation, and family conflict. Likewise, employees might dispro-

portionally withdraw from federal agencies where performance is emphasized due


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Cohen et al. 17

to the stressful nature of their work environments or simply because poor perform-ers are encouraged to “voluntarily” resign.

Last, as a final stage of analysis, we elaborated our model to assess the influence ofturnover intention as a predictor of actual turnover in the full model. Our purpose byso doing was to investigate whether turnover intention rate uniquely explains someaspect of actual turnover or whether controlling for intentions’ presence might enhanceour model’s overall predictive power. We found it does neither. As an individual pre-dictor in a model where agencies’ actual turnover rates are otherwise independentlyexplained, we found intentions’ individual effect to be weak and statistically insignifi-cant ( p = .177).

ConclusionPredicting employee turnover is an integral part of future organizational labor needs

planning in the federal government, state governments, and local governments (Broach& Dollar, 2006; Goodman, French, & Battaglio, 2015; Jung, 2010; Price, 2004). Theresults of this study suggest that at the organizational level at least, agencies’ actualturnover rate and turnover intention rate are distinct and contrarily explained con-structs. From a practical perspective, federal managers should be cognizant to the

possibility that turnover intention may be a poor proxy for actual turnover and that itsuse as such is potentially yielding dubious results.

Our argument that these constructs may not be as closely related as some theories postulate is supported by methodological and conceptual rationales. For instance,actual turnover is a discernable and objectively measureable phenomenon. Employeeturnover intentions, on the other hand, are indirectly and subjectively assessed. As anattitudinal construct, leave intentions is a process variable and, as such, is sensitive tointervening influences constantly in flux. Indeed, the intention to quit may vary as aresult of a dispute with a supervisor, praise for a job well done, or rumors about futurechanges such as merging or downsizing (Kirschenbaum & Weisberg, 1990).

In addition, changing circumstances often prevent employees from putting theirsincerest intentions into action. Such circumstances include macroeconomic condi-tions (Selden & Moynihan, 2000), health status (Price, 2004), family issues (Porter &Steers, 1973), or lack of alternative job opportunities (cf. Hom, Caranikas-Walker,Prussia, & Griffeth, 1992; Martin, 1979). It is therefore not surprising that where asso-ciations between employees’ stated intentions and quit behaviors have been observed,they have also often been found to diminish over time (Boe, Bobbitt, & Cook, 1997;LeCompte & Dworkin, 1991). Although these reasons by no means constitute a com-

prehensive list, they do serve to illustrate that the theoretical linkage between turnoverintentions and actual turnover is an unsettled scholarship worthy of examination.

This study’s finding that demographic composition of work units portends actualquits is supported and has long standing in the empirical literature. Pfeffer (1985), forinstance, demonstrated that differences in age, tenure, gender, and race in the workunit influence communication and networking patterns that, in turn, affect organiza-tional phenomena such as turnover. Thus, we conclude that public managers tasked


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with retention might be better served concentrating on their agencies’ unique demo-graphic characteristics, rather than on their employees’ self-reported leave intentions.Indeed, our study’s results might be applied to create preliminary profiles useful forthis purpose.

Our goal in this study was to ask, “Does turnover intention matter?” We understandthat this is a provocative question; one we do not claim to have answered. Nonetheless,we believe the question we ask is an important one and that our results, at a minimum,illustrate that the linkage between leave intentions and actual turnover at the organiza-tional level is a tenuous one.

It should be noted that our analysis of cross-sectional data represents the experienceof a particular set of agencies at a particular moment in time. In addition, data limita-tions prevented us from being able to test all factors discussed in the literature and tocontrol for important constructs such as agency type and size.

Future research could improve this study’s weakness by collecting data on theseomitted variables and investigate these testable propositions. Future research shouldalso examine the relationship between individual-level data and organization-levelturnover data to explore the differences in the concepts of actual turnover and turnoverintention. By incorporating these two levels of data, important management perspec-tives may be gained, as cumulative differences might combine to point the way to auseful empirically based organizational-level theory of turnover.

Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship,and/or publication of this article.

FundingThe author(s) received no financial support for the research, authorship, and/or publication ofthis article.

Notes

1. At its most basic level, turnover intention is measured as whether an employee intendsto leave the organization. Therefore, to create a variable that is directly comparable with“actual turnover,” we omit the option of “yes, to take another job within the federal gov-ernment” as it represents transferring rather than leaving (see Pitts, Marvel, & Fernandez,2011). We also omit the option of “Yes, other” because of its ambiguous nature.

2. In addition, we explored the use of robust variance estimates; but, because they had nosubstantive effect on the study’s statistical inferences, the standard deviations reported inassociation with modeled estimators are unadjusted throughout this analysis.

3. For these two models, two sets of diagnostic statistics were executed to test the data formulticollinearity and heteroskedasticity issues. For Model 1 (the “actual turnover model”),the Breusch–Pagan test for heteroskedasticity was non-significant ( p > .10) suggestingno problem of heteroskedasticity. The variance inflation factor (VIF) ranged from 1.46 to4.5, with an average of 2.35, also suggesting no problem of multicollinearity among theindependent variables. For Model 2 (the “turnover intention model”), a Breusch–Pagan test


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Cohen et al. 19

for heteroskedasticity also suggested no problem of heteroskedasticity ( p > .1). In addition,the VIFs all ranged in acceptable degrees from 1.31 to 4.52, with an average of 2.31, whichindicates no multicollinearity problems among the independent variables (Gujarati, 2003).

ReferencesAnderson, R. A., Corazzini, K. N., & McDaniel, R. R. (2004). Complexity science and

the dynamics of climate and communication: Reducing nursing home turnover. TheGerontologist , 44, 378-388.

Bae, K. B., & Goodman, D. (2014). The influence of family-friendly policies on turnover and performance in South Korea. Public Personnel Management , 43, 520-542.

Banaszak-Holl, J., & Hines, M. A. (1996). Factors associated with nursing home staff turnover.The Gerontologist , 36 , 512-517.

Baruch, Y., & Nicholson, N. (1997). Home, sweet work: Requirements for effective home

working. Journal of General Management , 23, 15-30.Belanger, F., & Collins, R. W. (1998). Distributed work arrangements: A research framework.The Information Society , 14, 137-152.

Bennett, N., Blum, T. C., Long, R. G., & Roman, P. M. (1993). A firm-level analysis ofemployee attrition. Group & Organization Management , 18, 482-499.

Bertelli, A. M. (2007). Determinants of bureaucratic turnover intention: Evidence from theDepartment of the Treasury. Journal of Public Administration Research and Theory , 17 ,235-258.

Blau, F. D., & Kahn, L. M. (1981). Race and sex differences in quits by young workers. Industrial & Labor Relations Review , 34, 563-577.

Boe, E. E., Bobbitt, S. A., & Cook, L. H. (1997). Whither didst thou go? Retention, reassign-ment, migration, and attrition of special and general education teachers from a national

perspective. Journal of Special Education , 30, 371-389.Bright, L. (2008). Does public service motivation really make a difference on the job satis-

faction and turnover intentions of public employees? The American Review of Public Administration , 38, 149-166.

Broach, D., & Dollar, C. (2006). Comparison of intent-to-leave with actual turnover withinthe FAA . Oklahoma City, OK: Federal Aviation Administration Civil Aerospace MedicalInstitution.

Carsten, J. M., & Spector, P. E. (1987). Unemployment, job satisfaction, and employee turn-

over: A meta-analytic test of the Muchinsky model. Journal of Applied Psychology , 72,374-381.

Cayer, N. J. (2003). Public employee benefits and the changing nature of the workforce. InS. W. Hays & R. C. Kearney (Eds.), Public personnel administration: Problems and pros-

pects (pp. 167-179). Upper Saddle River, NJ: Prentice Hall.Cho, Y. J., & Lewis, G. B. (2012). Turnover intention and turnover behavior implications for

retaining federal employees. Review of Public Personnel Administration , 32, 4-23.Choi, S. (2009). Diversity in the US federal government: Diversity management and employee

turnover in federal agencies. Journal of Public Administration Research and Theory , 19,603-630.

Cotton, J. L., & Tuttle, J. M. (1986). Employee turnover: A meta-analysis and review withimplications for research. Academy of Management Review , 11 , 55-70.

Cropanzano, R., Bowen, D. E., & Gilliland, S. W. (2007). The management of organizational justice. The Academy of Management Perspectives , 21(4), 34-48.


20/24


Currivan, D. B. (1999). The causal order of job satisfaction and organizational commitment inmodels of employee turnover. Human Resource Management Review , 9, 495-524.

Dalton, D. R., Johnson, J. L., & Daily, C. M. (1999). On the use of “intent to.” variables inorganizational research: An empirical and cautionary assessment. Human Relations , 52,

1337-1350.Farber, H. S. (1999). Mobility and stability: The dynamics of job change in labor markets. In

O. Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol. 3, pp. 2439-2483).Amsterdam, The Netherlands: Elsevier Science B.V.

Farnham, D., & Giles, L. (1996). People management and employment relations. In D. Farnham& S. Horton (Eds.), Managing the new public services (pp. 112-136). London, England:Macmillan.

Fernandez, S., & Moldogaziev, T. (2010). Empowering public sector employees to improve performance: Does it work? The American Review of Public Administration , 41, 23-47.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction totheory and research . Reading, MA: Addison–Wesley.

Gajendran, R. S., & Harrison, D. A. (2007). The good, the bad, and the unknown about telecom-muting: Meta-analysis of psychological mediators and individual consequences. Journal of

Applied Psychology , 92, 1524-1541.Gardner, T. M., Moynihan, L. M., & Wright, P. M. (2007). The influence of human resource

practices and collective affective organizational commitment on aggregate voluntary turn-over . Retrieved from http://digitalcommons.ilr.cornell.edu/cahrswp/479

Glebbeek, A. C., & Bax, E. H. (2004). Is high employee turnover really harmful? An empiricaltest using company records. Academy of Management Journal , 47 , 277-286.

Golden, T. (2007). Co-workers who telework and the impact on those in the office: Understandingthe implications of virtual work for co-worker satisfaction and turnover intentions. Human

Relations , 60, 1641-1667.Goodman, D., French, P. E., & Battaglio, R. P. (2015). Determinants of local government work-

force planning. The American Review of Public Administration , 45, 135-152.Gould-Williams, J. (2004). The effects of “high commitment” HRM practices on employee

attitude: The views of public sector workers. Public Administration , 82, 63-81.Greenhaus, J. H., Parasuraman, S., & Wormley, W. M. (1990). Effects of race on organizational

experiences, job performance evaluations, and career outcomes. Academy of Management Journal , 33(1), 64-86.

Griffeth, R. W., Hom, P. W., & Gaertner, S. (2000). A meta-analysis of antecedents and corre-lates of employee turnover: Update, moderator tests, and research implications for the nextmillennium. Journal of Management , 26 , 463-488.

Gujarati, D. N. (2003). Basic econometrics (4th ed). Boston, MA: McGraw-Hill.Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a

theory. Organizational Behavior and Human Performance , 16 , 250-279.Harrison, D. A., Newman, D. A., & Roth, P. L. (2006). How important are job attitudes? Meta-

analytic comparisons of integrative behavioral outcomes and time sequences. Academy of Management Journal , 49, 305-325.

Hausknecht, J. P., & Trevor, C. O. (2011). Collective turnover at the group, unit, and organi-

zational levels: Evidence, issues, and implications. Journal of Management , 37 , 352-388.Hausknecht, J. P., Trevor, C. O., & Howard, M. J. (2009). Unit-level voluntary turnover ratesand customer service quality: Implications of group cohesiveness, newcomer concentra-tion, and size. Journal of Applied Psychology , 94, 1068-1075.

http://digitalcommons.ilr.cornell.edu/cahrswp/479http://digitalcommons.ilr.cornell.edu/cahrswp/479


21/24

Cohen et al. 21

Hays, S. W., & Kearney, R. C. (2001). Anticipated changes in human resource management:Views from the field. Public Administration Review , 61, 585-597.

Hom, P. W., Caranikas-Walker, F., Prussia, G. E., & Griffeth, R. W. (1992). A meta-analyt-ical structural equations analysis of a model of employee turnover. Journal of Applied

Psychology , 77 , 890-909.Hom, P. W., Griffeth, R. W., & Sellaro, C. L. (1984). The validity of Mobley’s (1977) model

of employee turnover. Organizational Behavior and Human Performance , 34, 141-174.Huselid, M. A. (1995). The impact of human resource management practices on turnover,

productivity, and corporate financial performance. Academy of Management Journal , 38,635-672.

Ingraham, P. W., & Rubaii-Barrett, N. (2007). Human resource management as a core dimen- sion of public administration . Foundations of Public Administration Series. Retrieved fromhttp://www.aspanet.org

Ippolito, R. A. (1987). Why federal workers don’t quit. Journal of Human Resources , 22,281-299.

Iscan, O. F., & Naktiyok, A. (2005). Attitudes towards telecommuting: The Turkish case. Journal of Information Technology , 20, 52-63.

Johnston, M. W., Griffeth, R. W., Burton, S., & Carson, P. P. (1993). An exploratory investiga-tion into the relationships between promotion and turnover: A quasi-experimental longitu-dinal study. Journal of Management , 19, 33-49.

Jung, C. S. (2010). Predicting organizational actual turnover rates in the US federal government. International Public Management Journal , 13, 297-317.

Kellough, J. E., & Osuna, W. (1995). Cross-agency comparisons of quit rates in the federal ser-vice another look at the evidence. Review of Public Personnel Administration , 15(4), 58-68.

Kettl, D. F. (2005). Reforming the executive branch of the US Government. In J. D. Aberbach &M. A. Peterson (Eds.), The executive branch (pp. 344-378). Oxford, UK: Oxford UniversityPress.

Kim, S. (2005). Factors affecting state government information technology employee turnoverintentions. The American Review of Public Administration , 35, 137-156.

Kirschenbaum, A., & Weisberg, J. (1990). Predicting worker turnover: An assessment of intenton actual separations. Human Relations , 43, 829-847.

Kraut, A. I. (1975). Predicting turnover of employees from measured job attitudes. Organizational Behavior and Human Performance , 13, 233-243.

Lambert, E. G., Hogan, N. L., & Barton, S. M. (2001). The impact of job satisfaction on turn-over intent: A test of a structural measurement model using a national sample of workers.The Social Science Journal , 38, 233-250.

LeCompte, M. D., & Dworkin, A. G. (1991). Giving up on school: Student dropouts and teacherburnouts . Newbury Park, CA: Corwin Press.

Lee, G., & Jimenez, B. S. (2011). Does performance management affect job turnover intentionin the Federal Government? The American Review of Public Administration , 41, 168-184.

Lee, S. Y., & Hong, J. H. (2011). Does family-friendly policy matter? Testing its impact onturnover and performance. Public Administration Review , 71, 870-879.

Lee, S. Y., & Whitford, A. B. (2007). Exit, voice, loyalty, and pay: Evidence from the public

workforce. Journal of Public Administration Research and Theory , 18, 647-671.Lee, T. W., & Mowday, R. T. (1987). Voluntarily leaving an organization: An empirical inves-tigation of Steers and Mowday’s model of turnover. Academy of Management Journal , 30,721-743.

http://www.aspanet.org/http://www.aspanet.org/


22/24


Lewis, G. B. (1991). Turnover and the quiet crisis in the federal civil service. Public Administration Review , 51, 145-155.

Lewis, G. B., & Park, K. (1989). Turnover rates in federal white-collar employment: Arewomen more likely to quit than men? The American Review of Public Administration ,

19 , 13-28.Lewis, M. (2007). Stepwise versus hierarchical regression: Pros and cons . Retrieved from http://

www.academia.edu/1860655/Stepwise_versus_hierarchical_regression_Pros_and_consMartin, T. N. (1979). A contextual model of employee turnover intentions. Academy of

Management Journal , 22, 313-324.Meier, K. J., & Hicklin, A. (2008). Employee turnover and organizational performance: Testing

a hypothesis from classical public administration. Journal of Public Administration Research and Theory , 18, 573-590.

Mobley, W. H. (1977). Intermediate linkages in the relationship between job satisfaction andemployee turnover. Journal of Applied Psychology , 62, 237-240.

Mobley, W. H., Griffeth, R. W., Hand, H. H., & Meglino, B. M. (1979). Review and conceptualanalysis of the employee turnover process. Psychological Bulletin , 86 , 493-522.

Mobley, W. H., Horner, S. O., & Hollingsworth, A. T. (1978). An evaluation of precursors ofhospital employee turnover. Journal of Applied Psychology , 63, 408-414.

Moynihan, D. P., & Landuyt, N. (2008). Explaining turnover intention in state government:Examining the roles of gender, life cycle, and loyalty. Review of Public Personnel

Administration , 28, 120-143.Office of Personnel Management. (2005). Federal civilian workforce statistics: Employment

and trends as of January 2005 . Retrieved from http://www.opm.gov/feddataOffice of Personnel Management. (2011). Federal employee viewpoint survey. Empowering–

inspiring change . Retrieved from www.fedscope.opm.govO’Reilly, C. A., Chatman, J., & Caldwell, D. F. (1991). People and organizational culture: A

profile comparison approach to assessing person-organization fit. Academy of Management Journal , 34, 487-516.

Park, H. Y., Ofori-Dankwa, J., & Bishop, D. R. (1994). Organizational and environmentaldeterminants of functional and dysfunctional turnover: Practical and research implications.

Human Relations , 47 , 353-366.Perry, J. L., Hondeghem, A., & Wise, L. R. (2010). Revisiting the motivational bases of pub-

lic service: Twenty years of research and an agenda for the future. Public Administration Review , 70, 681-690.

Pfeffer, J. (1985). Organizational demography: Implications for management. California Management Review , 28, 67-81.

Pitts, D., Marvel, J., & Fernandez, S. (2011). So hard to say goodbye? Turnover intention amongUS federal employees. Public Administration Review , 71, 751-760.

Porter, L. W., & Steers, R. M. (1973). Organizational, work, and personal factors in employeeturnover and absenteeism. Psychological Bulletin , 80, 151-176.

Price, J. L. (2004). The development of a causal model of voluntary turnover. In R. W. Griffeth& P. W. Hom (Eds.), Innovative theory and empirical research on employee (pp. 3-32).Greenwich, CT: Information Age Publishing.

Price, J. L., & Mueller, C. W. (1981). A causal model of turnover for nurses. Academy of Management Journal , 24, 543-565.Sackett, P. R., DuBois, C. L., & Noe, A. W. (1991). Tokenism in performance evaluation: The

effects of work group representation on male–female and white–black differences in per-formance ratings. Journal of Applied Psychology , 76 , 263-267.

http://www.academia.edu/1860655/Stepwise_versus_hierarchical_regression_Pros_and_conshttp://www.academia.edu/1860655/Stepwise_versus_hierarchical_regression_Pros_and_conshttp://www.opm.gov/feddatahttp://www.fedscope.opm.gov/http://www.fedscope.opm.gov/http://www.opm.gov/feddatahttp://www.academia.edu/1860655/Stepwise_versus_hierarchical_regression_Pros_and_conshttp://www.academia.edu/1860655/Stepwise_versus_hierarchical_regression_Pros_and_cons


23/24

Cohen et al. 23

Selden, S. C., & Moynihan, D. P. (2000). A model of voluntary turnover in state government. Review of Public Personnel Administration , 20(2), 63-74.

Shaw, J. D., Delery, J. E., Jenkins, G. D., & Gupta, N. (1998). An organization-level analysis ofvoluntary and involuntary turnover. Academy of Management Journal , 41, 511-525.

Spector, P. E. (1985). Measurement of human service staff satisfaction: Development of the jobsatisfaction survey. American Journal of Community Psychology , 13, 693-713.

Spell, C. S., & Blum, T. C. (2005). Adoption of workplace substance abuse prevention pro-grams: Strategic choice and institutional perspectives. Academy of Management Journal ,48, 1125-1142.

Steel, R. P., & Ovalle, N. K. (1984). A review and meta-analysis of research on the relationship between behavioral intentions and employee turnover. Journal of Applied Psychology , 69,673-686.

Terborg, J. R., & Lee, T. W. (1984). A predictive study of organizational turnover rates. Academy of Management Journal , 27 , 793-810.

Tett, R. P., & Meyer, J. P. (1993). Job satisfaction, organizational commitment, turnover inten-tion, and turnover: Path analyses based on meta-analytic findings. Personnel Psychology ,46 , 259-293.

Trevor, C. O., & Nyberg, A. J. (2008). Keeping your headcount when all about you are los-ing theirs: Downsizing, voluntary turnover rates, and the moderating role of HR practices.

Academy of Management Journal , 51, 259-276.Tsui, A. S., Egan, T. D., & O’Reilly, III, C. A. (1992). Being different: Relational demography

and organizational attachment. Administrative Science Quarterly , 37 , 549-579.U.S. Congress House. (1999). Department of Transportation and Related Agencies

Appropriations Act of 2000. Public Law 106–069 106th Cong., 2nd sess. Oct 9.Vandenberg, R. J., & Nelson, J. B. (1999). Disaggregating the motives underlying turn-

over intentions: When do intentions predict turnover behavior? Human Relations , 52,1313-1336.

Van Marrewijk, M., & Timmers, J. (2003). Human capital management: New possibilities in people management. Journal of Business Ethics , 44, 171-184.

Wiersema, M. F., & Bird, A. (1993). Organizational demography in Japanese firms: Groupheterogeneity, individual dissimilarity, and top management team turnover. Academy of

Management Journal , 36 , 996-1025.Wright, P. M., & Boswell, W. R. (2002). Desegregating HRM: A review and synthesis of

micro and macro human resource management research. Journal of Management , 28 ,247-276.

Yanadori, Y., & Kato, T. (2007). Average employee tenure, voluntary turnover ratio, and labour productivity: Evidence from Japanese firms. The International Journal of Human Resource Management , 18, 1841-1857.

Author BiographiesGalia Cohen is a senior lecturer and an associate director in the Justice Administration andLeadership program at the University of Texas at Dallas. She received her BA in psychologyand her MA in organizational sociology from Bar-Ilan University, Israel. She holds a PhD in

public affairs from the University of Texas at Dallas. Her scholarly interests include organiza-tional behavior, human resource management, and leadership.

Robert S. Blake has a PhD in public affairs program from the University of Texas at Dallas. Hisresearch interests include public human resource management and effective administration of


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health care policies designed to promote institutional accountability and improved quality and patient safety. He is the director of strategic planning at the Medical Center of Plano.

Doug Goodman is a professor of public affairs and the Master of Public Affairs director in theSchool of Economic, Political, and Policy Sciences at The University of Texas at Dallas. Hisresearch and teaching interests include public human resource management, workforce plan-ning, public management, and other topics in public administration. He has published in numer-ous public administration outlets.

does turnover intention matter? evaluating the usefulness of turnover intention rate as a predictor...

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