Journal of Applied Psychology1999, Vol. 84, No. 4, 496-513

Copyright 1999 by the American Psychological Association, Inc.0021-9010/99/S3.00

Flexible and Compressed Workweek Schedules:A Meta-Analysis of Their Effects on Work-Related Criteria

Boris B. BaltesWayne State University

Thomas E. Briggs, Joseph W. Huff, Julie A. Wright,and George A. NeumanNorthern Illinois University

Meta-analytic techniques were used to estimate the effects of flexible and compressedworkweek schedules on several work-related criteria (productivity/performance, job satisfac-tion, absenteeism, and satisfaction with work schedule). In general, the effects of bothschedules were positive. However, the effects of both flextime and compressed workweekschedules were different across the outcome criteria (e.g., compressed workweek schedulesdid not significantly affect absenteeism). Thus, the level of positive impact associated witheither schedule is dependent on the outcome criterion under consideration. Further, severalvariables were found to be moderators of flexible work schedules. For example, highlyflexible flextime programs were less effective in comparison to less flexible programs, andthe positive benefits of flextime schedules were found to diminish over time.

Alternative work schedules, such as flextime and com-pressed workweeks, have been adopted by an increasingnumber of organizations over the past several decades(Pierce & Dunham, 1992). A recent report that sur-veyed 1,035 organizations found that 66% offered flexiblework schedules (up 6% from the year before) and 21%offered compressed work schedules (Hewitt AssociatesLLC, 1995).

Much of the increased use of alternative work schedulesis due to societal changes, such as increasing numbers ofwomen in the workforce, dual-career households, andwork-leisure time expectations (Hochschild, 1997; Pierce,Newstrom, Dunham, & Barber, 1989; Ronen, 1984). Thesechanges have increased employee demands for flexibility intheir work schedules so that they can better adjust to andmaster life outside the workplace. The positive benefits ofthese alternative work schedules for employees' quality oflife outside of work are well documented (Lee, 1983; Meij-

Boris B. Baltes, Psychology Department, Wayne State Univer-sity; Thomas E. Briggs, Joseph W. Huff, Julie A. Wright, andGeorge A. Neuman, Department of Psychology, Northern IllinoisUniversity.

A preliminary version of this article was presented at the 104thAnnual Convention of the American Psychological Association,Toronto, Ontario Canada, August, 1996. We thank Rob Altmannand Ken McGraw for their comments on a draft of this article.

Correspondence concerning this article should be addressed toBoris B. Baltes, Psychology Department, 71 West Warren, WayneState University, Detroit, Michigan 48202. Electronic mail may besent to bbaltes@sun.science.wayne.edu.

man, 1992; Ronen & Primps, 1981; Stevens & Elsworth,1979; Thierry & Meijman, 1994). However, research resultsregarding benefits to the employing organizations that haveimplemented these alternative work schedules are far moreambiguous (Pierce et al., 1989). This question is the primaryfocus of the present study.

Organizational gains that are presumed to result fromalternative work schedules are many and diverse, but theygenerally include increased employee job satisfaction, re-duction of overtime, decreased absenteeism, and increasedproductivity (deCarufel & Schaan, 1990; Pierce et al.,1989). However, although originally assumed to have pri-marily positive effects on both the employee and the orga-nization, alternative work schedules can have unintendednegative effects. These negative consequences include in-creased need for managerial planning, the inability of thesupervisor to be present at all times when employees are onthe job, and extra implementation costs (Coltrin &Barendse, 1981). In addition, Nollen (1981) has proposedthat alternative work schedules may create problems withinterface and coverage with suppliers and customers, as allunits are not working on the same schedule.

Because organizational use of these alternative workschedules is growing, it seems imperative that researchersprovide organizational leaders with the information neededto determine whether an alternative work schedule would bebeneficial or detrimental. In this vein, the next sectionaddresses several problems with the current literature thatleave open the questions of if, when, and how these work-schedule interventions are effective for organizations (Dun-

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WORK SCHEDULES 497

ham, Pierce, & Castaneda, 1987; Golembiewski & Proehl,1978, 1980; Pierce et al., 1989).

Problems With the Current Literature

There are several deficiencies within the available evi-dence that can be identified. First, with few exceptions, theresearch has not been based on theoretical models (Pierce etal., 1989; Thierry & Meijman, 1994). Second, researchershave not always agreed in their interpretation of the evi-dence on the effects of alternative work schedules. Althoughearlier literature reviews have revealed overall positive ef-fects of certain alternative work schedules (Golembiewski& Proehl, 1978, 1980; Ronen & Primps, 1981), a morerecent literature review (Dunham et al., 1987) revealed thatthe results of studies on work-schedule interventions werehighly mixed. For example, the effect of flextime scheduleson work-related criteria (e.g., productivity) are highly vari-able and range from zero or little change to substantialpositive change (Dunham et al., 1987; Pierce et al., 1989).These mixed results may point to the existence of modera-tors (e.g., employee type) that need to be identified to betterunderstand the relationship between these alternative workschedules and various outcome measures. Third, much ofthe literature is nonexperimental in nature and is "stronglycharacterized by (1) anecdotal reports of flexible working-hour systems, (2) the use of nonstandardized researchscales, (3) failure to include statistical treatment of thereported data, and (4) the absence of other systematic data-collection strategies" (Pierce & Newstrom, 1983, p. 247).Consequently, the internal validity of the individual studiesincluded in previous literature reviews is questionable,which could, in turn, affect the validity of each of the earlierreviews. Finally, very few researchers have looked at alter-native work schedules as multidimensional (Pierce et al.,1989). For example, the particular design features of aflextime schedule, such as amount of core hours, have rarelybeen considered as moderators of the effectiveness of aflextime intervention.

In the current study, we have attempted to address theseproblems by introducing two substantial improvements overprevious research. First, we used two theoretical models andprior research to formulate hypotheses about the effects ofthese alternative work schedule interventions on variousoutcomes. Second, we used quantitative meta-analytic tech-niques to more accurately assess the effects of flexible andcompressed workweek schedules on multiple criteria. Fi-nally, we were able to assess how specific moderatorsinfluenced the effects of both alternative work schedules. Toaccomplish these goals, we collected data from experimen-tal studies that examined the effects of flextime and/orcompressed workweek schedules on at least one of thefollowing four work-related criteria: productivity/perfor-

mance, overall job satisfaction, absenteeism, and satisfac-tion with work schedule.

Overview of Alternative Work Schedules

Alternative work schedules are schedules that do not fitthe fixed 8-hr day, 40-hr week definition. Examples ofcommon alternative work schedules include flexible work-ing hours (flextime), compressed workweek, part time, andtelecommuting. This study considers two of the most com-mon alternative schedules: flextime and compressed work-week. It may be helpful to the reader at this juncture to pointout that shiftwork arrangements, although alternative, wereexcluded from this study because they do not always matchthis definition of an alternative work schedule.1

Flextime

Under a flextime schedule, employees exercise a decisionregarding the time of day they will arrive at and leave fromwork. The employer creates a band of core time where eachemployee must be present (normally 9 or 10 a.m. to 2 or3 p.m.). For example, a flexible work schedule where allemployees have to be present from 10 a.m. to 3 p.m. wouldhave 5 core hours. Employees are free to arrive before thecore start time and leave after the core finish time, buttypically there is a limit as to how early the employees canarrive and how late they can leave (e.g., cannot start before7 a.m. and cannot stay past 9 p.m.). Another importantflextime characteristic is the degree of carryover that ispermitted. Some organizations do not permit any carryoverof hours (i.e., the employee must work 8 hr per day),whereas others permit carryover on a weekly basis (i.e., norequirement for 8 hr per day but must work 40 hr per week),and a few organizations even allow monthly carryover.

Our review of the literature revealed that flextime sched-ules are used almost exclusively in nonmanufacturing orga-nizations. This may be because of the fact that a flextimeschedule is more difficult to implement in continuous pro-cess operations, such as assembly lines (Ronen, 1981). Thatis, allowing employees to attend work at different timesdoes not mesh well with the interdependence requiredamong workers in a manufacturing setting.

Compressed Workweek

Under a compressed workweek schedule, the workweekis compressed into fewer than 5 days by increasing the

1 Shiftwork schedules are often still 8-hr day 40-hr workweekswith abnormal starting and ending times. Thus, many of theseschedules are not alternative according to our definition. Further-more, these schedules introduce factors not applicable to otheralternative work schedules, such as constantly trying to adjust fromday to night shifts as the shift schedule dictates.

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498 BALTES, BRIGGS, HUFF, WRIGHT, AND NEUMAN

number of hours an employee is required to work per day.The most common form of compressed workweek in theUnited States is the 4-day, 40-hr workweek (4/40), in whichemployees typically work four, 10-hr days (Latack & Fos-ter, 1985; Pierce et al., 1989). Commonly, employees willhave either Friday or Monday off, extending their weekendto three days. In more recent years, 3/36, 3/38, and 3/40schedules have been adopted by some organizations.

Our review of the compressed workweek literature re-vealed that these schedules are most commonly used inmanufacturing settings, which may be because of two rea-sons. First, because compressed workweek schedules stillrequire workers to attend work at the same time, they meetthe interdependence requirement of assembly line (i.e, man-ufacturing) settings. Second, manufacturing organizationstypically do not offer services that require employees to bepresent at more regular time intervals (e.g., Monday-Saturday) to serve customers.

Hypothesized Benefits of AlternativeWork Schedules

Early narrative reviews attempting to summarize the ben-efits of alternative work schedules failed to use a theoreticalmodel to formulate hypotheses. However, a few attempts toconstruct theoretical models to explain the benefits of alter-native work schedules have been made. Pierce and New-strom (1980) used the work adjustment model (Dawis,England, & Lofquist, 1968) to explain how flextime sched-ules influence employees' attitudes and behaviors. Thework adjustment model leads to the prediction that highcorrespondence between an employee's abilities and theability requirements of the job should lead to high roleperformance. Further, high correspondence between an em-ployee's needs and the reinforcement system of the workenvironment should lead to more positive job attitudes.Work adjustment is high when individuals fulfill their work/role requirements and the organization simultaneously ful-fills the needs of the individual. More recent research hasmodified the model so that moderating relationships alsoexist between job attitudes and job performance (Dawis &Lofquist, 1984).

Another theoretical model, which we believe can be usedto explain the effects of alternative work schedules, isHackman and Oldham's (1976) job characteristics theory.The basis of this model is that core characteristics of the job(e.g., autonomy, task identity, etc.) induce psychologicalstates that in turn lead to outcomes such as job performanceand job satisfaction. The introduction of alternative workschedules can affect the core characteristics of a job andthus work outcomes. For example, a flextime scheduleshould positively affect employees' sense of autonomy,which in turn increases job satisfaction.

To develop hypotheses regarding the effects of both

alternative work schedules on our four work-related criteria,we drew on the aforementioned theoretical models, theresults of prior alternative-work-schedule research, and re-search findings from other areas (e.g., person-job fit, jobsatisfaction, job performance, etc.). These are presented inthe following sections.

Flextime

Productivity/Performance

Using the framework of the work adjustment model,Pierce and Newstrom (1980) suggested that flexible work-ing schedules affect employees' performance in the follow-ing ways: They may allow individuals to make more effi-cient use of their own circadian rhythms (the normal 24-hrphysiological cycle) and may decrease the amount of stress(e.g., work arrival related stress) experienced by employees.Employees making more efficient use of their circadianrhythms should result in a higher correspondence betweentheir abilities and the ability requirements of the job.Person-job fit research supports the idea of congruencebetween the individual and the job environment leading tohigher performance (Caldwell & O'Reilly, 1990; Chatman,1988). Although research results on the relationship be-tween job stress and job performance are mixed, it seemssafe to assert that there is a negative relationship betweennegative reactions to stressful job conditions and job per-formance (Jamal, 1984, 1985; Parker & Kulik, 1995; Sul-livan & Bhagat, -1992). Thus, if reduced job stress leads toa reduction in negative reactions, then one should expect tosee an increase in job performance.

The implementation of a flextime schedule also givesemployees more job autonomy. Hackman and Oldham's(1976) job characteristics theory would predict that in-creased job autonomy should lead to increased job perfor-mance. Indeed prior research has linked increased job au-tonomy to higher job performance (Dodd & Ganster, 1996;Roberts & Foti, 1998).

Finally, prior research has indicated no decrements inperformance with the introduction of a flextime scheduleand some increases (Pierce et al., 1989). Thus, with respectto productivity/performance, we expect that the introductionof a flexible work schedule will have positive effects.

Absenteeism

Organizational attendance (lower absenteeism) should in-crease as the amount of discretionary time increases (Pierceet al., 1989). Employees under a flextime schedule can moreeasily respond to work-nonwork conflicts, which can re-duce employee stress. Prior research has linked decreasedemployee stress to decreased absenteeism (Parker & Kulik,1995). Also, motivation to attend may be enhanced byincreased organizational loyalty and job satisfaction result-

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WORK SCHEDULES 499

ing from the implementation of a flexible work schedule(Pierce et al., 1989). Both organizational commitment andjob satisfaction have been positively linked to increasedorganizational attendance (Gellatly, 1995; Somers, 1995;Song, Daly, Rudy, Douglas, & Dyer, 1997) especially withrespect to voluntary absences (Sagie, 1998). Finally, mis-uses of sick leave may no longer be necessary because theemployee can adjust his or her time of attendance (Ronen,1981). Prior literature has supported the hypothesis thatattendance is positively affected with the advent of a flex-time schedule, with frequent dramatic drops in absenteeismbeing reported by organizations (Pierce et al., 1989; Ronen,1981). Thus, with respect to absenteeism we expect that theintroduction of a flexible work schedule will have positiveresults (i.e., lower absenteeism).

Job Satisfaction and Satisfaction With Schedule

Both Pierce and his colleagues (1989), using the workadjustment model, and Ronen (1981), using Herzberg'staxonomy of needs, concluded that the introduction of aflextime schedule should lead to more positive job attitudes(i.e., job satisfaction and satisfaction with schedule). Sev-eral reasons exist for this conclusion. First, employees'needs for autonomy/independence can be met by the intro-duction of a flextime work schedule that can help theemployee fulfill self-actualization needs (Ronen, 1981).Ronen's theory coincides with Hackman and Oldham's(1976) theory of job characteristics, which predicts thatincreased autonomy leads to increased job satisfaction. In-deed, previous research has found that increased job auton-omy is positively linked to job satisfaction (Fried, 1991;Fried & Ferris, 1987; Roberts & Foti, 1998). More specif-ically, Macan's (1994) research indicated a positive rela-tionship between employees' perceived control of time andjob satisfaction. Furthermore, prior altemative-work-schedule research has, for the most part, supported the ideathat job attitudes are favorably affected by the introductionof a flexible work schedule (Pierce et al., 1989; Ronen,1984). Therefore, we expect that the introduction of a flex-time work schedule will lead to increased job satisfactionand satisfaction with schedule.

Compressed Workweek

Productivity/Performance

Using the circadian rhythm approach, Pierce and hiscolleagues (1989) suggested that there are only a few hoursa day where employees enjoy their peak period and performat optimal levels. Thus, having employees work longerhours (as is required in a compressed workweek workschedule) should increase the amount of time they areworking at suboptimal levels. Within the framework of thework adjustment model, this decrease in performance is

linked to lower congruence between the employee's abili-ties and the ability requirements of the job. Research onperson-job fit supports the notion that a decrease in person-job fit (i.e., decrease in congruence between the employee'sabilities and the ability requirements of the job) would leadto decreased job performance (Caldwell & O'Reilly, 1990).Furthermore, prior research has shown that fatigue increaseswith the advent of a compressed workweek schedule(Ronen, 1984), which also could negatively affect perfor-mance. In addition, if increased fatigue is associated withincreased employee stress, then one would expect to see adecrease in productivity/performance.

The results of prior compressed-workweek-schedule re-search have been mixed (Pierce et al., 1989), with produc-tivity either improving or staying the same after the imple-mentation of a compressed workweek work schedule(Ronen, 1984). Thus, although theoretically we would ex-pect the implementation of a compressed workweek sched-ule to lead to lower productivity, prior research does notsupport this claim. Because of these apparent contradic-tions, we felt it wiser to make no hypotheses regardingthe impact of a compressed workweek schedule onproductivity/performance.

Absenteeism

As with flextime, the advent of a compressed workweekschedule should lead to more discretionary time, which inturn should lead to increased organizational attendance.Employees enjoying 3-day weekends should be better ableto balance work and nonwork demands. Being able to moreeasily respond to work-nonwork conflicts should reducestress, and as stated earlier, decreased employee stress hasbeen linked to decreased absenteeism (Parker & Kulik,1995). Furthermore, prior research strongly suggests thatemployee absenteeism may decrease following the imple-mentation of a compressed workweek study (Pierce et al.,1989). Thus, we expect that the introduction of a com-pressed work schedule will have positive effects onabsenteeism.

Job Satisfaction and Satisfaction With Schedule

According to Ronen (1984), compressed workweekschedules can affect job attitudes by enhancing or facilitat-ing production. Specifically, "increases in responsibility,autonomy, and job knowledge resulting from implementingthe schedule may be associated with more positive attitudestoward the job itself (Ronen, 1984, p. 57). As mentionedearlier, Hackman and Oldman's (1976) model would pre-dict that positive changes in these types of job characteris-tics lead to higher job satisfaction. Prior research has shownmixed results, but in general positive changes in job atti-tudes can be expected with the implementation of a com-

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500 BALTES, BRIGGS, HUFF, WRIGHT, AND NEUMAN

pressed workweek work schedule (Pierce et al., 1989).Thus, we expect that job satisfaction and satisfaction withschedule will be positively affected by the advent of acompressed workweek work schedule.

Moderators

Considering the evidence provided by primary-data stud-ies, it seems likely that a number of variables may moderatethe relationships between alternative work schedules andthe previously mentioned criteria. Moderators that havebeen suggested by earlier researchers include flexibility ofthe flextime schedule (Pierce & Newstrom, 1983) and timesince schedule implementation (Ivancevich & Lyon, 1977).Furthermore, we hypothesized that employee type andmethodological rigor could function as moderators.2 Wehave used our conceptual framework and prior research toformulate some hypotheses regarding the effects of ourmoderators. However, these hypotheses are exploratory innature.

Employee Type

The effects of alternative work schedules may vary as afunction of employee type. Specifically, managers and pro-fessionals may be less affected by schedule interventionsthan general employees (i.e., blue-collar, administrativesupport, service employees, etc.), particularly if they al-ready possess a large amount of autonomy regarding theirwork schedules before the introduction of the alternativeschedule. The theoretical underpinning for this effect is thatbecause the managerial employees may already possessfreedom in their schedule, the official implementation of aflexible or compressed workweek work schedule would notincrease the correspondence of the work environment totheir needs. That is, managers' working conditions alreadysatisfy their need for autonomy, and thus the introduction ofa formal alternative work schedule may not lead to higherlevels of need satisfaction. Therefore, it is hypothesized thatmanagers and professionals will be less affected by alter-native schedules than general employees.

Flexibility of the Flextime Schedules

With respect to flextime interventions, high amounts offlexibility (e.g., fewer daily core hours), coupled with em-ployees having the option to change the pattern of hoursworked without management approval, may produce morepositive effects than less flexible flextime schedules (Pierce& Newstrom, 1983).3 That is, increased flexibility may leadto a higher correspondence between employee needs (e.g,need for autonomy) and the work environment and thusincrease the positive effects on various outcome criteria(e.g., job satisfaction, job performance, absenteeism, etc.).Increased flexibility should lead to lower levels of employee

stress also enhancing any positive outcomes. Therefore, it ishypothesized that more flexible flextime interventions willlead to larger positive effects than less flexible flextimeinterventions.

Time Since Schedule Intervention

The amount of time that the program has been in place atthe time its effects are ascertained is likely to be an impor-tant moderator. Ivancevich and Lyon (1977) found that thelong-term impact of a 4/40 workweek was not as positive asthe short-term impact. Prior research has demonstrated thatextrinsic rewards may have a temporary effect on employ-ees (Ronen, 1981). That is, the effects of a work scheduleintervention may wane over time as employees eventuallybecome accustomed to the new amount of freedom. Theemployees may adjust their perceptions and desire evenmore discretionary time (i.e., an increase in needs), therebydecreasing the level of correspondence between employee'sneeds and the reinforcement system of the work environ-ment that was initially attained. Thus, the benefits of alter-native work schedules that are perceived as extrinsic (e.g.,improved job conditions) may have temporary effects.Therefore, it is hypothesized that the effects of alternativeschedules should decrease over time.

Methodological Rigor

The magnitude of the observed effects of alternative workschedules on work-related criteria may vary as a function ofmethodological rigor of the studies evaluating these effects.Some researchers have suggested that low methodologicalrigor may attenuate the size of effects observed in studies(e.g., Bullock & Svyantek, 1983), whereas others havesuggested that low experimental rigor may inflate the size ofeffects observed in studies (e.g., Terpstra, 1981). Giventhese mixed results, no specific hypothesis is proposed.However, we felt that an investigation of methodologicalrigor might add to the extant literature.

2 Type of intervention (flextime vs. compressed workweek) wasalso considered a moderator but was dropped from our analysesbecause type of organization was confounded with interventiontype. The flextime studies included in these analyses were donealmost exclusively in nonmanufacturing environments, whereasthe compressed workweek studies were done predominantly inmanufacturing environments (confounding type of organizationwith intervention type).

3 Other dimensions of a flexible work schedule, such as carry-over, variability of employees schedule, and supervisor's role, mayalso be important in determining the flexibility of a flexible workschedule. Unfortunately, the studies included in this meta-analysisonly allowed us to consider core hours as a moderator.

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WORK SCHEDULES 501

Method

Sample of Studies

Computer-based literature searches were conducted on Psycho-logical Abstracts (PsycLIT: 1974 to February, 1997), ABI/IN-FORM (1977 to February, 1997), Business Periodicals Index(1977 to January, 1997), and Dissertation Abstracts (1891 toMarch, 1997). These searches were conducted using the followingkey terms: alternative work schedules; all forms of compressedworkweek (e.g., workweek, work week, 3/36, 3/38, 4/40); and allforms of flextime (e.g., flexitime, flextime, flex time). A manualsearch of all articles uncovered by the broad term work scheduleswas also conducted. Reference lists of numerous review articles,books, and chapters of books were searched, as well as the refer-ence lists of all located studies. Finally, 20 larger U.S. corporationswho were reported to use alternative work schedules were con-tacted for possible data.

Inclusion Criteria for Studies

Studies were initially selected if they (a) evaluated a flextimeand/or compressed workweek schedule and (b) included a pre-post, control-experimental, or normative-experimental compari-son. This was done to ensure that all effect sizes included in thismeta-analysis reflected the difference between an experimentalgroup working under an alternative work schedule and a controlgroup not working under an alternative work schedule. Thus, theeffect sizes calculated would indicate the effect that each interven-tion had on work-related criteria. Unfortunately, because many ofthese studies are qualitative in nature and often based on anecdotalevidence, these criteria resulted in the loss of a large amount of theoriginal studies. Our problem with finding experimental alternativework schedule studies is not unique. For example, Ralston, An-thony, and Gustafson (1985) evaluated over 100 flextime studiesand found that only a few met their experimental criteria. Further-more, from the responses received from the corporations we con-tacted, we have deduced that many (if not most) businesses havenot conducted formal evaluations of their alternative work sched-ules. Finally, only studies that had the necessary statistics to beincluded in a meta-analysis could be used.

Our final criteria for inclusion in the sample resulted in a totalof 29 published sources and 2 unpublished sources remaining foranalysis, several of which contained more than one study. Severalsources in the sample had examined the effects of alternative workschedules across different employee groups (e.g., professionals,managers, general employees) and across different units or divi-sions of an organization. In these cases, separate effect sizes werecalculated and entered into the analyses. Also, some sources hadexamined the effects of alternative work schedules across time(e.g., 3 months, 6 months, and 12 months). As time since inter-vention was a key moderator of interest in the meta-analysis, adichotomy of 6 or less months from intervention to data collection(i.e., short) and more than 6 months from intervention to datacollection (i.e., long) was used to calculated effect sizes. Sixmonths was chosen as the cut point because it provided for a nearlyeven split between the number of studies that fell into both theshort and long time since intervention groups. If multiple effectsizes from a study fell into either time frame, they were averaged

into a single effect size; otherwise, separate effect sizes wereentered into the analyses (it should be pointed out that only onestudy had such longitudinal data). Finally, in one study (Dunhamet al., 1987), the authors had evaluated the effects of both aflextime intervention and a compressed workweek intervention(using different samples), and therefore, separate effects werecalculated for these samples. As a result, the original 31 sourceswere coded into 39 separate substudies. Because these 39 substud-ies measured multiple work-related criteria, we were providedwith 69 effect sizes. The key characteristics of the 39 substudiesare presented in Table 1. Although our inclusion criteria did resultin a large number of studies being excluded from our meta-analysis, we believe that the organizations in the studies we in-cluded make up a diverse and representative sample. For example,within both schedule interventions, we had governmental versusnongovernmental organizations, and the employee type variedfrom managers to blue-collar workers. Furthermore, within flex-time we had organizations representing the finance, insurance, andgovernmental sectors. Also, from our own knowledge of the stud-ies both included and excluded from the study, no discerning factorwas observed that would distinguish organizations that did conductformal evaluations from those that did not.

Variables Coded From Each Study

The following information was coded from each report: (a)outcome criteria, (b) sample size, (c) alternative work scheduleintroduced (flextime, compressed), (d) time since schedule imple-mentation at the time of evaluation (6 months or less, more than 6months), (e) flexibility of the flextime schedule (less than 5 corehours';' 5 or more core hours), (f) sample employee type (generalemployees, professional or management, mixed), and (g) method-ological rigor (high, low).4

The outcome criteria from the studies were coded into thefollowing dimensions: productivity, supervisor-rated perfor-mance, self-rated performance, absenteeism, job satisfaction,and satisfaction with schedule. Productivity, supervisor, andself-performance ratings were coded as separate outcome cri-teria because of the low correlations found between thesedifferent types of productivity/performance measures in previ-ous research (Conway & Huffcutt, 1997; Hoffman, Barry, &Holden, 1991).5 Criteria in the productivity dimensionincluded only objective criteria (e.g., amount of claims pro-cessed, etc.) measured at either the employee or unit level.6 Thesupervisor and self-rated performance dimensions were made

4 Although many other variables were initially coded from eachstudy (e.g., location of sample, gender ratio of sample), they werenot included in the analyses because of the small number of studiesthat had provided this information.

5 We thank an anonymous reviewer for pointing out that type ofperformance criteria would be a logical moderator.

6 Individual and unit level productivity data were combinedbecause of the small number of effect sizes associated with pro-ductivity. The effect sizes associated with productivity in bothwork schedules were homogenous (see Tables 3 and 4). Thisindicated that no statistical differences existed between productiv-ity effect sizes measured at the individual and unit levels.

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502 BALTES, BRIGGS, HUFF, WRIGHT, AND NEUMAN

Table 1Summary of Characteristics of Alternative Work Schedule Substudies

Characteristic

Type of organizationManufacturingOther

Employee typeEmployeesMan ./prof.MixedUnknown

CriteriaProductivityPerformance supervisor-ratedPerformance self-ratedJob satisfactionAbsenteeismSatisfaction with schedule

Degree of flexibilityLowHighUnknown

Experimental rigorLowHigh

Time since interventionShort (6 months or less)Long (more than 6 months)Unknown

All substudies(n = 39)

930

203

151

945

251313

———

1524

16194

Flextime(n = 27)

126

15381

5—5

1789

7164

918

13113

Compressed(n = 12)

84

5070

44—854

———

66

381

Note. One study (Dunham, Pierce, & Casteneda, 1987) is represented twice, once in flextime and once incompressed workweek. Man./prof. = manager/professional.

up of various subjective scales. All absenteeism criteria wereobjective and consisted of company attendance records. Finally,the job satisfaction and satisfaction with schedule dimensionsincluded various self-rating scales.

Methodological rigor was determined by the design of the study.Specifically, each study was coded as one of the following 14designs: (1) one sample t (i.e., normative-experimental compari-son); (2) unmatched, experimental-control; (3) unmatched, longi-tudinal, experimental-control; (4) matched, experimental-control;(5) matched, longitudinal, experimental-control; (6) pre-post withdifferent samples; (7) pre-post with the same samples; (8) pre-post, longitudinal; (9) pre-post, experimental-control with a focuson the pre-post comparison; (10) interrupted time series; (11)pre-post, experimental-control with a focus on the interactioneffect; (12) experimental-control comparison of pre-post differ-ence scores; (13) experimental-control comparison of pre-postdifference scores, longitudinal; and (14) pre-post, experimental-control with pre scores serving as a covariate.

We generated a measure of experimental rigor that was based onthe experimental design used and the method of sample selection.First, the above designs were reduced to five categories of increas-ing rigor. Specifically, Designs 1-3 were given a rigor score of 1;Designs 4-6 were given a rigor score of 2; Designs 7-8 weregiven a rigor score of 3; Designs 9-10 were given a rigor score of4; and finally, Designs 11-14 were given a rigor score of 5.Second, a rigor score of 1 was added to the existing rigor score ifthe sample selection was random or representative random. Giventhe low number of studies included in the sample, these scores

were further reduced to a low versus high rigor dichotomy, withthe low-rigor category consisting of studies assigned a rigor scoreof 3 or less, and the high-rigor category consisting of studiesassigned a rigor score of 4 or more.

The studies were coded by three of the authors, and intraclasscorrelation coefficients (Shrout & Fleiss, 1979) were calculated forall the moderators used in the analysis. These correlation coeffi-cients were as follows: amount of flexibility (1.00), time sinceintervention (.88), methodological rigor (1.00), and employee type(.91). All disagreements were resolved through discussion.

Meta-Analytic Procedures

Computation of Effect Sizes and Outlier Analysis

The first step in the analysis involved converting the results of thevarious studies to a common statistic. The results were convertedto 69 Pearson correlations (r), reflecting the degree and valence(positive vs. negative) of the relationship between the type of schedule(i.e., standard vs. alternative) and the work-related criteria. Theseconversions were done with Johnson's (1993) DSTAT computerprogram. These Pearson correlations were then converted into a totalof 69 d statistics using the aforementioned program. A list of thePearson correlations calculated for each of the 39 substudies in eachof the six criteria and information about the attributes used in ourmoderator analysis are provided in Table 2.

The computation of r was based on (a) Fisher's F ratio or t testsfor 50% of the effects; (b) means and standard deviations or error

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WORK SCHEDULES 503

Table 2Study Characteristics and Effect Sizes for Alternative Work Schedules

Effect size (r)

Study Prod.

Bohen & Viveros-Long (1981)Calvasina & Boxx (1975)A .078Calvasina & Boxx (1975)B .001Coston(1973) .134Dalton & Mesch (1990)Dalton & Todor (1984)Dunham, Pierce, & Casteneda (1987)A

Dunham, Pierce, & Casteneda (1987)B

Evans (1975)Golembiewski & Hilles (1977)Golembiewski, Yeager, & Hilles (1975)A

Golembiewski, Yeager, & Hilles (1975)B

Goodale & Aagaard (1975) .000Harvey & Luthens (1979)Hausser (1980)Hicks & Klimoski (1981)Hodge & Tellier (1975)Ivancevich (1974)Ivancevich & Lyon (1977)A

Ivancevich & Lyon (1977)B

Kim & Campagna (1981) .129Krausz & Freibach (1983)Maklan (1977)McGuire & Lira (1986)McGuire & Liro (1987)Millard, Lockwood, & Luthans (1980)Morgan (1977)Narayanan (1982)Narayanan & Nath (1984)A

Narayanan & Nath (1984)B

Narayanan & Nath (1984)c

Orpen (1981) .205Ralston (1989)Ralston & Flanagan (1985)1

Ralston & Flanagan (1985)2

Schein, Maurer, & Novak (1977)A .276Schein, Maurer, & Novak (1977)B .460Venne (1993)Welsch & Gordon (1980) .213

Sup. Self Jobperf. perf. sat.

.000

.086.054 .081

.111

.075

.094

.277

.503

.018

.077

.463.310 .569.006 .056.332 .497

.000

.076

.180

.330

.049 .040

.134 .010-.078 .019

.061 .041.356

.000 .328

.075

Abst.

.343

.423

.020

.019-.012-.082

.060-.197

-.073-.142

.870...

.840

.859

Sch.sat.

.231

.392

.714

.278

.380

.348

.261

.218

.177

.292

.005

.000

.099

Sch.int.3

FXCWCWCWFXFXCWFXFXFXFXFXCWFXFXFXCWCWCWCWFXFXCWFXFXCWFXFXFXFXFXFXFXFXFXFXFXCWFX

Coreflex."

high—

highlowhighlowhigh

—highlow

highlow

highhigh

highhighhighhighhighhighlowlowlowhighhigh

—

Timec

longlongshortlonglongshortshortshortlonglonglonglongshortlonglonglonglonglonglongshortlong——

longshortshortshortshortshortshortshortlongshortlongshortshortlong

—

Orgtype"

OMMM0OOO0OOOMOOOOMMMO0MOO0MOOO0OOO0OOOO

Jobtype"

EMPBMPEMPEMPEMPEMPMIXMIXEMPMIXEMPM/PMIXEMPMIXEMPMIXMIXMIXMIXMIX

—EMPMIXMIXMIXMIXEMPEMPM/PM/PEMPMIXEMPEMPEMPEMPEMPEMP

Rigor5

lowlowlowlowhighhighhighhighlowhighhighhighlowhighhighlowlowhighhighhighhighlowlowlowhighhighlowhighhighhighhighhighhighhighhighlowlowhighlow

Note. Positive effect sizes refer to positive effects of intervention (i.e., absenteeism effect sizes have been reversed). Superscript A, B, C refer to differentsamples of participants (e.g., different units, different divisions). Superscript 1 and 2 refer to different posttest times (e.g., 6 months, 12 months). Dashesindicate cells in which data were applicable but not obtained. Prod. = production; sup. perf. = supervisor performance; sat. = satisfaction; abst. =absenteeism; sch. sat. = schedule satisfaction.a Schedule intervention: FX = flextime; CW = compressed workweek. b Flexibility: high = flextime core hours of 5 or less; low = flextime core hoursof more than 5. c Time since intervention: short = 6 months or less since intervention; long = more than 6 months since intervention. d Organizationtype: M = manufacturing, O = other. e Job type: EMP = employee; M/P = manager/professional; MIX = mixed group. f Experimental rigor: on thebasis of experimental design and participant selection method.

terms for 18% of the effects; (c) means and estimated error termsfor 16% of the effects; (d) proportions of standard and alternative-work-schedule participants using extreme category responses onmeasures with two or more response categories for 10% of theeffects (in cases where frequencies were reported on a stronglysatisfied to strongly dissatisfied scale, proportions were calculatedby comparing the extreme category of strongly satisfied to all othercategories combined); and (e) chi square for 2% of the effects.7 Inthree cases, the authors stated that there were no significant dif-ferences without reporting any statistic or providing informationthat would allow for the calculation of an effect size. In these

cases, a Pearson correlation (r) of zero was used. It should benoted that in all of these cases, the sample size exceeded 100;therefore, it can be assumed that the value of zero was a relativelyaccurate estimate of the actual effect size obtained. There were

7 If several comparisons were conducted using the same crite-rion, more accurate estimates of the Pearson correlations wereobtained by estimating the standard deviation from significancelevels and means and then using the lowest estimate of the stan-dard deviations to estimate the effect sizes.

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504 BALTES, BRIGGS, HUFF, WRIGHT, AND NEUMAN

also two cases where the authors stated that the results werestatistically significant without reporting any statistic that wouldallow for the calculation of an effect size. In cases where asignificance level was not provided, a significance level of .05 wasassumed, and this significance level and the sample size were usedto estimate the effect size (Glass, McGaw, & Smith, 1981). Incases where the actual significance level was provided (e.g., .01,.001), these values and the sample sizes were used to estimate thePearson correlation (r).

To estimate the relative stability of unbiased effect-size magni-tudes, separate schematic plot analyses were conducted (Light,Singer, & Willett, 1994) for each criterion variable in the flextime andcompressed workweek samples, as recommended by Hedges andOlkin (1985). No outliers or extreme values were found in the com-pressed workweek sample. However, three outliers were found in theflextime sample: one outlier in productivity, one in job satisfaction,and one in schedule with satisfaction. Because these outliers made upsuch a small percentage of our original sample they were eliminatedfrom the remaining analyses to ensure that any moderator effects thatwere found were not of a spurious nature.

Statistical Methods

The overall progression of analyses was based on Hedges andOlkin's (1985) approach to meta-analysis. Categorical model anal-yses of the effects within schedule intervention type as well as the

effects' of moderators were conducted first. Then, additionalweighted multiple-regression analyses were conducted using themoderators, as well as different work-related criteria as predictors,to ensure that the categorical differences associated with eachmoderator were unique. Furthermore, by placing all the modera-tors into a regression model, we were able to test whether control-ling for other moderators would reveal the true relationship be-tween any particular moderator and our study effect sizes.

The homogeneity of within-class effect sizes, as well as thesignificance of the between-class effects were assessed usingHedges and Olkin's (1985) statistical procedures, which are incor-porated into Johnson's (1993) DSTAT computer program. Thebetween-class effect was estimated by QB, which has a chi-squaredistributionp — 1 degrees of freedom (p is the number of classes).The homogeneity of effect sizes within each class was estimatedby Qw, which also has a chi-square distribution with k — 1 degreesof freedom (k is the number of effect sizes within the class).

Results

Categorical Analyses

Flextime

All of the results with respect to the flextime categoricalanalysis that are mentioned in the following sections arepresented in Table 3.

Table 3Effects of Flextime Work Schedules on Positive Work Outcomes With Between- and Within-Homogeneity Tests Across Study Characteristics

Moderator

OverallType of work-related

criteriaProductivityPerformance self-ratedAbsenteeismJob sat.Sat. with schedule

Employee typeEmployeeManager/prof.

Degree of flexibility0

LowHigh

Time since scheduleintervention"1

ShortLong

Methodological rigorc

LowHigh

k

41

458

168

318

1127

2215

1031

N

4,492

316563

1,0342,025

554

3,936556

1,2982,617

2,0371,753

1,3562,136

Meanweighted

effectsize (d)

0.30

0.450.040.930.150.32

0.410.01

0.490.28

0.350.30

0.110.37

95% CI

0.26

0.26-0.06

0.830.090.20

0.36-0.09

0.400.23

0.290.23

0.030.32

forrf

0.35

0.640.141.030.210.44

0.460.11

0.570.34

0.410.38

0.190.42

Meanweighted

(r)

.15

.22

.02

.42

.07

.16

.20

.01

.24

.14

.17

.15

.05

.19

Homogeneity tests

(a,r (sjb

1004.55**193.57**

1.577.08

728.95**48.90**24.50*

47.99**913.49**

7.3716.21**

503.17**462.63**

0.66

639.25**343.00**

28.39**38.28**

937.89**

Note. Significant effect sizes are indicated by confidence intervals that do not include 0. Positive effect sizesrefer to positive effects of intervention (i.e., effects for absenteeism have been reversed), k = number of effectsizes; CI = confidence interval; sat. = satisfaction; prof. = professional.a Significance indicates effects differ as a function of study characteristics. b Significance indicates rejection of thehypothesis of homogeneity. c High = less than 5 core hours; low = 5 or more core hours. d Short = 6 or lessmonths since intervention; long = more than 6 months since intervention. e Low = low rigor; high = high rigor.* p < .01. **p < .001.

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WORK SCHEDULES 505

Work-related criteria. Most hypotheses regarding theeffect of flextime work schedules on work-related criteriawere supported as indicated by positive effect sizes whose95% confidence intervals did not include zero. Flexiblework schedules favorably influenced productivity, job sat-isfaction, absenteeism, and satisfaction with work schedule.However, flexible work schedules did not seem to have aneffect on self-rated performance. Furthermore, the test ofthe categorical model for type of work-related criteria wassignificant, x*(4, N = 41) = 193.57, p < .001, indicatingthat the various work-related outcomes are affected quitedifferently by the introduction of a flextime work schedule.Absenteeism was the most effected by the introduction of aflextime schedule (d = .93), whereas self-rated perfor-mance was the least affected (d = .04).

Employee type. As predicted, the three flextime studiesthat included managers and professionals revealed an effectthat was not significantly different from zero (d = .01).Furthermore, the <2W statistic for the flextime work scheduleindicates that this result is homogenous across studies, andthus, a reliable estimate of the effect size, ^(7, N =8) = 7.37, ns. Thus, it would appear that managers andprofessionals were not appreciably affected by the imple-mentation of flextime work schedules. Employees, on theother hand, were affected by the advent of a flextime workschedule (d = .41).

Flextime flexibility. Flextime interventions can varyas a function of the number of core hours of mandatoryattendance (see footnote of Table 2). The number of corehours provides a good operational definition of the de-gree of flexibility of the flextime schedule, and one of theresearch questions of interest to us was whether increasedflexibility enhances organizational and employee out-comes. Contrary to our hypothesis, less flexible sched-ules (5 or more core hours) resulted in larger effect sizesthan more flexible schedules (less than 5 core hours)across all positive work outcomes, ^(1, N = 38) = 16.21,p < .001.8

Time since implementation. Most human resource prac-titioners should be concerned that the effects of an inter-vention may be short-lived. An intervention that initiallycreates positive results can return to baseline levels overtime. Contrary to our hypotheses, however, the short inter-val flextime intervention effects were not larger than thelong interval flextime intervention effects across all positivework outcomes, x*(l, N = 37) = 0.66, ns.

Methodological rigor. High-rigor studies showed largereffect sizes than low-rigor studies across all positive workoutcomes, ^(1, N = 41) = 28.39, p < .001. Thus, it wouldappear that high-rigor studies allowed researchers to findstronger effects across all the outcome criteria included inthe flextime studies.

Compressed Workweek

All of the results with respect to the compressed work-week categorical analysis that are mentioned in the follow-ing sections are presented in Table 4.

Work-related criteria. Support for our hypotheses re-garding the effects of compressed workweek schedules onwork-related criteria were mixed. Compressed workweekschedules positively affected supervisor performance rat-ings, job satisfaction, and satisfaction with work schedulebut did not affect productivity. Contrary to our hypotheses,however, absenteeism was not significantly affected. Thetest of the categorical model for type of work-related criteriawas significant, ^(4, N = 25) = 74.85, p < .001. Thisindicates that the introduction of a compressed workweekschedule influences the various work-related criteriadifferently.

Time since implementation. Contrary to our predictions,a reduced effect of length of intervention was not found inthe compressed workweek interventions, ^(1, N =23) = 0.04, ns.

Methodological rigor. There was not significant differ-ence between high- and low-rigor compressed workweekstudies, ̂ (1, N = 25) = 1.12, ns. However, because of thesmall number of low-rigor studies, this result should beinterpreted cautiously.

Weighted Regression Analyses

To determine which study characteristics were uniquelyrelated to our effect sizes, we conducted weightedmultiple-regression analyses. These regressions areweighted because the variances of each individual effect-size estimate are inversely proportional to the sample sizeof the study (Hedges & Olkin, 1985). The study charac-teristics are entered as predictors, effect size (d) as thecriterion, and w as the weighting factor. Thus, this anal-ysis gives more weight to effect sizes that are estimatedmore reliably. Because the standard errors for the regres-sion coefficients were incorrect, by a factor of the squareroot of the residual mean square (see Hedges & Olkin,1985, p. 174), they needed to be corrected using John-son's (1993) DSTAT program. The unstandardized par-tial regression coefficients from the multiple-regressionanalyses indicate the association of each study character-istic with the ds, while statistically controlling for theother variables in the regression analyses.

The study characteristics were dummy coded in bothregression analyses with four orthogonal dummy vectors

8 Positive work outcomes refers to an analysis of effects acrossall criteria (productivity, job satisfaction, absenteeism, andsatisfaction with schedule) after reversing the effect sizes forabsenteeism.

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506 BALTES, BRIGGS, HUFF, WRIGHT, AND NEUMAN

Table 4Effects of Compressed Work Schedules on Positive Work Outcomes With Between-arid Within-Homogeneity Tests Across Study Characteristics

Study characteristics

OverallType of work-related

criteriaProductivityPerformance sup.

ratedAbsenteeismJob satisfactionSat. with schedule

Time since scheduleintervention

Short"Long

Methodological rigorLowb

High

k

25

44

584

815

817

Totaln

2,921

770312

507855477

4901,883

1,0891,832

Meanweighted

effectsize (d)

0.29

0.040.42

0.010.590.40

0.290.27

0.250.31

95% CI

0.23

-0.070.27

-0.130.480.25

0.160.21

0.160.24

ford

0.34

0.150.57

0.140.690.55

0.420.34

0.340.38

Meanweighted

W

.14

.02

.21

.00

.28

.19

.14

.14

.13

.15

Homogeneity tests

(QbY (2Jd

210.58**74.85**

2.5116.09*

87.71**21.17**8.26*

.04

28.21**173.16**

1.1264.65**

144.82**

Note. Significant effect sizes are indicated by confidence intervals that do not include 0. Positive effect sizesrefer to positive effects of intervention (i.e., effects for absenteeism have been reversed), k = number of effectsizes; CI = confidence interval; sup. = supervision; sat. = satisfaction."Time since intervention: short = 6 or less months since intervention, long = more than 6 months sinceintervention. b Rigor: low = low rigor, high = high rigor. c Significance indicates effects differ as a functionof study characteristics. d Significance indicates rejection of the hypothesis of homogeneity.*p<.0l. **/?<.001.

representing the different work-related criteria. Thisdummy coding allowed us to make more specific com-parisons between the various work-related criteria thanwas allowed in the categorical analyses. The behavioral-attitudinal vector (vector 1) compared the two behavioralcriteria (productivity and absenteeism) against the twoattitudinal criteria (job satisfaction and satisfaction withschedule). The attitudinal vector (vector 2) compared thetwo attitudinal criteria (job satisfaction and satisfactionwith schedule), and the behavioral vector (vector 3) com-pared the two behavioral criteria (productivity and ab-senteeism). Finally, the productivity/performance vector(vector 4) compared the productivity criterion to theperformance criterion. This comparison was possible be-cause in each regression only one performance criterion(self ratings vs. supervisor ratings) was present.

Finally, the assumption of independence was violated inboth regressions (i.e., multiple effect sizes from the samestudy), which can lead to increased Type-1 error rate. Toaddress this issue, we calculated an adjusted alpha level asrecommended by Stevens (1996). For both regression anal-yses, an alpha level of .01 was required. It should bementioned that this method was very conservative becauseonly half (53%) of our 39 substudies had more than oneeffect size, only a few (20%) had more than two effect sizes,and none had four effect sizes.

Flextirhe

In the fiextime regression analysis (see Table 5) all butone of the study characteristics had significant regressioncoefficients. Managers and professionals were less affectedby fiextime schedules; less flexible schedules resulted inlarger effect sizes than more flexible schedules; high-rigorstudies showed larger effect sizes than low-rigor studies,and the various work-related criteria were affected quitedifferently by the introduction of a fiextime work schedule.Specifically, behavioral outcomes were more greatly ef-fected than attitudinal outcomes, productivity/performanceeffect sizes were larger than effect sizes associated withabsenteeism, and effect sizes associated with productivitywere greater than effect sizes measured through self-ratedperformance scales. Thus, the regression results supportedour categorical analyses with one important exception. Theregression analyses found that the time since schedule in-tervention produced a significant negative unstandardizedregression coefficient (B = -.60, p < .001). The negativevalue indicates that lower effect sizes are found as the timeof criterion measurement, after the intervention is intro-duced, increases. It seems that controlling for other studycharacteristics has allowed time since schedule interventionto explain a significant amount of variance in our effectsizes. Follow-up partial correlation analyses indicated thatwhen one controls for degree of flexibility a significant

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WORK SCHEDULES 507

effect of time since schedule intervention on the flextimeeffect sizes occurs.9 This result, not found in the categoricalanalysis, demonstrates the importance of conductingweighted multiple-regression analyses. Categorical analysesdo not allow researchers to discover such interactionsamong moderators.

The QR statistic indicated that a substantial and signifi-cant proportion of variance (40%) in the effect-size esti-mates is explained by the study characteristics. However,the QE statistic was also significant, indicating that a sig-

nificant proportion of the variance in the effect sizes was notexplained by the study characteristics. This result indicatesthat other moderators may exist.

Compressed Workweek

In the compressed-workweek weighted-regression analy-sis (see Table 6), all but three of the study characteristics

had significant regression coefficients. Thus, the regressionanalyses supported the results of our compressed-workweekcategorical analysis. That is, short interval intervention ef-fects were similar to the long interval intervention effects;high-rigor and low-rigor studies are similar; and various

Table 5Partial Multiple Regression Coefficients for StudyCharacteristics Predicting Positive Work Outcomes inFlextime Studies (k = 36, N = 3,790)

Positive work outcomes

Predictor

Employee typeDegree of flexibilityTime since schedule interventionMethodological rigorVector 1Vector 2Vector 3Vector 4

Intercept

Overall R2

Qr

B

-.24**-.94**-.60**

.96**

.22**-.03-.33**

49**1.40

.40387.23**591.40**

ft

-.13-.57-.39

.47

.29-.03-.20-.32

Note. In the dummy vector design for degree of flexibility, low flexibil-ity = 0 and high flexibility = 1. For time since schedule intervention,short = 0 and long = 1. For methodological rigor, low = 0 and high = 1.In the four dummy vectors for type of positive work outcome, productivityand absenteeism = 1 and job satisfaction and satisfaction with schedule =— 1 in Vector 1 (comparing behavioral vs. attitudinal outcomes). In Vec-tor 2, productivity and absenteeism = 0 and job satisfaction = 1 andsatisfaction with schedule = — 1 (comparing attitudinal outcomes). InVector 3, productivity = 1; absenteeism = -1; job satisfaction = 0;satisfaction with schedule = 0 (comparing behavioral outcomes). In Vec-tor 4, productivity = 1; self-performance ratings = — 1; absenteeism = 0;job satisfaction = 0; satisfaction with schedule = 0. k = number of effectsizes; B = unstandardized partial multiple regression coefficient; )3 =standardized partial multiple regression coefficient.**p < .001.

Table 6Partial Multiple Regression Coefficients for StudyCharacteristics Predicting Positive Work Outcomes inCompressed Workweek Studies (k = 21, N = 2,373)

Positive work outcomes

Predictor

Time since schedule interventionMethodological rigorVector 1Vector 2Vector 3Vector 4

Intercept

Overall R2

Qr

Qe

B

-.01-.32-.22**

.15**

.10-.37**

.88

.52101.33**93.84**

ft

-.01-.35-.52

.22

.17-.55

Note, k = number of effect sizes. In the dummy vector design for timesince schedule intervention, short = 0 and long = 1. For methodologicalrigor, low = 0 and high = 1. In the four dummy vectors for type of positivework outcome, productivity and absenteeism = 1 and job satisfaction andsatisfaction with schedule = -1 in Vector 1 (comparing behavioral, vs.attitudinal outcomes). In Vector 2, productivity and absenteeism = 0 andjob satisfaction = 1 and satisfaction with schedule = — 1 (comparingattitudinal outcomes). In Vector 3, productivity = 1; absenteeism = -1;job satisfaction = 0; satisfaction with schedule = 0 (comparing behavioraloutcomes). In Vector 4, productivity = 1; supervisor performance rat-ings = —1; absenteeism = 0; job satisfaction = 0; satisfaction withschedule = 0. B = unstandardized partial multiple regression coefficient;/3 = standardized partial multiple regression coefficient. ** p < .001.

work-related criteria were affected quite differently by theintroduction of a compressed workweek work schedule.Specifically, attitudinal outcomes were more greatly af-fected than behavioral outcomes, effect sizes associatedwith job satisfaction were larger than effect sizes associatedwith satisfaction with schedule, and effect sizes associatedwith productivity were much smaller than effect sizes mea-sured through supervisor-rated performance scales.

Although the QR statistic indicated that a substantial andsignificant proportion of variance in the effect-size esti-mates (52%) were explained by the study characteristics, the

QE statistic was also significant indicating that a significantproportion of the variance in the effect sizes was not ex-plained. As mentioned earlier, this result may indicate thepresence of additional moderators.

9 An anonymous reviewer suggested that the time since sched-ule intervention could only be appropriately tested with studiesthat included pretest scores. Thus, to confirm our flextime andcompressed workweek results, we conducted both regressions withonly pre-post test studies (about two thirds of the original sample).Time since schedule intervention was still found to be a significantnegative predictor in the flextime regression (B = —.64, p <.001), and it was not a significant predictor in the compressedworkweek regression. Both of these findings supported our earlierresults.

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508 BALTES, BRIGGS, HUFF, WRIGHT, AND NEUMAN

Discussion

The primary rationale for this study was to provide state-of-the-art evidence regarding the effects of flextime andcompressed workweek schedules on organization-relevantoutcomes. Specifically, this study attempted to address sev-eral problems in prior alternative-work-schedule researchby using meta-analytic techniques to predict and assess theimpact of flexible and compressed workweek schedules onseveral work-related criteria. This study also tested a num-ber of hypotheses derived in a cumulative sense from pastempirical findings and previously introduced theories.

In discussing our results, we attempt to integrate thefindings of this study with previous research and discuss theimplications of our findings for organizational decisionmaking. We begin with a review of the specific effects andmoderators of both intervention types on work-relatedcriteria.

Specific Effects

In general, the effects of both flextime and compressedworkweek schedules were positive and consistent with ourpredictions with a few important exceptions. Furthermore,differences in effect sizes were found within interventiontype across the outcome criteria.

Flextime

The predictions made for flextime were upheld in all butone case. Flexible work schedules had positive effects onemployee productivity, job satisfaction, satisfaction withwork schedule, and employee absenteeism. However, thesizes of these effects were significantly different. For ex-ample, the effect size associated with absenteeism wassignificantly larger than that for productivity. This result isconsistent with the conjectures made by Pierce et al. (1989),that an alternative work schedule would be more likely toimpact attendance and/or retention than directly impactworker effectiveness. Contrary to expectations, self-ratedperformance was not positively affected by the introductionof a flextime schedule. The fact that self-rated performancewas not affected is surprising given that productivity in-creased. However, research on the psychometric propertiesof self-rated performance scales has found that the self-report of performance tends to be more lenient than ratingsmade by others (Ford & Noe, 1987). It could be that aceiling effect came into play with respect to self-ratedperformance; that is, there was no room for self-rated per-formance to improve.

Compressed Workweek

As predicted, compressed workweek schedules did posi-tively affect job satisfaction and satisfaction with work

schedule. Contrary to predictions, however, absenteeism didnot decrease. Perhaps most surprising was that althoughproductivity was not positively affected, the supervisor-rated performance criteria did show a positive increase. Thisresult may seem surprising because the more objectivemeasure of productivity indicated no increase. However,prior research has found very low correlations betweenobjective and subjective measures (Alexander & Wilkins,1982).

The nonsignificant effect size reported for absenteeismsuggests that compressed workweek schedules do not en-hance the motivation of employees to attend. However,because this effect size was only calculated from five stud-ies, it should be interpreted cautiously.

As with flextime, the size of effects across the criteriawas significantly different for compressed workweek. How-ever, contrary to the flextime findings, the compressed-workweek effect sizes associated with behavioral work-related criteria (productivity and absenteeism) were smallerthan those for attitudinal work-related criteria (job satisfac-tion and satisfaction with schedule). Because our hypothe:

ses did predict that the compressed workweek would notaffect behavioral work-related criteria as positively (i.e.,only absenteeism should be positively affected) as attitudi-nal work-related criteria, this result is not that surprising.

Moderators

Overall, four significant moderator effects were found forflextime work schedules, whereas compressed-workweekeffect sizes exhibited no significant differences acrossmoderators.

Employee Type

In general, flextime work schedules demonstrated posi-tive effects on work-related outcome criteria for generalemployees, whereas they had no effect for professionals andmanagers. This finding is consistent with our hypothesisthat alternative work schedules are unlikely to benefit thosewho already have a high degree of work autonomy. How-ever, because of the small number of studies that includedmanagers, these results need further replications.

Flextime Flexibility

A significant yet counterintuitive finding of the presentanalysis was the diminished effectiveness of highly flexibleflextime programs in comparison to less flexible programs.This finding is consistent, however, with earlier researchwhere it had been found that the gains incurred from ahighly flexible schedule may be offset by the extra controlrequired to monitor the number of hours worked by theemployee (Coltrin & Barendse, 1981). Furthermore, it ispossible that the increased flexibility may have become

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WORK SCHEDULES 509

more of an inconvenience for employees than a benefit. Forinstance, employees may experience negative consequencesfrom flextime when they cannot communicate and/or coop-erate with other employees because they are not at workduring the same time period (Nollen, 1981). In this vein,Ronen stated that "since the total work force is availableonly during core time, problems with scheduling are inher-ent in flextime and can affect communication, supervision,and task performance, especially if tasks are highly inter-dependent" (Ronen, 1981, p. 65). Thus, too much flexibilitymay cause problems for employees who rely on one anotherfor task completion. Furthermore, as pointed out by ananonymous reviewer, when flexibility is very high, individ-ual managers may have more control over how the flextimeschedule is actually implemented. This fact could causevariations between organizations and thus also be responsi-ble for our finding.

It should be pointed out that there are other measures offlexibility (e.g., carryover, supervisor's role, etc.). Becausemost studies did not provide this information, we could nottake them into account as possible moderators. It is plausi-ble that one—or even all—of these other important factorsare confounding our findings with regard to degree offlexibility.

Time Since Intervention

In the flextime regression analysis, when other variableswere controlled for, a significant difference for time sinceschedule intervention was found. This result indicates thatflexible work schedules may have waning effects over time.The decrease in positive outcomes seen with a flextimeintervention is important for human resource practitioners toconsider. As argued earlier, this may be a direct result ofemployees becoming accustomed to the new schedules andeventually accepting them as the norm (Ronen, 1981).

Methodological Rigor

The results of these analyses were somewhat inconsis-tent. That is, methodological rigor only affected flextimeeffect sizes. High-rigor flextime studies exhibited largereffect sizes than their low-rigor counterparts. The fact thathigh-rigor studies led to larger effect sizes for flextimestudies is encouraging in that it helps promote the idea thatwell-developed studies with stringent methodological stan-dards can lead to more precise and, in this case, strongerresults.

Integration With Previous Researchand Practical Implications

The most recent major integrative review of the evidencefor both flextime and compressed workweek schedules iscontained in Pierce et al. (1989).10 By and large, our flex-

time, findings are consistent with this Pierce et al. review,which reported flexible work schedules to have generallypositive effects across all of the criteria we considered (i.e.,productivity, absenteeism, job satisfaction, and satisfactionwith schedule).

However, beyond the Pierce et al. (1989) review, we didfind that these effects varied significantly across criteria andthat positive effects were not found for managers/profes-sionals. Furthermore, we found that these effects differed asa function of time since schedule implementation and de-gree of flexibility. Specifically, the findings that the effectsof flexible interventions seem to decline with time and thattoo much flexibility may actually decrease the positiveeffects of this intervention on work-related criteria providenew evidence that we deem to be important from bothpractical and theoretical standpoints.

For example, our results concerning the degree of flex-time flexibility suggest that human resource practitionersneed to carefully examine the work that is being done byindividuals in their organization to determine the degree ofinterdependence between jobs. Too much flexibility (interms of core hours) for employees with highly interdepen-dent jobs may lead to lesser gains for the company than alow flexibility schedule. Also, our results indicate that hu-man resource practitioners may see a reduction in the initialpositive gains as time goes by after the introduction of aflextime intervention. To gain a better insight into thisprobtem, it seems imperative to link work-related findingswith trajectories of job and life satisfaction, including thoseassociated with nonwork contexts, such as family life. It isalso important to point out that because of the relativelysmall number of studies available the effects of these mod-erators (e.g., time since schedule intervention) need to beinterpreted cautiously. Further research investigating theseeffects is needed.

The compressed-workweek literature is not nearly asextensive as the literature concerning flexible work sched-ules. Regarding compressed workweek schedules, our re-sults also generally support earlier findings (Pierce et al.,1989). However, there are a few important exceptions. Jobsatisfaction was positively affected, and contrary to earlierstudies (Goodale & Aagaard, 1975; Latack & Foster, 1985),we found that this type of schedule did not affect absentee-ism rates.

As mentioned earlier, one of the most significant findingsof the present meta-analysis is the differential effects of thetwo schedule types across the four criterion measures. Thesedifferences demonstrate the need to examine the effects ofinterventions across a range of potential organizational out-

10 This meta-analysis included 15 studies not cited in the orig-inal Pierce et al. (1989) review, including two that were completedafter 1989 (Dalton & Mesch, 1990; Venne, 1993).

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510 BALTES, BRIGGS, HUFF, WRIGHT, AND NEUMAN

come criteria. Past meta-analyses of organizational devel-opmental interventions (e.g., Guzzo, Jette, & Katzell, 1985)have focused on a single class of outcome measure (e.g.,performance or attitudinal) and on broader classes of inter-vention types (e.g., technostructural interventions). Thepresent findings demonstrate the need to examine specificinterventions across a range of outcomes and the develop-ment of detailed intervention profiles. For example, on thebasis of our findings, an organization implementing a com-pressed workweek schedule may consider it a failure if theyare attempting to lower absenteeism. On the other hand, thesame organization may be very satisfied with the introduc-tion of their compressed workweek if their outcome crite-rion is job satisfaction. Thus, implementation of an inter-vention may result in organizational gains depending on theoutcome criterion being considered.

In summary, it is important to note that both flextime andcompressed workweek schedules had primarily positive andno negative effects on work-related criteria. These positivebenefits are consistent with historical changes toward morealternative work schedules, and as such they should easeemployers' worries over the outcomes they will experiencewith the implementation of a flexible or compressed work-week work schedule. Similarly, the findings should be re-assuring to organizations that may view the demand foralternative work schedules as originating from outside oftheir own work-related contexts, such as societal changes indual-career households and work-leisure time expectations.However, the results presented in this study also make itclear that employers and employees are well advised towork together to ensure that alternative work schedulesprovide the most positive benefits to individuals andorganizations.

Limitations and Future Research

Meta-analytic studies can also be used to identify weak-nesses in research and subsequently avenues for furtherresearch. From such a point of view, several limitations ofprior research, as well as the current study, deserve atten-tion. With respect to prior research, the relatively smallnumber of alternative work schedule programs that havebeen formally evaluated distresses us. Because organiza-tions are increasingly using these new work schedules, itseems desirable that more formal evaluations be done sothat future researchers can more accurately assess the ben-efits and/or losses associated with alternative work sched-ules. Finally, these formal evaluations should use a multi-dimensional conceptualization (e.g., carryover or supervisorrole) of the alternative work schedule being investigated.

With respect to the current study, we wish to point out thefollowing. First, on a methodological level, although bothregression models explained a significant amount of vari-ance in their respective effect sizes, they also left a signif-

icant amount of variance unexplained. It is probable thatunknown moderators may be related to the effect sizes inboth the flextime and compressed workweek samples. Forexample, the result that a low degree of flexibility is betterthan a high degree of flexibility may change with theintroduction of other moderator variables, such as car-ryover. Second, because our setting of a 6-month cutoff inthe time since schedule intervention dichotomy was arbi-trarily set to ensure us somewhat equal groups, future re-search should attempt to replicate these findings with amore suitable design and, perhaps more importantly, a the-oretically inspired time scale. Third, because of the smallnumber of effect sizes associated with any specific criterionmeasure, we could not assess whether our moderator vari-ables, such as flextime flexibility, have the same effectsacross all the outcome criteria. Thus, future research shouldattempt to assess moderators at the individual criterionlevel. Fourth, although we attempted to use theoreticalmodels to generate hypotheses, the studies included in themeta-analysis did not allow us to make a direct test of thesetheoretical assumptions. For instance, we hypothesized thatbecause flextime meets employees' needs for autonomy, jobsatisfaction will increase. However, we were not able todirectly test this proposition because available studies didnot measure employees' need for autonomy. Therefore, weencourage future research to use measures that directly tapthe mediating variables that are part of the major theoreticalframeworks in the field. In general, altemative-work-schedule research would benefit from the testing of modelsthat include individual level variables (e.g., need for auton-omy). These variables may help shed further light on out-come differences found in the alternative-work-scheduleliterature. For example, it may be that the reason flextimeinterventions have varying effects on job satisfaction is thatdifferent types of employees have different levels of needfor autonomy. Furthermore, the integration of these individ-ual level variables into alternative-work-schedule researchwould be necessary for any meaningful cross-cultural re-search attempting to discern differences, if any, that theimplementation of alternative work schedules may have indifferent cultures.

There are also issues of theory in other areas of industrialpsychology, such as training, where an increased concernfor the role of multiple contexts of life and historical timehas been observed (Warr, 1994). In this vein, as one eval-uates the effects of alternative work schedules, it is impor-tant to recognize that the effect patterns obtained are contextdependent. Two such context dependencies seem of partic-ular significance. One is the link between work contexts andother contexts of life such as family functioning and leisureactivity. It would seem important in future work on alter-native work schedules to link these contexts more explicitlythan past research has done. The second relevant context ishistorical time. There are major historical changes in aspects

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WORK SCHEDULES 511

of social communication, technology, and work environ-ments, and it has been found that incentives that motivateand satisfy older workers are irrelevant to younger workers(Forteza & Prieto, 1994). Thus, it may be that babyboomers, who grew up with parents who worked in a morestructured work environment, are more affected by theadvent of an alternative work schedule than a later genera-tion of employees who have come to expect such consid-erations on the part of their employer. It seems desirable,therefore, in future studies and meta-analyses, to includesuch comparative and historical dimensions. Moreover, be-cause almost all of the formal evaluations were done quitesome time ago, it is important to conduct more up-to-dateevaluations to determine whether societal changes may havechanged the impact that these interventions may have on amore modern workforce.

In summary, we believe that this meta-analysis providesboth researchers and practitioners with the most accurateassessment of these two alternative work schedules that hasbeen presented to date. Furthermore, the results of thismeta-analysis and their theoretical implications will hope-fully serve as a framework and/or inspiration for futureflextime and compressed workweek research.

References

References marked with an asterisk indicate studies usedin the meta-analysis.

Alexander, E., & Wilkins, R. D. (1982). Performance rating va-lidity: The relationship of objective and subjective measures ofperformance. Group and Organization Studies, 7, 485-496.

*Bohen, H. H., & Viveros-Long, A. (1981). Balancing jobs andfamily life: Do flexible work schedules help? Philadelphia: Tem-ple University Press.

Bullock, R. I, & Svyantek, D. J. (1983). Positive-findings bias inpositive findings research. Academy of Management Proceed-ings, 221-224.

Caldwell, D. F., & O'Reilly, C. A., III. (1990). Measuring person-job fit with a profile-comparison process. Journal of AppliedPsychology, 75, 648-657.

*Calvasina, E. J., & Boxx, W. R. (1975). Efficiency of workers onthe four-day workweek. Academy of Management Journal, 18,604-610.

Chatman, J. A. (1988). Matching people and organizations: Se-lection and socialization in public accounting firms. Unpub-lished doctoral dissertation, University of California, Berkeley.

Coltrin, S. A., & Barendse, B. D. (1981). Is your organization agood candidate for flextime? Personnel Journal, 60, 712-715.

Conway, J. M., & Huffcutt, A. I. (1997). Psychometric propertiesof multisource performance ratings: A meta-analysis of subor-dinate, supervisor, peer, and self-ratings. Human Perfor-mance, 10, 331-360.

*Coston, R. D. (1973). An investigation of the economic effects ofthe four-day workweek on women apparel workers in Georgia.Unpublished doctoral dissertation, University of Arkansas, Lit-tle Rock.

*Dalton, D. R., & Mesch, D. J. (1990). The impact of flexiblescheduling on employee attendance and turnover. Administra-tive Science Quarterly, 35, 370-387.

*Dalton, D. R., & Todor, W. D. (1984). Unanticipated conse-quences of union-management cooperation: An interrupted timeseries analysis. Journal of Applied Behavioral Science, 20,253-264.

Dawis, R. V., England, G. W., & Lofquist, L. H. (1968). A theoryof work adjustment (Rev. ed.). University of Minnesota Studiesin Vocational Rehabilitation XXIII, Bulletin 47.

Dawis, R. V., & Lofquist, L. H. (1984). A psychological theory ofwork adjustment: An individual differences model and its ap-plications. Minneapolis, MN: University of Minnesota Press.

deCarufel, A., & Schaan, J. L. (1990). The impact of compressedworkweeks on police job involvement. Canadian Police Col-lege Journal, 14, 81-97.

Dodd, N. G., & Ganster, D. C. (1996). The interactive effects ofvariety, autonomy, and feedback on attitudes and performance.Journal of Organizational Behavior, 17, 329-347.

*Dunham, R. B., Pierce, J. L., & Castaneda, M. B. (1987). Alter-native work schedules: Two field quasi-experiments. PersonnelPsychology, 40, 215-243.

*Evans, M. G. (1975). A longitudinal analysis of the impact offlexible working hours. Studies in Personnel Psychology, 6,1-10.

Ford, K. J., & Noe, R. A. (1987). Self-assessed training needs: Theeffects of attitudes toward training, managerial level, and func-tion. Personnel Psychology, 40, 39-53.

Forteza, J. A., & Prieto, J. M. (1994). Aging and work behavior. InHL-C. Triandis, M. D. Dunnette, & L. M. Hough (Eds.), Hand-book of industrial & organizational psychology (pp. 447-484).Palo Alto, CA: Consulting Psychologists Press.

Fried, Y. (1991). Meta-analytic comparison of the Job DiagnosticSurvey and Job Characteristics Inventory as correlates of worksatisfaction and performance. Journal of Applied Psychol-ogy, 76, 690-697.

Fried, Y., & Ferris, G. R. (1987). The validity of the job charac-teristics model: A review and meta-analysis. Personnel Psychol-ogy, 40, 287-322.

Gellatly, I. R. (1995). Individual and group determinants of em-ployee absenteeism: Test of a casual model. Journal of Orga-nizational Behavior, 16, 469-485.

Glass, G. V., McGaw, B., & Smith, M. L. (1981). Meta-analysis insocial research. Beverly Hills, CA: Sage.

*Golembiewski, R. T., & Hilles, R. (1977). Drug company work-ers like new schedules. Monthly Labor Review, 2, 65-69.

Golembiewski, R. T., & Proehl, C. (1978). A survey of empiricalliterature on flexible workhours: Character and consequences ofa major innovation. The Academy of Management Review, 3,837-853.

Golembiewski, R. T., & Proehl, C. (1980). Public sector applica-tions of flexible workhours: A review of available experience.Public Administration Review, 40, 72-85.

*Golembiewski, R. T., Yeager, S., & Hilles, R. (1975). Factoranalysis of some flextime effects: Attitudinal and behavioralconsequences of a structural intervention. Academy of Manage-ment Journal, 18, 500-509.

*Goodale, J. G., & Aagaard, A. K. (1975). Factors relating to

This

doc

umen

t is c

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ight

ed b

y th

e A

mer

ican

Psy

chol

ogic

al A

ssoc

iatio

n or

one

of i

ts a

llied

pub

lishe

rs.

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arti

cle

is in

tend

ed so

lely

for t

he p

erso

nal u

se o

f the

indi

vidu

al u

ser a

nd is

not

to b

e di

ssem

inat

ed b

road

ly.

512 BALTES, BRIGGS, HUFF, WRIGHT, AND NEUMAN

varying reactions to the 4-day workweek. Journal of AppliedPsychology, 60, 33-38.

Guzzo, R. A., Jette, R. D., & Katzell, R. A. (1985). The effects ofpsychologically based intervention programs on worker produc-tivity: A meta-analysis. Personnel Psychology, 38, 275-291.

Hackman, J. R., & Oldham, G. R. (1976). Motivation through thedesign of work: Test of a theory. Organizational Behavior andHuman Performance, 16, 250-279.

*Harvey, B. H., & Luthens, F. (1979). Flextime: An empiricalanalysis of its real meaning and impact. MSU Business Top-ics, 27, 31-36.

*Hausser, D. (1980). The impact of flexitime on organizationalfunctions: A field study. Washington, DC: U.S. GovernmentPrinting Office.

Hedges, L. V., & OIkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic Press.

Hewitt Associates LLC. (1995). Work and family benefits providedby major U.S. employers in 1994. New York: Author.

*Hicks, W. D., & Klimoski, R. J. (1981). The impact of flexitimeon employee attitudes. Academy of Management Journal, 24,333-341.

Hochschild, A. R. (1997). The time bind: When work becomeshome and home becomes work. New York: MetropolitanBooks.

*Hodge, B. J., & Tellier, R. D. (1975). Employee reactions to thefour-day week. California Management Review, 18, 25-30.

Hoffmann, C. C., Barry, N. R., & Holden, C. (1991). A compar-ison of validation criteria: Objective versus subjective perfor-mance measures and self versus supervisor ratings. PersonnelPsychology, 44, 601-619.

*Ivancevich, J. M. (1974). Effects of the shorter workweek onselected satisfaction and performance measures. Journal of Ap-plied Psychology, 59, 111-111.

*Ivancevich, J. M., & Lyon, H. L. (1977). The shortened work-week: A field experiment. Journal of Applied Psychology, 62,34-37.

Jamal, M. (1984). Job stress and job performance controversy: Anempirical assessment. Organizational Behavior and HumanPerformance, 33, 1-21.

Jamal, M. (1985). Relationship of job stress to job performance: Astudy of managers and blue-collar workers. Human Relations, 5,409-424.

Johnson, B. T. (1993). DSTAT 1.10: Software for the meta-analyticreview of research literatures. Hillsdale, NJ: Erlbaum.

*Kim, J. S., & Campagna, A. F. (1981). Effects of flexitime onemployee attendance and performance: A field experiment.Academy of Management Journal, 24, 729-741.

*Krausz, M., & Freibach, N. (1983). Effects of flexible workingtime for employed women upon satisfaction and absenteeism.Journal of Occupational Psychology, 56, 155-159.

Latack, J. C., & Foster, L. W. (1985). Implementation of com-pressed work schedules: Participation and job redesign as crit-ical factors for employee acceptance. Personnel Psychology, 38,75-92.

Lee, R. A. (1983). Flexitime and conjugal roles. Journal of Occu-pational Behavior, 4, 297-315.

Light, R. J., Singer, J. D., & Willett, J. B. (1994). The visualpresentation and interpretation of meta-analysis. In H. Cooper &

L. V. Hedges (Eds.), Handbook of research synthesis. NewYork: Sage.

Macan, T. H. (1994). Time management: Test of a process model.Journal of Applied Psychology, 79, 381-391.

*Maklan, D. M. (1977). The four-day workweek: Blue-collar ad-justment to a nonconventional arrangement of work and leisuretime. New York: Praeger.

*McGuire, J. B., & Liro, J. R. (1986). Flexible work schedules,work attitudes, and perceptions of productivity. Public Person-nel Management, 15, 65-73.

*McGuire, J. B., & Liro, J. R. (1987). Absenteeism and flexiblework schedules. Public Personnel Management, 16, 47-59.

Meijman, T. F. (1992). Verkoten van de werkweek en verlangenvan de wekdag [Shortening the workweek and lengthening theworkday]. Amsterdam: Studiecentrum Arbeid en Gezondheid.

*Millard, C. W., Lockwood, D. L., & Luthans, F. (1980). Theimpact of a 4-day workweek on employees. MSU BusinessTopics, 28, 31-37.

*Morgan, F. T. (1977). Your (flex)time may come. PersonnelJournal, 56, 82-85.

*Narayanan, V. K. (1982). Hierarchical level and the impact offlexitime. Industrial Relations, 21, 216-230.

*Narayanan, V. K., & Nath, R. (1984). The influence of groupcohesiveness on some changes induced by flexitime: A quasi-experiment. Journal of Applied Behavioral Science, 20, 265-276.

Nollen, S. D. (1981). The compressed workweek: Is it worth theeffort? Industrial Engineer, 13, 58-64.

*Orpen, C. (1981). Effect of flexible working hours on employeesatisfaction and performance: A field experiment. Journal ofApplied Psychology, 66, 113-115.

Parker, P. A., & Kulik, J. A. (1995). Burnout, self- and supervisor-related performance, and absenteeism among nurses. Journal ofBehavioral Medicine, 18, 581-599.

Pierce, J. L., & Dunham, R. B. (1992). The 12-hour work day: A48-hour, eight-day week. Academy of Management Journal, 35,1086-1098.

Pierce, J. L., & Newstxom, J. W. (1980). Toward a conceptualclarification of employee responses to flexible working hours: Awork adjustment approach. Journal of Management, 6, 117-134.

Pierce, J. L., & Newstrom, J. W. (1983). The design of flexiblework schedules and employee responses: Relationships andprocess. Journal of Occupational Behavior, 4, 247-262.

Pierce, J. L., Newstrom, J. W., Dunham, R. B., & Barber, A. E.(1989). Alternative work schedules. Needham Heights, MA:Allyn and Bacon.

*Ralston, D. A. (1989). The benefits of flextime: Real or imag-ing Journal of Organizational Behavior, 10, 369-373.

Ralston, D. A., Anthony, W. P., & Gustafson, D. J. (1985).Employees may love flextime, but what does it do to theorganization's productivity? Journal of Applied Psychology, 70,272-279.

*Ralston, D. A., & Flanagan, M. F. (1985). The effect of flextimeon absenteeism and turnover for male and female employees.Journal of Vocational Behavior, 26, 206-217.

Roberts, H. E., & Foti, R. J. (1998). Evaluating the interaction

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between self-leadership and work structure in predicting jobsatisfaction. Journal of Business & Psychology, 12, 257-267.

Ronen, S. (1981). Flexible working hours. New York: McGraw-Hill.

Ronen, S. (1984). Alternative work schedules: Selecting, imple-menting, and evaluating. Homewood, IL: Dow Jones-Irwin.

Ronen, S., & Primps, S. B. (1981). The compressed workweek asorganizational change: Behaviors and attitudinal outcomes.Academy of Management Review, 6, 61-74.

Sagie, A. (1998). Employee absenteeism, organizational commit-ment, and job satisfaction: Another look. Journal of VocationalBehavior, 52, 156-171.

*Schein, V. E., Maurer, E. H., & Novak, J. F. (1977). Impact offlexible working hours on flexibility. Journal of Applied Psy-chology, 62, 463-465.

Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses inassessing rater reliability. Psychological Bulletin, 86, 420-428.

Somers, M. J. (1995). Organizational commitment, turnover andabsenteeism: An examination of direct and interaction effects.Journal of Organizational Behavior, 16, 49-58.

Song, R., Daly, B. J., Rudy, E. B., Douglas, S., & Dyer, M. A.(1997). Nurses' job satisfaction, absenteeism, and turnover afterimplementing a special care unit practice model. Research inNursing and Health, 20, 443-452.

Stevens, E. D., & Elsworth, R. (1979). Flexitime in the Australianpublic service: Its effect on nonwork activities. Public Person-nel Management, 8, 196-205.

Stevens, J. (1996). Applied multivariate statistics for the socialsciences. Mahwah, NJ: Erlbaum.

Sullivan, S. E., & Bhagat, R. S. (1992). Organizational stress, jobsatisfaction, and job performance: Where do we go from here?Journal of Management, 18, 353-374.

Terpstra, D. E. (1981). Relationship between methodological rigorand reported outcomes in organizational development evalua-tion research. Journal of Applied Psychology, 66, 541-543.

Thierry, H., & Meijman, T. (1994). Time and behavior at work. InH. C. Triandis, M. D. Dunnette, & L. M. Hough (Eds.), Hand-book of industrial & organizational psychology (pp. 341-414).Palo Alto, CA: Consulting Psychologists Press.

*Venne, R. A. (1993). Alternative worktime arrangements: Thecompressed workweek. Unpublished doctoral dissertation, Uni-versity of Toronto, Toronto, Ontario, Canada.

Warr, P. (1994). Age and employment. In H. C. Triandis, M. D.Dunnette, & L. M. Hough (Eds.), Handbook of industrial &organizational psychology (pp. 485-550). Palo Alto, CA: Con-sulting Psychologists Press.

*Welch, J. R., & Gordon, D. (1980). Assessing the impact offlexitime on productivity. MSU Business Horizons, 23, 61-65.

Received March 13, 1998Revision received September 23, 1998

Accepted September 28, 1998 •

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