A meta-analysis of 111 samples from 93 studies estimated the correlation between attitudinal organizational commitment and job performance to be 0.20. The correlation was stronger for extra-role performance compared to in-role performance, for white-collar workers compared to blue-collar workers, and for self-rated performance compared to supervisor ratings or objective measures. Moderators like commitment measure, job level, age, and tenure did not significantly impact the correlation.

1. Journal of Organizational Behavior
J. Organiz. Behav. 23, 257–266 (2002)
Published online 1 March 2002 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/job.141
Attitudinal organizational commitment
and job performance: a meta-analysis
MICHAEL RIKETTA*
University of Mannheim, Germany
Summary A meta-analysis was conducted to estimate the true correlation between attitudinal organiza-
tional commitment and job performance and to identify moderators of this correlation. One-
hundred and eleven samples from 93 published studies were included. The corrected mean
correlation was 0.20. The correlation was at least marginally significantly stronger for: (a)
extra-role performance as opposed to in-role performance; (b) white-collar workers as
opposed to blue-collar workers; and (c) performance assessed by self ratings as opposed to
supervisor ratings or objective indicators. Four other assumed moderators (commitment mea-
sure: Affective Commitment Scale versus Organizational Commitment Questionnaire, job
level, age, and tenure) did not have at least marginally significant effects. Copyright #
2002 John Wiley & Sons, Ltd.
Introduction
According to its most often cited definition, attitudinal (or affective) organizational commitment
(AOC) is ‘the relative strength of an individual’s identification with and involvement in a particular
organization’ (Mowday, Steers, & Porter, 1979, p. 226). This variable is one of the most often studied
variables in organizational behavior research (for recent reviews see Mathieu & Zajac, 1990; Meyer &
Allen, 1997). Probably the main reason for the extensive and long-lasting research interest in AOC is
that it is assumed to influence almost any behavior that is beneficial to the organization such as per-
formance, attendance, and staying with the organization (see Mathieu & Zajac, 1990; Meyer & Allen,
1997; Mowday, Porter, & Steers, 1982; Randall, 1990). The present study focuses on the relationship
between AOC and performance in particular.
The assumption that employees who feel attached to and identify with their organization work
harder, is a popular one and may provide the rationale for many organizational attempts to foster
employees’ organizational commitment or identification. Given its popularity, an empirical test of this
assumption is urgent. A prerequisite for a causal influence of AOC on performance is that both
variables are correlated. However, previous quantitative reviews suggest that the AOC–performance
correlation is moderate at best (Allen & Meyer, 1996; Cohen, 1991; Mathieu & Zajac, 1990; Mowday
et al., 1982; Organ & Ryan, 1995; Randall, 1990). For example, Mathieu and Zajac (1990) reported an
* Correspondence to: Michael Riketta, Department of Social Psychology, University of Mannheim, 68131 Mannheim, Germany.
E-mail: mriketta@psychologie.uni-mannheim.de
Received 6 March 2001
Revised 12 September 2001
Copyright # 2002 John Wiley & Sons, Ltd. Accepted 22 January 2002

2. 258 M. RIKETTA
estimated true AOC–performance correlation of r ¼ 0.13 (k ¼ 8) and Randall (1990) and Cohen (1991)
reported estimated true correlations between organizational commitment (affective as well as calcu-
lative) and performance of r ¼ 0.21 (k ¼ 7) and 0.13 (k ¼ 14), respectively.
Although these correlations may appear disappointingly low, their relevance to the AOC–
performance relationship is limited due to a number of shortcomings of the mentioned reviews. First,
all of these reviews used only few samples reporting an AOC–performance correlation (ks 14). This
is only a small part of the relevant empirical research that is available today.
Second, all of the mentioned reviews used either too restricted or too comprehensive samples of stu-
dies. In particular, Randall (1990) and Cohen (1991) did not distinguish between AOC and other forms
of organizational commitment (normative and calculative) in their analyses pertaining to performance.
Given the conceptual and empirical differences between these three commitment types (see Allen &
Meyer, 1996; Mathieu & Zajac, 1991; Randall, 1990), it may be that Randall’s and Cohen’s results
would not replicate for AOC in particular. Allen and Meyer (1996), Mathieu and Zajac (1990), and
Organ and Ryan (1995) did focus on AOC but considered only studies that, respectively, employed a
specific AOC measure (the Affective Commitment Questionnaire [ACS] by Allen & Meyer, 1990), used
a specific operationalization of performance (objective indicators), and focused on a specific perfor-
mance type (extra-role behavior). It is not clear whether the conclusions of these three studies are
generalizable to other AOC measures, performance measures and performance types, respectively.
The aim of the present study is to overcome these shortcomings. It reports the results of a meta-analysis
that is based on a comprehensive sample of studies dealing specifically with the AOC–performance rela-
tionship. This meta-analysis is not only to provide an updated and specific estimate of the true AOC–
performance correlation but also to identify moderators of this correlation. In this respect, this study repli-
cates and extends the meta-analytic moderator analyses by Cohen (1991) and Randall (1990).
Two classes of moderators are considered herein. The first class comprises two methodological vari-
ables: the operationalization of performance and of AOC. Randall (1990) already investigated these
moderators and found stronger organizational commitment–work behavior correlations for self-reports
and objective indicators than for supervisor reports of performance and for Mowday et al.’s (1979)
pervasive Organizational Commitment Questionnaire (OCQ) than for other commitment measures.
However, Randall’s moderator analyses have the drawback that they do not pertain to AOC and per-
formance per se. Rather, she included studies pertaining to both affective and calculative organiza-
tional commitment and used a composite index of work behavior, which encompassed performance,
tardiness, absentism, turnover, and effort.
The second class of moderators comprises substantive ones. They follow from the pervasive hypoth-
esis that the impact of AOC on performance is positively correlated with autonomy at work (e.g.,
Kalleberg & Marsden, 1995; Meyer & Allen, 1997, p. 39; van Knippenberg, 2000; see also Judge,
Thoresen, Bono, & Patton, 2001). Cohen (1991) and Randall (1990) tested this hypothesis with three
indicators of autonomy. Randall assumed that white-collar workers have more autonomy at work than
blue-collar workers. In line with this, she found that they displayed a stronger commitment– perfor-
mance correlation than blue-collar workers. However, the already mentioned shortcomings of her
moderator analysis apply also to this finding. Cohen assumed that employees cumulate relevant work
experience in the course of time and thus increase their autonomy. Providing support for this assump-
tion, he found stronger commitment–performance correlations for samples with older mean age and
longer mean tenure. As already mentioned, however, he used only a small sample of studies (k ¼ 14)
and, like Randall, did not distinguish between affective and other forms of commitment. In the present
study, the impact of the same three moderators is investigated.
In addition, this study deals with the moderating impact of two further possible indicators of auton-
omy. The first one is performance type: in-role versus extra-role. In-role performance is defined as
behavior required by formal job descriptions. Extra-role performance is defined as behavior that is
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3. ORGANIZATIONAL COMMITMENT AND PERFORMANCE 259
beneficial to the organization and also goes beyond formal job requirements (e.g., extra hours, altruis-
tic behavior, and donating). Because extra-role behavior often is voluntary, it should depend on intrin-
sic motivational factors to a greater extent than does in-role behavior. Thus, AOC should relate more
strongly to extra-role behavior than to in-role behavior. A comparison of previous meta-analyses pro-
vides preliminary support for this hypothesis: Mathieu and Zajac’s (1990) correlation (r ¼ 0.13)
between AOC and performance (obviously in-role behavior for the most part) is lower than Organ
and Ryan’s (1995) correlations (r ¼ 0.23, k ¼ 5, and r ¼ 0.30, k ¼ 4) between AOC and two facets
of extra-role behavior.
The second additional assumed moderator is job level (supervisor versus subordinate). It is assumed
that supervisors have more autonomy than subordinates. So the AOC–performance correlation should
be stronger among supervisors.
Organizational Context
Features of the Samples Included in the Meta-Analysis
The results for most (75 per cent) of the 111 samples analysed herein were published in the years
1990–2001, 20 per cent in the years 1980–1989, and 5 per cent in the years 1975–1980. The average
sample consisted of 59 per cent men and 41 per cent women (gender proportions were reported for
38 per cent of the samples). Mean age and tenure across samples were 35.93 and 6.90 years, respec-
tively (age and tenure information was available for 56 per cent and 43 per cent of the samples respec-
tively). The huge bulk of the samples (86 per cent) was drawn from Anglo-American countries
(above all, the USA, 81 per cent), 4 per cent from from the European continent (in particular,
Germany, Belgium, and Netherlands), 4 per cent from eastern Asian countries (in particular, Japan,
Korea, and Singapore), and 3 per cent from Israel; the nationalities of the other samples (4 per cent)
were mixed or not evident from the respective studies. Most samples were drawn from the service
sector, in particular: 18 per cent from financial service organizations (banks, insurances, and account-
ing firms), 16 per cent from health or social service organizations (above all, hospitals), and
14 per cent from other non-public services (e.g., food, retailing, and research and development).
In addition, 14 per cent of the samples were from the public sector, except health and social
services (e.g., education, police, and armed forces), and 8 per cent from manufacturing firms; the
other samples (27 per cent) were from unspecified or diverse industries. The most prominent
occupational groups among the analysed samples were salespeople (18 per cent of the samples)
and nurses (5 per cent); the remainder of the samples (77 per cent) comprised other, unspecified,
or diverse occupational groups.
Method
Search for relevant studies
Only published studies were included in the meta-analysis. It was decided not to conduct a search for
unpublished findings because there was reason to assume that such a search would result only in a very
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4. 260 M. RIKETTA
selective sample of unpublished studies. For example, in their meta-analysis of the commitment–
loyalty literature, Tett and Meyer (1993, p. 266) mention that from 33 dissertation authors solicited
for information, only four answered and eventually two provided usable information. Considering
such a small part of the unpublished research, therefore, would not substantially reduce file-drawer
bias (i.e., the bias resulting from considering only published research; Rosenthal, 1991); rather, it
may introduce other biases, depending on the reasons for the solicited authors’ cooperation or non-
cooperation. The problem of file-drawer bias is addressed again in the Discussion section.
To identify relevant published studies, a search was conducted in the electronic databases PsycLIT
(covering the years 1887–2001, April), ABI/INFORM (covering 1971–2001, June), and Social
Sciences Citation Index (covering 1998–2001, June) for one of the keywords ‘organizational commit-
ment’ and ‘organizational identification’ alongside one of the keywords ‘performance’, ‘in-role’,
‘extra-role’, and ‘organizational citizenship’. Moreover, the reference lists of previous reviews of
AOC research were inspected.
Only those studies should be considered further that dealt with AOC rather than calculative or other
forms of organizational commitment. To accomplish this, all studies using a scale explicitly devised to
measure either AOC (e.g., ACS or OCQ) or the related constructs of organizational identification and
internalization of organizational values (e.g., the scales by O’Reilly & Chatman, 1986) were retained,
whereas all studies using a scale explicitly devised to measure non-affective forms of commitment
(e.g., Hrebiniak & Alutto’s, 1972, calculative commitment scale; Allen & Meyer’s, 1990, Continuance
Commitment Scale and Normative Commitment Scale) were discarded. When a study used a commit-
ment scale the type of which was not specified, the study was included only if the scale had some face
validity as an AOC measure (i.e., if at least one item seemed to tap affective attachment to the orga-
nization). Face validity was judged by the author; five of the samples included in the final meta-ana-
lysis were judged that way. Moreover, studies using measures of effort as indicators of performance or
measuring AOC otherwise than by self-reports were discarded. From the remaining studies, only those
studies were retained that reported zero-order correlation coefficients or data allowing computation of
such correlation coefficients (e.g., t and F values; cf. Hunter & Schmidt, 1990).
The final sample for the meta-analysis comprised 111 individual samples (n ¼ 26 344) from 93 pub-
lished studies. Sixty-nine studies (74 per cent) have not been included in any of the previous quanti-
tative reviews of the AOC–performance relationship (Allen & Meyer, 1996; Cohen, 1991; Mathieu &
Zajac, 1990; Mowday et al., 1982; Organ & Ryan, 1995; Randall, 1990). A list of the individual studies
and their characteristics is available from the author upon request.
Coding of sample characteristics
All sample characteristics (including correlation coefficients and reliabilities) were coded by the
author and an independent rater. Interrater agreement was at least 87 per cent for every variable. Incon-
sistencies that were not due to errors were resolved by discussion.
Performance type was coded in-role, extra-role or mixed. If the study authors explicitly stated which
type of performance they sought to measure, their sample was coded correspondingly, i.e. in each other
case, the coders inspected the respective performance measures to find out whether they tapped in-role
or extra-role performance or both. The above definitions served as guidelines for this judgment. Type
of worker was coded blue-collar (i.e., all study participants were blue-collar workers), white-collar
(i.e., all study participants were white-collar workers) or mixed/not stated. Job level was coded super-
visor (i.e., all study participants held supervisory or managerial positions), subordinate (i.e., none of
the study participants held supervisory or managerial positions), or mixed/not stated. Age and organi-
zational tenure were coded by their sample means into Cohen’s (1991) categories: up to 29, 30–39, and
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5. ORGANIZATIONAL COMMITMENT AND PERFORMANCE 261
40 years and over for age and up to 2, 3–8, and 9 and over for tenure. Commitment measure was
coded into two categories denoting the two most pervasive measures, ACS and OCQ, and into
others/mixed/not stated. The labels AOC and OCQ were used for both the full-length version and
shortened versions of either measure. Within the OCQ category, specific labels were assigned to each
of the two most pervasive versions of the OCQ: the 9-item version and the 15-item version. Source of
performance data was coded self-ratings, supervisor ratings, peer ratings, objective indicators, or
others/mixed/not stated. Self-reported supervisor ratings and self-reported objective indicators
were coded supervisor ratings and objective indicators, respectively. Because the peer rating category
comprised only five samples, it was not included in the moderator analyses. However, as described in
the next section, peer ratings were corrected for unreliability in a special manner. Finally, the
percentage of women in the sample, type of organization, occupations of participants, and country
where the investigation was conducted were coded for descriptive purposes (see Contextual Sidebar
for the categories and results).
When a study reported separate correlations pertaining to different levels of the same moderator
(e.g., for both in-role and extra-role behavior), the correlations were averaged across moderator levels
for all analyses except the analysis of the effect of that moderator. In the latter case, the separate cor-
relations were used. This was done with the moderators ‘performance type’ and ‘source of perfor-
mance data’. In averaging across moderator levels, the same formula was used as in averaging
across samples (see next section).
Meta-analytic procedure
The present study employed the meta-analytic methods of Hunter and Schmidt (1990). Hunter and
Schmidt suggested that a meta-analysis not only aggregate data across studies but also correct the data
for artefacts as far as possible. The current meta-analysis controlled for the artefacts of sampling and
measurement error.
In the first step, every individual correlation coefficient was divided by the square-root of the reli-
abilities of the involved variables. With some exceptions, which are described in turn, the sample-
specific reliability coefficients (usually internal consistency coefficients) reported in the respective
study were used. When the authors did not report reliability coefficients, the average reliability coeffi-
cient for each variable across all samples included in the meta-analysis was used. Objective perfor-
mance indicators for which no reliability coefficient was reported and factor scores were assigned a
reliability coefficient of 1.00. Moreover, following the recommendations by Viswesvaran, Ones, and
Schmidt (1996), interrater reliability rather than internal consistency was used to disattenuate correla-
tions involving supervisor and peer ratings of performance. Because no study included in the present
meta-analysis reported interrater reliabilities, Viswesvaran et al.’s meta-analytical estimates of the
interrater reliability of supervisor ratings (0.52) and peer ratings (0.42) were used to correct correla-
tions computed from such ratings.
In the next step, the correlation coefficients were averaged across samples according to the recom-
mendations by Hunter and Schmidt (1990, pp. 148–150). Specifically, every corrected correlation
coefficient was weighted with the product of sample size and the reliability coefficients for the two
correlated variables. Then the weighted coefficients were summed and divided by the sum of the
weights. The result is an estimate of the true population correlation (). Note that this estimate is neces-
sarily flawed by all artefacts not corrected for here: all artefacts besides measurement error and sam-
pling error. Reliability coefficients were averaged analogously, with sample sizes as weights.
Another population parameter of interest was the variance of the true population correlations. The
estimate recommended by Hunter and Schmidt (1990, p. 150) was employed here, that is, the
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6. 262 M. RIKETTA
difference between the variance of the corrected correlation coefficients and their average
squared standard error. The latter term is an estimate of the variance attributable to the corrected
artefacts.
The statistical significance of the estimated variance of the population correlation was computed
with Hunter and Schmidt’s (1990, p. 151) Q test. A significant result points to the existence of mod-
erators. The statistical significance of specific moderator effects was tested with Hunter and Schmidt’s
(1990, pp. 437–438) z test. This test reveals the significance of the difference in observed mean cor-
relation coefficients (corrected only for sampling error) between two subsamples in a meta-analysis
(here: between two subsamples representing different levels of the respective moderator). A prerequi-
site for the z test is that the compared samples are independent. Therefore, from each sample contri-
buting correlations to more than one level of the moderator (e.g., correlations for both in-role and
extra-role behavior), only one correlation was included in the moderator analysis. This was always
the correlation at the moderator level for which fewer samples were available.
All mean correlations (r) reported in the following are corrected for sampling error and attenuation.
All ps reported in the following are two-tailed, with a significance level of p 0.05. Effects with
p 0.10 are considered marginally significant.
Results
Table 1 shows the results of the meta-analysis. The mean corrected correlation between AOC and
performance was 0.20 (k ¼ 111). The 95 per cent confidence interval did not include zero; so the
correlation was statistically significant.
Moreover, 62 per cent of the variance of the observed AOC–performance correlations were not attri-
butable to the controlled artefacts. Hunter and Schmidt (1990) assume that if this proportion exceeds
25 per cent, the existence of moderators is likely. The Q test for significance of unexplained observed
variance points to the same direction—Q ¼ 2(112) ¼ 300.17, p 0.001. These results were prerequi-
site for the moderator analyses.
From the methodological variables, only source of performance data had a marginally significant
effect: the correlation was stronger for self-ratings of performance (r ¼ 0.24) than for supervisor rat-
ings (r ¼ 0.19) and objective indicators ( r ¼ 0.13) ( ps ¼ 0.09 and 0.10, respectively). However, the
AOC–performance correlation did not depend on the commitment measure used. Although the ACS
yielded a slightly stronger correlation (r ¼ 0.23) than the OCQ in general (r ¼ 0.18) and its two most
pervasive versions (the 9-item and 15-item version, rs ¼ 0.19 and 0.18), none of these differences
reached significance ( ps 0.18).
From the substantive assumed moderators, only job type and worker type had at least marginally
significant effects. Both effects were in line with the predictions. First, AOC related signifcantly
more strongly to extra-role performance (r ¼ 0.25) than to in-role performance (r ¼ 0.18), p ¼ 0.03.
Second, the AOC–performance correlation was significantly stronger among white-collar workers
(r ¼ 0.20) than among blue-collar workers (r ¼ 0.10) ( p ¼ 0.01). A problem with the worker type ana-
lysis is that there were only four samples in the blue-collar category. Nonetheless, the category was
analysed here to allow for a tentative test of the relevant hypothesis.
The differences for job level, age, and tenure were non-significant ( p ¼ 0.43, ps 0.13, and
ps 0.16, respectively). Contrary to the predictions, the AOC–performance correlation even decreased
as age and tenure increased.
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7. ORGANIZATIONAL COMMITMENT AND PERFORMANCE 263
Table 1. Results of the meta-analysis
Moderator k n r rc SD CI z
Total 111 26 344 0.146 0.198 0.108 0.032, 0.363
Commitment measure
1. ACS 21 5072 0.174 0.233 0.086 0.071, 0.395
2. OCQ (all versions) 65 15 511 0.132 0.181 0.093 0.014, 0.348 1.36
3. OCQ-9 21 4322 0.126 0.178 0.047 À0.002, 0.358 1.21a
4. OCQ-15 28 7099 0.142 0.191 0.116 0.032, 0.351 0.73,b 0.20c
Source of performance data
1. Objective indicators 18 5801 0.111 0.125 0.123 À0.001, 0.259
2. Self-ratings 32 8060 0.183 0.235 0.138 0.085, 0.385
3. Supervisor ratings 59 14 906 0.131 0.194 0.041 0.015, 0.374 1.65,y 0.64, 1.72y ,d
Age
1. Up to 29 years 10 1385 0.241 0.300 0.138 0.104, 0.496
2. 30–39 years 34 8282 0.162 0.231 0.103 0.061, 0.400
3. 40þ years 18 4879 0.145 0.198 0.121 0.042, 0.355 1.27, 1.46, 0.42d
Tenure
1. Up to 2 years 8 1213 0.209 0.297 0.027 0.077, 0.515
2. 3–8 years 24 5183 0.169 0.238 0.141 0.064, 0.412
3. 9þ years 16 4654 0.138 0.203 0.046 0.039, 0.367 0.68, 1.37, 0.70d
Job level
1. Supervisor 9 1774 0.167 0.200 0.107 0.034, 0.366
2. Non-supervisor 44 11 272 0.128 0.178 0.089 0.016, 0.339 0.78
Performance type
1. In-role 87 20 973 0.130 0.178 0.096 0.011, 0.344
2. Extra-role 42 10 747 0.185 0.252 0.093 0.093, 0.412 2.20*
Worker type
1. Blue-collar 4 1024 0.067 0.098 0 À0.085, 0.281
2. White-collar 84 17 554 0.150 0.201 0.124 0.026, 0.375 2.55*
Notes: k—number of averaged correlations; n—number of individuals; r—mean correlation corrected for sampling error; rc —
mean correlation corrected for sampling error and attenuation; SD —estimated standard deviation of the population
correlations; CI—95 per cent confidence interval for rc; z—result of the significance test on the difference in r between two
moderator levels (levels 1 and 2 except where stated otherwise).
*p 0.05; y p 0.10.
a
Moderator levels 1 versus 3. bModerator levels 1 versus 4. cModerator levels 3 versus 4. dModerator levels 1 versus 2, 1 versus 3,
and 2 versus 3, respectively.
Discussion
When interpreting the results, the reader should keep in mind three limitations of this meta-analysis.
First, only published studies were considered. Exclusion of null findings from publication (file-drawer
bias) may have inflated the estimated true correlation. However, file-drawer bias may be less of a pro-
blem here because in the analysed studies the AOC–performance correlation was often reported only
as an ancillary result. In this case, the non-significance of this correlation may not have affected the
publication chances of the respective study. Results reported by Allen and Meyer (1996, Table 5) are
conclusive in this context. These authors reported six AOC–in-role performance correlations and
five AOC–extra-role performance correlations from four and three unpublished studies, respectively,
all of which used the ACS (sample sizes and reliabilities were not reported). The unweighted means of
these correlations were 0.188 and 0.248, respectively, and thus even larger than the corresponding
unweighted mean correlations for the published ACS studies analysed herein (0.157 and 0.183).
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8. 264 M. RIKETTA
Moreover, the problem of file drawer bias does not challenge the practical significance of the present
moderator analyses because file drawer bias is more likely to blur moderator effects rather than to
inflate them. If real moderators exist, studies pertaining to the levels with the smaller true effect sizes
have less of a chance of yielding significant results than studies pertaining to the other moderator
levels. Hence, provided that significant results have a better chance to be published than non-signifi-
cant ones, there are more unpublished non-significant studies pertaining to the moderator levels with
smaller true effect sizes than to the other moderator levels. So the published effect sizes for the former
moderator levels should in total be more strongly upwardly biased than the published effects sizes for
the other moderator levels. Thus, the fact that only published data were considered herein likely lead to
an underestimation of the true moderator effects. This renders the significant moderator effects
obtained herein even more remarkable.
A second limitation of this study is that the correlations for young employees, low-tenure employees,
supervisors, and blue-collar workers were based on only few ( 10) samples. Hence, these
correlations may be altered by few additional studies or may have been substantively biased by single
non-representative findings. Therefore, the moderator analyses for age, tenure, job level, and worker
type are somewhat preliminary.
Finally, in the population of the analysed studies, employees from Anglo-American countries
(especially the USA) and white-collar workers (especially salespeople) were clearly overrepresented
(see Contextual Sidebar). As a consequence, one should be particularly cautious with generalizing the
present results to other, especially collectivistic (e.g., Asian), cultures and to blue-collar workers.
This having been said, the research and practical implications of the results are outlined in the
following. The estimated true AOC–performance correlation obtained herein (0.20) was similarly
strong as the corresponding estimates reported in the previous meta-analyses of the commitment–
performance relationship (Cohen, 1991; Mathieu Zajac, 1990; Randall, 1990; see introductory
section). Thus, after one decade of additional research, one has still to conclude that the AOC–
performance correlation is weak. However, whereas the correlations reported in those meta-analyses
are based on 14 samples or less, the correlation reported herein is based on 111 samples. Hence, it is
less likely than it was with the previous meta-analyses that additional research will alter the estimate of
the true AOC–performance correlation.
Furthermore, the present study was concerned with moderators of the AOC–performance relation-
ship. One methodological variable (source of performance data) and two substantive variables (job
type and worker type) turned out to be at least marginally significant moderators. One further meth-
odological variable (commitment measure) and three further substantive variables (age, tenure, and job
level) did not have significant moderator effects, with the tendencies for age and tenure being contrary
to expectations. Thus, the autonomy–moderator hypothesis, which was used to predict the effects of
the substantive moderators, recieved only mixed support.
A reason for the non-significance and the partly unexpected directions of the effects by age, tenure,
and job level may be that those variables do not constitute adequate operationalizations of autonomy.
Rather, they may be confounded with a number of other variables that may moderate the AOC–per-
formance correlation (e.g., economic dependency on the job [Brett et al., 1995], work load, and health
status). These variables may have effects that run counter to the effects by autonomy. Hence, future
research should test the autonomy–moderator hypothesis more directly, either by using self-report
measures of autonomy (but see Kalleberg Marsden, 1995, for a null finding obtained with this
method) or by experimental manipulations of this variable.
It should be mentioned that originally it was intended to explore the moderating impact of an addi-
tional methodological feature—study design (longitudinal versus cross-sectional). Also Randall
(1990) included this variable in her meta-analysis and found a non-significantly weaker correlation
for longitudinal studies. However, a replication of this analysis turned out to be problematic here
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9. ORGANIZATIONAL COMMITMENT AND PERFORMANCE 265
because the longitudinal studies included in the present meta-analyses were extremely heterogenous in
terms of the reported time lag between measurement of commitment and performance (ranging from
two weeks to four years). Hence, it would have been necessary to divide the longitudinal studies into
subgroups with different time lags to allow for more meaningful analyses. Yet, this was not possible
because the time lag was reported for only eight samples in the longitudinal category. This suggests
that researchers studying the AOC–performance link provide detailed information about the time when
their measures were collected so that the moderating impact of design can be assessed in future meta-
analyses.
Now that a reliable (though weak) correlation between AOC and performance has been demon-
strated, the question of causality arises. Moderator analyses are but one way to test causal hypotheses.
Other suitable methods are experiments and crucial tests of alternative structural equation models
(see Farkas Tetrick, 1989, for an example). At a basic level, the research agenda proposed by Judge
et al. (2001) could serve as a guideline and integrative framework for such research.
Provided that AOC does cause performance, the results of this meta-analysis have practical impli-
cations in two respects. First, the results suggest that AOC is a better predictor of performance when:
(a) performance is measured by self-reports rather than supervisor reports or objective indicators; (b)
extra-role performance rather than in-role performance is predicted; and (c) white-collar workers
rather than blue-collar workers are studied. Conclusion (c) is only tentative, given the small number
of analysed blue-collar samples. Second, with the same caveat, conditions (b) and (c) point to circum-
stances under which attempts to increase productivity through AOC may be particularly effective.
Author biography
Michael Riketta received diplomas (M.A. equivalent) in economics (University of Augsburg,
¨
Germany, 1997) and psychology (Catholic University of Eichstatt, Germany, 1999). In 1999 and
2000, he was research assistant at the Department of Economic and Social Psychology, Catholic
¨
University of Eichstatt. Since 2000, he has been research assistant at the Department of Social
Psychology, University of Mannheim, Germany. His areas of research are context dependence of
the self-concept, organizational commitment and identification, and social psychological aspects of
European integration.
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