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Do Role Clarity and Job Satisfaction Mediate theRelationship between Telework and Work Effort?James Gerard Caillier aa Department of Public Administration , The College at Brockport, State University of NewYork , Rochester , New York , USAPublished online: 20 Feb 2014.

To cite this article: James Gerard Caillier (2014) Do Role Clarity and Job Satisfaction Mediate the Relationship betweenTelework and Work Effort?, International Journal of Public Administration, 37:4, 193-201, DOI: 10.1080/01900692.2013.798813

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International Journal of Public Administration, 37: 193–201, 2014Copyright © Taylor & Francis Group, LLCISSN: 0190-0692 print / 1532-4265 onlineDOI: 10.1080/01900692.2013.798813

ARTICLES

Do Role Clarity and Job Satisfaction Mediate the Relationshipbetween Telework and Work Effort?

James Gerard CaillierDepartment of Public Administration, The College at Brockport, State University of New York,

Rochester, New York, USA

The empirical association between telework and work effort, as well as how this relationshipis mediated by role clarity and job satisfaction, is lacking in the literature. As a consequence,the direct and indirect impact of telework on work effort in U.S. federal government agencieswas examined in the article. Results indicate that telework was inversely related to work effort.Moreover, role clarity and job satisfaction did not mediate the relationship between teleworkand work effort. The implications these results have for theory and practice are thoroughlydiscussed in the article.

Keywords: telework, job satisfaction, work effort, role clarity

INTRODUCTION

Telework, also referred to as telecommuting, occurs “whenemployees perform all or a substantial part of their workphysically separated from the location of their employer,using IT for operation and communication” (Baruch, 2001,p. 114). Due to innovations in information and communica-tion technology (ICT) over the past 20 years, the number ofworkers and organizations taking part in this non-traditionalwork arrangement increased drastically during that timeperiod (Marintez-Sanchez, Perez-Perez, de-Luis-Carnicer, &Vela-Jimenez, 2007). In fact, a recent survey indicated that51 percent of organizations in the United States offered someform of telecommuting to employees (Society for HumanResource Management, 2009).

Because of the surge in telecommuting, scholars haveexamined this work arrangement in each sector, concludingthat it reduces overhead expenses (e.g., Bailey & Kurland,2002), enhances recruitment (e.g., Potter, 2003), and canimprove work-life flexibility, job satisfaction, and reten-tion (e.g., Golden, 2009). Telecommuting is not without

Correspondence should be addressed to James Gerard Caillier, TheCollege at Brockport, State University of New York, 107 Metro Center, 55St. Paul Street, Rochester, NY 14604. E-mail: jcaillie@brockport.edu

shortcomings, however. For instance, research examiningtelework found that it reduces interpersonal relationships,which in turn leads employees to feel isolated from co-workers and supervisors (e.g., Kurland & Cooper, 2002).

Even though this stream of research has enlightenedour understanding of telework and its consequences, suchunderstanding of the empirical associations among this workarrangement, work effort, and role clarity is lacking. Thepresent article therefore extends existing literature by empiri-cally examining the relationship between telework and workeffort, as well as how this relationship is mediated by roleclarity and job satisfaction. Furthermore, this analysis isconducted on U.S. federal government agencies, which areheavily invested in increasing the number of teleworkers.For instance, Congress passed the Telework EnhancementAct of 2010, which “asks agencies to step up their efforts”(U.S. Office of Personnel Management, 2011, p. 1). The U.S.Office of Personnel Management publishes a report enti-tled “Status of Telework in the Federal Government” eachyear, which is designed to inform Congress about efforts toimplement telework. And, the Government AccountabilityOffice frequently reports on the implementation of teleworkin agencies (e.g., U.S. Government Accountability Office,2007).

To examine teleworking and work attitudes and behav-iors, this article is structured in the following manner.

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First, hypotheses are drawn from relevant literature. Second,the methodology specifies items from the 2011 FederalEmployee Viewpoint survey used to test the hypotheses.Third are the results. Last is the discussion and conclu-sion section containing the managerial and organizationalimplications derived from the results.

REVIEW OF LITERATURE

Research has examined the effect of teleworking on turnoverintentions (Caillier, 2013a; Golden, 2006a; Golden, 2007;Golden, Veiga, & Dino, 2008), commitment (Golden, 2008;Hyland, Rowsome, & Rowsome, 2005; Golden, 2006a;Hunton & Norman, 2010; Caillier, 2012), role clarity(Igbaria & Guimaraes, 1999), and job satisfaction (Igbaria& Guimaraes, 1999; Golden, 2006b; Gajendran & Harrison,2007; Fonner & Roloff, 2010; Virick, DaSilve, & Arrington,2010; Caillier, 2012; Masuda, Poelmans, Allen, et al., 2012).Although employee attitudes about job satisfaction and roleclarity have previously been examined in such arrangements,their potential mediating effect on the relationship betweentelework and work effort—a type of discretionary behaviorsimilar to organizational citizenship behaviors describingactions that go above and beyond what is in formal jobdescriptions—has not. Following are the rationales behindthe expected associations between these factors.

Telework and Work Effort

According to social exchange theory and the norm of reci-procity, when workers are afforded a work-life benefit (e.g.,telecommuting), they are appreciative and feel compelledto respond in ways that are critical to the agency (Haar,2006; Gould-Williams, 2007; Noblet & Rodwell, 2009;Anderfuhren-Biget et al., 2010; Caillier, 2011), includingincreasing their levels of organizational citizenship behav-ior (Lambert, 2000). Indeed, individuals often feel that it istheir moral obligation to reciprocate by exerting extra effortafter being granted these extra benefits (Eisenberger, Fasolo,& Davis-LaMastro, 1990; Eisenberger, Armeli, Rexwinkel,Lynch, & Rhoades, 2001). Using data from a manufac-turing firm, Lambert (2000) provides support for socialexchange theory in that a positive relationship was foundbetween organizational citizenship behaviors and the amountof work-life benefits employees and their family membersreceived.

On the other hand, teleworkers often face obstaclesthat may reduce their work effort. Kurland and Cooper(2002), for example, conducted a qualitative examinationof managers in the private sector and concluded that,because of physical separation (i.e., isolation), teleworkersdiscretionary-related behaviors decreased. Another quali-tative study conducted in the private and public sectorsby the same authors also suggested that such behaviors

of teleworkers may be lower due to isolation (Cooper &Kurland, 2002). And, Johnson et al. (2007) suggested thatdistractions from family members and neighbors negativelyaffected the job-related behaviors of home-based teleworkers(Johnson, Andrey, & Shaw, 2007).

Since research suggests that telework may have eithera positive or a negative effect on discretionary behaviors,the following hypothesis is proposed concerning the discre-tionary behavior work effort:

Hypothesis 1: Telework will be associated with work effort.

Role Clarity as a Mediator of Work Effort

Telework may also have an indirect impact on work effortthrough role clarity—the degree to which tasks are clearlydefined (Sawyer, 1992). For instance, because of physicalseparation, supervisors tend to manage teleworkers differ-ently. That is, such managers use output-based controlsinstead of judging performance based on observable activ-ities (Gajendran & Harrison 2007, p. 1527). This entailsclearly defining duties and tasks (Kurland & Cooper, 2002).The outcome is that teleworkers report lower levels of roleambiguity than traditional workers (Igbaria & Guimaraes,1999). Furthermore, research indicates that discretionarybehaviors are negatively impacted in instances where rolesare ambiguous (Henry & Julius, 2011; Lambert, Hogan,Dial, Altheimer, & Baronb-Bellessa, 2012). This suggeststhe positive effect of teleworking on role clarity (the inverseof role ambiguity) is likely to increase work effort. That leadsto the following hypothesis:

Hypothesis 2: Teleworking will increase role clarity, which,in turn, will increase work effort.

Job Satisfaction as a Mediator of DiscretionaryBehaviors

Workers tend to perform more discretionary behaviors whenthey are satisfied with their jobs, as demonstrated by LePineet al. (2002) in a meta-analytic study. Moreover, manage-ment scholars view telework as an important antecedentto job satisfaction. According to social exchange theory,teleworkers are more satisfied with their jobs than theiroffice-based counterparts, because they are afforded a greaterability to manage work and personal responsibilities duringtraditional working hours (Caillier, 2012). Teleworkers alsohave a greater sense of autonomy than office-based work-ers (Golden, 2009). That also suggests teleworkers are moresatisfied, since autonomy is a critical psychological predic-tor of job satisfaction in Hackman and Oldham’s (1976) jobcharacteristics model.

Though not supported by all, the majority of researchhas found support for teleworkers being more satisfied thantraditional workers. For instance, Gajendran and Harrison

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ROLE CLARITY AND JOB SATISFACTION 195

(2007) conducted a meta-analysis on telework studies andfound a positive association between telework and job sat-isfaction. Given that teleworkers tend to have higher jobsatisfaction (Gajendran & Harrison 2007) and that such sat-isfaction enhances discretionary behaviors (LePine, Erez, &Johnson, 2002), telework may enhance work effort throughjob satisfaction. To test this effect, the following hypothesisis proposed:

Hypothesis 3: Teleworking will increase job satisfaction,which, in turn, will increase work effort.

METHOD

To test the hypotheses, data were derived from the2011 Federal Employee Viewpoint Survey—a surveydesigned to assess human resource management systems,agencies’ progress toward human capital initiatives, and theefforts of senior managers. The survey was administered dur-ing April and May 2011 by the U.S. Office of PersonnelManagement to full-time, permanent workers in U.S. federalagencies. Of the 540,727 employees that received the sur-vey, 266,376 returned the questionnaire for a response rateof 49.3 percent. Following is a discussion of the items fromthe survey that were used as dependent and independent vari-ables. Only certain key items are listed in this section. Theremaining items and the manner in which they were codedare in the Appendix.

Dependent Variable

The dependent variable in the model is work effort, and it issimilar to discretionary behaviors, or organizational citizen-ship behaviors. For instance, consistent with discretionarybehaviors, work effort involves extra things employees doto benefit the organization but that they are not required todo (George & Brief, 1992; Kim, 2005). These behaviors canalso be distinguished from performance which consists ofofficial duties and tasks. In the model work effort was mea-sured with the following items: “When needed I am willingto put in the extra effort to get a job done” and “I am con-stantly looking for ways to do my job better.” These twoitems were aggregated to form a single measure, and theCronbach’s alpha for the scale was .74.

Independent Variables

Several classes of factors presumed to have an impacton work effort were incorporated in the models. Theseinclude social exchange factors, work attitude factors, jobstressors, leadership-employee interactions, and control vari-ables. Each of these classes is discussed below.

The first category is social exchange factors. Socialexchange factors are voluntary benefits undertaken by

managers that compel employees to reciprocate in waysthat are important to the organization, such as elevatedwork attitudes and behaviors (Caillier, 2012). As mentioned,telework is one such benefit. In the questionnaire, employeeswere asked to indicate their type of work arrangement. Thecategories for these arrangements were:

• I do not telework because I choose not to telework;• I do not telework because I did not receive approval to

do so, even though I have the king of job where I cantelework;

• I do not telework because I have technical issues thatprevent me from teleworking;

• I do not telework because I have to be physicallypresent on the job;

• I telework very infrequently, on an unscheduled orshort-term basis;

• I telework, but no more than 1 or 2 days per month;• I telework 1 or 2 days per week;• I telework 3 or more days week.

Thus, the categories ranged from not teleworking, which rep-resented four separate categories, to four increments regard-ing time spent telecommuting. Similar to previous research,teleworkers were those teleworking three or more days aweek, or the majority of the work week, whereas office-basedworkers were the remaining categories or those that did nottelecommute most of the work week (Igbaria & Guimaraes,1999; Konradt & Hertel 2003; Fonner & Roloff, 2010). Thenotion is that employees need to telecommute most of thetime to be considered a teleworker. Furthermore, teleworkerswere coded 1 and office-based workers 0, which is alsosimilar to the research just referenced.

Another social exchange factor is training. Training fitswithin the social exchange framework because it is voluntaryand assists employees in their development. Furthermore,training has been found to affect attitudes and behavior (Lee,Lee, & Wu, 2010). The last factor in this category is pay, atraditional social exchange factor used by managers to com-pel reciprocity. Similar to Caillier (2012), satisfaction withpay is examined in the model instead of actual pay.

The second class represents two factors that are strongdrivers of work motivation in Locke and Latham’s (2004)theory of work motivation: job satisfaction and job involve-ment. The most often studied work attitude in administrativeliterature is job satisfaction. Job satisfaction was measuredwith a single-item. Next is job involvement. Job involvementrefers to the psychological importance of an employee’s job.This variable was measured using three items similar to mea-sures in previous research (Caillier, 2012). The Cronbach’salpha was .79. A caveat is that this measure was not a perfectmeasure of job involvement.

Third is the category for leader-employee interactions.Research has consistently found that behaviors and atti-tudes are affected by the interaction between managers

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196 CAILLIER

and employees. This is evident in studies examiningtransformational leadership (Bass & Riggio, 2006). Whilethis study does not examine transformational leadershipor any other validated leadership scale, it does examinesome important aspects of employee perceptions regard-ing leaders, including support, communication, and respect.In so doing, seven items were included in the model. TheCronbach’s alpha was .94 and a factor analysis demonstratedthat the items could be aggregated into one variable as theyloaded on one factor, explaining 73 percent of the variance.

Next are job stressors. Job stressors are factors that drainthe mental resources of employees so as to negatively affecttheir work. The first is role clarity (low role clarity is a jobstressor) which was measured using two items similar tothe ones constructed by Rizzo, House, and Lirtzman (1970).The Cronbach’s alpha for the scale was .74. The next wasemployee workload. It was included because a high work-load can lead to burnout, and subsequently diminished work-related attitudes and behaviors (Cole, Panchanadeswaran, &Daining, 2004).

A factor analysis with a rotation was conducted on theitems. It indicated that one of the involvement items loadedon a separate factor (see Appendix for the item). As a result,it was removed from the scale. Moreover, this did not impactthe Cronbach’s alpha which remained unchanged at .79.

Finally, consistent with other studies examining percep-tual data, employee personal characteristics were included inthe model as control variables (e.g., Boardman & Sundquist,2009). Such data are typically used to see if hypothesizedassociations will hold when these variables are included.These characteristics include gender, ethnicity, age, agencytenure, managerial status, and pay category.

To create consistency across measures, the multi-itemscales were averaged, making 1 the lowest score and 5 thehighest. Table 1 depicts the means and standard devia-tions of the factors. It is clear from the mean of workeffort that employees reported high levels. Indeed, this factorposted the highest score. In terms of teleworking, 3.4 per-cent of employees teleworked 3 or more days each weekand were thus placed in the variable telework. Even thoughthis percentage of teleworkers is small, it fits well within theframework of social exchange, in that workers may not viewit as a discretionary benefit if nearly everyone teleworked(Lambert, 2000).

Table 2 displays the correlations for the measures.As demonstrated, some of the combinations of variableswere correlated above .5. Hence an additional collinearitydiagnostic test was performed to detect the level of multi-collinearity between variables. The variance inflation factor(VIF) from this test—not shown in any of the tables—demonstrated that the highest score was 2.6, with most wellbelow 2, suggesting that multicollinearity was not a problemand that each of the variables could be placed in the modeltogether. For instance, VIFs greater than 3.4 are consideredproblematic (Diamantopoulos & Siguaw, 2006).

TABLE 1Measures of Means, Standard Deviations, and Reliability

Mean Std. Dev.Cronbach’s

Alpha

Work Effort 4.5 .57 .74Male .52 – –Minority .34 – –Age – – –Tenure – – –Manager .27 – –Pay Category – – –Telework .034∗ –Role Clarity 3.90 .85 .74Job Satisfaction 3.81 1.02 –Job Involvement 4.05 .87 .79Training 3.34 1.14 –Pay Satisfaction 3.64 1.10 –Reasonable Workload 3.36 1.13 –Leader-Employee Interaction 3.90 .91 .94

3.4 percent of employees teleworked.The range for the Likert-type items was 1 to 5.

Given that all study variables were derived from employ-ees, the presence of common source bias was examined.Common source bias can result in biased estimates and isthought to be serious when any one component explainsmore than half of the variance in a Harman’s single-factortest (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Afterconducting the test, the largest component explained lessthan 28 percent of the variance for each of the models. Thus,common source bias did not unduly affect the models.

RESULTS

Ordinal logit regression was used to estimate the impactof the study variables on work effort, as well as the medi-ators. This estimator was employed because it is suitablein cases where the dependent variable is ordinal and there-fore does not have a continuous normal distribution (Cohen,Cohen, West, & Aiken, 2003). A critical condition for ordi-nal logit regression is that the proportional odds assumptionnot be violated. After the model was run, it did indicatethat this assumption was violated. That was not surpris-ing, as the test for this assumption is too sensitive to largesample sizes. Simply put, this assumption is easily violatedin large samples (Allison, 1999), especially ones with over200,000 respondents, such as this one. To determine if thisviolation was due to the sample size and not the model,a random sample of 500 observations were selected, simi-lar to Caillier (2013b). That number was chosen because itrepresents a fairly sizeable sample and thus a stringent testfor the proportional odds assumption. In each of the sam-ples, proportional odds were not violated. That suggests theassumption was only violated because of the sample size andthat ordinal regression is a reasonable estimator.

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Because ordinal logit regression estimates are not trans-parent, odds ratios indicate the extent to which a givenvariable decreases or increases the likelihood of an employeereporting high work effort, role clarity, or job satisfaction.For instance, the greater the odds ratio is above 1, the greaterthe positive impact; the greater the odds ratio is below 1, thegreater the negative impact; and an odds ratio close to 1 indi-cates no impact. The coefficients from which the odds ratioswere derived were excluded because of limited space and canbe obtained by emailing the author.

The results of the analysis are listed in Table 3. As demon-strated in Model 1, teleworkers were less likely to reportwork effort than office-based workers (P < .001). Morespecifically, the odds ratio indicates that shifting from office-based workers to teleworkers results in the odds of reportinghigh work motivation (i.e., the highest rating on the measure-ment scale) to be 11 percent less likely (i.e., .89 subtractedfrom 1). Support was therefore found for hypothesis 1, whichstated that teleworking will be associated with work effort.

Next, several steps in accordance with research were fol-lowed to test the mediation hypotheses (Baron & Kenny,1986; Kohler & Matheiu, 1993). First, as demonstrated inModel 1, telework was inversely related to work effort.Second, role clarity was regressed on telework (Model 2),and it indicates that telework was positively related to roleclarity (p < .001). Teleworkers were 1.20 times more likelyto report high role clarity. Third, work effort was regressedon role clarity (Model 3), and it was positively associated towork effort (p < .001). The odds ratio indicates that workerswho perceive their roles are clear were 1.27 times more likelyto report high work effort. Last, to determine if full mediationoccurred, work effort was regressed on both telework androle clarity. Full mediation occurs when the predictor is notsignificant and the mediator is significant, and partial medi-ation is evidenced when both the predictor and the mediatorare significant, but the path between the predictor and theoutcome variable is significantly reduced. As indicated, bothtelework and role clarity were significant in Model 4, butthe strength of the relationship between telework and workeffort was not lower than it was in Model 1. Hypothesis2, which stated that teleworking would increase role clar-ity, which, in turn, would increase work effort, was notsupported.

Hypothesis 3 predicted that job satisfaction would par-tially mediate the relationship between telework and workeffort. As mentioned, the first condition was met; teleworkwas associated with work effort in Model 1. Model 5 demon-strates that telework was associated with higher job sat-isfaction (i.e., 1.25 times greater; p < .001) and Model6 demonstrates a negative association between job satisfac-tion and work effort (i.e., 11 percent less likely; p < .001).Furthermore, both telework and job satisfaction were statis-tically significant (p < .001) and associated to work effortin Model 7, but the relationship between telework and workeffort was not reduced from Model 1. Support was therefore

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TABLE 3Results of Ordinal Regression Analysis

H1: Work Effort H2: Work Effort Mediated by Role ClarityH3: Work Effort Mediated by Job

Satisfaction

Study Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Male 0.81∗∗∗ 0.98∗ 0.81∗∗∗ 0.81∗∗∗ 1.03∗∗ 0.81∗∗∗ 0.81∗∗∗Minority 1.11∗∗∗ 1.22∗∗∗ 1.10∗∗∗ 1.10∗∗∗ 1.04∗∗∗ 1.11∗∗∗ 1.11∗∗∗Age 1.01∗∗ 0.99∗ 1.02∗∗ 1.02∗∗ 1.04∗∗∗ 1.01∗∗ 1.01∗∗Tenure 0.87∗∗∗ 1.10∗∗∗ 0.87∗∗∗ 0.87∗∗∗ 0.99 0.87∗∗∗ 0.87∗∗∗Manager 1.41∗∗∗ 1.13∗∗∗ 1.41∗∗∗ 1.41∗∗∗ 1.15∗∗∗ 1.42∗∗∗ 1.42∗∗∗Pay Category 1.08∗∗∗ 0.96∗∗∗ 1.09∗∗∗ 1.09∗∗∗ 0.90∗∗∗ 1.08∗∗∗ 1.08∗∗∗Telework 0.89∗∗∗ 1.20∗∗∗ 0.88∗∗∗ 1.25∗∗∗ 0.89∗∗∗Role Clarity 1.27∗∗∗ 1.27∗∗∗Job Satisfaction 0.89∗∗∗ 0.89∗∗∗Job Involvement 2.87∗∗∗ 3.12∗∗∗ 2.67∗∗∗ 2.67∗∗∗ 5.19∗∗∗ 3.06∗∗∗ 3.06∗∗∗Training 1.02∗∗∗ 1.70∗∗∗ 0.99∗∗ 0.99∗∗ 1.40∗∗∗ 1.04∗∗∗ 1.04∗∗∗Pay Satisfaction 0.98∗∗∗ 1.04∗∗∗ 0.98∗∗∗ 0.98∗∗∗ 1.65∗∗∗ 1.00 1.00Reasonable Workload 0.98∗∗∗ 1.42∗∗∗ 0.95∗∗∗ 0.95∗∗∗ 1.36∗∗∗ 0.99∗∗ 0.99∗∗Leader-Employee Interaction 1.23∗∗∗ 2.05∗∗∗ 1.16∗∗∗ 1.17∗∗∗ 2.61∗∗∗ 1.27∗∗∗ 1.28∗∗∗N = 211,737Cox & Snell R Square 0.218 0.524 0.221 0.221 0.606 0.219 0.219Nagelkerke R Square 0.236 0.540 0.240 0.240 0.653 0.237 0.237McFadden 0.096 0.211 0.098 0.098 0.355 0.097 0.097

Note: Work effort is the dependent variable in Model 1; role ambiguity is the dependent variable in Model 2; work effort is the dependent variable inModels 3 and 4; job satisfaction is the dependent variable in Model 5; work effort is the dependent variable in Models 6 and 7.

∗∗∗p < .001; ∗∗p < .01; ∗p < .05.

not found for hypothesis 3, which expected job satisfactionto positively mediate the relationship between telework andwork effort.

Furthermore, models including only control variables,telework, and the potential mediators (i.e., role clarity andjob satisfaction) were run to test hypotheses 2 and 3. In otherwords, these models excluded job involvement, training,pay satisfaction, reasonable workload, and leader-employeeinteraction. These models also indicated that role clarity andjob satisfaction did not mediate the relationship betweentelework and work effort.

DISCUSSION AND CONCLUSION

The mediating effect of job satisfaction and role clarity onthe relationship between telework and work effort, a typeof organizational citizenship behavior, has not been previ-ously explored. To fill this void in organizational literatureand to inform practitioners, the present article examined thisrelationship in U.S. federal government agencies by usingdata from the 2011 Federal Employee Viewpoint Survey.Even though support was found for only one of the threehypotheses, the results indicate significant implications fororganizations and management theorist.

The first significant implication was that teleworkers wereless likely to report high levels of work effort compared tooffice-based workers. Contrary to social exchange theory,it appears that telework may not be a lever that managerscan use to increase the work effort of workers. There are

several possible reasons why. First, teleworkers perform theirduties away from workers, managers, and the organization(Cooper & Kurland, 2002; Kurland & Cooper, 2002; Golden,Veiga, & Dino, 2008), often causing them to feel out ofthe loop (i.e., out of sight, out of mind) and socially dis-connected from co-workers (Baruch, 2000). Furthermore, alikely outcome of such professional isolation is that moraleand ultimately work outcomes are reduced (Golden, Veiga,& Dino, 2008), such as discretionary behaviors (Cooper &Kurland, 2002; Kurland & Cooper, 2002). Second, althoughthe dataset did not capture where workers telecommutedfrom, they often do so from home. This means the linesbetween home and work are often blurred, possibly lower-ing work effort. For instance, teleworkers can be interruptedby phone calls or a knock on the door. Family members mayalso increase their demands on the teleworker. And, environ-mental cues (e.g., cleaning) may distract teleworkers awayfrom their duties (Johnson, Andrey, & Shaw, 2007). A finalreason why work effort may have been lower for teleworkersis simply because workers participating in this arrangementwant to better manage work and family obligations (Caillier,2012). In other words, they have family responsibilities out-side of work which may cause them to exhibit lower levelsof work effort than office-based workers.

The next important implication derived from the model isthat telework does not appear to affect work effort throughrole clarity. Thus role clarity was not found to be a mediator.Nevertheless, teleworkers were found to be more likely toreport high levels of role clarity than office-based workers,which supports a similar finding by Igbaria and Guimaraes

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(1999). The suggestion is that teleworkers have clearer rolesand responsibilities, ostensibly because they are manageddifferently. For instance, supervisors cannot physically seeteleworkers performing their duties like they can office-basedworkers. As a consequence, teleworkers are managed morebased on output controls that necessitate clearly definedexpectations (Kurland & Cooper, 2002).

The third implication is that job satisfaction does notappear to mediate the relationship between telework andwork effort. In fact, job satisfaction was inversely related towork effort in the model, a very intriguing finding. As men-tioned, LePine et al. (2002) conducted a meta-analysis andfound a positive association between job satisfaction andorganizational citizenship behaviors. The negative findingalso ran contrary to the bivariate analysis in Table 2. To fur-ther examine this effect, ordinary least squares regressionwas used as an estimator, and the outcome was identical tothat of the ordinal regression analysis. A possible reason thisfinding deviated from previous ones could be due to slightdifferences in the discretionary behavior measures (i.e., orga-nizational citizenship behaviors versus work effort). Clearlymore research is needed to find out exactly why job satisfac-tion was inversely associated. The managerial implication istherefore that supervisors may not be able to enhance workeffort by increasing the job satisfaction of workers.

On the other hand, teleworkers did report higher jobsatisfaction than office-based workers. In doing so, it con-firms others who found a positive association between jobsatisfaction and teleworking (e.g., Gajendran & Harrison,2007). It could be that teleworkers are more satisfied becausethey are afforded a greater ability to manage work and per-sonal responsibilities during traditional work hours (Caillier,2012). Another possibility could be that they have a greatersense of autonomy (Golden, 2009), which is viewed as apowerful antecedent of job satisfaction in the job character-istics model (Hackman & Oldham, 1976).

The last major implication was that the effect sizes for thedirect relationships between telework and work effort, roleclarity, and job satisfaction were fairly modest. For instance,the odds ratios ranged from .88 to 1.25. Hence, telework didnot have a robust negative impact on work effort, nor didit have a robust positive impact on role clarity and job sat-isfaction. Since telework was adopted by Congress, in part,to increase the work attitudes of employees (U.S. GeneralAccounting Office, 2003), an obvious policy implication isthat these outcomes don’t seem to be realized. However,teleworking does provide other advantages to federal agen-cies, namely it reduces expenses (Bailey & Kurland, 2002)and allows government operations to continue in the event ofa disaster (U.S. Office of Personnel Management, 2011), toname a few.

As is the case with any research endeavor, there are afew caveats and limitations to consider. The first is that thisarticle examined data derived solely from employees. Eventhough common source bias was not serious as evidenced by

Harman’s single-factor test, this is something to be noted.Along those lines, supervisory or independent reporting ofwork effort would have been more beneficial. The next lim-itation is that the Federal Employee Viewpoint Survey wasintended for other purposes. Thus some of the items werenot ideal measures. For instance, the measure for job involve-ment was not a validated measure. Previously developed andtested items for such measures as role clarity would have alsobeen more ideal. Another is that this article utilized a cross-sectional research design, making it impossible to determinecausality. Therefore, causality could only be inferred. Forinstance, scholars believe teleworking increases role clar-ity (Igbaria & Guimaraes, 1999). However, it is possiblethat organizations may only allow employees to teleworkwhen their roles and duties are clearly defined, as men-tioned by an editor. Last, the data did not stipulate whereworkers telecommuted from and this could possible impactthe findings. For instance, federal employees can teleworkfrom any number of locations, including home, regionaltelework centers, or on the road. It is therefore possible thatwork effort could be impacted differently in each of theselocations.

In the future, researchers should continue to examinetelework, especially in government agencies, as there is stillmuch to be learned. A good place to start would be to admin-ister surveys with validated measures of key constructs.Examining the mediating and moderating effect of isola-tion would also be valuable, since it is perhaps the greatestdrawback to telecommuting. Next, researchers could exam-ine differences at each level of government, as well as acrosscountries. Last, researchers could examine such managementand leadership practices as transformational and transac-tional leadership and their effect on teleworking. Doing sowill inform organizations about which leadership practicesengender the best outcomes in teleworking environments.

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APPENDIX

Variables and Survey Items

The Likert-type scales for work effort, training, satisfactionwith pay, role clarity, job satisfaction, job involvement, andrange from 1 (strongly disagree) to 5 (strongly agree) or from1(very dissatisfied) to 5 (very satisfied).

∗Work Effort

• “When needed I am willing to put in the extra effort toget a job done.”

• “I am constantly looking for ways to do my job better.”

∗Work Arrangements (As mentioned, teleworking 3 or moredays a week was coded as 1 and the remaining workarrangements were coded as 0)

• “I do not telework because I choose not to telework.”• “I do not telework because I did not receive approval to

do so, even though I have the kind of job where I cantelework.”

• “I do not telework because I have technical issues thatprevent me from teleworking.”

• “I do not telework because I have to be physicallypresent on the job.”

• “I telework very infrequently, on an unscheduled orshort-term basis.”

• “I telework, but no more than 1 or 2 days per month.”• “I telework 1 or 2 days per week.”• “I telework 3 or more days week.”

∗Training

• “My training needs are assessed.”

∗Satisfaction with Pay

• “Considering everything, how satisfied are you withyour pay?”

∗Role Clarity

• “I have enough information to do my job well.”• “I know what is expected of me on the job.”

∗Job Satisfaction

• “Considering everything, how satisfied are you withyour job.”

∗Job Involvement

• “My work gives me a feeling of personal accomplish-ment.”

• “I like the kind of work I do.”• “I recommend my organization as a good place to

work.” (removed from model due to low factor load-ing)

∗Workload

• “My workload is reasonable.”

∗Leader-Employee Interactions

• “My supervisor supports my need to balance work andother life issues.”

• “My supervisor/team leader provides me with oppor-tunities to demonstrate my leadership skills.”

• “Discussions with my supervisor/team leader aboutmy performance are worthwhile.”

• “My supervisor/team leader provides me with con-structive suggestions to improve my job performance.”

• “My supervisor/team leader listens to what I have tosay.”

• “My supervisor/team leader treats me with respect.”• “In the last six months, my supervisor/team leader has

talked with me about my performance.”

Gender (male = 1; female = 0)Ethnicity (minority = 1; non-minority = 0)Age (1 = 29 and under; 2 = 30 to 39; 3 = 40 to 49; 4 = 50 to59; 5 = 60 or older)Agency Tenure (1= up to 3; 2 = 4 to 5 years; 3 = 6 to10 years; 4 = 11 to 14 years; 5 = 15 to 20 years; 6 = morethan 20 years)Managerial Status (manager = 1; non-manager = 0)Pay category (1 = federal wage system and GS 1–12; 2 =GS 13–15; 3 = SES/SL/ST/other)

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