The Causal Relation Between Job Attitudes and PerformanceA .docx

The Causal Relation Between Job Attitudes and Performance:
A Meta-Analysis of Panel Studies
Michael Riketta
Aston University
Do job attitudes cause performance, or is it the other way
around? To answer this perennial question, the
author conducted meta-analytic regression analyses on 16
studies that had repeatedly measured perfor-
mance and job attitudes (i.e., job satisfaction or organizational
commitment). The effect of job attitudes
on subsequent performance, with baseline performance
controlled, was weak but statistically significant
(� � .06). The effect was slightly stronger for commitment than
for satisfaction and depended negatively
on time lag. Effects of performance on subsequent job attitudes
were elusive (� � .00 across all studies),
which suggests that job attitudes are more likely to influence
performance than vice versa.
Keywords: job satisfaction, organizational commitment,
performance, organizational citizenship behav-
ior, meta-analysis
Few topics in industrial and organizational psychology have
received as much attention as has the relation between job
attitudes
and performance (e.g., Brief & Weiss, 2002; Judge, Thoresen,
Bono, & Patton, 2001; Meyer & Allen, 1997; Mowday, Porter,
&
Steers, 1982; Staw, Sutton, & Pelled, 1994). Numerous meta-

analyses (e.g., Cooper-Hakim & Viswesvaran, 2005; Harrison,
Newman, & Roth, 2006; Judge et al., 2001; Meyer, Stanley,
Herscovitch, & Topolnytsky, 2002; Riketta, 2002) have demon-
strated that positive job attitudes, such as commitment and
satis-
faction, are accompanied by better work outcomes. Although the
existence of positive correlations is well established, the causal
relationship between job attitudes and performance is still
unclear.
Do job attitudes increase performance? Is it the other way
around?
Or are the frequently observed correlations between job
attitudes
and performance spurious (e.g., due to common causes)? The
vast
majority of empirical studies on job attitudes and performance
are
mute on these issues because of their cross-sectional designs.
The
same is true of the aforementioned meta-analyses. Thus, the
long-
standing debate about the causal relationship between job
attitudes
and individual performance (e.g., Harrison et al., 2006; Judge et
al., 2001; March & Sutton, 1997; Organ, 1977; Schwab & Cum-
mings, 1970) is far from being resolved.
The goal in this article is to contribute to this debate by provid-
ing the most controlled (to date) meta-analytic test of causal
links
between job attitudes and performance. This article is built
around
a meta-analysis of panel studies on these two constructs. These
studies permit the extent to which job attitudes predict perfor-
mance to be disentangled from the extent to which performance

predicts job attitudes. In this article, meta-analytic regression
anal-
ysis is applied to the aggregated correlations to estimate the
unique
effect of job attitudes on performance (with baseline
performance
controlled) and the unique effect of performance on job
attitudes
(with baseline job attitudes controlled). Differences between
forms
of job attitudes (organizational commitment and job
satisfaction)
and performance (in-role and extra-role), as well as the
moderating
role of measurement interval, are explored.
This article provides the first meta-analysis that estimates lon-
gitudinal effects between job attitudes and performance while
controlling for baseline scores (for similar methods that
examine
team cohesion instead of job attitudes, see the meta-analysis by
Mullen & Copper, 1994; for a meta-analysis of zero-order longi-
tudinal correlations between job attitudes and performance, see
Harrison et al., 2006). The present meta-analysis therefore
extends
previous meta-analyses of the job attitude–performance
relation-
ship by way of a more rigorous test of causal hypotheses.
Definitions and Theoretical Models
Throughout this article, the term job attitude refers to the eval-
uation or personal importance of job-related targets (e.g.,
organi-
zation, work group, job as a whole). The two most frequently
investigated job attitudes probably are job satisfaction, defined

as
a cognitive and/or affective evaluation of one’s job as more or
less
positive or negative (Brief & Weiss, 2002), and attitudinal or
affective organizational commitment, defined as “the relative
strength of an individual’s identification with and involvement
in
a particular organization” (Mowday, Steers, & Porter, 1979, p.
226; see also Allen & Meyer, 1990). The following arguments
and
empirical analyses refer to these job attitudes only. Other forms
of
job attitudes include organizational identification (see Riketta,
2005); job involvement (see Brown, 1996); continuance and
nor-
mative commitment (Allen & Meyer, 1990); and satisfaction
and
(affective) commitment with reference to targets other than job
or
organization, such as work group, career, or occupation (see,
e.g.,
Becker, 1992; Cohen, 2003; Meyer, Allen, & Smith, 1993).
These
I wish to thank Paula Brough, Ann Davis, Rolf Van Dick,
Xiaojing Yan,
and René Ziegler for helpful comments; Claudia Sacramento for
assistance
with coding; and Frank Bond, Jeremy Dawson, Günter Maier,
and Mike
West for making unpublished results available.
Correspondence concerning this article should be addressed to
Michael
Riketta, Work & Organisational Psychology Group, Aston
Business

School, Aston University, Birmingham B4 7ET, United
Kingdom. E-mail:
[email protected]
Journal of Applied Psychology Copyright 2008 by the American
Psychological Association
2008, Vol. 93, No. 2, 472– 481 0021-9010/08/$12.00 DOI:
10.1037/0021-9010.93.2.472
472
forms of job attitudes are not considered within this article, due
to
a lack of published investigations that contain these
constructs.1
In the literature, performance is usually divided into in-role
performance (similar to task performance), defined as
fulfillment
of tasks that are required by the formal job description, and
extra-role performance (similar to organizational citizenship be-
havior or contextual performance), defined as behavior that is
beneficial to the organization and goes beyond formal job
require-
ments (e.g., helping colleagues at work, working extra hours,
making suggestions for improvement; Borman & Moto-
widlo, 1997; Organ, 1988). This meta-analysis considers both
forms of performance.
At least four interpretations of positive correlations between job
attitudes and performance are possible. Because these
viewpoints
have been laid down many times (e.g., Brief & Weiss, 2002;
Brown & Peterson, 1993; Harrison et al., 2006; Judge et al.,
2001;

Meyer & Allen, 1997; Mowday et al., 1982; Staw et al., 1994),
only a brief summary is given below. This meta-analysis tests
all
four cases by estimating the unique effects of job attitudes on
later
performance (with baseline performance controlled) and of per-
formance on later job attitudes (with baseline job attitudes con-
trolled).
Case 1: Job attitudes cause performance. Arguments that sup-
port this view usually refer to the functions of attitudes as
guide-
lines and facilitators of behavior (e.g., Eagly & Chaiken, 1993;
Fishbein & Ajzen, 1974; see Judge et al., 2001); the energizing
and
facilitative effects of positive affect (as one component of satis-
faction) at the workplace (e.g., Staw et al., 1994); or the
motiva-
tional effects of the personal importance or identification with
the
job or organization (e.g., as a component or consequence of
commitment; see, e.g., Meyer, Becker, & Vandenberghe, 2004).
In
this meta-analysis, this view would receive support if job
attitudes
predicted later performance.
Case 2: Performance causes job attitudes. Two common argu-
ments that support this view are (a) that performance often
leads to
internal and external rewards (e.g., pay, recognition, feeling
good
at work), which in turn may foster positive job attitudes (e.g.,
Lawler & Porter, 1967), and (b) that people adjust their
attitudes to
their behavior, due to strivings for cognitive consistency or as a

rationalization for their actions (e.g., as assumed by
psychological
theories of cognitive dissonance and of self-perception,
Festinger,
1957, and Bem, 1972, respectively; see, e.g., Staw, 1975). This
view would receive support if performance predicted later job
attitudes.
Case 3: Performance and job attitudes cause each other. This
case results from the combination of Cases 1 and 2.
Case 4: Performance and job attitudes are causally unrelated. In
this case, the positive concurrent correlations between them
would
be due to research artifacts (e.g., common source bias) or to
third
variables that influenced both constructs (see, e.g., Brown &
Peterson, 1993; Judge et al., 2001). Although it is virtually
impos-
sible to rule out Case 4 with correlational data, this meta-
analysis
tests one possible implication of this case: that job attitudes and
performance do not predict each other over time.
This study explores the moderating roles of type of job attitude
(satisfaction vs. commitment), performance type (in-role and
extra-role performance), and timing (shorter vs. longer intervals
between measurement waves). In theory, panel designs require
that
the time between two measurement waves matches the time that
the effects under investigation presumably take to unfold. How-
ever, little is known about this process (e.g., how long it takes
for
satisfaction to influence performance or vice versa). Because of
this lack of knowledge and the common constraints of field

studies
(e.g., the organization may provide access only at certain inter-
vals), the time lags in most studies seem to be set independently
of
theoretical considerations, as reviews of panel studies have ad-
monished (e.g., Williams & Podsakoff, 1989). Through its com-
parison of attitude–performance effects between different time
lags, this meta-analysis may inform decisions on timing issues
in
future panel studies.
Method
Study Search and Coding
Studies had to meet the following criteria to be included in the
meta-analysis:
1. Participants were employees in an organization. Thus,
studies in other contexts, such as classrooms, sports
teams, or artificial environments (e.g., laboratory, sce-
nario), were excluded (e.g., Dorfman & Stephan, 1984;
Grieve, Whelan, & Meyers, 2000).
2. The study examined job satisfaction or organizational
commitment (attitudinal or affective).
3. The study examined job performance.
4. Job attitudes and performance were measured at each of
at least two measurement waves. That is, the study had a
panel design.
5. No major changes in the work environment, such as an
organizational merger or a change in the task of the
participants, occurred between the measurement waves

(e.g., Jetten, O’Brien, & Trindall, 2002).
6. Data were analyzed at the individual level rather than at
the group level. This criterion was included because most
theoretical accounts of the job attitude–performance re-
lation refer to individual processes and because
individual-level correlations are not comparable with
group-level correlations.
7. The complete matrix of the zero-order correlations for
job attitude and performance was available for at least
two measurement waves. Thus, the report of the study
had to contain the two synchronous correlations, the two
cross-lagged correlations, and the two stabilities for job
1 The literature search for this meta-analysis did refer to studies
on all of
the mentioned job attitudes. After the search, all job attitudes
were to be
excluded for which fewer than five independent studies were
available.
This step would reduce the heterogeneity of the data set for the
meta-
analysis and would ensure that the meta-analytic results for
single job
attitudes were generalizable to some extent. Only job
satisfaction and
organizational commitment met this criterion (and the other
inclusion
criteria described in the Method section). For ease of
presentation, the
excluded constructs are not mentioned further.
473RESEARCH REPORTS

attitude and performance. Only studies that reported the
complete set of correlations were considered in the meta-
analysis. This criterion ensured that the attitude–perfor-
mance and the performance–attitude paths within an
analysis were from the same studies and thus ruled out
differences between studies as confounds of the observed
effects. In other words, studies with missing correlations
were excluded listwise.
This study used several strategies for identification of published
and unpublished studies that met these criteria. A range of elec-
tronic databases was searched, including ABI/Inform (covering
published articles and unpublished dissertations, some of them
in
full text); Business Source Premier (covering published
articles);
PsycINFO (covering published articles, chapters, and books and
unpublished dissertations); and Web of Science (the former
Social
Sciences Citation Index; covering published articles). The
follow-
ing search terms, decomposed into smaller search terms as
neces-
sary, were used: (satisfaction or commitment) and (work or job
or
organization) and (cross-lagged or longitudinal) and ( perfor-
mance, in-role, or extra-role; or citizenship or effort or
productiv-
ity or work motivation). Moreover, the lists of studies included
in
previous meta-analyses and qualitative reviews on satisfaction,
commitment, and performance were checked, as were the refer-
ences of several papers on cross-lagged panel analysis (most
notably, Clegg, Jackson, & Wall, 1977; Williams & Podsakoff,
1989). Further, colleagues who research actively in the area of

job
attitudes were asked if they knew of relevant research; a request
for unpublished data was sent via the mailing list of the German
association of industrial and organizational psychologists (in
Sep-
tember 2006); a request for unpublished data was posted on the
web page of the Society for Industrial and Organizational
Psychol-
ogy (in September 2006); the abstracts of recent annual confer-
ences of that society (2005–2007) and of the Academy of Man-
agement (2000 –2007) were searched, and several papers were
requested; and authors of published studies that met all but the
last
inclusion criterion (i.e., that failed to report the complete
correla-
tion matrix) were contacted and asked for the missing
correlations.
The references of each relevant paper retrieved were scanned
for
additional studies. Study search was completed in June 2007.
All usable studies were coded by Michael Riketta and a doctoral
student into two categories according to the nature of
performance
the studies measured (in-role and extra-role). When a
performance
measure included items that referred to both performance types,
it
was coded into the category to which most of its items referred.
Intercoder agreement was 100%, and each study could be unam-
biguously assigned to one of the two categories. Because all
other
data to be coded did not require subjective judgments, they were
coded by Michael Riketta.
As is common in meta-analyses, the moderator effects were

explored by repeating the analyses for discrete values of the
moderator (Hunter & Schmidt, 1990). Thus, the moderator vari-
able time lag was categorized into 1– 6 months, 7–12 months,
and
13� months. These categories were chosen as a compromise
between the two conflicting demands of (a) having a reasonable
number of studies in each category (which could be best
fulfilled
by a small number of categories) and (b) having a category
system
differentiated enough for detection of nonmonotonic moderator
effects (which could be best fulfilled by a large number of cate-
gories). Given the small number of available studies, a larger
number of time lag categories did not seem meaningful. (None-
theless, a more differentiated analysis for the total sample is
reported in Footnote 3.)
Features of the Analyzed Studies
The literature search yielded 16 usable studies (see Table 1).
The average sample size of these studies was 192, with a range
from 35 to 526. Mean time lag between the coded waves of
measurement averaged 9.2 months, with a range from 1 month
to
18 months. Mean organizational tenure of participants at the be-
ginning of data collection was 4.5 years (k � 10). Mean
proportion
of women was 55% (k � 11). The majority of the studies were
conducted in English-speaking countries (10 in the United
States,
2 in the United Kingdom, 1 in Australia). Two studies were
conducted in Germany; for another study, the country was not
stated. Eight studies were conducted in service organizations, 3
were conducted in manufacturing organizations, and 4 examined
participants from multiple organizations and industries (e.g.,

alumni of the study authors’ institutions). One study was con-
ducted in an organization within an unspecified industry.
Of the studies, 14 examined job satisfaction with a variety of
measures. The most frequent measure was the Job Descriptive
Index (Smith, Kendall, & Hulin, 1969), which was used in 3
studies. Five studies examined organizational commitment,
mea-
sured with the Organizational Commitment Questionnaire
(Mow-
day et al., 1982). In-role and extra-role performance were mea-
sured by 11 and 5 studies, respectively. Extra-role performance
was measured with self-reports in 4 studies and with both self-
reports and peer ratings in the remaining study. In-role perfor-
mance was measured with supervisor ratings in 6 studies, with
objective indicators in 3 studies, with both supervisor ratings
and
objective indicators in 1 study, and with self-reports in another
study. No single performance measure was used more than
twice.
Data Aggregation
One requirement of a meta-analysis is independence of the
aggregated data points (here, correlations). Thus, a study must
not
contribute more than one correlation to each aggregated correla-
tion. When a study provided correlations for (a) more than one
job
attitude or performance form or (b) more than one measure for
the
same job attitude or performance form, the correlations were
averaged, such that the study contributed no more than one set
of
correlations (two stabilities, two synchronous correlations, two
cross-lagged correlations) to each of the following analyses. For

example, when a study provided correlations for commitment
and
satisfaction that had the same outcome, these correlations were
averaged for the overall analyses. The single correlations for
commitment and satisfaction were used, however, in the
separate
analyses for commitment and satisfaction.
The issue of independent correlations is also relevant to studies
with more than two waves of measurement (here, three studies
with three waves). To ensure independence of data points, this
analysis used only the data from the first two measurement
waves,
except in the analyses that compared different time lags. In this
latter case, if a study reported correlations for more than one
time-lag category (i.e., �7, 7–12, and 13� months), these corre-
474 RESEARCH REPORTS
lations were included in the analyses for the respective
categories.
Again, when a study reported more than one set of correlations
relevant to the same time-lag category, only the set of
correlations
for the two earliest measurement waves was considered.
In longitudinal studies, changes in reliability between the mea-
surement waves can bias estimates of cross-lagged effects
(Kenny,
1975; Williams & Podsakoff, 1989). To correct for this, the cor-
relations were disattenuated. The study-specific reliability esti-
mates for the relevant measurement waves were used if
available.
These reliability estimates were internal consistencies in all

cases.
When reliability information was lacking, imputed estimates
made
the analysis for these studies more comparable with those for
the
other studies. Specifically, when reliability information was
avail-
able for only one measurement wave (as was the case in one
study), this value was imputed as the reliability estimate for the
second measurement wave. Reliabilities of single-item rating
scales were set at .70 (Wanous & Hudy, 2001). In all other
cases,
missing reliabilities for job satisfaction, organizational commit-
ment, and (in- or extra-role) performance were set at .83, .83,
and
.85, respectively. These values were the average reliabilities
(mostly internal consistencies) from a recent, extraordinarily
large
meta-analysis on these constructs (Cooper-Hakim &
Viswesvaran,
2005; these estimates were based on 949, 311, and 159 studies,
respectively).2
The next step consisted of averaging the disattenuated correla-
tions across studies, after weighting them with the product of
sample size (to correct for sampling error) and the squared
disat-
tenuation factor (i.e., the square of the ratio of uncorrected to
corrected correlation; Hunter & Schmidt, 1990). The resulting
weighted correlation was an estimate of the mean population
correlation. Its standard error was computed as the standard
devi-
ation of the corrected correlations divided by the square root of
the
number of studies. Thus, as recommended by Hunter and

Schmidt
(1990), this meta-analysis used a random effects model. The
variance of the population correlations was computed as the dif-
2 Schmidt and Hunter (1996) and Viswesvaran, Ones, and
Schmidt
(1996) argued that interrater reliabilities are better estimates of
measure-
ment error than are internal consistencies. In a meta-analysis,
Viswesvaran
et al. estimated the interrater reliability of supervisor ratings of
perfor-
mance at .52 and suggested that this estimate be used for
disattenuation in
meta-analyses. When their estimate (rather than the internal
consistencies
from the original studies) was used in this study for supervisory
perfor-
mance ratings, the paths between job attitudes and performance
tended to
be slightly weaker but showed patterns largely similar to those
in the
present analysis. In particular, the job attitudes–performance
and perfor-
mance–job attitudes effects were, respectively, � � .04 and .00
overall; .02
and .00 for satisfaction; .07 and .02 for commitment; .4 and .01
for in-role
performance; .05 and �.02 for extra-role performance; .11 and
.03 for a
time lag of 1– 6 months; .01 and �.08 for 7–12 months; and .01
and .05 for
13� months. A limitation of this method is that it does not
consider
changes in reliability within studies, although this is advisable
for the

analysis of panel data (Kenny, 1975). Moreover, several authors
have
argued that the use of interrater reliabilities in disattenuation
may bias
correlations (Murphy & De Shon, 2000; Sackett, Laczo, &
Arvey, 2002).
Hence, only the analysis that used internal consistencies is
reported in the
text.
Table 1
Studies Included in the Meta-Analysis
Study N Participants Country Lag Variables
Coded correlations
A1A2 P1P2 A1P1 A2P2 A1P2 P1A2
Ashforth & Saks (1996) 222 University graduates U.S. 6 JS, IP
.64 .69 .11 .21 .14 .20
Bateman & Organ (1983) 77 Nonacademic university staff U.S.
1.5 JS, EP .71 .80 .41 .41 .43 .39
Bechtold et al. (1981) 64 Medical center employees U.S. 18 JS,
IP .53 .57 .15 .21 .17 .19
Bond & Bunce (2003) 412 Call-center employees U.K. 12 JS, IP
.66 .21 .26 .17 .05 .66
Borrill et al. (2003) 370 Hospital employees U.K. 12 JS, EP .67
.48 .28 .25 .20 .17
Crampon et al. (1978) 46 Management trainees U.S. 2 OC, IP
.72 .78 .16 .16 .36 .00
Donaldson et al. (2000) 157 Nonprofessionals U.S. 6 OC, EP
.71 .25 .20 .25 .14 .71
Griffin (1991)a 526 Bank tellers U.S. 18 JS, IP .61 .53 .04 .06
�.02 .06

OC, IP .74 .53 �.03 .05 .05 .03
Maier & Rosenstiel
(2006)
185 University graduates Germany 14 JS, EP .57 .57 .11 .195
.09 .17
OC, EP .75 .57 .18 .185 .24 .18
Maier & Rosenstiel
(2006)
216 University graduates Germany 14 JS, EP .64 .65 .16 .16 .14
.14
OC, EP .84 .65 .18 .14 .12 .13
Nathan et al. (1991) 300 Managers and professionals Not stated
3.5 JS, IP .56 .23 .06 .17 .14 �.02
Sheridan & Slocum
(1975)
59 Machine operators U.S. 11 JS, IP .45 .50 �.03 .15 �.08 �.06
Sheridan & Slocum
(1975)
35 Managers U.S. 12 JS, IP .68 .49 .20 .21 .21 .24
Szilagyi (1980) 128 Controllers and accountants U.S. 3 JS, IP
.62 .65 .09 .05 .09 .09
Tharenou (1993) 200 Electrical apprentices Australia 12 JS, IP
.48 .64 .19 .08 .11 .08
Wanous (1974) 80 Telephone operators U.S. 2 JS, IP .73 .44 .09
.15 .18 .24
Note. Lag � time lag between the coded measurement waves in
months; A1 and A2 � job attitude at first and second coded

wave, respectively; P1 and
P2 � performance at first and second coded wave, respectively;
JS � job satisfaction; IP � in-role performance; EP � extra-
role performance; OC �
organizational commitment.
a Only Time 2 and Time 3 were coded because of an
intervention between Time 1 and Time 2.
475RESEARCH REPORTS
ference between the variance of the corrected correlation coeffi-
cients and their average squared standard errors (Hunter &
Schmidt, 1990). Heterogeneity of population correlation was
tested with Hunter and Schmidt’s chi-square test (Q test). A
significant result would indicate that there was more than one
population correlation.
Meta-Analytic Regression Analysis
For the causal analyses, the matrix of the corrected mean cor-
relations served as input into a meta-analytic regression
analysis
(Judge & Piccolo, 2004; Viswesvaran & Ones, 1995). The soft-
ware MPlus 4.2, using maximum likelihood estimation, was
used
for these computations. To increase the sensitivity of
significance
tests, the sum of the sample sizes of the relevant studies (rather
than, e.g., the average) was used to compute the standard errors
for
the regression coefficients (see Cheung & Chan, 2005). Specifi-
cally, across all studies, performance or job attitude at the
second
coded measurement wave was regressed on both performance

and
job attitudes at the first coded measurement wave. The
standard-
ized regression coefficients provided by this analysis estimated
how well job attitudes and performance predicted each other,
with
baseline scores of the criterion variable being controlled. These
analyses were conducted across all job attitudes and
performance
forms, irrespective of time lags (called overall analysis
hereafter),
for each combination of satisfaction or commitment with in-role
or
extra-role performance and with each time-lag category.
Results
Table 2 shows the mean corrected correlations (as estimates of
the mean population correlations; symbolized by r in the
following
text); their standard errors (as indicators of the precision with
which the mean population correlations were estimated); and the
estimated standard deviations of the population correlations (as
estimates of the extent to which the population correlations vary
around the mean population correlation). Data are shown for the
overall analysis and for each category of each moderator (job
attitude, performance type, time lag, and all possible
combinations
thereof).
The mean cross-sectional correlations between job satisfaction
and organizational commitment with performance were weakly
positive and statistically significant (rs between .10 and .21, ps
�
.05). These findings are consistent with those of previous meta-
analyses (e.g., Cooper-Hakim & Visweswaran, 2005; Judge et

al.,
2001; Meyer et al., 2002; Riketta, 2002). The stabilities of job
attitudes and performance were remarkably high (rs � .52
across
all time lags, i.e., for an average time lag of 9 months).
Table 3 shows the results of the meta-analytic regression anal-
yses conducted on the correlations from Table 2. The upper
panel
of Table 3 shows general analyses, which averaged across at
least
one of the moderator variables. Overall, job attitudes were weak
predictors of performance (� � .06, p � .001). This effect
tended
to be stronger for commitment than for satisfaction (� � .08 vs.
.03, ps � .05) and stronger for shorter than for longer time lags
(� � .12, p � .001, for 1– 6 months; � � .02, ns, for 7–12
months;
� � .03, ns, for more than 12 months). It did not differ between
in-role and extra-role performance (�s � .05, ps � .05).
Effects of performance on job attitudes in the general analyses
were more elusive. The only significant effect was a negative
effect for moderate time lag (� � �.08, p � .001, 7–12
months).
Because no studies of organizational commitment were
available
for this time-lag category, this effect was entirely due to
satisfac-
tion. All other effects of performance on job attitudes in the
general analyses were nonsignificant (�s � .04).3
Table 3, lower panel, shows more specific analyses, which
examined all possible combinations of job attitude, performance
type, and time lag. Because most of these analyses were
conducted

on three samples or fewer, they have to be interpreted with
great
caution. The most remarkable finding may be that the tendency
for
higher effects of job attitudes on performance for shorter time
lags
was replicated for all four job attitude–performance type combi-
nations.
Discussion
With its use of meta-analytic regression analysis and its exclu-
sive focus on studies with repeated measurements, this meta-
analysis accomplished a more rigorous test of causal relations
between job attitudes and performance than did previous meta-
analyses on these relations. The results provide some support
for
the common assumption that job attitudes influence
performance.
Across job attitudes and performance forms, the effect was
weak
but significant (� � .06). The effect was significant for both
satisfaction and commitment and for both in- and extra-role per-
formance. The effect tended to be stronger for shorter time lags
between measurement waves and for commitment rather than for
satisfaction. Almost no statistically significant evidence for the
reverse causal direction emerged, with the effect size in the
overall
analysis being � � .00. This finding suggests that job attitudes
are
more likely to influence performance than vice versa.
Limitations
Before the implications of the findings for research and practice
are discussed, several limitations of this meta-analysis should

be
noted. First of all, the number of studies was small, especially
for
commitment and extra-role performance (only five studies each)
and for the moderator analysis for time lag (fewer than eight
studies in each category).
Moreover, almost all studies on extra-role performance mea-
sured this construct with self-reports only. Common source bias
and socially desirable self-presentation may have distorted the
3 Additional analyses explored the effects of time lag in more
detail by
repeating the computations (across job attitudes and
performance types) for
every available time lag (rounded to months). The job attitudes–
perfor-
mance and performance–job attitudes effects were, respectively,
� � .18
and .06 for a time lag of 2 months (k � 3, n � 203); .03 and .03
for 3
months (k � 1, n � 128); .20 and �.12 for 4 months (k � 1, n �
300); .06
and .08 for 6 months (k � 2, n � 379); �.03 and .01 for 9
months (k �
1, n � 64); �.08 and �.05 for 11 months (k � 1, n � 59); .02
and �.08
for 12 months (k � 4, n � 1017); .01 and .03 for 14 months (k
� 2, n �
401); and .02 and .06 for 18 months (k � 2, n � 590). Weighted
linear
regression analyses on these data (with the inverse of n as
weights)
revealed that time lag related strongly and negatively to the job
attitudes–
performance effects (� � �.69, p � .04) but not to the

performance–job
attitudes effects (� � .13, p � .73).
Table 2
Aggregated Zero-Order Correlations
Analysis k n A1A2 P1P2 A1P1 A2P2 A1P2 P1A2
Overall 16 3,077 .77
(.02, .06)
.67
(.04, .15)
.17
(.03, .09)
.21
(.03, .06)
.17
(.03, .07)
.13
(.03, .05)
37.66** 114.21*** 33.12** 23.33 26.90 22.25
JS 14 2,874 .75
(.02, .06)
.63

(.04, .14)
.17
(.03, .08)
.21
(.03, .06)
.14
(.03, .08)
.13
(.03, .06)
31.59** 97.24*** 25.98* 22.24 27.07* 20.56
OC 5 1,130 .84
(.03, .05)
.70
(.06, .13)
.10
(.06, .12)
.13
(.04, .01)
.15
(.05, .09)
.10
(.04, .01)
15.47* 38.32*** 16.27* 5.07 11.87* 5.14
In-role 11 2,072 .75

(.03, .07)
.65
(.04, .14)
.12
(.03, .05)
.18
(.03, .05)
.13
(.03, .05)
.09
(.03, .01)
26.24** 68.03*** 14.59 15.34 14.7 11.17
Extra-role 5 1,005 .80
(.02, .03)
.71
(.07, .16)
.29
(.03, .00)
.27
(.03, .00)
.25
(.04, .02)
.21
(.03, .00)

7.21 44.38*** 4.42 3.78 5.29 4.03
Lag, 1–6 7 1,010 .79
(.04, .09)
.70
(.08, .21)
.18
(.04, .06)
.23
(.04, .00)
.24
(.04, .05)
.17
(.05, .09)
19.59** 65.52*** 9.06 6.71 8.65 11.98
Lag, 7–12 6 1,140 .74
(.03, .03)
.67
(.05, .11)
.26
(.03, .00)
.26
(.03, .00)
.19
(.03, .00)

.12
(.03, .00)
7.68 29.64 5.3 5.13 4.67 5.27
Lag, 13� 4 991 .79
(.03, .04)
.66
(.06, .12)
.07a
(.05, .06)
.13
(.04, .02)
.09
(.04, .04)
.11
(.04, .02)
8.36* 24.94*** 7.15 4.42 5.17 4.22
JS, extra-role 4 848 .80
(.02, .00)
.68
(.08, .16)
.28
(.05, .04)
.28
(.00, .03)

.23
(.05, .05)
.22
(.00, .04)
1.70 35.01*** 5.11 3.09 5.72 3.70
Lag, 1–6 1 77 .86 .86 .47 .46 .49 .45
Lag, 7–12 1 370 .79 .53 .32 .28 .23 .19
Lag, 13� 2 401 .79 .86 .19 .23 .16 .20
(.03, .00) (.03, .00) (.02, .00) (.02, .00) (.03, .00) (.02, .00)
0.99 0.99 0.25 0.22 0.28 0.20
JS, in-role 10 2,026 .73
(0.03, 0.07)
.64
(0.05, 0.13)
.13
(0.03, 0.03)
.18
(0.03, 0.06)
.11
(0.03, 0.06)
.10
(0.03, 0.02)
23.68* 61.06*** 11.09 14.71 15.66 10.46
Lag, 1–6 4 730 .79
(0.06, 0.1)

.62
(0.11, 0.21)
.11
(0.01, 0.00)
.20
(0.04, 0.00)
.17
(0.02, 0.00)
.14
(0.06, 0.07)
15.74** 37.22*** 0.10 2.71 0.90 6.10
Lag, 7–12 5 770 .71
(.00, .03)
.67
(.04, .06)
.21
(.00, .03)
.23
(.00, .04)
.15
(.00, .04)
.08
(.00, .04)
4.49 13.86* 3.46 3.94 3.77 3.23

Lag, 13� 2 590 .69
(.00, .02)
.58
(.00, .01)
.06
(.00, .03)
.09
(.00, .04)
.01
(.00, .05)
.09
(.00, .04)
0.43 0.07 0.72 1.27 1.91 0.97
OC, extra-role 3 558 .84
(.05, .08)
.86
(.02, .00)
.25
(.02, .00)
.21
(.02, .00)
.24
(.04, .00)
.18

(.02, .00)
15.37*** 1.14 0.53 0.45 2.19 0.35
Lag, 1–6 1 157 .70 .87 .30 .24 .31 .17
Lag, 13� 2 401 .88
(.03, .04)
.86
(.00, .03)
.22
(.00, .04)
.20
(.00, .02)
.22
(.00, .05)
.19
(.00, .02)
5.65* 0.99 0.01 0.28 1.51 0.33
OC, in-role 2 572 .85
(.00, .00)
.60
(.06, .07)
�.02a
(.04, .00)
.07
(.02, .00)

.08a
(.07, .07)
.03
(.00, .00)
0.07 7.27* 1.51 0.54 4.23* 0.01
Lag, 1–6 1 46 .87 .92 .19a .19a .43 .01a
Lag, 13� 1 526 .85 .58 �.03a .06a .06a .03a
Note. Sample-size weighted and disattenuated zero-order
correlations. The standard errors of these mean correlations and
the standard deviations of the
population correlations are given in parentheses. The chi-square
values (from the test of heterogeneity of the individual
correlations, df � k �1) are given
below them. All correlations shown are significant at p � .05,
except where indicated. A1 and A2 � job attitude at first and
second coded wave, respectively;
P1 and P2 � performance at first and second coded wave,
respectively; JS � job satisfaction; OC � organizational
commitment; Lag � time lag between
the coded measurement waves in months.
a Nonsignificant correlation ( p � .05).
* p � .05. ** p � .01. *** p � .001.
477RESEARCH REPORTS
scores on these measures and may have inflated their
correlations
with job attitudes in particular, thereby introducing bias in the
regression analyses. This possibility, together with the small

num-
ber of studies available, renders the present findings on extra-
role
performance particularly tentative. Future research on the
causal
link between job attitudes and extra-role performance should
use
observer ratings or objective indicators as alternative or
additional
measures.
One limit to the generalizability of the data is that the majority
of the studies were from English-speaking countries, primarily
the
United States. Thus, the generalizability to other countries is
unclear. Moreover, due to the small number of studies, it was
not
possible to include studies that measured performance at the
firm,
rather than individual, level. It is still possible that job attitudes
and
performance show stronger, or different, relations at aggregated
than at individual levels (see Koys, 2001, and Schneider,
Hanges,
Smith, & Salvaggio, 2003, for examples of firm-level analyses).
Finally, because the present analysis is based on correlational
rather than experimental data, it allows for only tentative causal
conclusions and cannot rule out some alternative causal
explana-
tions (e.g., that third variables inflated the cross-lagged paths;
see,
e.g., Cherrington, Reitz, & Scott, 1971; Brown & Peterson,
1993).
Although the present analysis accomplished a more rigorous test
for causality than did previous meta-analyses in this domain, it

still
suffers from the usual weakness of correlational designs.
Experi-
ments are required to provide compelling evidence of causal
relations.
Implications for Research
Notwithstanding the limitations mentioned above, this is the
first meta-analysis to predict later performance by job attitudes
over and above initial performance and vice versa. The findings,
obtained using a comprehensive database, support the common
view that job attitudes increase performance. They provide
weaker
support for the alternative view that performance shapes job
atti-
tudes. Effects of this latter type remain elusive.
At first glance, this conclusion seems to be at odds with Judge
et al.’s (2001) assertion that there is evidence for effects in both
directions, which led them to propose a model of reciprocal
satisfaction–performance relationships (p. 390). Several points
are
worth mentioning in this regard. For one, the present findings
represent average trends across studies and do not rule out
recip-
rocal or mere performance–attitude effects in specific contexts.
Further, many studies that claim to test causal relations between
job attitudes and performance are cross-sectional and thus are
able
to provide only weak evidence of causality. In fact, Judge et
al.’s
conclusion is based on a qualitative review of 4 longitudinal
and
12 cross-sectional studies (pp. 377–379). Finally, even
longitudi-

nal studies, especially those published in the 1970s and 1980s,
often use inappropriate methods to test for causality (see
Williams
& Podsakoff, 1989). Rather than taking the analyses and
interpre-
tations in the primary studies at face value, the present research
reanalyzed their correlation matrices, thus overcoming some
lim-
itations of the studies.
Table 3
Regression Analyses: Subsequent Attitude or Performance
Regressed on Preceding Attitude and Performance
Analysis k n
Criterion: Subsequent attitude Criterion: Subsequent
performance
Attitude Performance R2 Attitude Performance R2
General analyses
Overall 16 3,077 .77 (65.98)*** .00 (0.08) .59 .06 (4.27)*** .64
(48.75)*** .45
JS 14 2,874 .75 (59.87)*** .00 (0.21) .56 .03 (2.11)* .65
(44.86)*** .42
OC 5 1,130 .84 (51.70)*** .02 (1.00) .71 .08 (3.81)*** .69
(32.16)*** .50
In-role 11 2,072 .75 (51.24)*** .00 (0.00) .56 .05 (3.15)** .64
(38.37)*** .43
Extra-role 5 1,005 .81 (40.84)*** �.02 (1.22) .64 .05 (2.08)*
.70 (30.05)*** .51
Lag, 1–6 7 1,010 .79 (40.06)*** .03 (1.47) .63 .12 (5.23)*** .68
(30.11)*** .50
Lag, 7–12 6 1,140 .76 (37.08)*** �.08 (3.79)*** .55 .02 (0.74)
.67 (29.24)*** .45

Lag, 13� 4 991 .75 (35.15)*** .00 (0.12) .56 .03 (1.36) .62
(24.96)*** .40
Specific analyses
JS, extra-role 4 848 .80 (37.33)*** .00 (0.20) .64 .04 (1.64) .67
(25.51)*** .46
Lag, 1–6 1 77 .83 (12.70)*** .06 (0.90) .74 .11 (1.70) .81
(12.50)*** .75
Lag, 7–12 1 370 .81 (22.13)*** �.07 (1.91) .63 .07 (1.32) .51
(9.98)*** .29
Lag, 13� 2 401 .78 (25.10)*** .05 (1.67) .63 .00 (0.14) .86
(33.16)*** .74
JS, in-role 10 2,026 .73 (47.71)*** .00 (0.33) .53 .03 (1.76) .61
(34.15)*** .37
Lag, 1–6 4 730 .78 (34.47)*** .05 (2.36)* .63 .10 (3.56)*** .61
(21.01)*** .40
Lag, 7–12 5 770 .73 (28.08)*** �.07 (2.69)** .51 .00 (0.12) .74
(29.81)*** .55
Lag, 13� 2 590 .69 (23.07)*** .05 (1.64) .48 �.03 (0.74) .58
(17.32)*** .34
OC, extra-role 3 558 .85 (35.81)*** .03 (1.35) .71 .03 (1.20)
.86 (38.29)*** .74
Lag, 1–6 1 157 .71 (11.96)*** .04 (0.74) .49 .05 (1.31) .85
(20.81)*** .76
Lag, 13� 2 401 .88 (36.23)*** .00 (0.16) .77 .03 (1.24) .85
(32.71)*** .74
OC, in-role 2 572 .85 (38.78)*** .05 (2.14)* .73 .09 (2.77)**
.60 (18.11)*** .37
Lag, 1–6 1 46 .90 (12.84)*** .16 (2.30)* .78 .27 (6.01)*** .87
(19.74)*** .91
Lag, 13� 1 526 .85 (37.27)*** .06 (2.43)* .73 .08 (2.19)* .58
(16.46)*** .34

Note. Standardized path coefficients, with t scores in
parentheses. JS � job satisfaction; OC � organizational
commitment; Lag � time lag between the
coded measurement waves in months.
* p � .05. ** p � .01. *** p � .001.
Though statistically significant in several cases, the effects of
job attitudes on performance were generally weak in the present
analyses (e.g., � � .06 overall, .03 for satisfaction, .08 for
com-
mitment). One reason may be that these effects are short lived.
As
might be expected (see Hulin, Henry, & Noon, 1990), the
effects
were stronger for shorter time lags between measurement
waves,
though only slightly so (e.g., � � .11 for time lags of 6 months
or
less). It is noteworthy that the time intervals in the analyzed
studies
were quite long, with a range of 1.5 months to 18 months. Re-
search still has to explore whether stronger effects emerge for
shorter intervals (e.g., a few days). In general, to the extent that
time lags are longer than the actual duration of the effects of
interest, these effects are reflected in the cross-sectional, not
the
cross-lagged, relationships (Clegg et al., 1977). More
theoretical
and empirical work on the temporal characteristics of job
attitude–
performance effects is necessary to help researchers choose

opti-
mal time intervals.
Furthermore, cross-lagged effects between job attitudes and
performance may be stronger under certain circumstances (see
also
Judge et al., 2001). Possible moderators of these effects include
autonomy at work (Kalleberg & Marsden, 1995; Riketta, 2002);
the stimulating nature of jobs (Ivancevich, 1979);
organizational
tenure (Cohen, 1993; Wright & Bonett, 2002); and the degree to
which job attitude and performance are measured with regard to
the same, rather than different, targets (Fishbein & Ajzen, 1974;
Lee, Carswell, & Allen, 2000; Riketta & Van Dick, 2005; Van-
denberghe, Bentein, & Stinglhamber, 2004). The studies
included
in the present meta-analysis did not provide enough information
to
test the effects of these moderators. Additional panel studies in-
cluding these moderators would be valuable.
A theoretical challenge is to explain the counterintuitive nega-
tive effect of performance on job satisfaction for moderate time
lags (� � �.08, time lag � 7–12 months). One explanation is
that
the effect is due to people who perform strongly but who do not
perceive themselves to be adequately rewarded for their perfor-
mance. In this case, high performers may be less satisfied than
are
low performers, especially when enough time has passed to
stifle
their hope for performance-adequate rewards. This would
explain
why the effect is evident only for longer time lags. Thus, reward
systems and justice perceptions may be additional moderators,
especially for job attitude–performance effects. Because the

effect
is weak and was based on only six studies, replication attempts
are
advisable before further interpretation is attempted.
From a practical perspective, the longitudinal effects of job
attitudes on performance might be weaker than many
practitioners
have hoped. Thus, researchers should be cautious with practical
recommendations such as, “Increase job satisfaction or commit-
ment to increase productivity.” Especially when effect sizes are
small, it is important to communicate them so that practitioners
understand their practical significance (McCartney & Rosenthal,
2000). A useful tool for this purpose is the binomial effect-size
display (Rosenthal & Rubin, 1982). Translated into this display,
the present finding of a job attitudes–performance effect of � �
.06 means that, among the half of employees with higher job
attitudes, 53% also belong to the half with higher performance;
whereas, among the half of employees with lower job attitudes,
47% belong to the half with higher performance. It is up to
practitioners to decide, on the basis of this information, whether
their organizations would substantially benefit from higher job
attitudes.
Conclusions
This meta-analysis provides some support for effects of job
attitudes on performance and little support for the reverse
effects.
In light of the weak effects observed in the present analyses,
future
research should focus on moderators of the relations between
job
attitudes and performance. The present findings suggest timing
of

measurement as one moderator, with effects being more likely
to
emerge for shorter (vs. longer) time spans. Moreover, research
should increasingly use panel designs to broaden the database
for
follow-up meta-analyses on the causal link between job
attitudes
and performance.
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Received March 28, 2007
Revision received June 27, 2007
Accepted July 30, 2007 �
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481RESEARCH REPORTS
Gident University II MGT9 Mod – Analytic Tool

Identification and Assessment of Meta-Analysis Stages and
Practices
(based on Figure 1, p. 307, Kirca & Yaprak, 2010)
Practice
1. Formulate problem
Identify and read relevant theoretical and empirical articles
Identify important, common variables, study, and measurement
characteristics to assess
Identify contradictory findings
Decide on meta-analysis objectives
Develop preliminary theoretical framework
2. Collect Data
Conduct key-word database searches
Search references of key studies

Manually search relevant publications
Solicit studies from key researchers
Post requests for articles and working papers on academic list-
serves
Develop elimination criteria
Screen studies according to elimination criteria
Decide whether data is sufficient to proceed with meta-analysis
3. Evaluate Data
Identify common metrics across studies (e.g. correlation
coefficient)
Identify key substantial and methodological study
characteristics

Prepare coding form and manual
Code each study
Prepare database and check for errors
4. Analyze Data
Conduct univariate analysis of mean effect sizes
Search for moderators using bivariate analysis of effect sizes
Search for moderators using multivariate analysis of effect sizes
Construct and analyze the meta-analytical correlation matrix to
test theory
5. Interpret and discuss
Report univariate and bivariate findings using tables and figures
Make detailed recommendations for future research based on the
findings

1
Mo4 – SL4
JOB ATTITUDES
Kirca and Yaprak (2010) develop a five-stage framework and
research practices commonly associated with each of the five
stages in their framework, which they summarize in Table 1 on
p. 307. In this fourth SLP assignment, your job is to:
· Apply the Kirca and Yaprak five-stage framework to identify
the meta-analysis stages and practices described in the articles
by Riketta (2008) and Harrison, Newman, and Roth (2006).
SLP Assignment Expectations
1. Fill in the table in the Identification and Assessment of Meta-
Analysis Stages and Practices to focus and structure your work.
2. Search carefully within each article for information as to
whether and how the authors describe the five meta-analytic
stages and associated practices proposed by Kirca and Yaprak.
Note that the information regarding a practice may appear in
more than one place in the article, or in a section that seems
unrelated to the section heading. In other words, the evidence of
each practice could potentially appear anywhere in the article.
3. Once you have assembled this information in the table,
briefly critique the extent to which "the coding process
proceeded with rigor and that all relevant possible studies were
included in the database" (Kirca & Yaprak, p. 311) in the
Riketta (2008) and Harrison, et al. (2006) studies, and to make
recommendations to the authors for how they could strengthen
confidence in the comprehensiveness of the studies included in
their sample.
General Expectations

1. Length: 4 pages of double-spaced, 12-point text, plus cover
and reference pages.
2. Structure: Narrative style with brief introduction, relevant
section headings, and summary conclusion section.
3. References: Follow Campion's (1997) rules for references
(see Background page).
4. Style: APA format.
5. Proofread your paper before uploading it.
6. Upload your paper by the module due date.
· Kirca and Yaprak five-stage framework to identify the meta-
analysis stages and practices described in the articles by Riketta
(2008) and Harrison, Newman, and Roth (2006).
Kirca and Yaprak
Kirca, A. H., & Yaprak, A. (2010). The use of meta-analysis in
international business research: Its current status and
suggestions for better practice. International Business Review,
19(3), 306-314.
Link: file:///C:/Users/owner/Downloads/The_use_of_meta-
analysis_in_international_business.pdf
· Riketta (2008)
Riketta, M. (2008). The Causal Relation Between Job Attitudes
and Performance: A Meta-Analysis of Panel Studies. Journal Of
Applied Psychology, 93(2), 472-481.
Harrison, et al. (2006)
Harrison, Newman, and Roth (2006).
important are job attitudes? Meta-analytic comparisons of
integrative behavioral outcomes and time sequences. Academy
Of Management Journal, 49(2), 305-325.
doi:10.5465/AMJ.2006.20786077

Link:
http://www.business.unr.edu/faculty/simmonsb/mgt486/amjjobat
titudes.pdf
Link:
Background
Required Materials
There are two sections of required readings in this module. The
first section introduces Meta-analysis as a quantitative approach
to reviewing and synthesizing the results of a large number of
empirical research studies on a particular topic. The goal is to
help you understand, critique, and effectively draw upon meta-
analysis research review articles. The second section provides
breadth and depth of foundation in theory and research related
to job attitudes, and includes recent, high quality meta-analyis
research articles that illustrate the concepts introduced in the
articles in the first section.
Note: The required readings are described in the order in which
you are encouraged to read them, as each provides concepts that
will help you make sense of the subsequent readings.
I. Meta-Analysis
In Module 3 of ORG601 you were introduced to three basic
types of academic articles that contribute to the creation and
testing of new knowledge:
1. Theoretical articles that develop new theoretical constructs
and propose conceptual relationships among those constructs
2. Empirical research articles in which quantitative data is
collected and analyzed, most often to test hypotheses that are
derived deductively from a theory, and
3. Review articles that provide an in-depth overview of the

theory and empirical research related to a particular
phenomenon.
A meta-analysis article could be seen as a hybrid of the second
and third types of academic articles. It employs an empirical,
meta-analytic research method to quantitatively review and
synthesize empirical research findings from a large number of
studies on a particular topic.
The following article provide an overview of the nature, role,
methods, and relative use of meta-analysis in two fields related
to business administration: information systems and
international business research.
· Kirca, A. H., & Yaprak, A. (2010). The use of meta-analysis
in international business research: Its current status and
suggestions for better practice. International Business Review,
19(3), 306-314.
II. Job Attitudes
In this module, we focus on the two most widely researched job
attitudes - organizational commitment and job satisfaction.
However, a wide variety of job attitudes have been
conceptualized, operationalized, and researched. In their meta-
analysis of the relationships between age and job attitudes, Ng
and Feldman (2010) provide a succinct overview of job
attitudes. Table 1 lists and defines 35 job attitudes that have
been researched in relation to age in the workplace, with
references to the original sources of those definitions. This
provides a very handy guide that can help identify job attitudes
that may be relevant to your own research. Please read the Job
Attitudes section that begins on p. 679, up to the Theoretical
Background heading on p. 683 (unless you are specifically
interested in the impact of age on job attitudes, then of course
read the whole article):
· Ng, T. H., & Feldman, D. C. (2010). The relationships of age
with job attitudes: A meta-analysis. Personnel
Psychology, 63(3), 677-718. doi:10.1111/j.1744-
6570.2010.01184.x

In RES601, Module 2, you were introduced to the
operationalization of constructs through multi-item scales.
Researchers in organizational behavior develop and apply such
scales extensively in their work. You have already been
introduced to a variety of such measures in this course.
However, are multi-item scales always the best way to measure
a construct? When does it make theoretical, empirical, and/or
practical sense to use a single item to operationalize a
construct? In the following meta-analysis, Wanous, et al. (1997)
empirically compared single-item measures vs. multi-item
scales measuring the job attitude of overall job satisfaction. In
doing so, they provide clear guidance regarding these questions:
· Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall
Job Satisfaction: How Good Are Single-Item Measures?. Journal
Of Applied Psychology, 82(2), 247-252.
In the following meta-analysis, Harrison, Newman, and Roth
(2006) take a novel, integrative approach to empirically
addressing an age old question about the relative importance
and impact of job attitudes on work related behaviors. Pay
careful attention to the compatibility principle (p. 309), and the
ways in which the authors conceptualize and measure the job
attitude constructs that they use in their study:
important are job attitudes? Meta-analytic comparisons of
integrative behavioral outcomes and time sequences. Academy
Of Management Journal, 49(2), 305-325.
doi:10.5465/AMJ.2006.20786077
In the "Limitations and Research Directions" section of their
article, Harrison, et al. (2006) note that a criticism of the
method that they used is that it "does not allow for clear-cut
cause-effect conclusions (as time-lagged and especially cross-
sectional correlations cannot definitely establish temporal
precedence..." Keep that criticism in mind as you read the

following brief meta-analysis that attempts to test the nature
and direction of causality in relationships between job attitudes
and performance. Also, please pay careful attention to the
author's conceptualization and measurement of the primary job
attitudes included in his research:
· Riketta, M. (2008). The Causal Relation Between Job
Attitudes and Performance: A Meta-Analysis of Panel
Studies. Journal Of Applied Psychology, 93(2), 472-481.
In 2002, Swailes critiqued the conceptualization and
measurement of the job attitude of organizational commitment.
Ideally, the later Harrison, et al. (2006) Riketta (2008) meta-
analyses addressed and resolved the issues that Swailes
identified. As you read this, you may find it helpful to take
notes wherever you notice issues that are relevant to either or
both of the subsequent Harrison, et al. and Riketta meta-
analyses, as your observations will help you in completing your
Case Assignment:
· Swailes, S. (2002). Organizational commitment: A critique of
the construct and measures. International Journal Of
Management Reviews, 4(2), 155.
Supplemental Materials
You may have noticed in the required readings that many
authors cite Azjen (2001) as a key source to support their
conceptualization and/or definition of job attitudes. Whenever
you notice a source like that being cited by multiple authors, it
may mean that it is a foundational theoretical or empirical
article. If that construct were central to your own research,
then you would be expected to go back and read the original
source, not just the more recent authors who cite that source.
Icek Azjen is a psychologist, and his review of the nature and
operation of attitudes provides a solid conceptual foundation
that is broader than the the specific, contextualized focus on job
attitudes in our field. Many constructs in organizational studies
are based on foundational theory and research in other fields,
including psychology. If you plan to include one or more job

attitudes in your doctoral research model, it would be important
for you to read this review:
Ajzen, I. (2001). Nature and operation of attitudes. Annual
Review of Psychology, 52(1), 27-58.
Abstract: This survey of attitude theory and research published
between 1996 and 1999 covers the conceptualization of attitude,
attitude formation and activation, attitude structure and
function, and the attitude-behavior relation. Research regarding
the expectancy-value model of attitude is considered, as are the
roles of accessible beliefs and affective versus cognitive
processes in the formation of attitudes. The survey reviews
research on attitude strength and its antecedents and
consequences, and covers progress made on the assessment of
attitudinal ambivalence and its effects. Also considered is
research on automatic attitude activation, attitude functions, and
the relation of attitudes to broader values. A large number of
studies dealt with the relation between attitudes and behavior.
Research revealing additional moderators of this relation is
reviewed, as are theory and research on the link between
intentions and actions. Most work in this context was devoted to
issues raised by the theories of reasoned action and planned
behavior. The present review highlights the nature of perceived
behavioral control, the relative importance of attitudes and
subjective norms, the utility of adding more predictors, and the
roles of prior behavior and habit.
The following meta-analysis examines the impact of individual
personality (which you studied in Module 1 in this course) as
the IV, on the attitude of job satisfaction (the focus of this
module), as the DV. It illustrates the fact that a construct can
serve as an IV, DV, moderator, or mediator in a study,
depending on the focus of the research.
However, some constructs cannot logically serve as either an IV
or a DV. For example, could pay satisfaction cause personality?
It is highly unlikely that a transitory attitude like pay

satisfaction could substantively influence the far more stable
enduring nature of individual personality.
Bruk-Lee, V., Khoury, H. A., Nixon, A. E., Goh, A., & Spector,
P. E. (2009). Replicating and Extending Past Personality/Job
Satisfaction Meta-Analyses. Human Performance, 22(2), 156-
189. doi:10.1080/08959280902743709
Abstract: A meta-analysis summarizing results of 187 studies
reporting cross-sectional and longitudinal relationships between
job satisfaction and personality is described. The Big Five
factor of Neuroticism related most strongly and negatively to
job satisfaction (–.25), with the other factors ranging from .16
(Conscientiousness) to –.02 (Openness to Experience). Job
satisfaction was positively related to internal locus of control
(LOC), positive affectivity, and Type A (achievement striving).
Results showed negative relationships with external LOC, trait
anger, Machiavellianism, negative affectivity/trait anxiety, and
Type A (global and impatience/irritability). Job satisfaction had
a very weak, negative correlation with narcissism that was
indistinguishable from zero. These relationships were similar,
although the effect sizes were generally not as strong, when
examined in a longitudinal context. The distinctiveness of
Extraversion and positive affectivity, as well as that of global
and composite measures of job satisfaction, are discussed.
An earlier meta-analysis on the impact of personality on job
satisfaction distinguishes between studies that take
a direct vs. indirect approach to empirically testing that
relationship. Consider how the distinction between direct and
indirect approaches might impact the more recent Bruk-Lee, et
al. (2009) meta-analysis (above):
Dormann, C., & Zapf, D. (2001). Job satisfaction: A meta-
analysis of stabilities. Journal Of Organizational
Behavior, 22(5), 483-504. doi:10.1002/job.98
Abstract: Evidence suggesting that job satisfaction is caused by
individual dispositions is reviewed, and stability coefficients
for job satisfaction in previous studies are analysed with a
meta-anjilytic procedure. Previous longitudinal studies

analysing job changer samples imply an upper limit estimate of
0.51 for direct dispositional influences on job satisfaction. A
study of job changers considering the stability of working
conditions suggests that this estimate has to be considerably
corrected downwards. At present, it is concluded that it is more
likely that dispositions indirectly affect job satisfaction via
selection and self-selection processes. Implications for job
satisfaction as a tool for organizational assessment are
discussed.
The following meta-analysis examined a broad spectrum of the
antecedents and outcomes of the attitude of pay level
satisfaction:
Williams, M. L., McDaniel, M. A., & Nguyen, N. T. (2006). A
meta-analysis of the antecedents and consequences of pay level
satisfaction. Journal Of Applied Psychology, 91(2), 392-413.
doi:10.1037/0021-9010.91.2.392
Abstract: This study reports results from a meta-analysis of 28
correlates of pay level satisfaction involving 240 samples from
203 studies conducted over the past 35 years. Results are
presented in 4 categories: primary determinants, antecedents,
correlates, and outcomes of pay satisfaction. The authors
controlled for pay in examining relations between correlates and
pay level satisfaction, as suggested by theory and when primary
studies were available to do so. The authors found support for
many of the relations suggested by a theoretical model and also
note some limitations in the research that has tested this model.
The authors recommend changes and additions to the model and
suggest additional primary research in specific areas.
Moderators
Wright and Bonett (2002) meta-analytically test the moderating
impact of employee tenure on the relationship between
organizational commitment (as the IV) and job performance (as
the DV):
Wright, T. A., & Bonett, D. G. (2002). The Moderating Effects
of Employee Tenure on the Relation Between Organizational
Commitment and Job Performance: A Meta-Analysis. Journal Of

Applied Psychology, 87(6), 1183-1190. doi:10.1037//0021-
9010.87.6.1183
Abstract: This meta-analysis investigated the correlation
between attitudinal commitment and job performance for 3,630
employees obtained from 27 independent studies across various
levels of employee tenure. Controlling for employee age and
other nuisance variables, the authors found that tenure had a
very strong nonlinear moderating effect on the commitment–
performance correlation, with correlations tending to decrease
exponentially with increasing tenure. These findings do not
appear to be the result of differences across studies in terms of
the type of performance measure (supervisory vs. self), type of
tenure (job vs. organizational), or commitment measure
(Organizational Commitment Questionnaire [L. W. Porter, R.
M. Steers, R. T. Mowday, & P. V. Boulian, 1974] vs. other).
The implications and future research directions of these results
are discussed.
In the following meta- analysis, the job attitudes of affective
commitment and job satisfaction serve as the DV, with HR
practices as the IV, and Age - which is the focus of the study,
as moderating the relationship between HR practices and job
attitudes:
Kooij, D. M., Jansen, P. W., Dikkers, J. E., & De Lange, A. H.
(2010). The influence of age on the associations between HR
practices and both affective commitment and job satisfaction: A
meta-analysis. Journal Of Organizational Behavior, 31(8), 1111-
1136. doi:10.1002/job.666
Abstract: Research on the association between high commitment
Human Resource (HR) practices and work-related outcomes at
the individual level rarely focuses on age differences. To fill
this knowledge gap, a meta-analysis has been conducted to
examine how the relationships between the availability of high
commitment HR practices, as perceived by employees, and
affective commitment and job satisfaction change with age.
Drawing on Selection, Optimization, and Compensation (SOC)

theory and on Regulatory Focus theory, we identify a bundle of
maintenance HR practices and a bundle of development HR
practices, and hypothesize that the association between
maintenance HR practices and work-related attitudes
strengthens with age, and that the association between
development HR practices and work-related attitudes weakens
with age. Our meta-analysis of 83 studies reveals that, in line
with social exchange and signaling theories, employees’
perceptions of HR practices are positively related to their work-
related attitudes, and that calendar age influences this
relationship largely as expected. These results are discussed in
light of the above mentioned theories.

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