Journal of Applied Psychology
2006, Vol. 91, No. 4, 884 – 899
Copyright 2006 by the American Psychological Association
0021-9010/06/$12.00 DOI: 10.1037/0021-9010.91.4.884
Psychological Collectivism: A Measurement Validation and Linkage to
Group Member Performance
Christine L. Jackson
Jason A. Colquitt
Purdue University
University of Florida
Michael J. Wesson
Cindy P. Zapata-Phelan
Texas A&M University
University of Florida
The 3 studies presented here introduce a new measure of the individual-difference form of collectivism.
Psychological collectivism is conceptualized as a multidimensional construct with the following 5 facets:
preference for in-groups, reliance on in-groups, concern for in-groups, acceptance of in-group norms, and
prioritization of in-group goals. Study 1 developed and tested the new measure in a sample of consultants.
Study 2 cross-validated the measure using an alumni sample of a Southeastern university, assessing its
convergent validity with other collectivism measures. Study 3 linked scores on the measure to 4
dimensions of group member performance (task performance, citizenship behavior, counterproductive
behavior, and withdrawal behavior) in a computer software firm and assessed discriminant validity using
the Big Five. The results of the studies support the construct validity of the measure and illustrate the
potential value of collectivism as a predictor of group member performance.
Keywords: collectivism, individual differences, teams, groups, personality
Nason, & Smith, 1999; Murphy, 1999; Perrewe & Spector, 2002).
Highly collective individuals see themselves as members of one or
more in-groups, are primarily motivated by the norms of those
in-groups, prioritize the goals and well-being of those in-groups,
and emphasize their connectedness to other in-group members
(Triandis, 1995). Scholars have theorized that collectivism could
enhance one’s tendency to cooperate in group contexts (Chen,
Chen, & Meindl, 1998; Cox, Lobel, & McLeod, 1991; Earley &
Gibson, 1998; Wagner, 1995). Still, a fundamental question remains largely unexplored: Does collectivism predict members’ job
performance in work group settings?
One reason for the lack of research linking collectivism to group
member job performance may be the reliability and validity of
collectivism measures. In a recent review, Earley and Gibson
(1998) concluded, “What seems to be a safe, but pessimistic,
conclusion is that if further research is to be conducted using
individualism– collectivism, then continued efforts need to be
made in improving its measurement” (p. 296; see also Kagitcibasi,
1994; Oyserman, Coon, & Kemmelmeier, 2002; Triandis, Chan,
Bhawuk, Iwao, & Sinha, 1995). With that in mind, we report the
results of three independent studies that (a) validate a new measure
of collectivism for use in work contexts and (b) link scores on that
measure to group member performance. The first study developed
and tested the new measure in a sample of management consultants. The second study cross-validated the measure in a university
alumni sample and examined its convergent validity with other
collectivism scales. The third study assessed the discriminant
validity of the new measure with established individual differences
and tested the predictive validity of the measure by linking it to
supervisor ratings of group member job performance in a sample
of computer software developers.
As the nature of work has changed, cooperation among employees has become increasingly important (Hedge & Borman, 1995;
Mohrman & Cohen, 1995; Motowidlo & Schmit, 1999). Work is
increasingly scheduled around group-based projects rather than
individual job descriptions, with individual employees working in
several different groups at any given time (Ilgen & Pulakos, 1999;
Mohrman & Cohen, 1995). The performance evaluation and compensation systems that govern the employees in such groups often
build in cooperative components, with members appraised and
rewarded on the basis of shared achievements (e.g., Bartol &
Durham, 2000; DeMatteo, Eby, & Sundstrom, 1998). Such systems may also supplement the traditional focus on “taskwork”
behaviors by considering “teamwork” behaviors on the part of
individual group members (e.g., LePine, Hanson, Borman, &
Motowidlo, 2000). As a result, the very conceptualization of job
performance has changed, as the concept of “doing a good job” has
become more dependent on the concept of “being a good group
member.”
As job performance takes on a more cooperative character, it
becomes necessary to identify employees with a propensity to be
effective group members. Reviews in the personality and teams
literatures have speculated that collectivism could take on a new
importance in the contemporary workplace (Kozlowski, Gully,
Christine L. Jackson, Department of Management, Krannert School of
Business, Purdue University; Jason A. Colquitt and Cindy P. ZapataPhelan, Department of Management, University of Florida; Michael J.
Wesson, Department of Management, Texas A&M University.
Correspondence concerning this article should be addressed to Christine
L. Jackson, Department of Management, Krannert School of Business,
Purdue University, 403 West State Street, West Lafayette, IN 47907-2076.
E-mail: jackson2@krannert.purdue.edu
884
PSYCHOLOGICAL COLLECTIVISM
885
Collectivism: Origins and Measurement
Families of Measures
Much of the attention devoted to collectivism can be attributed
to Hofstede’s (1980) cross-cultural study that identified
individualism– collectivism as one of four major cultural variables.
Scholars have continued to examine individualism– collectivism as
a cultural variable, often using the country rankings provided by
Hofstede as proxies. Others have developed new scales using
Hofstede’s variables as a basis for their research. For example, the
Global Leadership and Organizational Behavior Effectiveness
(i.e., GLOBE) Project created scales to assess two forms of collectivism (one referenced to the collective distribution of resources
and one referenced to the cohesion of organizations and families)
with the intention of predicting organizational practices and leader
attributes and behaviors (House, Javidan, Hanges, & Dorfman,
2002).
Other studies have operationalized individualism and collectivism as individual-difference variables. For example, Triandis and
colleagues proposed that both could be viewed as psychological
dimensions that correspond to the constructs at the cultural level
(Triandis, Leung, Villareal, & Clack, 1985). The authors suggested
that the individual version of collectivism be termed allocentrism
and the individual version of individualism be termed idiocentrism. The value of this perspective can be seen in arguments by
Triandis and Suh (2002), who suggested that only around 60% of
the members of an individualistic culture will be idiocentric as
individuals and 60% in collective cultures will be allocentric. This
view is consistent with a recent meta-analysis by Oyserman et al.
(2002), which found that between-culture differences in collectivism were “neither as large nor as systematic as often perceived” (p.
40). Like Triandis et al. (1985), the studies reported here cast
collectivism as an individual difference. However, we eschewed
the allocentrism label in favor of psychological collectivism, a less
confusing term that has been used occasionally in the literature
(e.g., Hui, Triandis, & Yee, 1991). An individual-difference-based
view of collectivism has become more common in organizational
behavior, with several studies occurring within one culture (e.g.,
Colquitt, 2004; Colquitt, Noe, & Jackson, 2002; Cox et al., 1991;
Dickson & Weaver, 1997; Eby & Dobbins, 1997; Moorman &
Blakely, 1995; Pillai & Meindl, 1998; Van Dyne, Vandewalle,
Kostova, Latham, & Cummings, 2000) or using an individualdifference approach in multiculture studies (e.g., Earley, 1994;
Gibson, 1999; Gomez, Kirkman, & Shapiro, 2000; Kirkman &
Shapiro, 2001).
The studies reported here also follow an emerging consensus
in the literature that collectivism and individualism are not
polar opposites but rather orthogonal, independent constructs.
Oyserman et al.’s (2002) review summarized this position as
follows:
Most of the major measures of collectivism can be categorized
into three principal families that tend to share items across scale
versions. Appendixes A and B summarize these families, including
the articles that introduced and revised the collectivism scales, the
in-groups referenced by those scales, the labels for scale facets in
cases where the measure was multidimensional, and sample items.
We also provide the number of Web of Science citations from
2000 to 2005 for each of the articles, to provide a sense of the
current visibility of the various scales. The first family, shown in
Appendix A, is the Triandis family, introduced in Hui’s (1988)
dissertation, based on earlier work (Hui & Triandis, 1986). The
first four versions of the Triandis scales included between five and
seven facets (Hui, 1988; Triandis et al., 1986; Triandis, Bontempo,
Villareal, Asai, & Lucca, 1988; Triandis, McCusker, & Hui, 1990),
whereas the more recent versions collapsed those facets into two
broader dimensions (Gelfand & Realo, 1999; Singelis, Triandis,
Bhawuk, & Gelfand, 1995; Triandis & Gelfand, 1998). The second
family, summarized in Appendix B, is the Earley family, introduced in Erez and Earley (1987) and revised and expanded in
Earley (1993). Unlike the Triandis scales, Earley’s measures are
intended to capture a unidimensional form of collectivism. Appendix B also summarizes the Wagner family. The measure introduced in Wagner and Moch (1986) includes three facets, reflecting
collective beliefs, values, and norms. Wagner (1995) introduced a
different version that utilized many of the Triandis items.
Scales from all three of the major families have struggled with
issues of reliability. Hui’s (1988) own validation study yielded an
average reliability of .60 for his subscales. Studies using Singelis
et al.’s (1995) scales have found reliabilities as low as .59 (Oishi,
Schimmack, Diener, & Suh, 1998; Probst, Carnevale, & Triandis,
1999; Robert, Probst, Martocchio, Drasgow, & Lawler, 2000;
Triandis & Singelis, 1998). In a review article, Earley reported that
his measures had poor psychometric properties and recommended
that other scales be used (Earley & Gibson, 1998). Some studies
using Wagner and Moch’s (1986) scale have also failed to reach
the .70 standard (Chen, Meindl, & Hunt, 1997; Eby & Dobbins,
1997). Finally, Wagner’s (1995) scale has failed to reach the .70
standard in some instances, with reliabilities as low as .58 for the
subscales (Ramamoorthy & Carroll, 1998). These reliability problems were noted in Oyserman et al.’s (2002) review, which estimated that half of the cross-cultural studies on collectivism have
used scales with poor reliability.
The collectivism scales, as a set, have also been criticized with
respect to content validity (Earley & Gibson, 1998; Fiske, 2002;
Kagitcibasi, 1994; Oyserman et al., 2002; Triandis, 1995). For
example, Earley and Gibson (1998) argued, “Quite frankly, if one
simply observed the highly varied operationalizations of
individualism– collectivism without reference to the underlying
construct, it might appear that these measures tap unrelated constructs” (p. 291). In commenting on the inconsistent results of
Oyserman et al.’s (2002) meta-analytic review, Fiske (2002)
concluded,
Although the assumption of IND being the conceptual opposite of
COL may be intuitively appealing, an accumulation of recent research
suggests this simple approach does not sufficiently represent the
impact of IND and COL on basic psychological processes. Instead,
IND and COL are better understood as domain-specific, orthogonal
constructs. (p. 8; see also Ho & Chiu, 1994; Triandis, 1995)
Given our focus on predicting the performance of group members,
the remainder of this article focuses solely on collectivism.
This can only mean that the various scales are not measuring the same
two constructs—in other words, the various scales that are intended to
measure IND are actually measuring several different, still unidentified attributes of the respondents and that scales designed to measure
COL are measuring a variety of unknown attributes as well. (p. 80)
JACKSON, COLQUITT, WESSON, AND ZAPATA-PHELAN
886
Concerns about content validity are underscored by the correlations among the various scale facets. Studies using scales from the
Triandis family have sometimes yielded near-zero or even negative correlations among facets purported to represent collectivism
(Hui & Yee, 1994). Moreover, when scale scores were used as
indicators of a higher order collectivism construct, those scores
had either low factor loadings or poor internal consistency (Chen
et al., 1997; Hui & Yee, 1994). Studies using scales from the
Wagner family have also yielded small or near-zero correlations
among facets (Moorman & Blakely, 1995; Ramamoorthy & Carroll, 1998; Wagner, 1995; Wagner & Moch, 1986). Such results
raise questions about whether the various facets named in the
Appendixes are actually assessing the same underlying construct.
Study 1
In response to the concerns raised about collectivism measurement (Earley & Gibson, 1998; Kagitcibasi, 1994; Oyserman et al.,
2002; Triandis et al., 1995), in Study 1 we developed and tested a
new collectivism measure to be used in organizational research.
The content of our measure was initially based in Triandis’s
various discussions of the defining attributes of collectivism (Triandis, 1989, 1995, 1996; Triandis & Bhawuk, 1997). Triandis
argued that collectivists (a) use groups as the units of analysis in
their social space, preferring to draw meaning from group memberships; (b) emphasize a sense of collective responsibility and
common fate; (c) feel great concern about what happens to ingroup members; (d) emphasize group norms as a driver of behavior rather than individual attitudes; and (e) prioritize the goals of
the in-group rather than personal goals. Triandis further suggested
that these defining attributes should be moderately correlated—
that the attributes should converge to capture the common core of
collectivism (Triandis, 1995; Triandis & Bhawuk, 1997).
We compared these attributes with descriptions of collectivism
in other reviews of the literature. Ho and Chiu (1994) derived a
classification scheme for collectivism that consisted of 18 different
“component ideas” for the construct. Almost all of the components
could be viewed as more specific or nuanced versions of the
Triandis attributes described above. Exceptions included economic
and religious philosophies that are more relevant at the national
culture level, along with the importance of privacy. Oyserman et
al. (2002) presented a summary of all of the concepts included
within collectivism measures in their meta-analysis. Most of the
concepts could again be viewed as more specific or nuanced
versions of the Triandis attributes. Exceptions included the importance of advice and a focus on hierarchy. It should be noted that
Oyserman et al. argued that many of these exceptions, including
privacy and advice issues, should more appropriately be conceptualized as consequences of collectivism, not defining qualities of
the construct itself.
We therefore limited the content of our measure to the five
facets summarized in Table 1: preference, reliance, concern, norm
acceptance, and goal priority. Our terminology and discussion of
each facet in Table 1 are distilled from the key sources cited in the
table in an attempt to emphasize the commonalities across multiple
descriptions of the collectivism construct. A close inspection of the
measures in the Appendixes illustrates that the scales in the Triandis family sample most, if not all, of the facets in Table 1
(though often with low reliability or near-zero correlations among
facets). The scales in the Earley family sample the preference,
reliance, and norm acceptance facets (though in a purportedly
unidimensional fashion). The scales in the Wagner family sample
most, if not all, of the Table 1 concepts (though again with
near-zero correlations among subscales).
We designed our measure around a second-order factor structure, with the five facets serving as latent indicators of a higher
order collectivism construct. This structure is consistent with Triandis’s view that the defining attributes of collectivism should be
moderately correlated (Triandis, 1995; Triandis & Bhawuk, 1997)
Table 1
Key Facets of the Collectivism Construct
Facet
Preference
Reliance
Concern
Discussion
Key sources
Collectivists emphasize relationships with in-group members and prefer to exist
within the bounds of the in-group. They are affiliative by nature and believe
that collective efforts are superior to individual ones.
Collectivists believe that one person’s responsibility is the responsibility of the
entire in-group. This sense of collective responsibility makes them comfortable
relying on other members of the in-group.
Collectivists are motivated not by self-interest but by a concern for the well-being
of the in-group and its members.
Triandis (1995)
Ho & Chiu (1994)
Oyserman et al. (2002)
Triandis (1989)
Ho & Chiu (1994)
Oyserman et al. (2002)
Triandis (1989, 1995,
1996)
Triandis & Bhawuk (1997)
Ho & Chiu (1994)
Oyserman et al. (2002)
Triandis (1989, 1995,
1996)
Triandis & Bhawuk (1997)
Ho & Chiu (1994)
Oyserman et al. (2002)
Triandis (1989, 1995,
1996)
Triandis & Bhawuk (1997)
Ho & Chiu (1994)
Oyserman et al. (2002)
Norm acceptance
Collectivists focus on the norms and rules of the in-group and comply with those
norms and rules in order to foster harmony within the collective.
Goal priority
Collectivists’ actions are guided by the consideration of the in-group’s interests.
Thus in-group goals take priority over individual goals, even if this causes the
in-group member to make certain sacrifices.
PSYCHOLOGICAL COLLECTIVISM
and is also consistent with the conceptualization of most multidimensional constructs in organizational behavior (Law, Wong, &
Mobley, 1998). We felt that this strategy would allow us to create
a more reliable collectivism scale because the five facets should be
more internally consistent than the dimensions used in other scales.
We also felt that this strategy would maximize both “bandwidth”
and “fidelity” (Cronbach, 1990). Bandwidth refers to the amount
of information and is increased by assessing a wide variety of
construct elements. Fidelity refers to the reliability of the information and is decreased by assessing too many loosely related
concepts. Of this bandwidth–fidelity tradeoff, Triandis et al.
(1995) wrote,
It should be noted here that researchers in this area ask for an
instrument that will measure the common core, and yet not be too
long. However, because allocentrism and idiocentrism are very broad
constructs, their measurement with a few items is very difficult. Most
short scales have low fidelity (Cronbach, 1990), and it is not surprising that in most past studies the Cronbach alphas have been unsatisfactory (lower than .70). Selection of a narrow aspect of the constructs
can result in high alphas, e.g. patriotism can be measured with a .95
alpha (Schmitz, 1992), but then one needs a measure for each collective, which is not convenient since most researchers want a few items
to measure the broader construct. (p. 464)
Our goal was therefore to create a small set of items to measure
each of the five facets. The second-order factor structure of the
measure could then balance fidelity (within the five latent indicators) with bandwidth (within the higher order factor). Because our
five facets should be significantly correlated, our measure should
possess adequate reliability at the scale level, not just the facet
level. Moreover, the second-order structure could provide researchers with a great degree of flexibility. When psychological
collectivism is not the central focus of a study, the general factor
can be used as a single variable. However, when psychological
collectivism is the central focus, the facets can be assessed separately in order to gain a more fine-grained understanding of collectivism effects. Oyserman et al. (2002) suggested that the use of
such a facet-level approach could improve the theoretical clarity
found in the literature.
We should also note that all five of the facets in Table 1 appear
relevant to work contexts in which cooperation is important. K. G.
Smith, Carroll, and Ashford (1995) reviewed a number of theoretical perspectives that can be used to explain cooperation. One
perspective is attraction theories, which argue that cooperation is
fostered when individuals have a natural affinity for one another.
The preference and concern facets seem relevant to cooperation
from this standpoint, as both should create a sense of attraction to
the group. Another perspective is power and conflict theories,
which argue that cooperation is fostered when goals and tasks are
interdependent. The goal priority and reliance facets seem relevant
here, as both should facilitate the development of interdependence
within the group. A third perspective discussed by K. G. Smith et
al. is modeling theories, which link cooperation to the development of shared norms for prosocial behavior. The norm acceptance
facet seems most relevant to this perspective. Thus, each of the
facets seems relevant to cooperation as it is conceptualized in the
literature.
887
Method
Item Generation
The first step in constructing the measure was to decide what in-group
to reference in the items. The choice of in-group is critical, as the very
definition of collectivism is in-group based (Triandis, 1995). Indeed,
Deaux and Reid (2000) suggested that “collectivism should be assessed in
terms of specific group memberships rather than some general feelings
toward groups in general. We suggest that people may be collective in their
attitudes toward some groups but not others” (pp. 181–182). The instructions leading into our measure therefore referenced work groups as the
in-group, as opposed to the friend, family, neighbor, and kin groups used
in past research. We felt that this would make the measure more relevant
to applied research in organizational behavior and industrial–
organizational psychology.
The second step in constructing the measure was generating items that
reflect each of the facets in Table 1. We generated 5 items for each facet
with the goal of eventually retaining the strongest 3 (the final, shortened
form of the measure is shown in Table 2). We then asked 10 subject matter
experts to blindly classify the 25 items into the five facets in Table 1. Items
were correctly classified 95% of the time, and only 2 of the items were
misclassified by more than one expert. We should note that 2 of the
preference items were similar to those used in past research (Earley, 1993;
Erez & Earley, 1987; Wagner, 1995; Wagner & Moch, 1986). We then
piloted the 25 items in a sample of 540 undergraduates taking management
courses at a large Southeastern university to gauge item readability and
form an initial picture of the factor structure.
Sample
Study 1 participants were 241 full-time employees of a multinational
consulting firm. All participants in the study were at the same level
(consultant) within the organization, and all were based in the United
States. The consultants’ projects centered on the installation and customization of various software systems (e.g., Peoplesoft systems, SAP systems). Owing to the anonymous nature of the responses to the survey, exact
gender distribution cannot be determined. However, the initial sample
population was 65% male, with an average age of 27.5. Listwise deletion
of missing data resulted in a usable sample of 235 participants.
Consultants represent an interesting setting for examining cooperative
phenomena. Their tasks are organized around projects rather than jobs,
with some projects taking only a few days and others taking several
months. Although the task requirements vary from project to project,
cooperation among the consultants is always a necessity. Unfortunately,
one barrier to cooperation is that projects are often staffed with consultants
who lack experience or familiarity with one another. Thus, individuals
must supply the ingredients for cooperation themselves, rather than relying
on past history and experience to supply them.
Procedure
Data were collected through the use of an Internet survey. A total of 643
usable e-mail addresses were obtained through the participating company.
These e-mails contained an offer to participate in the study, which could be
accessed by following a link in the e-mail. Owing to the nature of the
survey, respondents were guaranteed complete anonymity in both their
responses and their decision to participate. In responding to the 25 collectivism items, respondents were asked to think about the work groups to
which they currently belong and have belonged in the past. All items were
assessed using a 5-point Likert scale with anchors of 1 (strongly disagree)
and 5 (strongly agree). A total of 241 consultants chose to participate,
resulting in a response rate of 37%.
JACKSON, COLQUITT, WESSON, AND ZAPATA-PHELAN
888
Table 2
Psychological Collectivism Measure Items
Measure item
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Facet
I preferred to work in those groups rather than working alone.
Working in those groups was better than working alone.
I wanted to work with those groups as opposed to working alone.
I felt comfortable counting on group members to do their part.
I was not bothered by the need to rely on group members.
I felt comfortable trusting group members to handle their tasks.
The health of those groups was important to me.
I cared about the well-being of those groups.
I was concerned about the needs of those groups.
I followed the norms of those groups.
I followed the procedures used by those groups.
I accepted the rules of those groups.
I cared more about the goals of those groups than my own goals.
I emphasized the goals of those groups more than my individual goals.
Group goals were more important to me than my personal goals.
Preference
Preference
Preference
Reliance
Reliance
Reliance
Concern
Concern
Concern
Norm acceptance
Norm acceptance
Norm acceptance
Goal priority
Goal priority
Goal priority
Note. The instructions participants read were as follows: “Think about the work groups to which you currently
belong, and have belonged to in the past. The items below ask about your relationship with, and thoughts about,
those particular groups. Respond to the following questions, as honestly as possible, using the response scales
provided. (1 ⫽ Strongly Disagree to 5 ⫽ Strongly Agree).”
Results and Discussion
A confirmatory factor analysis of the 25 items yielded a reasonable fit for a second-order structure with the five facets as latent
indicators of the higher order psychological collectivism factor:
␹2(270, N ⫽ 235) ⫽ 532.26, p ⬍ .001; ␹2/df ⫽ 1.97; incremental
fit index (IFI) ⫽ .94; comparative fit index (CFI) ⫽ .94; rootmean-square error of approximation (RMSEA) ⫽ .06. Good model
fit is typically inferred when ␹2/df falls below 3 and IFI or CFI rise
above .90 (Kline, 1998). RMSEA is interpreted as follows: Greater
than .10 is poor fit, .08 to .10 is mediocre fit, .05 to .08 is
reasonable fit, and less than .05 is good fit (Browne & Cudeck,
1993).
We used the results of the confirmatory factor analysis to select
the best 15 items from the larger set of 25. Reducing the length to
15 items made our measure more similar in length to the most
commonly used collectivism measures, which range from 8 (Earley, 1993; Gelfand & Realo, 1999; Triandis & Gelfand, 1998) to
11 items (Wagner & Moch, 1986). However, the 15-item length
still devoted 3 items to each facet, allowing facet level analyses to
be performed when desired. The best 15 items were chosen on the
basis of factor loadings, skewness and kurtosis, residual values,
and item content.
The fit of the 15-item model was strong: ␹2(85, N ⫽ 235) ⫽
117.51, p ⬍ .05; ␹2/df ⫽ 1.38; IFI ⫽ .98; CFI ⫽ .98; RMSEA ⫽
.04. As shown in Figure 1, each of the collectivism items loaded
highly on its intended facet, with loadings ranging from .64 to .95,
with an average of .82. Moreover, the five collectivism facets
loaded strongly on the general factor: .56 for preference, .49 for
reliance, .61 for concern, .65 for norm acceptance, and .46 for goal
priority. All 10 of the interfacet correlations were statistically
significant. Thus, the general factor seemed to be driving the five
facets to an approximately equal degree. The psychological collectivism measure also possessed strong reliability, whether used
as an overall scale (␣ ⫽ .84) or as five more specific facets.
Facet-level reliabilities were as follows: .86 for preference, .81 for
reliance, .90 for concern, .90 for norm acceptance, and .87 for goal
priority.
Study 2
The purpose of Study 2 was to cross-validate the shorter 15-item
version of our collectivism measure and to assess its convergent
validity. Convergent validity refers to the extent to which alternative measures of the same construct share variance (D. T. Campbell & Fiske, 1959; Schwab, 1980). We tested the convergent
validity of our collectivism measure by correlating its scores with
those derived from other measures of the construct. We chose a
representative version of the scales in each of the families summarized in the Appendixes. Specifically, we included the most
recent scale from the Triandis family, used by Triandis and Gelfand (1998) and Gelfand and Realo (1999), adapted from Singelis
et al. (1995). We also included the most recent scale from the
Earley family, in the form of Earley (1993). Finally, we included
Wagner and Moch’s (1986) scale, chosen over Wagner (1995)
because the latter includes items from the other two families.
Method
Sample
Study 2 participants were 140 individuals working in a variety of
organizations and industries. Of the participants, 115 were registered
alumni of a large, Southeastern university. The remaining 25 were students
in an executive weekend master of business administration (MBA) course
at the same university. Participants were 68% male and averaged 40 years
of age. Listwise deletion of missing data resulted in a usable sample of 139
participants.
Procedure
A list of 325 names was randomly drawn from a database of 7,000
registered alumni who were known to be employed full time. We mailed
PSYCHOLOGICAL COLLECTIVISM
Figure 1.
889
Confirmatory factor analysis results for Study 1.
the alumni a survey along with a letter explaining that the purpose of the
study was to assess attitudes about working in groups. Participants were
told that they were eligible for the study if their job contained a significant
group component, meaning they needed to collaborate with others to
perform day-to-day tasks and achieve collective work goals. Participants
earned a $5 cash payment for their participation. A total of 115 surveys
were received, indicating a response rate of 35%. In addition, 43 executive
MBA students were given a copy of the survey during their weekend visit
to the campus, with the same cash incentive offered. Twenty-five surveys
were received, for a response rate of 58%. On average, the participants
worked in groups of eight people when performing their job duties and
reported high levels of task interdependence with their fellow group
members (M ⫽ 4.24 out of 5.00 using the measure developed by Pearce &
Gregersen, 1991). All items were assessed using a 5-point Likert scale with
anchors of 1 (strongly disagree) and 5 (strongly agree).
Results and Discussion
Cross-Validation of Collectivism Measure
The fit of the 15-item model was strong: ␹2(85, N ⫽ 139) ⫽
101.91, p ⬍ .10; ␹2/df ⫽ 1.20; IFI ⫽ .99; CFI ⫽ .99; RMSEA ⫽
.04. Each of the items loaded highly on its intended facet, with
loadings ranging from .56 to .95, with an average of .83. Four of
the five facets loaded strongly on the general collectivism factor:
.68 for preference, .69 for reliance, .47 for concern, and .42 for
goal priority. The norm acceptance facet had a weaker loading of
.21, though it was still statistically significant. Nine of the 10
interfacet correlations were statistically significant, with the exception being reliance and norm acceptance. The measure possessed strong reliability, whether used as an overall scale (␣ ⫽ .85)
or as five, more specific facets (␣s ranging from .78 to .93).
Convergent Validity
Convergent validity can be judged by examining the correlation
between our collectivism measure and the Triandis, Earley, and
Wagner measures, shown in Table 3. Our measure was strongly
correlated with the Triandis measure (r ⫽ .52), the Earley measure
(r ⫽ .50), and the Wagner measure (r ⫽ .50). These correlations
provide strong support for convergent validity given the different
in-groups referenced by the scales, the varying coverage of the five
collectivism facets in Table 1, the different item styles used (e.g.,
attitudes, past behaviors, norms, beliefs), and the lower reliabilities
of some of the other measures (.29 for Earley, .68 for Wagner).
The convergent validity correlations for our measure were also
stronger than the correlations for the other three, as the Triandis,
Earley, and Wagner measures had intercorrelations ranging from
.23 to .35.
The facet-level correlations in Table 3 can be used to provide a
more fine-grained picture of convergent validity. With respect to
the Triandis scale, preference and concern should correlate most
strongly with the horizontal facet, which assesses collectivism
from an equal status perspective. Norm acceptance, on the other
hand, should correlate most strongly with vertical collectivism,
which focuses on hierarchical contexts in which norms and rules
feed down to group members. With respect to the Wagner scale,
preference and reliance should correlate most strongly with the
values facet, which captures the value placed on working in
groups. Norm acceptance should correlate most strongly with
the beliefs facet, which captures the belief that members should
do what the group thinks is best. Goal priority should correlate
most strongly with the norms dimension, which captures the
need to sacrifice individual goals for the good of the group. The
JACKSON, COLQUITT, WESSON, AND ZAPATA-PHELAN
890
Table 3
Convergent Validity Correlations
Variable
␣
M
SD
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1. Psychological collectivism
2. Preference facet
3. Reliance facet
4. Concern facet
5. Norm acceptance facet
6. Goal priority facet
7. Triandis collectivism
8. Horizontal facet
9. Vertical facet
10. Earley collectivism
11. Wagner collectivism
12. Beliefs facet
13. Values facet
14. Norms facet
.85
.93
.85
.89
.78
.89
.75
.72
.70
.29
.68
.81
.74
.79
3.46
3.17
3.29
3.96
3.87
3.05
4.07
4.02
4.12
3.40
3.70
3.78
2.95
4.09
0.49
0.86
0.86
0.65
0.58
0.88
0.46
0.54
0.58
0.37
0.37
0.73
0.71
0.46
—
.73*
.68*
.63*
.40*
.65*
.52*
.57*
.29*
.50*
.50*
.11
.68*
.15
—
.43*
.36*
.17*
.29*
.30*
.38*
.12
.39*
.45*
.01
.80*
.07
—
.31*
.03
.23*
.28*
.36*
.10
.46*
.32*
.03
.49*
.09
—
.17*
.22*
.55*
.65*
.26*
.31*
.21*
.03
.30*
.06
—
.18*
.25*
.15
.26*
.06
.31*
.26*
.16
.14
—
.30*
.28*
.22*
.28*
.29*
.09
.31*
.14
—
.81*
.83*
.23*
.35*
.35*
.10
.30*
—
.34*
.30*
.31*
.04
.39*
.15
—
.10
.27*
.12
.12
.26*
—
.33*
.11
.45*
.06
—
.58*
.56*
.70*
—
⫺.07
.12
—
.13
—
Note. N ⫽ 139.
* p ⬍ .05, one-tailed.
correlations in Table 3 match that expected pattern, with the
exception of the relation between goal priority and norms. We
should note, however, that Wagner’s norms facet yielded very
few significant correlations across the table. In fact, the three
Wagner facets were not correlated with one another (rs ranging
from ⫺.07 to .13).
Study 3
Having described the development and validation of our new
measure of psychological collectivism, in Study 3 we seek to make
a more substantive contribution. As noted at the outset, the relationship between collectivism and group member performance
remains largely untested. Past research has linked collectivism to
reward generosity (Gomez et al., 2000), preference for groupbased appraisals (Ramamoorthy & Carroll, 1998; Waldman,
1997), self-efficacy for teamwork (Eby & Dobbins, 1997), and
cooperative choices in a prisoner’s dilemma game (Cox et al.,
1991). We explored collectivism’s linkage with four dimensions of
job performance: (a) task performance—the proficiency with
which members perform the activities recognized as part of their
group role (Borman & Motowidlo, 1993); (b) citizenship behaviors— discretionary behaviors that lie outside of an employee’s job
description and may not be formally rewarded by the group (C. A.
Smith, Organ, & Near, 1983); (c) counterproductive behaviors—
intentional behaviors on the part of employees that are contrary to
the group’s legitimate interests (Sackett & DeVore, 2001); and (d)
withdrawal behaviors—a more passive response to dissatisfaction
characterized by psychological and physical avoidance of the
group and its work (Hulin, 1991).
Task Performance
According to J. P. Campbell, McCloy, Oppler, and Sager’s
(1992) theory of performance, the most proximal individual predictors of task performance are motivation and declarative and
procedural knowledge, which are themselves predicted by personality, interest variables, and past experience, among others. In
group settings, the collectivism facets should supply the interest
criterion needed to foster declarative and procedural knowledge, as
collective members prefer to work in groups and are concerned
about group welfare. Collective members may also have more past
experience working in groups, as past research has linked those
two variables (Eby & Dobbins, 1997). In addition, the collectivism
facets may supply motivation in task contexts, as the tendency to
prioritize group goals should foster a more intense and long-lasting
exertion of effort. We are aware of only one study that has linked
a member’s collectivism levels to his or her task performance in an
actual group setting (Shaw, Duffy, & Stark, 2000). Although this
study did not find a significant zero-order link between the two
variables, an interaction effect was uncovered in which the relationship between collectivism and task performance became more
positive at higher levels of perceived task interdependence. We
therefore predicted the following:
Hypothesis 1: Psychological collectivism will be positively
related to supervisor ratings of group member task
performance.
Citizenship Behavior
Citizenship behaviors form the core of what is more commonly
called “teamwork” (LePine et al., 2000), and such behaviors are
typically explained using a social exchange theory lens (Blau,
1964). Specifically, Organ (1990) argued that a social exchange
relationship is a prerequisite for citizenship behaviors, given that
such behaviors may not be formally rewarded by the organization.
Triandis and Bhawuk (1997) suggested that collectivists are more
likely to adopt a social exchange mindset as they are predisposed
to “play relationships by ear,” adopt a long-term time perspective,
give more than they receive, and ground their willingness to
reciprocate in a deeply held concern for their group. Consistent
with this reasoning, past research has supported the linkage between collectivism and individual citizenship behaviors in two
studies (Moorman & Blakely, 1995; Van Dyne et al., 2000). We
therefore predicted the following:
PSYCHOLOGICAL COLLECTIVISM
Hypothesis 2: Psychological collectivism will be positively
related to supervisor ratings of group member citizenship
behavior.
Counterproductive Behavior
Robinson and Bennett’s (1997) model of workplace deviance
argues that counterproductive behaviors are triggered by provocations such as inequity and poor work conditions. These provocations create two forms of motivation for engaging in counterproductive behavior: instrumental (to resolve the disparity in the
exchange relationship) and expressive (to vent one’s anger). These
motivations can be neutralized by a number of constraints, including internalization of norms, bonds to a social system, and the
potential for formal and internal sanctions. Collectivism should
foster two of these constraints given that collective members are
more likely to internalize the norms of the group and possess
strong bonds to the social system (Ho & Chiu, 1994; Oyserman et
al., 2002; Triandis, 1989, 1995). Although we are not aware of any
studies linking collectivism to the counterproductive behaviors of
individual group members, we predicted the following:
Hypothesis 3: Psychological collectivism will be negatively
related to supervisor ratings of group member counterproductive behavior.
Withdrawal Behavior
Some forms of withdrawal are found within taxonomies of
counterproductive behavior, including poor attendance, tardiness,
and the withholding of effort (Robinson & Bennett, 1997; Sackett
& DeVore, 2001). We separated the two constructs in this study,
however, as withdrawal behaviors are more passive responses
relative to actions like sabotage, intentional breaking of group
rules, and verbal abuse of group members. Most models of withdrawal view the psychological and physical disengagement from
work as a response to dissatisfaction (Hulin, 1991; Johns, 2001).
Although past research has linked collectivism to member satisfaction in student project groups (Shaw et al., 2000), researchers
have argued that satisfaction alone is incapable of explaining a
large portion of variance in withdrawal (Johns, 2001). Such behaviors also depend on contextual variables like job embeddedness, which is characterized by strong ties to other members in a
group’s social network (Mitchell, Holtom, Lee, Sablynski, & Erez,
2001). Collectivists form stronger bonds with in-group members
and are more likely to make sacrifices for the good of the group
(Ho & Chiu, 1994; Oyserman et al., 2002; Triandis, 1995), making
withdrawal less likely. Although we are not aware of any studies
linking collectivism to the withdrawal of individual group members, we hypothesized the following:
Hypothesis 4: Psychological collectivism will be negatively
related to supervisor ratings of group member withdrawal
behavior.
Linking collectivism to group member performance makes a
substantive contribution to the literature while also testing the
predictive validity of our measure. We also compared our measure’s predictive validity with that of the most recent scale from
the Triandis family: Triandis and Gelfand (1998). We chose this
891
measure because its 214 citations since 2000 make it the most
commonly used measure and because it was shown to have acceptable reliability and interfacet correlations in Study 2. We also
included the Big Five personality factors in Study 3 in order to
assess discriminant validity, which provides evidence that measures that purportedly assess different constructs in fact do so
(D. T. Campbell & Fiske, 1959; Schwab, 1980).
Method
Sample
Study 3 participants were 186 full-time employees of a computer software firm. The organization is a supplier and developer of software
systems based in the southwest United States. The company splits its
workforce into groups on the basis of clients or regions served. The sample
consisted of programmers, software support, and client support, with all
jobs being fairly technical in nature. In all cases, participants worked in
groups that were highly interdependent, requiring cooperation to meet the
specific needs of their clients. Participants were 71% male and averaged
31.54 years of age and 3.35 years with the organization. Listwise deletion
of missing data resulted in a usable sample of 178 participants.
Procedure
All measures were assessed through the use of an Internet-based survey.
A total of 262 usable e-mail addresses were obtained from the participating
organization. These e-mails contained an offer to participate in the study,
which could be accessed by following an Internet link within the e-mail. A
total of 186 of the employees chose to participate, resulting in a response
rate of 71%. Participants’ supervisors were also asked to fill out a survey
assessing job performance in the context of work group tasks. In all cases,
the supervisor who filled out this survey was the same person who
performed the employee’s formal performance appraisal (though it was
stressed that these rankings did not feed into that system in any way). Of
the 186 participants, complete supervisor data were obtained for 128, a
response rate of 69%.
Measures
All measures were assessed using a 5-point Likert scale with anchors of
1 (strongly disagree) and 5 (strongly agree).
Task performance. Supervisors rated their employees using the 7-item
scale developed by Williams and Anderson (1991), with the wording
changed to reflect work group duties and responsibilities. Supervisors were
asked, “How much do you agree with the following statements: This
employee _____,” with sample items including “adequately completes
assigned work group duties,” “fulfills responsibilities specified by his/her
work group,” “meets formal requirements of the work group,” and “fails to
perform essential duties for the work group” (reverse scored).
Citizenship behavior. Supervisors rated their employees using the 16item measure created by Lee and Allen (2002), with the wording changed
to reflect work-group-directed citizenship. Sample items included “willingly gives time to help other group members who have work-related
problems,” “shows genuine concern and courtesy toward other group
members, even under the most trying business or personal situations,”
“attends functions that are not required but that help the work group’s
image,” “defends the work group when other employees criticize it,” and
“offers ideas to improve the functioning of the work group.”
Counterproductive behavior. Supervisors rated their employees using
the nine-item scale developed by Robinson and O’Leary-Kelly (1998),
with the wording changed to assess work-group-directed counterproductive
behaviors. Supervisors were asked, “Within the last year, how often has
this person _____ ?” Sample items included “said or did something to
JACKSON, COLQUITT, WESSON, AND ZAPATA-PHELAN
892
purposely hurt a work group member,” “damaged property being used by
the work group,” “deliberately bent or broke a group rule(s),” and “said
rude things about work group members.”
Withdrawal behavior. Supervisors also rated their employees with 10
items developed by Lehman and Simpson (1992). The item wording was
again changed to reflect the work group context and was also altered to
allow for supervisor-reported rather than self-reported data. Supervisors
were asked, “Within the last year, how often has this person _____ ?”
Sample items included “been absent from work group activities,” “left
work early without permission,” “seemed to be daydreaming rather than
working,” “spent time on personal matters rather than group duties,” and
“talked about leaving current job or work group.”
Psychological collectivism. The measure shown in Table 2 was again
used.
Triandis collectivism. As in Study 2, the scale used by Triandis and
Gelfand (1998) and Gelfand and Realo (1999), adapted from Singelis et al.
(1995), was used.
Big Five. We measured these personality dimensions using the Big
Five Inventory (John, Donahue, & Kentle, 1991). Sample items included “I
persevere until the task is finished” (Conscientiousness, 9 items), “I am
helpful and unselfish with others” (Agreeableness, 9 items), “I am sometimes depressed or blue” (Neuroticism, 8 items), “I have an active imagination” (Openness to Experience, 10 items), and “I am outgoing and
sociable” (Extraversion, 8 items).
Results and Discussion
Cross-Validation of Factor Structure
We conducted another second-order confirmatory factor analysis to provide further cross-validation of the measure in Table 2.
The fit of the model was again strong: ␹2(85, N ⫽ 124) ⫽ 148.06,
p ⬍ .001; ␹2/df ⫽ 1.74; IFI ⫽ .95; CFI ⫽ .95; RMSEA ⫽ .06.
Each of the collectivism items loaded highly on its intended factor,
with loadings ranging from .59 to .97. Moreover, the five collectivism facets loaded significantly on the general factor: .53 for
preference, .50 for reliance, .61 for concern, .52 for norm acceptance, and .34 for goal priority. Nine of the 10 interfacet correlations were statistically significant, with the exception being preference and goal priority. The measure again possessed strong
reliability, whether used as an overall scale (␣ ⫽ .82) or as five,
more specific facets. Facet-level reliabilities were as follows: .89
for preference, .79 for reliance, .84 for concern, .82 for norm
acceptance, and .83 for goal priority.
Descriptive Statistics and Correlations
The means, standard deviations, reliabilities, and correlations
for all Study 3 variables are shown in Table 4. Three of the four
outcome measures had acceptable reliabilities, with the alpha for
withdrawal behavior falling below the .70 standard. The four outcomes were also weakly to moderately correlated, with correlations
ranging from ⫺.08 to .30. Our measure of psychological collectivism
was significantly correlated with Agreeableness, as suggested by
Triandis (1997), but was uncorrelated with the other Big Five
factors. These results provide evidence of discriminant validity for
the psychological collectivism measure, relative to the Big Five.
Tests of Hypotheses
Hypotheses 1– 4 predicted significant relationships between
psychological collectivism and the task, citizenship, counterproductive, and withdrawal dimensions of group member job performance. As shown in the top row of Table 5, all four hypotheses
received support. Psychological collectivism explained 10% of the
variance in task performance, 4% of the variance in citizenship
behavior, 10% of the variance in counterproductive behavior, and
5% of the variance in withdrawal behavior.
Table 5 decomposes the reasons for the psychological collectivism effects by providing information on the relative importance
of the five facets. In addition to the standardized regression
weights, the table provides two alternative methods for judging the
importance of correlated predictors: dominance analysis (Azen &
Budescu, 2003) and the epsilon statistic (Johnson, 2000). The
general dominance statistic (denoted D in the table and computed
using Dominance Analysis 4.30 by James LeBreton) is computed
as the squared semipartial correlation averaged across all possible
subset regression models. The values in the table are then rescaled
by dividing them by the total variance explained in the outcome to
provide an index of the average usefulness of each predictor. The
epsilon statistic (denoted ␧ in the table and computed using SPSS
syntax by Jeff Johnson) is calculated by deriving uncorrelated
principal components from a set of predictors and combining two
sets of standardized regression weights: the outcome regressed on
the principal components and the original predictors regressed on
the principal components. The resulting statistic provides an index
Table 4
Correlations Between Study 3 Variables
Variable
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Psychological collectivism
Task performance
Citizenship behavior
Counterproductive behavior
Withdrawal behavior
Triandis collectivism
Conscientiousness
Agreeableness
Neuroticism
Openness to experience
Extraversion
Note. N ⫽ 124.
* p ⬍ .05, one-tailed.
␣
M
SD
1
2
3
4
5
6
7
8
9
10
11
.82
.78
.94
.86
.50
.76
.70
.77
.76
.81
.79
3.67
4.37
4.41
1.44
1.65
3.46
2.56
2.68
2.72
2.59
3.56
0.42
0.54
0.63
0.48
0.32
0.58
0.50
0.58
0.61
0.62
0.63
—
.32*
.20*
⫺.31*
⫺.23*
.33*
⫺.02
.23*
⫺.01
⫺.07
.11
—
.20*
⫺.18*
⫺.26*
.16*
.10
.15*
⫺.06
⫺.05
⫺.01
—
⫺.30*
⫺.08
.09
.17*
.12
⫺.06
⫺.01
⫺.04
—
.20*
⫺.11
.05
.05
⫺.02
⫺.04
⫺.04
—
⫺.03
.10
⫺.07
.06
⫺.14
.06
—
⫺.05
.07
.00
.00
.22*
—
⫺.01
⫺.03
⫺.12
.07
—
.05
⫺.05
.11
—
⫺.09
⫺.15*
—
.03
—
PSYCHOLOGICAL COLLECTIVISM
893
Table 5
Relationships Between Psychological Collectivism and Group Member Performance
Member task
performance
Variable
R2
Psychological collectivism
.10*
␤
D
Member citizenship
behavior
␧
␤
R2
Member counterproductive
behavior
␧
D
R2
␤
.10*
⫺.31*
.11*
.01
⫺.07
⫺.13
⫺.18*
⫺.14
D
␧
Member withdrawal
behavior
R2
␤
.05*
⫺.23*
.09*
⫺.05
.03
⫺.14
⫺.16*
⫺.24*
D
␧
4.0
1.2
11.1
8.2
75.5
4.0
1.5
11.2
8.2
75.2
General factor
.32*
.04*
.20*
Specific facet
Preference
Reliance
Concern
Norm acceptance
Goal priority
.15*
.18*
.06
⫺.09
.12
.29*
18.2
9.1
2.1
14.3
56.3
19.2
8.1
1.4
13.8
57.5
.09*
⫺.09
.23*
.21*
⫺.04
.05
3.9
52.5
34.9
1.8
7.0
2.9
53.3
35.8
1.6
6.5
2.6
9.7
24.1
40.0
23.6
2.4
8.8
25.3
40.8
22.7
Note. N ⫽ 124.
* p ⬍ .05.
of the proportionate contribution each predictor makes to total
variance explained. A recent review suggested that dominance
analysis and the epsilon statistic are the two preferred methods for
judging the relative importance of correlated predictors (LeBreton,
Ployhart, & Ladd, 2004).
The results in Table 5 reveal that the five facets explain about
35% more variance in the outcomes than the higher order factor.
Each of the five facets significantly contributes to at least one of
the outcomes. Preference is relatively important in predicting task
performance, whereas reliance and concern contribute to the prediction of citizenship. Norm acceptance is important to the prediction of counterproductive behavior and, to a lesser extent,
withdrawal. Finally, goal priority has a high degree of importance
for two outcomes: task performance and withdrawal behavior.
These results illustrate that all five collectivism facets contributed
to the predictive validity of the psychological collectivism factor.
We also compared the predictive validity of our collectivism
measure with that of the Triandis scale. Triandis collectivism was
significantly correlated with member task performance (r ⫽ .16)
but was not correlated with the other three outcomes (see Table 4).
We compared the predictive validity levels using a usefulness
analysis, in which the incremental variance explained by each
measure was compared using two separate regressions. As shown
in Table 6, scores on our collectivism measure explained incremental variance in all four outcomes after controlling for scores on
the Triandis measure, with ⌬R2 values ranging from .03 to .08. In
contrast, scores on the Triandis measure failed to explain incremental variance.
Although we included the Big Five in Study 3 primarily to
gauge the discriminant validity of our collectivism measure, we
felt it would be worthwhile to compare the predictive validity of
psychological collectivism with that of the Big Five. Table 7
summarizes the effects of collectivism and the Big Five in combination. The results reveal that collectivism has the highest relative importance of the predictors, regardless of whether standardized regression coefficients, dominance analysis, or the epsilon
statistic are used to assess importance levels.
General Discussion
The three studies described in this article point to a number of
strengths for our psychological collectivism measure. The measure
Table 6
Usefulness Analysis With Triandis Collectivism
Member task
performance
Regression order
R2
⌬R2
Member citizenship
behavior
␤
R2
⌬R2
Member counterproductive
behavior
Member withdrawal
behavior
␤
R2
⌬R2
␤
R2
⌬R2
␤
.09
.19*
.01
.10*
.01
.09*
⫺.11
⫺.30*
.00
.06*
.00
.06*
⫺.03
⫺.25*
.20*
.02
.10*
.10*
.10*
.00
⫺.31*
⫺.01
.06*
.06*
.06*
.00
⫺.23*
.05
Order 1
1. Triandis collectivism
2. Psychological collectivism
.02
.10*
.02
.08*
.16
.30*
.01
.04†
.01
.03*
Order 2
1. Psychological collectivism
2. Triandis collectivism
Note. N ⫽ 124.
† p ⬍ .10. * p ⬍ .05.
.10*
.10*
.10*
.00
.32*
.05
.04*
.04†
.04*
.00
JACKSON, COLQUITT, WESSON, AND ZAPATA-PHELAN
894
Table 7
Psychological Collectivism and the Big Five in Combination
Member task
performance
Member citizenship
behavior
Member counterproductive
behavior
Member withdrawal
behavior
Predictor
R2
␤
D
␧
R2
␤
D
␧
R2
␤
D
␧
R2
␤
D
␧
Conscientiousness
Agreeableness
Neuroticism
Openness to experience
Extraversion
Psychological collectivism
.13*
.10
.09
⫺.08
⫺.02
⫺.08
.31*
7.7
11.1
3.8
1.2
7.8
76.2
5.4
11.2
4.1
1.7
1.4
76.2
.09†
.18*
.09
⫺.07
.02
⫺.09
.19*
38.2
12.3
4.9
0.1
9.6
44.6
34.1
11.6
4.9
0.1
5.0
44.4
.12*
.04
.13
⫺.04
⫺.05
⫺.02
⫺.34*
38.2
12.3
4.9
0.1
9.6
44.6
2.3
6.8
0.5
1.0
1.0
88.4
.10†
.07
⫺.04
.07
⫺.14
.10
⫺.24*
8.3
3.5
4.8
21.7
16.8
61.7
9.8
3.6
5.7
17.9
6.5
56.6
Note. N ⫽ 124.
† p ⬍ .10. * p ⬍ .05.
is focused solely on work groups as the in-group, making it more
relevant to applied research in the organizational sciences. The
measure was carefully constructed around the most frequently
discussed facets of the collectivism construct, as described in key
reviews of the literature (Ho & Chiu, 1994; Oyserman et al., 2002;
Triandis, 1989, 1995, 1996; Triandis & Bhawuk, 1997). The
second-order structure of the collectivism measure allowed us to
balance bandwidth and fidelity by capturing the broad spectrum of
collectivism facets while measuring each one in an internally
consistent fashion. This is in contrast to previous collectivism
scales, which tended to sample from widely different domains that
did not always seem to be tapping the same construct.
Taken together, three samples totaling 552 group members
supported the construct validity of our new measure on a number
of fronts. The measure was shown to be reliable at both the overall
scale level and the facet level, in contrast to other scales, whose
reliability sometimes falls below the .70 standard. In addition, the
15 items seemed to be valid indicators of the five facets, and the
five facets seemed to be valid indicators of the higher order factor
(as evidenced by the significant second-order loadings and the
significant interfacet correlations). Convergent validity was supported in the form of high correlations with representatives of all
three major scale families (Earley, 1993; Triandis & Gelfand,
1998; Wagner & Moch, 1986). Discriminant validity was supported by separating psychological collectivism from a measure of
the Big Five.
The creation of a construct valid measure of collectivism allowed us to test a key substantive question: Do collective group
members tend to be better performing group members? Study 3
provides some preliminary support for that question, as scores on
psychological collectivism were associated with supervisor ratings
of four separate dimensions of group member performance. Collective members performed their group tasks better, contributed
more discretionary citizenship, and were less likely to engage in
counterproductive or withdrawal behaviors. These results add to a
small body of research that supports the performance benefits of
collectivism with respect to individual group members (Moorman
& Blakely, 1995; Shaw et al., 2000; Van Dyne et al., 2000).
Our facet-level analyses shed further light on the performance
implications of collectivism. Oyserman et al. (2002) suggested that
facet-level analyses of collectivism could bring more theoretical
clarity to the literature by isolating the “active ingredients” in a
given relationship (p. 41). In other words, the facets can help
explain how and why collectivism predicts each of the outcomes.
Our results showed that each of the facets served as an “active
ingredient” for at least one performance dimension, with preference explaining task performance effects, reliance and concern
facets explaining citizenship effects, norm acceptance explaining
counterproductive and withdrawal effects, and goal priority explaining relationships with task performance and withdrawal.
The contributions above should be interpreted in light of this
article’s limitations. Our collectivism scale used self-report data
from a common source. This limitation creates the possibility that
the higher order factor observed in our confirmatory factor analysis results was (at least partially) an artifact of common method
variance. Parker (1999) illustrated how common method variance
can inflate the influence of a higher order factor in his examination
of the hierarchical model of psychological climate. A more stringent test of whether a psychological collectivism factor underlies
the five facets in our measure would require either multiple
sources (e.g., peers, significant others) or multiple methods (e.g.,
interview, projective test). These steps would allow the model in
Figure 1 to be tested in a multitrait–multimethod fashion. In
addition, we should note that the adequate reliability found for our
measure may be a function of the semantic similarities of the items
within each facet. However, it should be pointed out that the
reliability may also be a function of the significant correlations
found between the five facets, in contrast to other scales that have
used facets that are generally uncorrelated in empirical tests.
Moreover, we should note that the withdrawal behavior measure
used in Study 3 exhibited poor reliability. Lehman and Simpson’s
(1992) withdrawal items were intended to be self-report in nature,
and it may be that supervisors are unable to reliably evaluate subtle
behaviors like moonlighting, pretending to be busy, or discussing
nonwork topics.
A final limitation of our study concerns the assessment of our
measure’s predictive validity. Specifically, we chose to pit our
measure against the Triandis and Gelfand (1998) measure, which
is meant to be used in more diverse settings and is more general in
nature than our measure. Researchers generally agree that matching the specificity or breadth of predictors and criteria enhances
validity (Hogan & Roberts, 1996; Schneider, Hough, & Dunnette,
1996). The breadth of the Triandis and Gelfand (1998) measure
may have hindered its ability to predict the specific dimensions of
group member job performance assessed in our study. As such, the
deck may have been stacked in favor of our more narrowly focused
PSYCHOLOGICAL COLLECTIVISM
measure, which was developed with work contexts in mind. It may
be that scales from the Triandis family will be more significant
predictors of nonwork outcomes than our scale, because the Triandis items are more rooted in family, friend, and personal contexts. Thus, although the work-focused nature of our scale may
prove to be an advantage in predicting job attitudes and behaviors,
other scales may be more predictive of outcomes rooted in other
life domains.
Despite these limitations, our results suggest some avenues for
future research. First and foremost, research is needed to further
refine the measure introduced in this article, given that scale
development is always an iterative process. Future studies should
assess convergent validity with other collectivism scales, discriminant validity with other individual differences, and predictive
validity with other outcomes. With respect to the latter, we would
particularly encourage researchers to begin connecting scores on
our scale to some of the other commonly examined collectivism
outcomes, such as conflict resolution styles, communication styles,
and reward preferences (Earley & Gibson, 1998). Future research
should also continue to test the boundary conditions for collectivism effects. Shaw et al. (2000) showed that collectivism effects
became less positive as task interdependence decreased. We suspect that the predictive validity of our scale would decline if the
context changed from the highly interdependent groups in our
samples to the more casual work groups used in some organizations. A number of other variables could help capture such contextual boundaries, including group life span and member
proximity.
Finally, given that the five facets were differentially related to
the performance outcomes, an interesting avenue for future research would be to explore the theoretical underpinnings of these
facets. For example, Akers’s (1973) social learning approach to
deviant behaviors, which identifies key mechanisms that should
increase or decrease the likelihood of deviance, is one theoretical
framework that would be particularly helpful in explaining the
relationships found between the norm acceptance facet and counterproductive and withdrawal behaviors. Possible intervening
mechanisms for these relationships include collectivists’ imitation
of group members’ behavior as a means to comply with group
norms and the value that collectivists place on complying with
these norms. Social exchange theory (Blau, 1964), on the other
hand, would be useful in understanding the relationships between
the concern and reliance facets and citizenship. Specifically, the
nature of the exchange relationship that collectivists have with
their group could serve as an intervening mechanism of the facetlevel relationships. From a social exchange theory perspective,
collectivists’ concern for and reliance on group members should
foster a social exchange relationship, which in turn has been
argued to encourage more prosocial discretionary behaviors.
Our results also offer important practical implications. Given the
linkage between psychological collectivism and group member
performance, organizations should strive to maximize collectivism
using selection and placement or employee development systems.
In jobs where cooperation is vital, psychological collectivism
could be assessed during employment interviews or personality
tests. Such personality scores, which are routinely stored in the
employee profiles of many human resource software systems,
could also be used for placement decisions when staffing work
groups. Alternatively, employee development programs could tar-
895
get psychological collectivism when necessary. Whereas typical
development programs may be geared toward teaching task strategies that mimic high levels of conscientiousness or agreeableness,
other programs could be used to teach collective strategies. Such
programs could also be incorporated into the team-building programs routinely used to indoctrinate new group members.
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(Appendixes follow)
JACKSON, COLQUITT, WESSON, AND ZAPATA-PHELAN
898
Appendix A
The Triandis Family of Collectivism Measures
Measure
Hui (1988): 63 items (82
citations last 5 years)
Triandis et al. (1986,
1988, 1990): 17–29
items (347 citations last
5 years)
Singelis et al. (1995);
Triandis & Gelfand
(1998); Gelfand &
Realo (1999): 8–16
items (214 citations last
5 years)
Note. R ⫽ reverse scored.
In-groups
Dimension labels
Coworkers
Friends
Neighbors
Spouse
Parents
Kin
1. Consideration of
implications of one’s own
actions for others
2. Sharing of material resources
None
Coworker
Friend
Neighbor
Family
Spouse
Parent
Kin
Nationality
Work
group
Coworkers
Friends
Neighbors
Relatives
3. Sharing of nonmaterial
resources
4. Susceptibility to social
influence
5. Self-presentation and facework
6. Sharing of outcomes
7. Feeling of involvement in
others’ lives
1. Concern for in-group
2. Interdependence
3. Family integrity
4. Self-reliance (R)
5. Distance from in-groups (R)
1. Horizontal collectivism
(includes concern for ingroup, distance from ingroup, interdependence,
sociability)
2. Vertical collectivism
(includes concern for ingroup, family integrity)
Dimension sample items
1. The decision of where one is to work should be jointly
made with one’s spouse, if one is married.
2. I would help if a colleague at work told me that he/she
needed money to pay utility bills.
3. One needs to return a favor if a colleague lends a helping
hand.
4. My good friends and I agree on the best places to shop.
5. Each family has its own problems unique to itself. It does
not help to tell relatives about one’s problems.
6. The motto “sharing is both blessing and calamity” is still
applicable even if one’s friend is clumsy, dumb, and causes
a lot of trouble.
7. There is everything to gain and nothing to lose for
classmates to group themselves for study and discussion.
1. When my colleagues tell me personal things about
themselves, we are drawn closer together.
2. I would help within my means, if a relative told me that
s(he) is in financial difficulty.
3. One should live life independently of others as much as
possible. (R)
4. One does better working alone than in a group. (R)
5. My happiness is unrelated to the well-being of my
coworkers. (R)
1. The well-being of my co-workers is important to me.
I feel good when I cooperate with others.
To me, pleasure is spending time with others.
2. It is important to me that I respect the decisions made by
my groups.
Parents and children must stay together as much as possible.
Family members should stick together, no matter what
sacrifices are required.
PSYCHOLOGICAL COLLECTIVISM
899
Appendix B
The Earley and Wagner Families of Collectivism Measures
Measure
In-group
Dimension labels
Dimension sample items
Earley family
Erez & Earley (1987): 4
items (27 citations last
5 years)
Work group
Family
Unidimensional
Earley (1993): 8 items
(72 citations last 5
years)
None
Work group
Friends
Unidimensional
Only those who depend on themselves get ahead in life. (R)
One should live life independent of others as much as possible. (R)
Working with a group is better than working alone.
In society, people are born into extended families or clans who
protect them in shared necessity for loyalty.
One does better work working alone than in a group. (R)
If a group is slowing me down, it is better to work alone. (R)
Problem solving by groups gives better results than problem solving
by individuals.
An employee should accept the group’s decision even when
personally he or she has a different opinion.
Wagner family
Wagner & Moch (1986):
11 items (24 citations
last 5 years)
Work group
1. Beliefs
2. Values
3. Norms
Wagner (1995): 20 items
(72 citations last 5
years)
Work group
1. Self-reliance (R)
2. Competitive success (R)
3. Value attached to
working alone rather
than in a group (R)
4. Subordination of
personal needs to group
interests
5. Effects of personal
pursuits on group
productivity
1. My work group is more productive when its members follow
their own interests and concerns. (R)
2. I prefer to work with others in my work group rather than to
work alone.
3. People in my group should recognize that they are not always
going to get what they want.
1. Only those who depend upon themselves get ahead in life. (R)
2. Winning is everything. (R)
3. Working with a group is better than working alone.
4. People in a group should realize that they sometimes are going
to have to make sacrifices for the sake of the group as a whole.
5. A group is more productive when its members do what they
want to do rather than what the group wants them to do. (R)
Note. R ⫽ reverse scored.
Received December 20, 2004
Revision received July 28, 2005
Accepted August 10, 2005 䡲
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