a meta analysis of organizational identification

Journal of Vocational Behavior 66 (2005) 358–384

www.elsevier.com/locate/jvb

Organizational identification: A meta-analysisq

Michael Riketta*

Psychological Institute, University of Tubingen, Friedrichstr. 21, 72072 Tuebingen, Germany

Received 22 December 2003

Available online 19 August 2004

Abstract

The last two decades have witnessed a surge in interest in research on organizational iden-

tification (OI). This paper presents a comprehensive meta-analysis of this research (k = 96).

Results indicate that (a) OI is correlated with a wide range of work-related attitudes, behav-

iors, and context variables, (b) OI is empirically distinct from its closest conceptual neighbor,

attitudinal organizational commitment (AOC), and (c) the two most common OI measures

(the Mael scale and the Organizational Identification Questionnaire) produce very different

results. It is argued that OI scales, especially the Mael scale, may be preferable over AOC

scales for studies aimed at explaining, and partly also for studies aimed at predicting, work

behavior.

� 2004 Elsevier Inc. All rights reserved.

Keywords: Organizational identification; Organizational commitment; Work behavior; Meta-analysis

1. Introduction

Compared with other psychological variables assumed to be relevant to work

behavior, such as ability, job satisfaction, and work motivation, organizational

0001-8791/$ - see front matter � 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.jvb.2004.05.005

q Michael Riketta, Psychological Institute, University of Tubingen, Germany. I am grateful to Rolf van

Dick for helpful comments on this research project and to Timo Forster for assistance with coding.* Fax: +49-7071-29-5899.

E-mail address: [email protected].

mailto:[email protected].

M. Riketta / Journal of Vocational Behavior 66 (2005) 358–384 359

identification (OI) has received little attention as a unique research topic until re-

cently. Although the first detailed model of was OI proposed by March and Simon

(1958), only a few studies that explicitly dealt with OI were published in the follow-

ing 20 years (e.g., Brown, 1969; Lee, 1969; Patchen, 1970; Rotondi, 1975a, 1975b). In

the 1970s, Porter and his colleagues (e.g., Porter, Steers, Mowday, & Boulian, 1974;Mowday, Steers, & Porter, 1979) included identification as a component of attitudi-

nal organizational commitment (AOC) in their seminal definition of this construct.

Since then, several researchers have treated the terms OI and AOC as synonyms

(e.g., Griffin & Bateman, 1986; Mathieu & Zajac, 1990).

Researchers in organizational behavior, social psychology, and communication

re-discovered OI as a unique construct in the late 1980s. After Ashforth and Mael

(1989) outlined the relevance of these social psychological theories to organizational

behavior research, the number of empirical and theoretical analyses focussing on OIas a unique construct and employing these theories increased markedly in that dis-

cipline (e.g., Dutton, Dukerich, & Harquail, 1994; Elsbach, 1999; Mael & Ashforth,

1992; Pratt, 1998; Rousseau, 1998; Tyler, 1999; Wan-Huggins, Riordan, & Griffeth,

1998). At the same time, and seemingly independently of research in organizational

behavior, several social psychologists following the social identity tradition discov-

ered organizational settings as a new field of application of social identity theory

and self-categorization theory (e.g., Abrams, Ando, & Hinkle, 1998; Brown & Wil-

liams, 1984; Ellemers, de Gilder, & van den Heuvel, 1998; Haslam, 2001; van Knip-penberg & van Schie, 2000). Finally, in communication research, Cheney, Tompkins,

and their colleagues studied the relation of OI to control, socialization, and commu-

nication in organizations (e.g., Barker & Tompkins, 1994; Bullis & Tompkins, 1989;

Cheney, 1983).

To date, about 80 journal articles dealing with OI have been published, about the

half of them since 1998 (PsycINFO, electronic database, update from October 2003).

Moreover, OI has been addressed in special issues of Academy of Management Re-

view (Albert, Ashforth, & Dutton, 2000) and Group Processes and Intergroup Rela-

tions (van Knippenberg & Hogg, 2002). Given this surge in interest in OI, work

that summarizes and organizes the literature on this construct appears desirable.

Although recent qualitative reviews on OI are available, they did not aim at a com-

prehensive overview of this research (e.g., Elsbach, 1999; Haslam, 2001; van Dick,

2004). The present article provides a more comprehensive review and meta-analysis

of empirical research on OI.

This review addresses three issues that are crucial to OI research. First, it gives a

comprehensive quantitative overview of the most often studied correlates of OI. Sec-ond, it addresses the question of whether the operationalization of OI matters. Many

measures for OI are available. Yet, little is known about the consequences of using

one measure instead of the other. Third, this review presents evidence concerning the

empirical distinctiveness of OI and its closest conceptual neighbor, AOC. Many OI

researchers assert that this construct is distinct from AOC (e.g., Ashforth & Mael,

1989; Pratt, 1998; van Knippenberg & van Schie, 2000), while others challenge this

assumption on both empirical and theoretical grounds (e.g., Ouwerkerk, Ellemers, &

de Gilder, 1999; Sass & Canary, 1991; Stengel, 1987). The present review addresses

360 M. Riketta / Journal of Vocational Behavior 66 (2005) 358–384

this issue by comparing the results from the meta-analysis of OI research with results

from previous meta-analyses of AOC research.

To date, only one meta-analysis of OI research has been presented (Fontenot

& Scott, 2000; see also Fontenot & Scott, 2003). However, this prior work has

several limitations, which the present study overcomes. First, the Fontenot andScott meta-analysis focused on only four correlates (AOC, job satisfaction, intent

to leave, and organizational tenure). By contrast, in the present meta-analysis, all

correlates were considered for which a sufficient number of studies could be re-

trieved. Second, the prior meta-analysis included only studies conducted until

2000. Since then, many more OI studies have been published. Third, Fontenot

and Scott did not compare their results for OI with findings from AOC research.

Thus, they left open the question of whether the results were unique for OI re-

search. Finally, correlations were not disattenuated in the Fontenot and Scottmeta-analysis so that it probably underestimated population correlations (cf.

Hunter & Schmidt, 1990).

In the following, at first, I will present definitions of OI and discuss the relation

between OI and AOC. A meta-analysis of OI studies follows. Next, I will compare

the correlations of OI and AOC with other variables to explore the distinctiveness

of these two constructs.

2. Organizational identification and attitudinal organizational commitment

Many definitions of OI have been proposed. Most of them conceptualize OI as a

cognitive construct, in particular, as the congruence of individual and organizational

values (Hall, Schneider, & Nygren, 1970; Pratt, 1998; Stengel, 1987), as the ‘‘percep-

tion of oneness with or belongingness to’’ the organization (Ashforth & Mael, 1989,

p. 34), or as the process of incorporating the perception of oneself as a member of a

particular organization into one�s general self-definition (Dutton et al., 1994; Els-bach, 1999; Rousseau, 1998). Having a different focus, O�Reilly and Chatman

(1986), following Kelman (1961), defined OI in affective-motivational terms, that

is, as based on attraction and the desire to maintain an emotionally satisfying self-

defining relationship with the identification object. Further, the definition derived

from social identity theory—which is the most pervasive theoretical framework in

contemporary OI research (Haslam, 2001; van Dick, 2001)—combines cognitive

and affective components. According to this theory, social identity is ‘‘that part of

an individual�s self-concept which derives from his knowledge of his membershipof a social group (or groups) together with the value and emotional significance at-

tached to that membership’’ (Tajfel, 1978, p. 63; see Abrams et al., 1998; Benkhoff,

1997a; and Ouwerkerk et al., 1999; for an application of this definition to organiza-

tions). Finally, perhaps the most comprehensive definition of OI has been proposed

by Patchen (1970), who used the term OI for ‘‘a variety of separate, though related

phenomena . . . (1) feelings of solidarity with the organization; (2) [attitudinal and

behavioral] support for the organization; and (3) perception of shared characteristics

with other organizational members’’ (p. 155). Despite their heterogeneity, all these


definitions imply that the organizational member has linked his or her organizational

membership to his or her self-concept, either cognitively (e.g., feeling a part of the

organization; internalizing organizational values), emotionally (pride in member-

ship), or both. The link between self-concept and organization is used herein as

the working definition of OI.Depending on its specific definition, OI is more or less similar to other concepts of

organizational behavior research such as involvement, satisfaction, and, above all,

AOC (Ashforth & Mael, 1989; Mael & Tetrick, 1992; Pratt, 1998). The distinction

between AOC and OI, because it is the fuzziest one, requires examination.

According to its seminal definition, AOC is ‘‘the relative strength of an indi-

vidual�s identification with and involvement in a particular organization’’ (Mow-

day et al., 1979, p. 226). It has at least three related factors: (1) acceptance of the

organization�s goals and values; (2) willingness to work hard for the organization;and (3) a strong desire to remain in the organization. Recently, the reconceptual-

ization of AOC by Allen and Meyer (1990) has received growing attention in re-

search. These authors distinguished three forms of commitment: affective,

continuance, and normative. The definition of affective organizational commit-

ment resembles Mowday et al.�s definition and includes the following components:

‘‘employee�s emotional attachment to, identification with, and involvement in, the

organization’’ (Allen & Meyer, 1990, p. 1). In the present work, the term AOC

encompasses both the definition by Mowday et al. and the definition by Allenand Meyer. If one looks at the cited definitions, there is a clear overlap between

AOC and OI. In particular, identification is explicitly included in these definitions

of AOC.

There is also some overlap at the operational level. The two most often used AOC

scales are the Organizational Commitment Questionnaire (OCQ; Mowday et al.,

1979) and the Affective Commitment Scale (ACS; Allen & Meyer, 1990). Items in

these scales refer to (a) emotional attachment to the organization (e.g., OCQ: ‘‘I

am proud to tell others that I am part of this organization’’; ACS: ‘‘I do not feelemotionally attached to this organization’’), (b) involvement in organizational issues

(OCQ: ‘‘I really care about the fate of the organization’’; ACS: ‘‘I feel as if this orga-

nization�s problems are my own’’), (c) value congruence (OCQ: ‘‘I find that my val-

ues and the organization�s values are very similar’’; not represented in the ACS), and

(d) willingness to stay with the organization (OCQ: ‘‘There is not too much to be

gained by sticking with this organization indefinitely’’; ACS: ‘‘I would be very happy

to spend the rest of my career with this organization’’). Similar items are included in

many OI measures. For example, eight of the 25 items of the Organizational Identi-fication Questionnaire (OIQ; Cheney, 1983), one of the most often used OI measures,

are virtually identical with ACS and OCQ items.

Several researchers have asserted that AOC is a broader or vaguer construct

than OI (e.g., Ashforth & Mael, 1989; Edwards, 2002; Pratt, 1998; van Dick,

2004). The most influential scholars distinguishing between OI and AOC are Ash-

forth and Mael (1989), who stated that OI is ‘‘a perceptual cognitive construct

that is not necessarily associated with any specific behaviors or affective states’’

(p. 21). Their view has found many supporters (e.g., Edwards, 2003; Elsbach,


1999; Iyer, Bamber, & Barefield, 1997; Mael & Tetrick, 1992; Pratt, 1998; van

Knippenberg & van Schie, 2000; Wan-Huggins et al., 1998; but see Harquail,

1998; for a critique of this cognitively narrowed view). One of the most common

OI scales, the Mael scale (Mael & Tetrick, 1992), is based on this definition. Its

items do not overlap with items from OCQ and ACS (e.g., ‘‘When I talk aboutthis organization, I usually say �we� rather than they’’; ‘‘When someone praises

this organization, it feels like a personal compliment’’; ‘‘This organization�s suc-

cesses are my successes’’).

Other researchers evidently considered both AOC and OI as closely related or

even interchangeable, using the terms as synonyms (e.g., Benkhoff, 1997a; Ouwer-

kerk et al., 1999; Wallace, 1993), citing studies on OI as examples of commitment

research (e.g., A. Cohen, 1992; Mathieu & Zajac, 1990) or vice versa (e.g., Elsbach,

1999), and/or incorporating items from OI scales in their commitment measures (e.g.,Buchanan, 1974; Mottola, Bachman, Gaertner, & Dovidio, 1997) or vice versa (e.g.,

Cheney, 1983). Finally, some scholars did not mention AOC at all in their studies on

OI (partly because that concept was not yet introduced at that time of their studies)

so that it is unclear how they saw the relationship between OI and AOC (e.g., Padaki

& Gandhi, 1981; Patchen, 1970; Rotondi, 1975a, 1975b). All in all, there is no con-

sensus among OI researchers regarding the conceptual relation between OI and

AOC.

Empirical evidence on the OI–AOC relation is also inconsistent. The correlationbetween OI and AOC varied across studies between values around 0 (Beerman, 1987;

van Dick, Wagner, & Gautam, 2002; van Dick, Wagner, Lemmer, & Britt Lima,

2002) and .8 or above (Potvin, 1991; Sass & Canary, 1991; Sauer, 1997). Using struc-

tural equation techniques, Bergami and Bagozzi (2000), Gautam, van Dick, and

Wagner (in press), Mael and Tetrick (1992), and van Knippenberg and Sleebos

(2001) found that AOC and OI fit a two-factor model better than a single-factor

model. Nevertheless, the correlation between the two constructs was strong in these

studies (rs > .50). This begs the question of whether the distinctiveness of AOC andOI matters for any theoretically or practically significant purposes—for example,

when the goal is to explain or predict work outcomes.

One way of addressing this question is to compare the correlates of OI and AOC.

If no differences can be detected, or if they occurred only on correlates that were of

minor importance for research and practice, it would be wise to abandon one of

these terms to avoid conceptual confusion and redundant research efforts (see Sass

& Canary, 1991; for an empirical study based on this reasoning). Accordingly, in

the present study, the overlap in the phenomena tapped by OI and AOC measureswas examined by comparing their correlates. Given the heterogeneity of OI mea-

sures, some OI measures may be empirically distinct from AOC measures while oth-

ers are not. Furthermore, the same OI measure may have discriminant validity from

the ACS but not from the OCQ or vice versa. To account for this, I conducted sep-

arate analyses for the most often used OI measures on the one hand and for the ACS

and the OCQ on the other. In the following, I will give a meta-analytic overview of

the correlates of OI and then compare the results with findings from previous meta-

analyses of AOC research.


3. Meta-analysis of research on organizational identification

3.1. Study collection and coding of study characteristics

All empirical studies were of potential relevance that: (a) explicitly dealt with OIand (b) operationalized it with a measure labeled OI. Both published and unpub-

lished studies were considered. To identify published studies, I searched the keyword

‘‘organizational identification’’ in the electronic databases PsycINFO, Business

Source Premier, and Social Sciences Citation Index (all databases updated last time

in May 2003) and on the Internet with several search engines. To identify unpub-

lished studies, I sent e-mails to 21 researchers who either had published on OI or

whom I knew to have conducted unpublished studies on OI (e.g., from the research-

ers� Internet homepages). I asked those researchers whether they had unpublisheddata on OI and if yes, whether they would provide me with them. Eleven researchers

responded, and 8 provided usable unpublished data. The references in every research

report obtained were examined to identify other relevant studies.

The final meta-analysis was conducted on data from 96 independent samples (to-

tal N = 20,905; see References for the single studies). For each sample, the following

information was coded: sample size, name, and source the OI measure used, corre-

lations of OI with other variables (called correlates in the following), and reliabilities

of the measures involved in the coded correlations (i.e., internal consistencies orsplit-half reliabilities). Only those correlates were considered in the final analysis

for which data from at least five independent samples were available (see first column

of Table 1 for these correlates). Data referring to four of these correlates (job scope/

challenge, organizational prestige, in-role performance, and extra-role performance)

were coded independently by me and a graduate student who received detailed cod-

ing instructions. Intercoder agreement was at least 95% for the correlations and reli-

ability estimates in each category. Inconsistencies were resolved by discussion. Given

this high agreement, I coded all other information by myself. A list of the analyzedsamples with their coded characteristics is available from the author.

3.2. Computations

The computational procedure followed the meta-analytic approach of Hunter and

Schmidt (1990). Hunter and Schmidt suggested that a meta-analysis aggregate data

across studies and correct the data for artifacts as far as possible. The current meta-

analysis controlled for the artifacts of sampling and measurement error.To correct for measurement error, I disattenuated each coded correlation, that is,

divided it by the product of the square-roots of the reliability estimates of the in-

volved variables. With self-report data (except factor scores), the internal consisten-

cies or split-half reliabilities coded for the respective sample were used as reliability

estimates. Where no such estimate was coded, the N weighted average of all esti-

mates coded for the focal variable was inserted. In computing these averages, I

substituted the value 500 for all sample sizes larger than 500 to avoid that single

studies with extraordinarily large sample sizes overly dominated the weighted

Table 1

Correlates of organizational identification

Correlate k n rc SDrc SDrho % v2 CI

Demographic variables

Organizational tenure 25 5305 .13 .17 .11 30 83.67*** �.06 .32

Mael scale 6 1230 .16 .14 .12 27 22.39*** �.05 .36

Age 21 4802 .12 .17 .15 18 117.40*** �.13 .38

Mael scale 5 751 .07a .09 .03 90 5.55 .02 .12

OIQ 2 225 .60 .23 .22 8 24.75*** .24 .96

Job level 5 708 .24 .13 .09 46 1.94* .11 .42

Mael scale 2 428 .31 .11 .08 40 5.00* .17 .44

Female gender 18 4331 �.04a .11 .09 41 43.89*** �.18 .11

Mael scale 7 1100 �.11a .16 .14 29 24.31*** �.34 .11

Education 5 549 �.06a .12 .05 80 6.31 �.15 .02

Mael scale 3 330 �.01a .09 .00 100 2.23 — —

Work-related attitudes

AOC 16 4263 .78 .46 .46 1 2499.48*** .02 1.54

Mael scale 3 500 .79 .08 .06 47 6.44* .69 .89

OIQ 3 1022 .90 .08 .07 9 33.88*** .78 1.02

AOC measured with OCQ 8 2228 .79 .45 .45 1 1182.22*** .06 1.53

OIQ 2 572 .94 .01 .00 100 0.69 — —

AOC measured with ACS 7 1791 .71 .12 .12 11 61.82*** .53 .90

Mael scale 2 237 .74 .10 .07 53 3.75 .62 .85

Occupational attachment 13 2445 .47 .25 .24 9 145.73*** .08 .87

Mael scale 3 534 .39 .12 .09 43 7.02* .24 .54

Work group attachment 20 3867 .52 .24 .23 8 240.73*** .14 .91

Mael scale 4 446 .46 .17 .13 39 10.24* .25 .67

OIQ 2 238 .92 .09 .08 16 12.29*** .78 1.05

Job satisfaction 38 8759 .54 .20 .20 9 415.71*** .22 .86

Mael scale 7 1298 .47 .20 .19 12 56.56*** .16 .77

OIQ 8 1283 .68 .19 .18 9 85.27*** .38 .97

Organizational satisfaction 6 1530 .59 .17 .16 8 76.97*** .33 .86

Mael scale 2 297 .52 .05 .01 96 2.09 .50 .54

Job involvement 12 2837 .61 .13 .11 26 46.09*** .44 .79

Mael scale 6 1703 .60 .11 .09 39 15.26** .46 .75

Context characteristics

Job scope/challenge 10 1699 .33 .11 .07 61 16.42 .12 .54

Mael scale 2 342 .26 .08 .00 100 1.99 — —

Organizational prestige 16 5257 .56 .13 .12 19 82.57*** .37 .75

Mael scale 8 2423 .55 .13 .11 22 35.89*** .37 .73

Work-related intentions and behaviors

Intention to leave 34 7243 �.48 .20 .19 13 277.27*** �.79 �.17

Mael scale 9 2055 �.35 .15 .12 28 31.89*** �.55 �.15

OIQ 6 991 �.64 .13 .12 20 3.19*** �.84 �.44

In-role behavior 16 3009 .17 .14 .11 44 36.53** �.01 .35

Mael scale 5 891 .17 .17 .14 34 14.52** �.06 .40

Extra-role behavior 25 6644 .35 .19 .17 15 163.35*** .07 .63

Mael scale 9 2475 .39 .15 .13 22 4.23*** .18 .61

Absenteeism 6 1581 �.01a .06 .00 100 5.22 — —


Table 1 (continued)

Notes. Results for the Mael scale and the OIQ are reported only if k > 1. k, number of analyzed corre-

lations. n, sum of the sizes of the analyzed samples. rc, mean correlation corrected for sampling error and

attenuation. SDrc, observed standard deviation of the disattenuated correlations. SDrho, estimated stan-

dard deviation of the mean population correlation. %, percent variance of the observed correlations

attributable to artifacts. v2, result of the significance test for unaccounted variance, with df = k � 1. CI:

90% credibility interval around rc. Mael scale, studies using the scale developed by Mael (see Mael and

Tetrick, 1992). OIQ, studies using the Organizational Identification Questionnaire (Cheney, 1983). AOC,

attitudinal organizational commitment. ACS, Affective Commitment Scale (Allen and Meyer, 1990).

OCQ, Organizational Commitment Questionnaire (Mowday et al., 1979).a The 95% confidence interval around rc includes zero.* p 6 .05.

** p 6 .01.*** p 6 .001.


average. The average reliability estimates for all relevant variables were as follows:

AOC .79, extra-role performance (self-rating) .80, in-role performance (self-rating)

.83, intent to leave .81, job involvement .75, job satisfaction .84, job scope/challenge

.76, occupational attachment .83, OI .84, organizational prestige .79, organizational

satisfaction .86, and work group attachment .82. With other-reported data (here,

peer or supervisor ratings of in-role or extra-role performance), I used estimates

of interrater reliability rather than internal consistencies or split-half reliabilities in

disattenuating. Using the latter estimates would have underestimated true correla-tions because a type of measurement error that is specific to others� ratings (namely,

construct independent variation in responses due to rater idiosyncracies) would have

been uncorrected. Because no study included in the present meta-analysis reported

interrater reliabilities, Viswesvaran, Ones, and Schmidt�s (1996) meta-analytical esti-

mates of the interrater reliability of supervisor ratings (.52) and peer ratings (.42)

were used to correct correlations computed from such ratings. Finally, so-called hard

data (here: age, gender, education, organizational tenure, job level, absenteeism, and

objective indicators of in-role or extra-role performance such as sales figures andnumbers of contributions to suggestion programs) and factor scores of self-report

data were assigned reliability estimates of 1.00 in the disattenuation procedure

because (a) the forms of measurement error corrected in the other data were not

relevant here and (b) no other relevant reliability information for those data were

available from the analyzed studies.

In the next step, I averaged the disattenuated correlation coefficients for each var-

iable across independent samples. Following the recommendations of Hunter and

Schmidt (1990), to correct for sampling error, I weighted every corrected correlationcoefficient with the product of sample size and the reliability coefficients for the two

correlated variables. Sample size weights were again limited to 500. The average

weighted correlation coefficients are estimates of the population correlations. Note

that these estimates are necessarily flawed by all artifacts not corrected for, here,

all artifacts besides measurement and sampling error. To test the corrected mean cor-

relations for significance, I computed 95% confidence intervals around them using

the formulae by Hunter and Schmidt (1990). In the following, mean correlation coef-

ficients are called significant only if the confidence interval excludes zero.


Another parameter of interest was the variance of the population correlations.

The estimate recommended by Hunter and Schmidt (1990) was used, i.e., the differ-

ence 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 artifacts. The estimated variance of the population correlations wastested for significance with the v2 test of Hunter and Schmidt (1990). A significant

result would indicate that there is more than one population correlation, that is, that

moderators of the correlation exist. Further, 90% credibility intervals around mean

corrected correlations were computed by multiplying the square-root of the esti-

mated variance of the population correlations with the appropriate z value.

3.3. Results

Table 1 displays the results of the meta-analysis. Both overall results and results

for the the Mael scale and the OIQ are presented. These two measures were used in

their original form or a shortened version (i.e., a version consisting of at least half of

the items of the original scale) in 26 and 11 of the analyzed samples, respectively. All

other measures were used in less than eight samples. (A complete list of the measures

and their use frequencies is available from the author.) All ps reported in the follow-

ing are two-tailed. Results with p 6 .05, p 6 .10, and p > .10 are labeled significant,

marginally significant, and nonsignificant, respectively.

3.3.1. Overall analysis

For ease of presentation, the correlates were subdivided in four categories: demo-

graphic variables, work-related attitudes, context characteristics, and work-related

intentions and behaviors. In the demographic variables category, organizational ten-

ure, age, and job level related significantly to OI. The correlations were positive and

small to medium according to J. Cohen�s (1988) effect-size classification (rs = .13, .12,

and .24, respectively). The correlations with gender and education were nonsignifi-cant (rs = �.04 and �.06, respectively). The variables in the work-related attitudes

category all correlated significantly and positively with OI. The highest mean corre-

lation emerged between OI and AOC (r = .78). The mean correlations were similarly

strong for studies using the ACS and studies using the OCQ (rs = .71 and .79, respec-

tively; difference: z < 1). Further, OI correlated strongly with attachment to (i.e.,

either commitment to or identification with) one�s occupation and one�s work group

(rs = .47 and .52, respectively). Moreover, there was a strong overlap with job and

organizational satisfaction and job involvement (rs = .54, .59, and .61, respectively).Likewise, the correlations with the two context characteristics considered (job scope/

challenge and organizational prestige) were significant and positive. The correla-

tions were medium to large in size (rs = .33, and .56, respectively). Finally, of the

work-related intentions and behaviors considered, intention to leave correlated

strongly and negatively with OI (r = �.48), and in-role and extra-role performance

correlated weakly and moderately with OI (rs = .17 and .35, respectively). All cor-

relations were significant. The fourth variable in this category, absenteeism, was

unrelated to OI (r = �.01).


Note that the results of the analyzed OI studies were heterogeneous. For all vari-

ables except education, job scope/challenge, and absenteeism, (a) the v2 test lead to

the rejection of the null hypothesis that the individual correlations were equal (with

all ps < .01), and (b) more than half of the variance of the observed individual cor-

relations remained unexplained. Together, this indicates the likely existence of mod-erators for these variables. The following section deals with the question of whether

the operationalization of OI is a potential moderator. For this purpose, all studies

using either the OIQ or the Mael scale were analyzed separately.

3.3.2. Analyses for single OI measures

Separate correlations for the Mael scale and the OIQ were computed for every

correlate for which at least two samples using the respective measure were available.

In these analyses, all studies were considered that used either the original versions ofthese measures or shortened versions that included at least half of the items in the

original scales. To test for the moderating influence of using one of these scales in-

stead of any other scale, I conducted Hunter and Schmidt�s (1990) z test on the dif-

ferences between samples in which the Mael scale (OIQ) was used and all

independent samples in which other scales were used.

With regard to the Mael scale, data were available for all correlates considered in

the overall analyses except for OCQ and absenteeism. In general, the results paral-

leled the results from both the total analysis and the analysis for studies that usedanother OI scale (see Table 1 for details). The only significant difference emerged

in the category work-related intentions and behavior. The Mael scale correlated signif-

icantly less strongly with intent to leave (r = �.35) than did the other OI measures

(r = �.53, difference: z = 2.66, p = .01; all other zs < 1.57).

Correlations for the OIQ could be computed for six correlates only (see Table 1

for details). On four of them, significant differences between the OIQ and the other

measures emerged. In particular, the OIQ correlated significantly more strongly with

age (r = .60), work group attachment (r = .92), job satisfaction (r = .68), and intentto leave (r = �.64) than the other measures (rs = .09, .59, .42, and �.45, respectively;

zs > 2.21, ps < .04). Further, the OIQ correlated somewhat more strongly with AOC

in total (r = .90) and with the OCQ in particular (r = .94) than did the other OI mea-

sures (rs = .73 and .74, respectively). However, these differences were nonsignificant

(zs < 1.49).

3.4. Discussion

In interpreting the data, one should bear in mind that the correlations were very

heterogeneous in general. Thus, any conclusions drawn from the overall analysis

may not apply to single settings or measures in particular. Hence, the following

arguments apply to the fictitious average OI study. A particular real study may have

yielded quite different outcomes or may do so in the future.

The highest mean correlation obtained was the one with AOC (r = .78). This sug-

gests that the average OI study has focused on a construct that overlapped largely

with what is measured by AOC measures. However, the correlation is far from per-


fect; it corresponds to only 62% shared variance. Thus, OI as measured in the typical

OI study may well have unique features as compared with AOC in its common oper-

ational form. The next section deals with this possibility more in detail. Further, the

correlations with all other considered constructs to which OI is conceptually related

(job and organizational satisfaction, job involvement, and occupational and workgroup attachment) were markedly lower (shared variances below 40%). This suggests

that OI relates to but is distinct from these constructs.

The present analyses also revealed significant mean correlations with a range of

variables that may be either antecedents or consequences of OI, including tenure,

job scope/challenge, organizational prestige, intent to leave, and in-role and extra-

role performance. Because only correlational studies were analyzed, no attempt is

made here to interpret the results in terms of causality. The interested reader can find

detailed models of the causal relation of OI with most of the correlates consideredherein elsewhere (e.g., Bergami & Bagozzi, 2000; Cheney, 1983; Mael & Ashforth,

1992; van Knippenberg, 2000; Wan-Huggins et al., 1998). For present purposes, it

seems sufficient to note that the directions of all significant mean correlations re-

ported above are both intuitively plausible and consistent with the common causal

assumptions of OI researchers. From a more practical perspective, the significant

and partly strong correlations with those variables indicate that OI may be a useful

predictor of several variables that are relevant to organizational practice.

In light of the mentioned heterogeneity of the results of OI studies, it is revealingto look at the results for single measures. The Mael scale was used in the analyzed

samples most often. The results of the studies using this scale were close to the results

for studies using other measures as well as for the results for all measures. Hence, the

Mael scale seems to be the most representative OI measure with regard to its empir-

ical outcomes. Another interesting finding is that the correlations involving the Mael

scale showed much less variation than the correlations involving all OI measures.

For example, for several correlates, the correlations from all OI studies were strongly

significantly heterogeneous (ps < .001) but the correlations from studies using theMael scale were not (i.e., with regard to the correlates age, AOC as measured with

the ACS, and organizational satisfaction). Moreover, the credibility intervals were

typically narrower for correlations computed from studies using the Mael scale than

for the correlations computed from all studies. This suggests that (a) in general, the

operationalization of OI does matter for the correlations obtained (at least for the

variation in these correlations) and (b) the studies using the Mael scale comprise a

relatively homogenous subgroup within OI research with regard to their findings.

The second most often used measure in the samples analyzed was the OIQ.Although this scale is the most common OI measure in communication research

(see Miller, Allen, Casey, & Johnson, 2000), few studies have been reported correla-

tions with the OIQ that pertained to the same variables. In the studies analyzed here,

more than two correlations involving the OIQ were available only for six correlates.

Yet, it is striking that all six correlations deviated markedly (and four of them sig-

nificantly) from the average across the studies that used another measure. Especially

important, the OIQ was more strongly related to three constructs that are conceptu-

ally similar to OI: AOC, work group attachment, and job satisfaction. Thus, the OIQ


seems to have less discriminant validity with regard to AOC scales than do the other

OI measures in total and the Mael scale in particular. Specifically, the very strong

correlation with AOC (r = .90) suggests that the OIQ is almost interchangeable with

AOC measures, especially with the OCQ (r = .94). In this respect, the present re-

search is in accordance with the conclusions that Sass and Canary (1991) drew fromtheir study, namely that OIQ and OCQ refer to the ‘‘same cluster of attitudes’’ (p.

275). Further, the OIQ correlated more strongly with intent to leave and age than

did the other OI measures, including the Mael scale. The former correlation might

point to a higher usefulness of the OIQ as compared with the average other OI mea-

sure when the goal is to predict intent to leave. However, because the individual cor-

relations were strongly significantly heterogenous in all of these analyses (ps < .001),

the aforementioned conclusions regarding the OIQ are only tentative.

Because OI is strongly correlated with AOC, one may wonder whether it differsfrom this construct with respect to its correlates. In other words, does it make a dif-

ference whether one uses an OI scale or an AOC scale in predicting or explaining

other variables? The following section addresses this question.

4. Meta-analytic comparison between organizational identification and attitudinal

organizational commitment

As mentioned above, OI and AOC would prove distinct if they differed in the

strength of their respective correlations with same variables. To explore whether this

is the case, I compared the results reported above with results from previous meta-

analyses of AOC studies. Because the vast majority of AOC studies used either the

ACS or OCQ, I considered only studies that used one of these measures. This had

the advantage that the analyzed studies were rather homogenous with respect to

operationalization of AOC. Yet, because ACS and OCQ partly differ in the contents

of their items (see above), I conducted all comparisons separately for the ACS andthe OCQ. In addition, to account for the heterogeneity in the operationalization of

OI, I conducted separate analyses for the Mael scale and the OIQ alongside overall

analyses, which referred to all OI measures.

4.1. Study search and computations

To identify meta-analyses that reported correlations involving either the ACS or

the OCQ, I searched the keywords ‘‘commitment’’ and ‘‘meta-analysis’’ in the dat-abases Business Source Premier, PsycINFO, and Social Sciences Citation Index

(all databases updated last time in May 2003). I considered only those meta-analyses

that (a) reported mean correlations corrected for sampling error and unreliability,

(b) dealt with at least one correlate that was considered in the above analyses for

OI, and (c) reported, or allowed for the computation of, separate correlations for

the ACS and the OCQ. When results referring to the same correlate were available

from two or more meta-analyses, I considered only the result based the largest num-

ber of studies. The following meta-analyses met all these criteria and contributed at


least one mean correlation to the analyses reported below: Aven, Parker, and McE-

voy (1993), Cohen (1993), Meyer, Stanley, Herscovitch, and Topolnytsky (2002),

Riketta (2002), Tett and Meyer (1993), and Wallace (1993). Because Riketta

(2002) and Wallace (1993) did not report separate results for ACS or OCQ with re-

gard to the correlates that were of interest in the present study, I re-analyzed theirdatasets. In doing so, I included only the studies that used the OCQ (either in its ori-

ginal 15-item or a shortened version, as long as the latter contained at least eight

items of the long version). The data allowed for comparisons between OI and

AOC on 11 correlates.

4.2. Results

Table 2 shows the results for the ACS or the OCQ. To enable a quick comparisonwith the results of the above analyses for OI, Table 2 displays the corresponding cor-

rected mean correlations from Table 1 in addition (i.e., correlations for OI scales in

Table 2

Comparison of findings from research on organizational identification (OI) and attitudinal organizationa

commitment

Variable rc from OI studies Studies using the ACS Studies using the OCQ

Total Mael OIQ k n rc SDrc k n rc SDrc

Organizational

tenure

.13 .16 — 51 18630 .16 .13 48 26175 .10 .09

Age .12B .07a,B .60A,B 53 21446 .15 .10 53 26402 .20 .10

Female gender �.04 �.11 — 32 11764 �.03 .10 16 8979 �.01 .12

Education �.06 �.01 — 32 11491 �.02 .12 —

Occupational

attachment

.47 .39 — 13 3599 .51 .11 16 6414 .42 .15

Job satisfaction .54A .47A .68 69 23656 .65 .14 47 14597 .68 —

Job involvement .61a .60 — 16 3625 .53 .14 —

Intent to leave �.48a �.35A �.64 51 17282 �.56 .22 24 5403 �.53 —

In-role

performance

.17 .17 — 25 5938 .16 .12 47 11072 .18 .13

Extra-role

performance

.35B .39B — 22 6277 .32 .15 12 3446 .23 .08

Absenteeism �.01A — — 22 3543 �.15 .09 —

Notes. The data referring to the OCQ are from the following sources: Cohen (1993) for age and tenure

Aven et al. (1993) for female gender. Tett and Meyer (1993) for intent to leave and job satisfaction

(sample-size weighted averages of the results for OCQ-15 and OCQ-9; results for intent to leave are the

results that Tett and Meyer reported for both turnover intention and withdrawal cognition); Riketta

(2002) for in-role and extra-role performance (re-analyzed); Wallace (1993) for occupational attachmen

(re-analyzed). The data referring to the ACS are from Meyer et al. (2002). rc, mean correlation corrected

for sampling error and attenuation. ACS, Affective Commitment Scale (Allen and Meyer, 1990). OCQ

Organizational Commitment Questionnaire (Mowday et al., 1979). k, number of analyzed correlations. n

sum of the sizes of the analyzed samples. SDrc, observed standard deviation of the disattenuated corre

lations. Total, all OI studies. Mael, studies using the Mael scale. OIQ, studies using the OIQ.a Differs at p 6 .10 from the corresponding rc for the ACS.A Differs at p 6 .05 from the corresponding rc for the ACS.B Differs at p 6 .05 from the corresponding rc for the OCQ.

l

;

t

,

,

-


total, the Mael scale, and the OIQ). As far as the necessary data were available, all

possible differences in the correlations for all OI scales in total, the Mael scale, and

the OIQ on the one hand and ACS and OCQ on the other were tested for significance

with Hunter and Schmidt�s (1990) z test.

The analyses for all OI measures revealed at least marginally significant differ-ences on 6 of the 11 correlates. In particular, OI measures in total differed from

the ACS at least marginally significantly on the correlations with job satisfaction

(r = .54 vs. .65 for OI measures in total and the ACS, respectively; difference:

z = 2.96, p < .01), job involvement (r = .61 vs. .53, z = 1.65, p = .10), intent to leave

(r = �.48 vs. �.56, z = 1.77, p = .08), and absenteeism (r = �.01 vs. �.15, z = 4.59,

p < .001). Further, OI measures in total differed from the OCQ significantly on the

correlations with age (r = .12 vs. .20, z = 1.98, p = .05) and extra-role performance

(r = .35 vs. .23, z = 2.59, p = .01). Another marked difference between OI measuresin total and the OCQ and emerged with regard to job satisfaction (r = .54 vs. .68).

Significance testing was not possible in this case due to missing information. (All

other zs < 1.)

Results for the Mael scale were very similar. At least marginally significant differ-

ences occurred on four of the ten correlates for which comparison data were avail-

able. In particular, the Mael scale differed from the ACS at least marginally

significantly on the correlations with age (rs = .07 vs. .15, z = 1.71, p = .09), job sat-

isfaction (r = .47 vs. .65, z = 2.36, p = .02), and intent to leave (r = �.35 vs. �.56,z = 3.67, p < .001). The difference from job involvement was in the same order as

in the analyses for OI measures but nonsignificant (r = .60 vs. .53, z = 1.22). The

Mael scale differed from the OCQ significantly on the correlations with age

(rs = .07 vs. .20, z = 2.85, p < .01) and extra-role performance (r = .39 vs. .23,

z = 2.93, p < .01). Further, like OI measures in total, the Mael scale differed mark-

edly from the OCQ on the correlation with job satisfaction (r = .47 vs. .68). Again,

no significance testing was possible in this case. (All other zs < 1.60.)

The available data permitted comparisons for the OIQ on only three corre-lates. The OIQ differed from both ACS and OCQ significantly on the correlation

with age (r = .60 vs. .15 and .20, z = 2.81, p < .01, and z = 2.45, p = .01, respec-

tively). Note that the direction of the difference is opposite to the one for all

OI scales in total and the Mael scale in particular. Further, the OIQ showed

(nearly) the same correlation with job satisfaction as did the ACS and the

OCQ (r = .68 vs. .65 and .68, z < 1 and no significance testing possible, respec-

tively). Finally, the OIQ tended to show a stronger correlation with intent to

leave than did the ACS and the OCQ (r = �.64 vs. �.56 and �.53, respectively),although the differences were nonsignificant (z = 1.28) and not testable for signif-

icance, respectively.

4.3. Discussion

Despite the heterogeneity of the findings of OI research, this analysis revealed sev-

eral statistically significant differences in the average outcomes of OI and AOC re-

search. The two most important findings can be summarized as follows. First, OI


seems to overlap less strongly with job satisfaction and more strongly with job

involvement than does AOC. This hints to substantive differences between the con-

structs of OI and AOC. Second, OI seems to differ from AOC in the correlations

with some work-related behaviors and intentions. Specifically, OI was less strongly

related to absenteeism and intent to leave than was AOC but was more strongly re-lated to extra-role performance.

All mentioned differences except the one for job involvement were significant also

in the analyses for studies that used the Mael scale. This confirms the above conclu-

sion that the Mael scale is a rather typical or representative OI measure. By contrast,

the OIQ proved more similar to the AOC measures than to the typical OI measure.

Specifically, like the ACS and OCQ, the OIQ correlated more strongly with age, job

satisfaction, and intent to leave than did all OI measures in total and the Mael scale

in particular. The correlation with job satisfaction was even virtually identical to thecorrelations of the ACS and OCQ with this variable. Thus, the present findings are

consistent with Miller et al.�s (2000) and Sass and Canary�s (1991) conclusion that the

OIQ is almost interchangeable with AOC measures.

How can one explain the observed differences between OI in general and the Mael

scale in particular on the one hand and the ACS and the OCQ on the other? In gen-

eral, these differences seem to be consistent with the fact that many OI scales and

especially the Mael scale were designed to measure a more narrowly defined con-

struct than AOC. On the one hand, this fact can explain the relatively low correla-tion of OI with job satisfaction, absenteeism, and intent to leave. These variables can

have many other causes apart from identification. Some of these causes may be cap-

tured by AOC scales better than by OI scales. For example, some items of the OCQ

and the ACS refer to willingness to stay with the organization and to such evalua-

tions of the organization that are not necessarily related to identification but may

indicate or contribute to job satisfaction, such as, respectively, ‘‘I would be very hap-

py to spend the rest of my career with this organization’’ (ACS) or ‘‘There is not too

much to be gained by sticking with this organization indefinitely’’ (OCQ) and ‘‘Forme this is the best of all possible organizations to work for’’ (OCQ) or ‘‘I enjoy dis-

cussing my organization with people outside it’’ (ACS). Some OI scales, among them

the widely used Mael scale (and also, e.g., the scales by Bergami & Bagozzi, 2000;

Lee, 1969; O�Reilly & Chatman, 1986), do not include such items. This can explain

why OI scales in total and the Mael scale in particular relate less strongly to those

variables than do AOC measures.

On the other hand, the narrower focus of OI measures can explain why the cor-

relation of OI with extra-role behavior and job involvement is relatively high. Extra-role behavior is defined as voluntary behavior that is beneficial to the organization

(Organ, 1988). Among others, the motivation for such a behavior may stem from

internalization of organizational norms and emotional attachment to the

organization (van Knippenberg, 2000). These two variables, however, are at the core

of most definitions and measures of OI. Thus, in general, OI measures may be better

predictors of extra-role behaviors than are AOC measures because the former focus

more narrowly on these crucial causes of extra-role behaviors than do the latter,

which may also refer to variables that are not relevant to extra-role behaviors. Fur-


ther, job involvement overlaps conceptually with intrinsic motivation. Because OI as

commonly defined is likely to produce such an intrinsic motivation (van Knippen-

berg, 2000), OI should be an important predictor of job involvement. Thus, similarly

as in the case of extra-role behavior, the narrower focus of OI measures on factors

that contribute to intrinsic motivation as compared with AOC measures can explainthe formers� stronger correlation with job involvement.

In light of this reasoning, the finding that the OIQ proved empirically almost

interchangeable with AOC scales in so many respects becomes understandable. Un-

like other OI measures (including the Mael scale), the OIQ was based on a broad def-

inition of OI (namely, Patchen�s, 1970). Consequently, some items of the scale

obviously refer to variables that are conceptually distinct from OI (e.g., ‘‘I would

be willing to spend the rest of my career with the organization’’; ‘‘I am glad to have

chosen to work for the organization rather than another company’’; ‘‘I feel that theorganization cares about me’’). Thus, scores of the OIQ may reflect a similar broad

mixture of satisfaction, work-related intentions, and identification as do scores of

AOC measures. Hence, it should not be surprising that the OIQ has similar empirical

qualities as AOC scales (for similar conclusions, see Miller et al., 2000; Sass &

Canary, 1991).

5. General discussion

5.1. Correlates and distinctiveness of organizational identification

The results presented in the two foregoing main sections provided at least two

important insights. First, they showed that OI as it has been measured in previous

research correlates with a number of practically or theoretically interesting variables.

Some of them can plausibly be interpreted as either antecedents or consequences of

OI, for example, organizational tenure, job scope/challenge, organizational prestige,intent to leave, and in-role and extra-role performance. Thus, the present analysis

pointed to several variables that should be considered in research aimed at exploring

the nomological network around OI. Moreover, the results suggest that OI may be a

useful predictor for many practically relevant variables.

Second, the analyses indicated that OI as it has been measured in previous re-

search is empirically distinct from conceptually related constructs. For one, it proved

distinct from its closest conceptual neighbor, AOC (cf. Mael & Tetrick, 1992; Pratt,

1998; van Dick, 2001). Although the empirical overlap was large (61% shared vari-ance), OI proved distinct from AOC with respect to its correlates. In particular, OI

correlated less strongly with job satisfaction, intent to stay, and absenteeism, and

more strongly with extra-role behavior and job involvement than did AOC. The

results of these analyses suggest that empirical OI research has indeed dealt with a

construct that is more specific or homogeneous than is AOC (e.g., Ashforth & Mael,

1989; van Knippenberg & Sleebos, 2001). Moreover, the correlations with three

other conceptually related constructs, job and organizational satisfaction and job

involvement, indicated that OI was related to but nonetheless distinct from them


(less than 37% shared variance). Thus, and consistent with most current OI research-

ers� claims, OI as operationalized in the average previous OI study had unique empir-

ical qualities.

5.2. The role of the operationalization of organizational identification

The aforementioned conclusions were based on the overall analyses for all OI

studies. Individual studies may yield outcomes that are not compatible with the

above conclusions, that is, moderators may exist. The existence of moderators is very

likely because for most correlates considered herein, the observed variation in indi-

vidual correlations was statistically significant and remained substantial even after

correcting for sampling error and unreliability (i.e., these corrections mostly reduced

the observed variance by less than 50%).Because many different OI measures have been used in previous research, the

operationalization of OI suggests itself as such a moderator. To explore whether this

is the case, I conducted separate analyses for studies that used one of the two most

common OI measures, the Mael scale and the OIQ. It turned out that the Mael scale,

which is the most often used OI scale, yielded results that were close to the average

across studies that used other scales and were relatively homogenous. It follows that

the above conclusions may apply particularly to studies using the Mael scale.

Quite differently, the OIQ produced results that (a) differed from the average re-sults of studies that used other scales and (b) were especially heterogeneous. More-

over, the OIQ seems to be particularly similar to an AOC scale. This is perhaps most

clearly indicated by the facts that the OIQ correlated very strongly with AOC scales

(r = .90, 81% common variance) and correlated similarly strongly with job satisfac-

tion as did AOC (r = .68, 46% common variance). Thus, the conclusions from the

foregoing section might not apply to studies using the OIQ.

Because the results of OIQ studies were very heterogeneous and separate analyses

were possible only for few correlates, it may not be justified to draw specific conclu-sions about the OIQ on the basis of the available studies. Nevertheless, the results do

suggest that results obtained with the OIQ are not equivalent to results obtained with

the Mael scale. This has at least two implications. First, reviewers of the OI literature

should be more cautious with generalizing across OI measures than it has been the

case in previous reviews of OI research. Second, empirical OI researchers who want

to minimize redundancy of their findings with findings from AOC research should

use the Mael scale rather than the OIQ.

Overall, the results speak to the usefulness of the Mael scale. Given the additionaladvantages of this scale (short and easy to administer; widely used; many results

demonstrating its reliability and construct validity), it appears to be the best OI mea-

sure available to date.

5.3. Limitations

Several limitations of the present studies should be mentioned. For one, the num-

ber of analyzed studies was small for several correlates. Thus, in theory, a small num-


ber of future studies could markedly change the results for those correlates. This is

especially true for the analyses regarding the Mael scale and the OIQ, in which all

sample sizes were below 10. Less than 10 samples were available also with regard

to the following correlates: job level, education, organizational satisfaction, and

absenteeism. Thus, the results referring to these variables should be consideredrather preliminary.

A general problem of meta-analyses is that the observed effects of moderator vari-

ables may actually be due to variables confounded with the moderator. In particular,

it cannot be ruled out that the observed differences between OI and AOC research

reported herein reflect (also) other differences between these branches of research

rather than (only) differences between OI and AOC measures. For example, on aver-

age, OI and AOC research might differ also with respect to design, populations, or

operationalization of correlates. These variables may affect the results in addition tothe focal variable (OI vs. AOC). In general, too few studies were available for the

present analyses to systematically consider these variables. However, it is important

to note that the present results are partly in line with studies that compared the cor-

relates of OI and AOC within the same sample and hence held the mentioned fea-

tures constant. For example, also Mael and Tetrick (1992) and van Knippenberg

and Sleebos (2001) found that OI as measured with the Mael scale related less

strongly to job satisfaction than did AOC; in addition, like the present research,

van Knippenberg and Sleebos found a weaker relation of OI with intent to leave.Further, as shown above, especially the observed differences with respect to extra-

role behavior, intention to leave, job satisfaction, and job involvement can be plau-

sibly linked to common differences between OI and AOC measures (namely, the

usually narrower focus of the former ones on norm internalization or emotional

attachment). Thus, it appears likely that the observed differences reflect unique fea-

tures of OI and AOC measures rather than only other features of the analyzed

studies.

Finally, one should note that OI and AOC were only studied in their operationalform. The present analyses dealt only with the question of whether empirical OI re-

search has indeed studied something that is not identical to what has been empiri-

cally studied under the rubric of AOC. The present analyses did not address the

question of whether the OI measures used in the analyzed studies were adequate

in light of specific definitions of OI. For example, some items of the Mael scale seem

to refer to affective components of OI (e.g., ‘‘When someone praises this organiza-

tion, it feels like a personal compliment’’) whereas the OI definition underlying the

development of this scale explicitly excluded affective components (cf. Ashforth &Mael, 1989; Mael & Tetrick, 1992). Hence, in studies using this scale, there might

be a mismatch between the conceptualization and operationalization of OI (for this

criticism, see also van Dick, 2001). In general, this shows that the results reported

herein cannot be taken as evidence for the adequacy or the success of particular

OI concepts. Thus, although the results of the present analyses point to differences

between OI and AOC at the empirical level, the precise nature of this difference

and its implications for theorizing on OI still have to be explored (e.g., what the Mael

scale taps that AOC scales do not tap and vice versa).


5.4. Research recommendations

The present findings point to several promising areas for future research. For

example, the findings suggest that OI generally correlates with some variables that

play a crucial role in theorizing on OI, such as organizational prestige, intent toleave, and in-role and extra-role performance (for relevant models, see, e.g., Duke-

rich, Golden, & Shortell, 2002; Mael & Ashforth, 1992; Wan-Huggins et al., 1998;

van Knippenberg, 2000). Hence, in the next step, it would be necessary to explore

the causal relation of OI with these variables and thus to test the extant theories.

Moreover, in light of the heterogeneity of the results for almost all of the correlates

studied herein, research on moderators seems necessary. The present meta-analysis

studied only the operationalization of OI as moderator. Several other moderators

are conceivable. For example, regarding the relation between OI and work out-comes, research on AOC or on social identification suggest moderators such as

accountability (Barreto & Ellemers, 2000), economic dependency on the job (Brett,

Cron, & Slocum, 1995), intergroup comparisons (Ouwerkerk et al., 1999; Tajfel &

Turner, 1986), or organizational tenure (Wright & Bonett, 2002). These moderators

could not be examined meta-analytically herein due to small numbers of studies and/

or lack of information in the original reports.

Another important task for future research is to avoid a further fragmentation of

the field with respect to OI measurement. In more than a half of the samples includedin this meta-analysis, an OI scale was used that was constructed ad hoc. This heter-

ogeneity in the operationalization of OI makes it difficult to compare results between

studies. Further, the present analyses suggested that the choice of the OI measure

may influence the size of the correlations obtained (cf. the separate analyses for

the OIQ). Thus, the heterogeneity at the measurement level may have substantially

contributed to the large heterogeneity of the observed correlations. Hence, rather

than introducing new measures, future research should use already established mea-

sures whenever possible. As explained above, the Mael scale currently appears thepreferable measure. The use of another existing measure may be justified if the re-

search goal is to replicate or extend findings obtained with that measure. However,

even in this case, it seems advisable to include the Mael scale (or other OI scales) in

addition to explore the generality of the findings across operationalizations of OI.

This is not meant to say that OI research should not address conceptual issues any

more. For specific purposes, more differentiated conceptualizations of OI may prove

useful. For example, several studies have shown that different subtypes of identifica-

tion (e.g., affective vs. cognitive) relate differentially to work outcomes (see Bergami& Bagozzi, 2000; van Dick, Wagner, Stellmacher, & Christ, 2004; also Ellemers,

Kortekaas, & Ouwerkerk, 1999). Thus, further research on the dimensions of OI

does seem desirable. Yet, before developing completely new scales that measure

OI on these dimensions, researchers should explore to which extent existing scales,

for example the Mael scale, tap one or more of those dimensions and whether they

can either be decomposed in subscales or combined with each other to measure those

dimensions. This way, researchers could promote a more differentiated look at OI

while ensuring some comparability with previous research. However, if the develop-


ment of a new OI scale appears necessary for some theoretical reason, researchers

should demonstrate the incremental validity of the new scale over existing ones to

justify its use. In any case, before developing another OI measure, researchers should

carefully weight the potential benefits of doing so against the potential costs (e.g.,

noncomparability of the own findings with previous ones).A final issue refers to the distinction between OI and AOC in future research. This

distinction has a methodological and a theoretical aspect. The methodological aspect

can be expressed in the question: When should one use an OI scale rather than an

AOC scale and vice versa? The present findings suggest that either type of scale

can have unique advantages when the research is merely to predict other variables.

Specifically, OI scales (especially the Mael scale) may be superior in predicting extra-

role behavior whereas AOC scales may be superior in predicting absenteeism and in-

tent to stay. However, when the research goal is to test theoretical models, some OIscales (e.g., the Mael scale) appear basically superior to AOC scales because they

represent a more narrowly and precisely defined construct. Thus, they may enable

a more fine-grained empirical analysis of psychological processes than do AOC

scales. For example, several models clearly distinguish between (affective and/or cog-

nitive) OI and its evaluative and motivational consequences (such as job satisfaction

and intent to stay; e.g., Ashforth &Mael, 1989; van Dick, 2001). A test of these mod-

els requires that these constructs are measured separately. AOC scales, which obvi-

ously tap a mixture of (components of) OI and evaluative and motivationalvariables, appear less suited to meet this requirement than do certain OI scales with

their more specific focus.

A second and more theoretically important question in the context of the OI–

AOC distinction is: To what extent should researchers interested in OI take into

account findings referring to AOC and vice versa? In my view, the answer is: to

the greatest extent possible. There are at least two reasons for this. One reason

is that this can avoid redundant research efforts. The use of particular scales is of-

ten a matter of personal habit or a striving to appear consistent. A researcher whohas used the same AOC or OI scale in all of his or her previous studies will likely

use and may be expected to use this scale also in the future. Thus, AOC scales may

often have been used in studies for which an OI scale would have been more

appropriate and vice versa. Moreover, even if the focal construct (OI vs. AOC)

has been deliberately selected, the results may be relevant to research on both con-

structs because in many (albeit evidently not all) research contexts, OI and AOC

scales may fulfill almost identical roles. For example, the above analyses suggest

that OI and AOC may be similarly suited for the prediction of in-role behavior.In all mentioned cases, the findings are relevant to research on both OI and

AOC, irrespective of the rubric under which they have been reported. Hence, an

OI researcher who plans to test (what he or she believes to be) an innovative

hypothesis should examine whether this hypothesis has already been tested with

regard to AOC and vice versa. If this is the case, a test of the hypothesis may

not be much more than a replication, and it may be more appropriate to consider

the hypothesis as already confirmed and use it as a starting-point for addressing a

more sophisticated issue.


A second reason why OI researchers should be open-minded to AOC research and

vice versa is that researchers in both fields seem to be able to provide each other with

valuable theoretical input. For one, AOC researchers may profit from several models

and theories that have been used in OI research and that do not have parallels in

AOC research, for example, social identity theory (Tajfel & Turner, 1986), Elsbach’s(1999), taxonomy of identification types, Rousseau�s (1998) distinction between sta-

ble and temporary identification, and models of the dimensions of OI (Ellemers

et al., 1999; van Dick et al., 2004). Conversely, OI researchers may profit from ideas

that originated in AOC research and have not yet been taken up in OI research, for

example, assumptions concerning moderators of the AOC–work behavior relation

such as economic dependency on the job (Brett et al., 1995) and tenure (Wright &

Bonett, 2002). Further, OI and AOC researchers seem to have enough common

ground to engage in fruitful discussions about theoretical issues. Thus, they may ben-efit not only from transfer of theoretical models but also from mutual criticism.

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a meta analysis of organizational identification

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