The Academy of Management Journal, Vol. 36, No. 5. (Oct., 1993), pp

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1 Organizational Commitment and Turnover: A Meta-Analysis Aaron Cohen The Academy of Management Journal, Vol. 36, No. 5. (Oct., 1993), pp The Academy of Management Journal is currently published by Academy of Management. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is an independent not-for-profit organization dedicated to and preserving a digital archive of scholarly journals. For more information regarding JSTOR, please contact support@jstor.org. Mon May 7 05:26:

2 " Academy of Manageinent journal 1993,Vol. 36, No. 5, ORGANIZATIONAL COMMITMENT AND TURNOVER: A META-ANALY SIS AARON COHEN University of Haifa This meta-analysis examines whether differences in the lengths of time elapsed between the measurement of organizational commitment and departure interact with the career stages of employees in moderating the commitment-turnover relationship. For younger employees, the shorter the time separating the measurement of organizational commitment and the occurrence of departure, the stronger the correlations. The findings also show that the commitment measure used strongly affects the magnitude of the relationship. The findings are discussed in terms of their implications for future research. Most attention given to the concept of organizational commitment results from its relationship with turnover. By definition, highly committed employees wish to remain with their employing organizations (Mowday, Porter, & Steers, 1982). However, quantitative summaries of findings (Mathieu & Zajac, 1990; Randall, 1990), have demonstrated that the relationships between organizational commitment and turnover have produced few large correlations. One explanation for the low commitment-turnover correlations is that other variables probably moderate this relationship (Mathieu & Zajac, 1990), but little research has addressed that issue. Wiener and Vardi (1980) found that type of occupation had some effect on the commitmentturnover relationship. Werbel and Gould (1984) revealed an inverse relationship between organizational commitment and turnover for nurses employed more than one year, but my previous work (Cohen, 1991) has indicated that this relationship was stronger for employees in their early career stages (i.e., up to 30 years old) than those in later career stages. In two studies (Mathieu & Zajac, 1990; Randall, 19901, the findings showed that methodological factors, such as research design, operational definition of organizational commitment, sample selection, and observation technique, failed to account for a large proportion of the variance in the commitment-work outcomes relationship. The purpose of this meta-analysis was to examine the moderating effect of the interval between the measurement of an individual's organizational commitment and the occurrence of organizational departure upon the rela- Thanks to Brian Prat, Catherine Kirchmeyer, and the three anonymous reviewers for this journal for their helpful comments and suggestions. I would also like to thank Betty Siegler, Suzanne Kiely, and Marion Lupu for their editorial assistance.

3 1993 Cohen 1141 tionship between those variables. The expectation that the relationship would vary over different time spans was based on the argument that the shorter the time separating the measurement of an intention and the occurrence of a criterion behavior, the more accurate will be the prediction (Price & Mueller, 1981). The time variable is part of the criterion problem to the extent that different studies have measured turnover over different lengths of time. Mobley, Griffeth, Hand, and Meglino (1979) argued that the effect of differences in the lengths of time between the measurement of independent variables and organizational departure appeared to be a topic in need of additional research. Controlling the commitment-turnover relationship by the time variable might show that this relationship is stronger than has previously been found and might clarify the importance of organizational commitment as a predictor of turnover. THEORETICAL BACKGROUND AND HYPOTHESES Porter, Crampon, and Smith (1976), who compared stayers and leavers at three time points, each of which was prior to a set of leavers terminating, found that leavers who were a month and a half or less away from actually terminating reported significantly less commitment than stayers. When leavers were 2 to 3.5 months from actually terminating, they showed less commitment than equivalent stayers, but the difference was not significant. If eventual leavers were at least 6 months away from terminating, their commitment was almost the same as that of equivalent stayers. The findings of Porter and colleagues (1976) indicate that even though organizational commitment appears to be "somewhat more stable over time than job satisfaction" (Mowday et al., 1982: 28), decline in this attitude can be very rapid. If, for example, two employees report high levels of commitment at a given time, the prediction for both of them is that they will remain in their employing organization. If turnover data were collected one year later, and if decline in the level of organizational commitment occurred for one of them 2 months after its measurement and resulted in his or her leaving the organization, this observation would contradict the prediction and reduce its accuracy. The decline in organizational commitment would not have been captured because of the long interval between the measurement of the attitude and the collection of the turnover data. The longer this interval, the higher the probability that additional events in an organization will cause more employees to change their level of organizational commitment, which may increase the errors in prediction and lower the commitment-turnover relationship. Hypothesis 1: The relationship between organizational commitment and turnover will be stronger the shorter the time elapsed between the measurement of the two variables. What may determine the magnitude of the effect of time upon the commitment-turnover relationship is (1)the probability that a decline in organ-

4 1142 Academy of Management Journal October izational commitment will occur, and (2) the rapidity and intensity of this decline. If the probability of a decline in employees' organizational commitment is high and the process from decline to their leaving an organization is rapid, more employees who reported high levels of commitment at the time of an attitude survey and who were expected to remain in the organization might leave before turnover data could be collected; this set of circumstances would reduce the accuracy of prediction. In undertaking this research, I expected the probability and intensity of decline to differ across career stages. Early in individuals' careers, their levels of organizational commitment vary because of their differing propensities to become committed to an employing organization (Mowday et al., 1982); commitment level could also depend on an individual's opportunities and the availability of attractive alternatives (Mowday, Koberg, & McArthur, 1984). The effect of time upon the commitment-turnover relationship will be strong for these employees: the instability in their organizational commitment will result for many of them in rapid changes in its level over relatively short time spans. These changes will not be captured over long time intervals between the measurement of commitment and the collection of turnover data and will cause errors in turnover predictions based on commitment levels at the times of the surveys. Consequently, the shorter the measurement interval, the more accurate the prediction will be. Employees in later career stages are more oriented than new employees to settling down in an organization and are less willing to relocate or to leave the organization to achieve a promotion. For them, organizational commitment levels are more stable because of structural and behavioral bonds with the organization, a stronger need for stability, and increased difficulties in finding opportunities elsewhere (Levinson, Darrow, Klein, Levinson, & Mc- Kee, 1978; Super, 1957). These considerations will also increase the length of time for their job search and will result in a slow process from decline in commitment to actual departure. For such employees, fewer drastic changes of the kind that may cause errors in prediction are expected, and the measurement interval will not affect this relationship for them as it does for those in an early career stage. Hypothesis 2: For new employees, the relationship between organizational commitment and turnover will be stronger the shorter the interval between the measurement of commitment and the collection of turnover data. For employees in later career stages, the measurement interval will not affect the magnitude of the relationship. METHODS Studies that contained correlational data dealing with the relationship between organizational commitment and turnover published between 1967 and 1991 were identified by means of both manual and computer-assisted searches of the social science, psychology, and management literatures. The

5 1993 Cohen 1143 computer search scanned two data bases, psycinfo and ABIIINFORM. The manual search was based on scanning the reference lists of the two previous meta-analyses concerned with the focal relationship (Mathieu & Zajac, 1990; Randall, 1990) and on an article-by-article search performed for 24 organizational behavior, psychological, and sociological journals. Table 1lists the 34 studies found, which represented 36 independent samples. This study used the Hunter and Schmidt (1990) meta-analytic procedure, which consists of three basic steps: estimation of the mean correlation and variance for a population; correction for statistical artifacts; and analysis of moderating effects. The statistical artifacts controlled for in this study were sampling error and predictor unreliability. For two samples, the reliability of organizational commitment was not reported, and I therefore used the average reliability across all samples that reported information. Recent research on turnover (Kemery, Dunlap, & Griffeth, 1988; Steel, Shane, & Griffeth, 1990; Williams, 1990) has dealt with the question of whether and how turnover correlations should be adjusted for inopportune splits in the dichotomous variable whenever turnover deviates from 50 percent. Williams (1990) argued that analysts should correct for artifactual dichotomization when a dichotomous variable is assumed to represent a continuous theoretical construct, such as employee tenure. However, "unlike with tenure, there is a perfect correspondence between the dichotomous turnover construct and the dichotomous operational measure of turnover" (Williams, 1990: 732). Therefore, turnover correlations need not be corrected for dichotomization but for unequal sample sizes, a correction allowing estimation of what the maximum correlation would be if there had been a split in the distribution of the dichotomous variable. On the basis of empirical comparison of three correction formulas, Williams concluded that TABLE 1 Studies Used in the Meta-analysis Abelson 1983 Marsh & Mannari 1977 Arnold & Feldman 1982 Mayes & Ganster 1988 Blau 1989 Michaels & Spector 1982 Blau & Boa Miller et al Blegen et al Mowday et al Clegg 1983 Mowday et al Colarelli et al O'Reilly & Caldwell 1981 Decotiis & Summers 1987 O'Reilly & Chatman 1986 Ferris & Aranya 1983 Parasuraman 1982 Fisher 1985 Parasuraman & Alutto 1984 Hollenbeck 1989 Pierce & Dunham 1987 Hollenbeck & Williams 1986 Porter et al Hom & Hulin 1981 Porter et al Hom et al Steers 1977 Husfield & Day 1991 Stumpf & Hartman 1984 Kinicki et al Werbel & Gould 1984 Lee & Mowday 1987 Wunder et al. 1982

6 1144 Academy of Management Journal October correction for unequal samples is best accomplished by using the following formula offered by Kemery and colleagues (1988): corrected r =.7978 x r x (pq)'lzlh, where r is the observed correlation between organizational commitment, the continuous variable, and turnover, the truly dichotomous variable; p is the proportion of cases in either dichotomous category; q is (1- p); and h is the ordinate of the unit normal distribution at p. Information for proportion of leavers was available in 31 out of the 36 samples studied here. In the remaining five samples, I corrected the correlations by using the mean of the proportion of leavers in the 31 samples as the value for p. To define the time frames of turnover measurement, I divided the Samples studied into those in which turnover data were collected up to and including six months after organizational commitment was measured and those that reported collecting turnover data more than six months after the measurement of commitment. This categorization is based on the findings of Porter and colleagues (1976) reported earlier. For 3 of the 36 samples, there was no report of the interval of concern. There is ambiguity and inconsistency in the literature regarding the use of age, tenure, or both as career indicators because different measurements of career stage result in different patterns of affective reactions (Morrow & McElroy, 1987). In addition, in previous research (Cohen, 1991), I found a strong commitment-turnover relationship when age but not tenure was the career stage moderator and concluded that further research was needed to explore the differences between the two career indicators. The solution used in this study was to test both operational definitions. If the moderating effect of career stage differs according to which of the two indicators is used, it can be argued that age and tenure represent different moderation processes. To study the first indicator, employee age, I divided the samples into two subgroups based on the mean age of the employees they represented (1) up to and including 30 years and (2) over 30 years. As in previous operational definitions of time frame based on age (Rush, Peacock, & Milkovich, 1980; Slocum & Cron, 1985), the first subgroup represented the early career stage, and the second, the middle and the later stages. The second indicator, organizational tenure, was also divided into two subgroups: (1)up to two years and (2) two years or more. Again, as in previous definitions of time frame based on tenure (Gould & Hawkins, 1978; Stumpf & Rabinowitz, 1981), the first subgroup represents the early career stage, and the second, the middle and later stages. I defined career stage using two subgroups because most often the differences across age and stage categories were between individuals beginning their early careers and individuals in all later stages (Cohen, 1991; Gould & Hawkins, 1978; Stumpf & Rabinowitz, 1981). Information about the mean age of respondents was provided in 24 of the 36 samples, and information on tenure in 26. The other samples were treated as missing values for the analyses. Controlling the moderating effects of different measures of organizational commitment allowed for a comprehensive control of an important aspect of the commitment-turnover relationship and also for comparisons with two

7 1993 Cohen 1145 previous meta-analyses (Mathieu & Zajac, 1990; Randall, 1990). Those two meta-analyses compared results based on the Organizational Commitment Questionnaire (OCQ) of Porter, Steers, Mowday, and Boulian (1974) to results based on other, non-ocq attitudinal measures (Randall, 1990) or to results based on the continuance commitment measure developed by Ritzer and Trice (1969); it is known as the side-bet measure because it is conceptually based on the side-bet theory approach proposed by Becker (1969). However, one consideration not taken into account in the two previous meta-analyses was that the OCQ has two versions. O'Reilly and Chatman (1986) and Reichers (1985) criticized the original 15-item version because of the overlap of some items with the concept they are supposed to predict, turnover. This criticism has led some researchers to use a shorter version of the OCQ that omits the problematic items. This study therefore contains analyses (1)comparing the full and the shorter version of the OCQ and (2) comparing the side-bet measure to the non-ocq attitudinal measures. Both the full OCQ and the side-bet measure include items that refer to turnover (Reichers, 1985), and they will be compared to the short version of the OCQ and the non-ocq measures that do not include these items. The procedure of Hunter and Schmidt (1990: Chapter 9) for detecting a moderator among binary variables was applied in this study. If a metaanalysis is based on only a small number of studies, there will be an error in the estimates of means and standard deviations that they termed secondorder sampling error. They asserted that the observed difference between the means of the subgroup correlations may be due to this error and argued that the range of potential sampling error in each subgroup of studies can be estimated by computing a confidence interval for the mean correlation in each subgroup. They proposed a way to measure the extent of a confidence interval by computing a significance test on the difference between the two mean correlations. The formula for the significance test is: z = CI-, where C is the difference between the correlations and var (c) is SI2 + SZ2, where S12 is the variance of observed effect sizes divided by the number of samples for the specific subgroup and S,' is the same as S12 for its subgroup. Williams (1990) argued that conclusions regarding turnover relationships are valid and generalizable within particular combinations of conditions, rather than to any given correction of turnover correlations. One way of controlling for situational conditions is through the hierarchical breakdown of moderators in meta-analysis. This technique begins by grouping studies on the basis of one moderator variable, and then on the basis of the second, and so forth (Hunter & Schmidt, 1990; Williams, 1990). Studies within each cell are then meta-analyzed, and the pattern of effect sizes across cells examined. The interaction effect was examined here by dividing the total data set into four groups. Group 1included samples in which turnover data were collected six months or less after organizational commitment data and the individuals studied were in the early career stage, indicated by average ages up to and including 30 years or average tenures up to and

8 1146 Academy of Management Journal October including 2 years. Group 2 included samples in which turnover data were collected more than six months after commitment data and the individuals studied were again in the early career stage. Group 3 included samples in which turnover data were collected six months or less after organizational commitment data and the individuals studied were in later career stages, and group 4 represented a data collection interval of more than six months and late-career-stage individuals. Meta-analysis was conducted in each cell. To control for the type of commitment measurement used, I conducted additional meta-analyses in each cell. Two separate hierarchical analyses were performed: in the first, career stage was measured by age, and in the second, by tenure. Although meta-analysis serves to illustrate the unique impact of each moderator of concern, it is difficult to assess a moderator's relative importance with the method. Thus, I performed a multiple regression analysis with each of the moderators regressed on the organizational commitmentturnover correlation coefficients (Randall, 1990). Hunter and Schmidt (1990) criticized the regression approach because of the high probability of "capitalization by chance." As a researcher sorts through potential moderator variables, some study characteristics may have a high chance correlation with the sampling error. This variable then looks like a strong moderator. I therefore used the regression approach as a supplementary method for analyzing the data. In order to maintain consistency with the meta-analytic design that would allow comparison of the results of the two procedures, I analyzed time interval and career stage as dummy variables. In response to the argument about measuring age and tenure as ranges or as chronological indicators (Bedeian, Ferris, & Kacmar, 1992), I performed an additional regression analysis analyzing age and tenure as continuous variables. Commitment-turnover correlations were regressed onto time interval, career stage, and finally an interaction of age and tenure with time interval. A significant interaction would support the research hypothesis. I entered the type of organizational commitment measurement first, as a dummy and control variable. Finally, two separate regression analyses were performed: in the first, the career stage was operationally defined by age, and in the second, by tenure. RESULTS Table 2 shows results for two main effect analyses: the first included all 36 samples, and the second only the 33 samples in which information for the interval between the measurement of commitment and organizational departure was available. The moderator analysis controlled for the time frame of turnover measurement, career stage, and the measures of organizational commitment used. As Hypothesis 1 predicts, the commitment-turnover relationship is stronger when the interval between the measurement of commitment and organizational departure is short. It is significantly stronger when the inter-

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10 1148 Academy of Management Journal October val is six months or less (r = -.35) than when it is more than six months (r = -.22). The relationship is significantly stronger in the early career stage (r = -.35) than in the later stages (r = -.23) when age is the career stage indicator. There is no significant difference between the two career stages when tenure is the indicator. Finally, results also show a significantly stronger relationship between commitment and turnover when the former is measured by the full 15-item version of the OCQ (r = -.33) than by the shorter version (r = -.19). Also, the relationship was significantly stronger when the side-bet measure is used (r = -.28) than when other attitudinal non- OCQ measures are used (r = -.16). The moderator analysis shows a considerable increase in the percentage of variance across samples attributed to statistical artifacts in most of the subgroups. Results of the hierarchical moderator analysis, shown in Table 3, partly support Hypothesis 2 when age is the career stage indicator. The commitment-turnover relationship is the strongest for the shorter interval between the measurement of the two variables among employees in the early career stage (r = -,479. For the longer interval, the relationship is lower not only among employees in the later career stages but also among employees in the early stage. The Hunter and Schmidt test (1990) reveals significant differences between group 1and groups 2 (z = 2.33, p <.05) and 4 (z = 2.97, p <.05). The difference in the correlations between groups 3 and 4 was unexpected. Hypothesis 2 did not anticipate any effect of the time of measurement upon employees in the late career stages, but results show a low correlation in the shorter interval (r = -.05) and a higher correlation in the longer interval (r = -.22). It is important to note that the correlation in group 3 is based upon one sample; this result should therefore be interpreted cautiously. Although a large amount of variance remains unaccounted for by statistical artifacts when the samples are divided into the four groups, controlling each for organizational commitment measurement eliminates the unexplained variance, demonstrating strong homogeneity in the groups. Strong support for this homogeneity emerged in the results of the chi-square tests. In all four groups, the results of that test were not significant when commitment measures were controlled, showing a lack of support for the hypothesis of heterogeneity across sample correlations. Results do not support Hypothesis 2 when tenure is the career stage indicator (Table 4). The commitment-turnover relationship is the strongest in the shorter interval between the measurement of the two variables, but among employees in the later career stages (r = -.44), not among those in the early career stage (r = -.22), as expected. In the longer intervals, the relationship between organizational commitment and turnover is lower among employees in both the early (r = -.22) and later stages (r = -.25). The Hunter and Schmidt test (1990) reveals significant differences only between group 3 and groups 1 (z = 1.75,p <.05), 2 (z = 1.78,p <.05), and 4 (z = 1.70, p <.05). Controlling each of the subgroups for measurement type eliminates much of the unexplained variance, demonstrating strong homogeneity in the groups. In addition, in all the groups except one (group

11 TABLE 3 Meta-analytic Results of Hierarchical Moderator Analysis for Agea Variance Attributable Confidence Observed Residual to Statistical variablesb k N r r, Interval Variance Variance Artifacts Y Z Age, total 22 4, to.03,0170, % 96.29*** Group 1, total to -.I6,0271, *** 15-item OCQ to.48,0044, item OCQ to -.I2,0121, % Group 2 total to. I 0,0107, * 15-item OCQ , item OCQ I4 Group 3, total Group 4, total 12 3, to. I 2,0067, ** 15-item OCQ 3 1, to -.29, item OCQ 4 1,074. I 6 -.I * Non-OCQ measures I4 -.I6, Side-bet measure % " k = the number of samples in each analysis; N = the total number of individuals in the k samples; r = the mean weighted uncorrected correlation; and r, = the mean weighted correlation corrected for attenuation. The confidence interval is 95 percent for r,; the observed variance is the variance of the uncorrected correlations, and the residual variance is the latter corrected for statistical artifacts. Group 1:six months or less in turnover measurement, and 30 years or less in age or 2 years or less in tenure; Group 2: more than six months in turnover measurement, and 30 years or less in age or 2 years or less in tenure; Group 3: six months or less in turnover measurement, and more than 30 years in age or 2 years in tenure; Group 4: more than six months in turnover measurement and more than 30 years in age or 2 years in tenure. * p <.05 ** p <.O1 *** p <,001

12 TABLE 4 Meta-analytic Results of Hierarchical Moderator Analysis for Tenurea Variance Attributable Confidence Observed Residual to Statistical variablesb k N r r, Interval Variance Variance Artifacts yz Z Tenure, total Group 1,total 15-item OCQ 9-item OCQ Group 2, total 15-item OCQ 9-item OCQ Group 3, total 15-item OCQ 9-item OCQ Group 4, total 15-item OCQ 9-item OCQ Non-OCQ measures Side-bet measure " k = the number of samples in each analysis; N = the total number of individuals in the k samples; r = the mean weighted uncorrected correlation; and r, = the mean weighted correlation corrected for attenuation. The confidence interval is 95 percent for r,; the observed variance is the variance of the uncorrected correlations, and the residual variance is the latter corrected for statistical artifacts. "Group 1: six months or less in turnover measurement, and 30 years or less in age or 2 years or less in tenure; Group 2: more than six months in turnover measurement, and 30 years or less in age or 2 years or less in tenure; Group 3: six months or less in turnover measurement, and more than 30 years in age or 2 years in tenure; Group 4: more than six months in turnover measurement and more than 30 years in age or 2 years in tenure. * p <.05 ** p <.O1 *** p <.001

13 1993 Cohen , $item OCQ), results of the chi-square test were not significant when commitment measures were controlled. When age is the career indicator, regression results strongly support the interaction effect and confirm the results of the hierarchical meta-analysis (Table 5, model 4). The interaction effect is significant when career stage is defined as a dummy variable and as chronological age. Organizational commitment measures strongly affected the correlations. The measures explained 56 percent of the variance, indicating a strong effect. However, the age-time interaction contributed additionally and significantly to the explained variance, raising it to 70 percent. The interaction effect expected in Hypothesis 2 is not supported when tenure is the career indicator. Another difference is that in the regression equation for age, the time to turnover measurement has a significant effect when entered (model 2), but in the equation for tenure, tenure as career indicator has a significant effect (model 3), and the time to turnover measurement does not. It should be noted that when tenure was analyzed as chronological, the final equation (model 4) revealed a significant, positive effect of time to turnover that replaced the significant effect of tenure in model 3. This is the only difference found between the analysis of age and tenure as dummy rather than chronological variables. DISCUSSION The purpose of this study was to examine how the time elapsed between measurement of the variables can moderate the organizational commitmentturnover relationship. The main hypothesis, predicting that time span would have a differing effect across career stages, was supported when age served as the career stage indicator but was not when tenure did. Results of the regression analysis generally supported the meta-analytic findings while explaining considerable variance in the commitment-turnover correlations. The main conclusion that can be drawn is that both methodological and theoretical moderators strongly affect that relationship. Future research should therefore focus on examining these moderators before any conclusion can be reached regarding the value of organizational commitment as an attitude and as a research topic. In that regard, this study proposes a potential future research agenda. Age and Tenure as Career Stage Indicators The findings supported the research hypotheses when age was the career indicator. For younger employees, a prediction problem arises mainly because employees who report high levels of commitment may have a sharp decline shortly after a survey; this results in turnover and prediction errors that can cause a low organizational commitment-turnover relationship. Since changes in commitment occur quite often among new employees, one way to increase the accuracy of the prediction at the early stage is to measure the attitude frequently, for instance, at two-month intervals. For older em-

14 Academy of Management Journal TABLE 5 Results of Regression ~ n a l ~ s e s ~ ' ~ Model 1 Model 2 Model 3 Model 4 Variables 6 t P t P t P t Regression for age Commitment measures 9-item ocq -.7gf -5.02*** *** *** *** Non-OCQmeasures *** ** * * Side-bet measure I I I Interval to turnover measurementc * Aged OO Interval X age * RZ, dichotomous age (adjusted RZ)".63 [.56).70 [.63).72 [.63).79 (.70) RZ, continuous age [adjusted RZ).63 (.56).70 f.63).74 [.66).81 [.74) F. dichotomous age 10.06** 10.08*** 8.21*** 9.26** * F, continuous age 10.06** 10.08*** 9.15*** 10.85*** ARZ, dichotomous age ARZ, continuous age F for hr2, dichotomous age 4.41* * F for hr2, continuous age 4.41* " Regression for tenure Commitment measures 9-item OCQ -.61f -3.36** ** *** *** Non-OCQ measures * * ** ** Side-bet measure -.I I I Interval to turnover measurementc Tenure" * Interval X tenure RZ, dichotomous tenure [adjusted RZ)".40 [.31).45 j.33).58 (.47).60 (.47) RZ, continuous tenure (adjusted RZ).40 [.31).45 [.33).57 [.45).62 j.49) F, dichotomous tenure 4.72* 4.02* 5.30** 4.51% F, continuous tenure 4.72* 4.02* 4.94** 4.91** hrz,dichotomous tenure AR2, continuous tenure F for ARz. dichotomous tenure *.80 F for hr2, continuous tenure ,22* 2.64 " N = 22 for equations for age; h' = 25 for equations for tenure. "The negative signs of the commitment-turnover correlations were omitted, '0 = more than six months, 1 = six months or less. " 0 = more than 30 years for age and 2 pears for tenure, 1 = 30 years or less for age and 2 years or less for tenure. " Results are based on listwise deletion of missing values. 'The standardized regression coefficients are presented only for the analysis of age and tenure as dichotomous variables, The coefficients for the analysis of age and tenure as continuous are available from author. * p <.05 ** p <.01 *** p <,001

15 1993 Cohen 1153 ployees, errors in prediction occur more because employees report low levels of organizational commitment but may not leave their organization because of structural bonds, few employment alternatives, and a desire for stability. For such employees, predictions may be more accurate the longer the time interval between the measurement of the two variables, because it will cover the slow process from the decline of their organizational commitment to their departure decision and the longer time they need to seek and find alternative employment. Therefore, although for new employees frequent measurement of organizational commitment should increase the accuracy of prediction, for employees in later career stages, frequent collection of turnover data should increase that accuracy. Results do not support the research hypotheses when tenure is the career indicator. A proposed explanation is that tenure cannot be interpretable in the first years of employment because at this stage the commitmentturnover relationship is largely a reflection of affect triggered by unrealistic and inflated job previews and new employees' need to justify their choice among employment alternatives (Werbel & Gould, 1984). People need experience in an organization to develop a realistic evaluation of their relationship with it. This pattern may explain the lack of difference between the tenure groups in the meta-analysis (Table 2) and the lack of interaction between tenure and time spans in the regression results. The hierarchical moderator analysis, in which time lag has an effect only for employees with more than two years of experience (Table 4, group 3), also supports this explanation. In addition, the fact that the regression equation for tenure reveals a significant effect of time to organizational departure (Table 5, model 4) only when tenure is analyzed as a continuous variable suggests that tenure is better represented as such rather than as a dichotomous variable. The findings of this research support previous findings and arguments that age and tenure as career stage indicators represent different processes (Bedeian et al. 1992; Cohen, 1991; Morrow & McElroy, 1987; Werbel & Gould, 1984). Bedeian and colleagues (1992) argued that age and tenure are theoretically interesting variables whose relation to outcomes of interest and importance has been neglected. They suggested that theoretical and methodological advances concerning these two variables should prove fruitful scientifically and practically. More work is required, however, before more definite conclusions can be reached regarding the effects of age and tenure, Some Methodological Implications Several methodological implications of this research are worth noting. First, the effect of the type of measure of organizational commitment used is very strong. Such a strong effect did not emerge in previous meta-analyses (Mathieu & Zajac, 1990; Randall, 1990), probably because they did not control for the two different versions of the OCQ, as did this study. It is apparent that turnover's relationship with the full version of the OCQ is stronger than

16 1154 Academy of Management Journal October that with the shorter instrument, because the first measure includes items that refer to turnover. However, the problem is not whether the full or the short version of the OCQ should be applied. There is a need for a theoretical and conceptual reexamination of the definition and theory behind the measures of organizational commitment. Only after such a reexamination could a conclusion be reached as to whether the OCQ is the appropriate instrument for measuring this attitude and which of its versions, if any, should be applied in future research. At present, research regarding the focal relationship cannot be interpreted or conducted without considering the effect of the measurement of commitment upon findings. Second, the findings of this study showed the value of hierarchical moderator analysis, which has been used very rarely in the literature. Williams (1990: 736) argued that "hierarchical moderator analysis can provide a much richer means of understanding why turnover results vary from study to study." This study shows how theory-based systematic breakdowns of moderators can detect the conditions that affect the commitment-turnover relationship. Third, the study also shows the value of using various methods of study aggregation for testing the same hypothesis. Meta-analysis is a much more sensitive procedure than regression analysis because it can uncover and illustrate the unique impact of each moderator upon a relationship. This sensitivity was demonstrated in particular in the stronger relationship found for group 3, representing samples in which the measurement interval was six months or less and individuals had more than two years in tenure, which cannot be captured in regression analysis. The regression analysis can tell more about the relative importance of each of the moderators; the importance of the organizational commitment measurement was shown in this study, for instance. Regression analysis, despite its limitations (Hunter & Schmidt, 1990) can supplement meta-analytic findings. Finally, several limitations of the study should be mentioned. First, the results of the hierarchical moderator analysis are based on parts of the total sample, 22 and 25 out of 36 samples, because of missing values, especially for the career stage variables. Hunter and Schmidt (1990) argued that if the number of studies in the cells of a full hierarchical analysis is small, firmer conclusions must await the accumulation of more studies. Second, organizational commitment is not the only predictor of turnover. Mediators such as withdrawal cognition, or intention to leave an organization, and perceived alternative employment (Mowday, Koberg, & McArthur, 1984) that were not included in this study also affect the commitment-turnover relationship. However, the findings of this study show that organizational commitment and turnover are both dynamic concepts. Examining them by static linear statistical manipulations at two points of time cannot give an accurate estimation of their relationship. More empirical research is needed to validate the results and conclusions of this study. Longitudinal research designs that will measure the focal variables at more than one point of time are essential for clarifying their relationship. Some of the arguments and findings of this study may well be a good starting point for such research.

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20 LINKED CITATIONS - Page 1 of 3 - You have printed the following article: Organizational Commitment and Turnover: A Meta-Analysis Aaron Cohen The Academy of Management Journal, Vol. 36, No. 5. (Oct., 1993), pp This article references the following linked citations. If you are trying to access articles from an off-campus location, you may be required to first logon via your library web site to access JSTOR. Please visit your library's website or contact a librarian to learn about options for remote access to JSTOR. References Conceptualizing How Job Involvement and Organizational Commitment Affect Turnover and Absenteeism Gary J. Blau; Kimberly B. Boal The Academy of Management Review, Vol. 12, No. 2. (Apr., 1987), pp Organizational Career Stage as a Moderator of the Satisfaction-Performance Relationship Sam Gould; Brian L. Hawkins The Academy of Management Journal, Vol. 21, No. 3. (Sep., 1978), pp Voluntarily Leaving an Organization: An Empirical Investigation of Steers and Mowday's Model of Turnover Thomas W. Lee; Richard T. Mowday The Academy of Management Journal, Vol. 30, No. 4. (Dec., 1987), pp