The

Japanese Association of
Administrative Science
経営行動科学学会

jA
A

Discussion Paper Series

S<Contact>
Please submit your paper as an attached file via email tothe following address:
ushimaru@kisc.meiji.ac.jp
H A J I M E U S H I M A R U , M E I J I U N I V E R S I T Y.

<Rules regarding the writing of papers>
Please download from JAAS home page.
http://wwwsoc.nii.ac.jp/jaas2/

JAASDP2007-2E

Fitting with Organizations or Jobs? A Multilevel Investigation of HR Effects on Employee
Behaviors

Norihiko Takeuchi
School of Management
Tokyo University of Science, Japan
Email: ntake_8@yahoo.co.jp

Tomokazu Takeuchi
Department of Career Development
Kawaguchi Junior College, Japan
Email: tomo-t@mte.biglobe.ne.jp

Yutaka Toshima
College of Commerce
Nihon University, Japan
E-mail: toshima@nihon-u.ac.jp

April, 2007
1

Fitting with Organizations or Jobs? A Multilevel Investigation of HR Effects on Employee
Behaviors

ABSTRACTThis study attempted to clarify the mediating role of person-environment fit and multiple workcommitment in the relationship between an organization’s HRM practices and its employees’ behavioral
outcomes. Using a sample of 876 Japanese employees working for 37 healthcare establishments, the study
demonstrated that employees’ evaluations of their fit and commitment to their organizations were found to beimportant mediators of the relationship between HRM practices and employees’ behaviors, while theirevaluations of their fit to and involvement in their jobs were not. The possible contributions that this study
makes to the black box debate in strategic HRM are highlighted.

Keywords: Human resource practices, Person-environment fit, Multilevel analysis

2

INTRODUCTION
There is a general consensus among researchers that an employee’s commitment to both anorganization and a job is a key factor that links the human resource management (HRM) practices of anorganization to the work behaviors and outcomes of its employees in areas such as enhanced job performance,
reduced turnover, and absenteeism (Guest, 1997; Meyer & Allen, 1997; Meyer & Smith, 2000; Whitener,
2001; Wright, Gardner, & Moynihan, 2003). Guest (1997) lays out the clear causal path that many otherresearchers in this field assume. Human resource systems are established; they influence workplace practice;employee attitudes change with increased work commitment, which consequently affects work behavior; andthis in turn feeds through to the performance of the work unit and eventually of the firm.
Despite the increasing attention paid to such inner mechanisms, or the so-called black box of
HRM-performance relationships (Wright et al., 2003; Wright, Gardner, Moynihan, & Allen, 2005), previous
studies on strategic HRM (SHRM) tended to focus on examining the effectiveness of organizational-level
HRM practices and their impact on a firm’s economic performance (Datta, Guthrie, & Wright, 2005; Delery& Doty, 1996; Huselid, 1995; Youndt, Snell, Dean, & Lepak, 1996). Researchers are increasingly recognizing,
however, that the true effectiveness of a firm’s HRM policies and practices can be evaluated from the micro or
socio-cultural aspects of performance, including its employees’ attitudes and behaviors (Chang, 2005; Liao &Chuang, 2004; Whitener, 2001; Wright et al., 2003). Clearly, a greater effort needs to be made to explore themechanisms by which establishment-level HRM policies and practices influence the behaviors and outcomesof individual-level employees. Specifically, we need to further address the central part of Guest’s (1997)
hypothetical sequence; that is, the process by which work practices affect employee attitudes, which
eventually influence employee work behavior.
The present study will therefore examine how organizational HRM practices affect employees’
attitudes and behaviors, using a sample of 876 Japanese employees working for 37 healthcare establishments
in Japan. We hypothesize from our literature review of this field that the person-environment (P-E) fit, alongwith multiple aspects of work commitment, will be the important mediators linking organizational HRM
practices to the reduction in employee turnover, as well as to the enhancement of job performance quality3

(Lauver & Kristof-Brown, 2001; Meyer & Allen, 1997; Meyer & Smith, 2000; Wright et al., 2003). This
study is therefore expected to make a significant step forward in the areas of P-E fit and work commitment
research by clarifying their roles as mediators between establishment-level HRM practices and turnover andjob quality-enhancing behaviors. We also expect that the findings of this study will fuel the on-going debate
over the black box issue of HRM-performance linkages by providing significant insights into the process bywhich HRM practices influence employees’ behavioral outcomes.

LITERATURE REVIEW AND HYPOTHESESHRM Practices That May Facilitate Work Commitment and PerformanceHigh performance work practices in SHRM researchStudies in the SHRM field have consistently shown that certain types of HRM practices enablefirms to perform better by attracting, motivating, and retaining a number of highly committed humanresources (Appelbaum et al., 2000; Arthur, 1994; Wright et al., 2003). It is widely accepted that there are twomodels of HRM practices: the control model and the commitment model (Arthur, 1994; Wood & deMenezes, 1998). The intention of the control model is to reduce direct labor costs or improve efficiency bydefining strict work roles and procedures (Arthur, 1994). Following this approach, rules, sanctions, rewards,
and monitoring are considered to be the effective tools to manage the workforce. The intention of thecommitment model, on the other hand, is to induce desired employee attitudes and behaviors by forging
psychological links between the goals of the organization and the goals of the employee. Following thecommitment approach, development, involvement, participation, and long-term orientation are considered tobe the significant means of increasing human resource productivity and outcomes (Arthur, 1994; Wood & deMenezes, 1998). The commitment, rather than control, model of HRM practices is said to serve as a source ofcompetitive advantage by eliciting greater work commitment and motivation within an organization.
Although there is agreement among SHRM researchers that the commitment model of HRM
practices is the best approach, there is some discrepancy among them as to which elements of individual
HRM practices should be included in the commitment model. An intensive review of relevant literature
4

(Delery & Doty, 1996; Huselid, 1995; MacDuffie, 1995; Way, 2002; Wright et al., 2003; Whitener, 2001;
Youndt et al., 1996) led us to identify the following HRM practices as significant components in thecommitment model: (1) appropriate staffing and selection, (2) a fair appraisal system, (3) comprehensive
training and development, and (4) competitive compensation.
Another important argument in SHRM for enhancing work commitment and performance is thepenetrating debate as to whether or not the HRM practices as a set influence employees’ attitudes andbehaviors. Configurational theory suggests that there should be an internally appropriate fit or consistency
within the system for producing better performance and achievements (Delery & Doty, 1996; Guest, 1997).
MacDuffie (1995) introduced the concept of an HR bundle, and illustrated that the effect of HRM practices asa whole (or bundle) on performance was greater than that of individual HRM practices. Chang (2005)
supported this view, and explained that the bundle of HRM practices is more likely to enhance employees’
desired attitudes and behaviors since it can provide the mutually reinforcing conditions that support employee
motivation and skill acquisition. Relying on such a configurational view, this study examines how HRM
practices as a set (i.e., an HRM bundle) will enhance the attitudes and behaviors of employees.

Literature on HRM-commitment links
A review of the literature on the effects of HRM on work commitment reveals that there are two
types of studies. Earlier studies dealing with this issue focused on testing the direct impact of HRM practices
on work commitment (Gaertner & Nollen, 1989; Konovsky & Cropanzano, 1991; Ogilvie, 1986). For
instance, Ogilvie (1986) found that employees’ perceptions of two individual HRM practices, merit-systemaccuracy and the fairness of promotions, had direct influences on their commitment to the organization.
Similarly, Gaertner and Nollen (1989) showed a clear connection between an employee’s perceptions of
employment practices and his or her work commitment in a Fortune 100 manufacturing firm. They found that
employee commitment had direct associations with both actual and perceived HRM practices, includinginternal promotion, training opportunities, and employment security. Similarly, Konovsky and Cropanzano(1991) found that types of HRM programs as perceived by employees increased affective organizational
5commitment and other relevant attitudinal outcomes.
Recent studies on this issue, on the other hand, have assumed that the links between HRM practices
and an employee’s work commitment would be either indirect or conditional. Although limited, there is some
empirical evidence that certain intervening variables mediate (or moderate, in some cases) the relations
between HRM practices and employee commitment. Using a sample of 281 employees from several
organizations, Meyer and Smith (2000) found that the relationship between employees’ evaluations of HRM
practices and their commitment to the organization was mediated by perceived organizational support andprocedural justice. Moreover, Whitener (2001) revealed that the actual reward system of firms moderated thepositive relationship between organizational support and organizational commitment as perceived by
employees. Although the design of the research differs somewhat from researcher to researcher, a stream ofrecent studies suggests that the effects of HRM practices on employee commitment are neither direct nor
unconditional.
Following the recent empirical findings of the aforementioned studies, it is reasonable to assume
that there would be some intervening factors that mediate the relationship between HRM practices andemployees’ work commitment. Due to the limited number of studies that examine mediators between HRM
and work commitment, there are now a variety of speculations among researchers in this field as to what
would be the important factors that mediate this relationship. Nevertheless, some studies of P-E fit provide
important insights into this matter (Lauver & Kristof-Brown, 2001; O’Reilly, Chatman, & Caldwell, 1991).

Multiple Aspects of Work Commitment
Recently, studies on work commitment have tended to treat the concept of work commitment as
having multiple dimensions, and have classified them depending on particular commitment objectives, suchas organization, work group, occupation, union, and one’s job (Cohen, 1999). According to Cohen (1999),
such a broad concept of work commitment can be divided into five distinctive constructs based on the workof Morrow (1993). These are affective organizational commitment, continuance organizational commitment,
work ethic endorsement, career commitment, and job involvement. Morrow (1993) termed these constructs
6

the “universal forms of work commitment,” since these five foci of commitment were considered relevant toall employees.
Of these five forms of work commitment, we focused on the three forms that are considered most
relevant to the present study: (1) affective organizational commitment, (2) continuance organizational
commitment, and (3) job involvement. Since the aim of our study was to demonstrate how human resource
management affects work behaviors, including turnover intentions and job quality improvement, within afirm, we considered employees’ attitudes toward both their job and their organization to be the more relevant
constructs that mediate the link between HRM and work behavior. It is logical to assume that employees’commitment to their job and organization can be conditioned by their reactions toward HRM practices, while
the two other forms of commitment, work ethic endorsement and career commitment, are less likely to bechanged by HRM. Morrow (1993) similarly holds the view that the protestant work ethic and career
commitment are more dispositional, cultural, and cohort-based in nature, and are therefore relatively stable
over time. We therefore used the above three forms of commitment as important mediators of the relationshipbetween HRM practices and employee work behavior. Affective organizational commitment reflects anemotional attachment to, identification with, and involvement in the organization, while continuanceorganizational commitment is based on the perceived costs associated with discontinuing employment within
the organization (Meyer & Smith, 2000: p. 320). Job involvement is defined as the psychological
identification with one’s work and the degree to which the job situation is central to the person and his or her
identity (Lawler & Hall, 1970: pp. 310-311). It is generally accepted that the first two forms of commitment,
affective and continuance commitment to the organization, are the sub-dimensions of the broader concept oforganizational commitment, while the latter is treated as an independent concept of job involvement.

Person-Environment Fit as a Mediator between HRM Practices and Work Commitment
In recent years, management scholars have expressed a growing interest in the concept of
person-environment (P-E) fit, since it can offer many insights into the links between an organization’s policies
and practices, and employees’ attitudes and behaviors (Hoffman & Woehr, 2006; Lauver & Kristof-Brown,
7

2001; Verquer, Beehr, & Wagner, 2003). Prior research demonstrated that employees in work organizations
could distinguish two specific types of fit under the umbrella concept of P-E fit: person-organization (P-O) fit
and person-job (P-J) fit (Lauver & Kristof-Brown, 2001). P-O fit is defined as the compatibility between
people and organizations that occurs when at least one entity provides what the other needs, they share similar
fundamental characteristics, or both (Kristof, 1996: pp. 4-5). P-J fit, on the other hand, is defined as the match
either between the abilities of a person and the demands of a job, or the needs and desires of a person andwhat a job provides (Edwards, 1991).
Traditionally, studies of P-O and P-J fit have been conducted in the context of the selection andrecruitment processes of firms. A number of studies have examined the similarities and dissimilarities
between the recruiters’ own values, goals, and nature (i.e., personality) and those of the organization and thejob (Bretz, Rynes, & Gerhart, 1993; Cable & Judge, 1996). Recent studies, on the other hand, have shifted
their focus from the fit of the recruiters to that of existing employees, examining the relationship between
P-O/P-J fit and work attitudes (Lauver & Kristof-Brown, 2001; Verquer, Beehr, & Wagner, 2003). For
instance, P-O fit was found to have a positive relationship with organizational commitment, whereas P-J fit
was found to have a positive relationship with job involvement (Kristof, 1996; Saks & Ashfoth, 1997). The
relation between P-O fit and organizational commitment has been more extensively analyzed anddemonstrated in many prior studies. Verquer et al. (2003), who conducted a meta-analysis of this link by
examining 15 studies involving 18,776 participants altogether, reported that the mean correlation of P-O fit
with organizational commitment was r=.27.
More importantly, with an increasing number of empirical studies reporting a clear connection
between P-O/P-J fit and positive work attitudes and outcomes, the organizational and individual contexts that
affect employees’ perceptions of P-O/P-J fit are becoming an important issue (e.g., Lauver & Kristof-Brown,2001). Although few studies have examined what contextual variables lead to employees’ having greater
perceptions of P-O/P-J fit, there is no doubt that an organization’s HRM practices and the resulting level of its
employees’ evaluations of these programs can change their P-O/P-J fit perceptions.
For P-O fit, when HRM practices as a set are implemented in such a way as to facilitate the P-O fit,
8

employees will evaluate that they fit in well with their organizations’ values, goals, and climate. In other words,
rather than assuming that HRM practices directly stimulate an employee’s emotional attitudes, it is morelogical to consider that HRM can change an employee’s assessment of his or her fit within the organization(i.e., perceived P-O fit), which eventually influences more emotional aspects of organizational attitudes (i.e.,organizational commitment). For instance, Cable and Parsons (2001) demonstrated that the use of training
programs as part of HRM practices directly enhanced employees’ perceptions of P-O fit. Moreover, in theirattempt to test the effectiveness of training programs in firms, Klein and Weaver (2000) reported that
employees’ participation in training programs increased their P-O fit perceptions. Although these studies havefocused on training and its direct impact on P-O fit, it is more likely, following a configurational andcomplementary notion of SHRM, that a firm’s HRM practices as a bundle can be a direct and strong predictorof employees’ perceptions of P-O fit, which then affect their sense of organizational commitment (Chang,2005; Delery & Doty, 1996; Sekiguchi, 2006; Takeuchi, Wakabayashi, & Chen, 2003; Way, 2002). Thus, we
expect that the links between HRM practices and employees’ psychological attachment to an organization
will be conditioned by how the employees assess the degree of their congruence with their organizations.
Therefore, we developed the following hypotheses:

Hypothesis 1a: The relationship between establishment-level HRM practices and affective organizational
commitment will be positively mediated by the person-organization fit as perceived by employees.
Hypothesis 1b: The relationship between establishment-level HRM practices and continuanceorganizational commitment will be positively mediated by the person-organization fit as perceived by
employees.

In a similar vein, the other aspect of commitment, namely job involvement, and its links to HRM
practices is considered to be conditioned by the degree of employees’ P-J fit perceptions. Kristof (1996)
suggested that P-J fit should be more strongly associated with attitudes specific to the job (e.g., job
involvement) and P-O fit with attitudes about the organization in general (e.g., organizational commitment).
9Hence, it is reasonable to consider that HRM practices first affect employees’ assessment of their actual
degree of job match, which in turn leads to the increase (or decrease) of their emotional involvement with
their own job. Hence, the following hypothesis regarding the links between HRM practices, job involvement,
and P-J fit can be made:Hypothesis 1c: The relationship between establishment-level HRM practices and job involvement will bepositively mediated by the person-job fit as perceived by employees.

Consequences of Organizational Commitment and Job Involvement
As many studies have demonstrated, employees’ turnover intentions are one of the most salient
consequences of multiple work commitments, including affective and continuance commitment, and job
involvement. The findings of past studies in regard to the three forms of commitment are largely consistent, inthat employees’ turnover intentions were found to be negatively related to affective organizational
commitment (e.g., Chen & Francesco, 2003; Meyer, Herscovitch, & Topolnytsky, 2002; Shore, Newton, &Thorton III, 1990), continuance organizational commitment (e.g., Chen & Francesco, 2003; Meyer,
Herscovitch, & Topolnytsky, 2002), and job involvement (e.g., Brown, 1996; Morrow & McElroy, 1987).
Therefore, we can make the following hypotheses:

Hypothesis 2a: An employee’s turnover intention will be negatively influenced by affective organizational
commitment.
Hypothesis 2b: An employee’s turnover intention will be negatively influenced by continuanceorganizational commitment.
Hypothesis 2c: An employee’s turnover intention will be negatively influenced by job involvement.

Job performance is another important aspect of the consequences of work commitment according tomany of the prior studies. In such studies, a wide variety of job performance indexes or measures have been10

used: some used an objective index of job performance; others relied on subjective measures. Moreover, theaspects of an employee’s performance that were measured also differed depending on the researchers’ views
and/or intentions: some measured job performance in terms of its quality; others in terms of its quantity.
Our study focuses on the extent to which employees behave in such a way as to improve the overall
quality of their jobs. Since the sample of this study consists of healthcare workers whose ultimate objective intheir jobs is to offer an excellent quality of service to their clients, the use of job quality improvement as ameasure of the job performance of healthcare workers seemed appropriate.
A review of the literature on the relations between multiple work commitment and job performance
revealed that affective organizational commitment tended to affect job performance significantly andpositively, while continuance organizational commitment tended to affect it either not significantly or
negatively (e.g., Chen & Francesco, 2003; Diefendorff et al., 2002; Meyer et al., 2002; Somers & Birnbaum,1998). Meyer et al. (2002), who conducted a meta-analysis of the relations between the multi-dimensional
scales of organizational commitment and their outcomes, revealed that affective organizational commitment
had significant and positive correlations with overall job performance and organizational citizenship behavior.
In a similar vein, Diefendorff et al. (2002) found that job involvement had significant and positive effects on
both in-role and extra-role performance. As such, both affective commitment to the organization and job
involvement tended to positively correlate with job performance.
On the other hand, Chen and Francesco (2003) reported that subordinates’ continuancecommitment to the organization had no statistically significant effect on in-role performance as rated by theirsupervisors, but had a significant and negative effect on extra-role performance. Meyer et al. (2002) foundfrom their meta-analysis that overall job performance is negatively related to continuance organizational
commitment. These findings provide evidence that continuance organizational commitment does notnecessarily promote either in-role or extra-role performance, while affective organizational commitment andjob involvement function, more or less, to enhance job performance. Thus, based on the general trend of
empirical findings and the notions shared by scholars in this field concerning the relations between types ofwork commitment and job performance, we can make the following hypotheses:11Hypothesis 3a: Employees’ behavior that improves their job quality will be positively influenced byaffective organizational commitment.
Hypothesis 3b: Employees’ behavior that improves their job quality will be either negatively or not
significantly influenced by continuance organizational commitment.
Hypothesis 3c: Employees’ behavior that improves their job quality will be positively influenced by job
involvement.

METHOD
Sampling and Data Collection Procedures
In order to test the sets of hypotheses stated above, we attempted to collect individual employee data
by using a questionnaire survey method. Given that we aimed to test a cross-level interplay between
organizational-level HRM practices and individual-level attitudes and behaviors, we carefully designed andcollected the data that allowed us to estimate both the between-group and within-group variances of thevariables. Our target sample was Japanese employees who all belonged to Japanese private corporations
within a single industry category in Japan, the healthcare industry sector. This is because we wanted to control
for any possible bias that might stem from involving a variety of industry sectors. Within this industry sector,
we selected 80 service establishments as a source of information. These establishments were the member
organizations of one of the biggest cooperative unions located in the Northern part of Japan, where thehealthcare service businesses are very active due to the growing number of elderly people in this region. We
only targeted the relatively large service establishments within this industry (those with more than 40
employees) that were specialized in running businesses related to home care and short or long stay services.
Our criteria for the size of establishment (i.e., those with more than 40 employees) may not be as large as thatused in some other studies (e.g., Huselid, 1995), but in this service sector, establishments with more than 40
regular employees are considered quite large. In addition, we checked, at the time of data collection, whether
each establishment sampled had formal HRM functions and procedures. They all had written documents and12guidelines for staff recruitment, appraisal, compensation, and training. For data collection, we visited all theparticipating establishments and asked the section heads of the human resource functions at eachestablishment to distribute questionnaires to each of the full-time healthcare service employees with astamped envelope. Those employees who did not engage in a healthcare service job (e.g., clerical and/or othermanagerial jobs) or were part-time assistants were excluded from the sample. We left a total of 1,371
questionnaires at all the establishments sampled; 1,052 sets of completed questionnaires were then sent backdirectly to the senior author’s institution.
However, since our study involves testing the relations between variables at two hierarchical levels,
there needed to be an ample size of individual data for each establishment. For this reason, we omitted
employee data with less than five samples from one establishment. As a consequence, our final sample was
reduced to 876 full-time healthcare employees nested in 37 service establishments. The average number of
employees per establishment was 23.7, with the minimum and maximum numbers of employees at an
establishment being 5 and 151, respectively. The average establishment size of the respondents as measured
by the number of employees was 85.9, ranging from 40 to 225 employees. The average age of our
respondents was 34.1 and their average length of service was 3.9 years. Six hundred and fifty-nine of the
respondents (75.2%), or approximately three-quarters, were female while 217 (24.8%) were male. The
predominance of women in the sample reflects the overall population of this industry in Japan. The Japanese
Ministry of Health, Labor, and Welfare reported in 2004 that 79.1% (787,190 out of 995,374 employees) ofhealthcare industry jobs in Japan were occupied by women.

Measurement Instruments
Since our target sample was made up entirely of Japanese people who speak Japanese as a nativeand primary language, all of the survey instruments in the questionnaire needed to be prepared in Japanese. In
preparing the questionnaire, we first made it in English since most of the survey instruments we used in the
study had originally been developed by other researchers in English. The conventional method of translation
and back-translation (Brislin, Lonner, & Thorndike, 1973) was applied in developing the Japanese version of13

the questionnaire. The details of the survey instruments are explained below.

HRM practices
There are two different approaches used to measure organization-wide HRM practices (Gerhart etal., 2000). The first is to rely on the use of single-respondent designs, which have been frequently applied inprior SHRM research (e.g., Delery & Doty, 1996; Huselid, 1995; Huselid & Becker, 2000). Some recent
SHRM studies, on the other hand, have recommended taking an alternative approach that uses multiple
respondent designs, given the fact that single rater reliabilities of HRM measures have in the past beenfrighteningly low (Gerhart et al., 2000; Wall & Wood, 2005). Consistent with this notion, some recent studies
have used employees as the source of HRM practice data (Mayer & Smith, 2000; Cable & Parsons, 2001;
Wright et al., 2003, 2005). Hence, we attempted to measure establishment-level HRM practices by
aggregating the survey responses from all of the employees nested in each of the 37 healthcare
establishments.
Since there had been a lack of well-established HRM practice items to be surveyed for employees,
the HRM practice items used in this study were mostly developed by the authors, based on our extensive
review of the relevant SHRM literature. To capture the commitment model of HRM practices, we listed the
HRM practices from past literature so that we could find some common denominators in a number ofcommitment-enhancing HRM practices (Delery & Doty, 1996; Huselid, 1995; MacDuffie, 1995; Way, 2002;
Wright et al., 2003; Whitener, 2000; Youndt et al., 1996). As a consequence, we developed an 11-item HRM
practice measure which covers the four sub-dimensions of the major HRM functions; namely, appropriate
staffing and recruitment (two items), a fair performance appraisal system (four items), comprehensive training
and development (three items), and competitive compensation (two items). For each item, we used afive-point Likert-type response format, ranging from 5 (=agree) to 1 (=disagree).
To check the construct validity of our 11-item HRM practice measure, we performed a
confirmatory factor analysis on all the employee respondents. The results showed that the a priori four-factor
model fitted the data very well. Specifically, the various fit indices, including CFI, IFI, and TLI, had achieved14

a value well above the conventional criteria of .90 (CFI=.97, IFI=.97, and TLI=.96). In addition, following theconvention for testing the convergent and discriminant validity of items (e.g., Podsakoff & Organ, 1986), we
tested the hypothesized four-factor model against an alternative one-factor model, as well as a null model.
This additional analysis strongly supported the convergent and discriminant validity of the HRM measures.
Specifically, the four-factor model fitted significantly better than both the null model where all HRM items are
assumed to be totally unrelated (Δχ²=3610.96, Δdf=17, p<.001) and the one-factor model where all HRM
items are converged into a single factor (Δχ²=286.80, Δdf=6, p<.001).
Before creating establishment-level measures of HRM practices by aggregating individual
responses for each measure, we calculated the intraclass correlations, ICC(1) and ICC(2), that are commonlyused to justify the aggregation of the data to higher levels (e.g., Shrout & Fleiss, 1979). The value of ICC(1) is
known as an index to assess the reliability of a single-respondent measure, while that of ICC(2) is typified as
estimating the reliability of aggregated multiple respondents. We found that the ICC(1) values for both thefour underlying dimensions of HRM practices and the HRM practices as a whole (i.e., the HRM practices
bundle) were extremely low (ranging from .01 to .09), indicating that using a single respondent for measuring
HRM practices would result in extremely low reliability. ICC(2) values for these items, on the other hand,were all found to be well above the recommended value of .60 (Glick, 1985), ranging from .83 to .96. Thesefindings served as strong evidence to support the use of aggregated data for the four sub-divided measures of
HRM practices, as well as for the overall HRM bundle measure. Cronbach’s coefficient alphas for the four
HRM practices and the overall HRM practices bundle at the establishment-level were all found to achieve theconventional criteria of measurement reliability with alphas greater than .73.

Person-environment fit
Perceived P-O fit was measured using three items taken from Lauver and Kristof-Brown (2001)(sample item: “My values match or fit the values of this organization.”). The coefficient alpha of this scale
was .75. We measured perceived P-J fit using four items adopted from Lauver and Kristof-Brown (2001)(sample item: “My ability fits the demands of this job.”). Cronbach’s coefficient alpha for this scale was .82.
15

For these P-E fit measures, a five-point Likert-type response format, ranging from 5 (=agree) to 1 (=disagree),was used.
Then, we conducted a confirmatory factor analysis to check the convergent and discriminant
validity of the two underlying structures of perceived P-E fit measures. The results strongly supported astatistical validity of the P-E fit constructs. The expected two-factor model of P-O and P-J fit fitted better tothe present sample covariant matrix, with CFI, IFI, and TLI being .92, .92, and .90, respectively. In addition,
this a priori model fitted significantly better than both the null model (Δχ²=2443.02, Δdf=7, p<.001) and thealternative one-factor model (Δχ²=352.91, Δdf=1, p<.001). Thus, we may conclude from the above resultsthat the two underlying constructs of P-E fit are a valid operationalization in the present study.

Multiple work commitment
As explained earlier, two constructs of organizational commitment, namely affective andcontinuance commitment to the organization, were used in this study. To measure affective and continuanceorganizational commitment, we relied on the use of existing psychometric properties developed by Meyer,
Allen, and Smith (1993). We measured affective organizational commitment using four representative items
out of the six global items of Meyer et al. (sample item: “I really feel as if this organization’s problems are myown.”). The coefficient alpha of this scale was .80, indicating a sufficiently high reliability of the construct.
Continuance commitment was measured using three items from Meyer et al.’s original six items (sample
item: “Too much of my life would be disrupted if I decided I wanted to leave my organization right now.”).
The reliability coefficient of the three items was found to be .73. Job involvement was measured with three
items adopted from Lodahl and Kejner’s (1965) and Kanungo’s (1982) job involvement scales (sample item:“My present job is religion for me.”). The Cronbach’s coefficient alpha for this measure was .76. A five-point
Likert-type response format, ranging from 5 (=agree) to 1 (=disagree), was used for the above workcommitment scale.
The results of the confirmatory factor analyses for multiple work commitment indicated that the apriori three-actor model fitted better than both the null model (Δχ²=2781.66, Δdf=13, p<.001) and the16

one-factor model (Δχ²=479.57, Δdf=3, p<.001). The fit indices of CFI, IFI, and TLI that the a priori
three-factor model had achieved all exceeded the recommended level of .90.

Behavioral outcomes
Two outcome measures of the employees’ behavior were included in this study: turnover intention
and job quality improvement. The employees’ turnover intentions were tapped by using two items (“I want toleave the present organization.” and “I want to move to other organizations which are different from thepresent workplace.”). The coefficient alpha for this two-item measure was .77. Job quality improvement wasmeasured using four items taken from Randall, Fedor, and Longenecker (1990) with a slight modification of
their “behaviors indicating a concern for quality” items (sample item: “I always try to increase the quality of
my job.”). The reliability coefficient of this measure was found to be .74. We adopted a five-point Likert-type
response format, ranging from 5 (=agree) to 1 (=disagree), for this behavioral outcomes scale.
It was observed from the confirmatory factor analysis results that the behavioral outcome measuresused in the study subsumed the expected two factors, yielding sufficiently high values of various model fit
indices (CFI=.99, IFI=.99, and TLI=.97). Moreover, the a priori two-factor model fitted better than both thenull model (Δχ²=1251.11, Δdf=7, p<.001) and the one-factor model (Δχ²=344.50, Δdf=1, p<.001). Thesefindings give support to the validity of both the convergent and discriminant behavioral outcome scales usedin the study.

Control variables
We controlled for the following six background variables in the study: (1) gender (coded 1 for maleand 0 for female), marital status (coded 1 for married and 0 for not married), years of service (the actual
number of years of each respondent), job change experiences (1 was assigned when a respondent had everexperienced job hopping, while 0 was assigned when he or she had not), and company size (measured interms of the number of employees in each establishment).

17

Model Estimation Strategies
We developed three types of analytical model involving either the whole or part of the hypothesized
paths from HRM practices to P-O fit and P-J fit, commitment and involvement, and employee turnover andjob quality behavior. The three analytic models, tested using a structural equation modeling (SEM) method,
are depicted in Figure 1. The first model, Model A, only involves organization-related attitudes (e.g., P-O fit
and organizational commitment) as mediators of the relationship between HRM practices and an employee’s
turnover intention and job quality improvement. Specifically, we examined in this model the sequences ofpaths initiating from HRM practices to P-O fit, organizational commitment, and the two outcome variables ofturnover intention and job quality behavior. Using this model, we tested the six individual hypotheses,
Hypotheses 1a, 1b, 2a, 2b, 3a, and 3b. The aim of testing this model using SEM was to evaluate to what
extent an employee’s attitude toward the organization will help strengthen or weaken the effects of HRM
practices on the resulting level of employee turnover and quality improvement behavior.
The second model (i.e., Model B) focuses on the variables relating to P-J fit and job involvement inexamining the relationship between HRM practices and employee behavior to test Hypotheses 1c, 2c, and 3c.
Here, we examined the sequences of paths leading from HRM practices to P-J fit, job involvement, andoutcome variables. An examination of Model B using SEM helped us assess the role of job-related attitudes(e.g., P-J fit and job involvement) as a means of mediating the relationship between establishment-level HRM
practices and behavioral outcomes.
The third model (i.e., Model C) involves all the hypothesized paths regarding the effects of HRM
practices on an employee’s attitudes toward both his or her organization and job. This is considered a fullmodel since it aims to test all the hypothesized relationships at once in the same model. If this model were
found to be a valid operationalization, we would be able to consider that types of commitment HRM practices
facilitate an employee’s psychological attachment to both the job and the organization, which in turn promotes
job quality improvement behavior and employee retention in an organization. In our SEM analyses, we
attempted to compare all three models in terms of model fit and to test all the individual hypotheses statedabove.
18

Our data, as well as our hypotheses, involve cross-level interplay between organizational HRM
practices and individual (i.e., employee) perceptions of fit in an organization and a job. We decided that further
statistical investigations should be carried out to ascertain whether (and if so, to what extent) individual-level
differences in P-O/P-J fit can be explained by the establishment-level differences in HRM practices, after
eliminating the possible individual-level effects that may influence P-O/P-J fit perceptions. To rigorously
capture such multi-level effects, we performed a hierarchical linear modeling (HLM) analysis. The use of
HLM allows for the iterative investigation of multiple levels of relationships with individual-level (employee)dependent variables (Hofmann, Griffin, & Gavin, 2000).
A possible response bias for this study might exist regarding the common source variance problem(Podsakoff & Organ, 1986), since our study involved collecting all variable information from the same source.
Although the HRM practices data were nested in each establishment, it is likely that other individual data
might be subject to this problem. Following the recommended method of checking this (e.g., Podsakoff &Organ, 1986), we performed a Harman’s one-factor test on all of the 23 individual-level items, including theitems on P-O/P-J fit, affective and continuance commitment, job involvement, turnover intention, and job
quality-enhancing behavior. Using an eigenvalue with a criterion of greater than one, the expected sevenfactors were justified, and no general factor was present. In addition, the results of a confirmatory factor
analysis for the a priori seven-factor model of all items showed a reasonably good fit to the present sample
covariance matrix structure (χ2 = 940.81, df = 209; GFI = .91; CFI = .91; IFI = .91; RMSEA = .06). It is worth
noting that there was a significant increase in chi-square statistics from the seven-factor model to thesingle-factor model (Δχ2 = 2003.40, df = 21, p < .001) and to the null model (Δχ2 = 7618.88, df = 44, p< .001), indicating that the a priori seven-factor model was a better-fitting model for the observed data. These
results suggest that method variance may not be a suitable explanation for this study. A correlation matrix anddescriptive statistics of all the variables used in the study are presented in Table 1.
Insert Figure 1 about here

RESULTS
19

Hypothesis Testing based on SEM
Model comparisons
In order to identify which of the three models proposed in this study fits best with the present
sample covariance structure, we performed a series of SEM analyses for each model. The summary statistics
are displayed in Table 1. This table shows that Model A fitted the data better than the two other models did.
Various fit indices for Model A, including a comparative fit index (CFI), an incremental fit index (IFI), theTucker-Lewis index (TLI), and a root mean square error of approximation (RMSEA), showed 0.91, 0.92,
0.90, and 0.07, respectively, indicating that they all achieved the general acceptance limit of a model fit.
Nevertheless, these fit indices failed to reach the conventional criteria of a model fit for Models B and C—thevalues were all between 0.82 and 0.89 for CFI, IFI, and TLI, and 0.08 or more for RMSEA. This indicatesthat Models B and C, which involve job-related attitudes, including P-J fit and job involvement, as mediatorsof HRM-behavior relationships do not fit our sample well. In addition, it can be seen from Table 1 that theratio of the model chi-square to the degree of freedom of all three models was smallest in Model A. All thesefindings indicated that the model that best fit our data was Model A, which predicted that only one aspect of
P-E fit, namely P-O fit, would mediate the relationship between establishment-level HRM practices andorganizational commitment. Thus, we may conclude that the findings negate Hypotheses 1c, 2c, and 3c,which concern the relationships between HRM practices and job attitudes.
Insert Table 1 about here

Path analytical results
Since Models B and C, which include variables concerning job-related attitudes, failed to reach thelevel of acceptance in various fit indexes, we only depicted the results of a path model in Model A, as shownin Figure 2. The tests of the hypotheses regarding the individual paths are summarized below.
First, we hypothesized that a P-O fit would positively mediate the relationships between
establishment-level HRM practices and affective organizational commitment (Hypothesis 1a) and between
the practices and continuance organizational commitment (Hypothesis 1b). Figure 2 shows that organizational
20

HRM practices significantly and positively affected P-O fit as perceived by employees (β=.25, p<.001),
which in turn contributed significantly and positively to the two forms of organizational commitment,
affective and continuance (β=.85, p<.001 and β=.65, p<.001, respectively). This indicates that the positive
relationship between perceived HRM practices and the two forms of organizational commitment is mediatedby the extent to which an employee perceives himself or herself to fit in with the organization. Morespecifically, an establishment-level HRM practices bundle helps to promote both affective and continuanceorganizational commitment when employees feel that they fit well with the organization. These findings are
consistent with Hypotheses 1a and 1b of the present study.
Second, Hypothesis 2 stated that an employee’s turnover intention would be negatively influenced
by affective organizational commitment (Hypothesis 2a) and by continuance organizational commitment(Hypothesis 2b). As shown in Figure 2, both coefficients of the paths linking affective and continuancecommitment to turnover intention were found to be statistically significant and showed the predicted negative
effects (β=-.54, p<.001 and β=-.34, p<.001, respectively). These negative paths mean that the higher the levelof affective and continuance commitment to an organization an employee shows, the lower the intention toquit. These findings give consistent support to Hypotheses 2a and 2b that predicted the negative effects of an
employee’s organizational commitment and the resulting level of his or her turnover intention.
Third, Hypothesis 3 stated that an employee’s behavior that leads to an improvement in his or her
job quality would be positively influenced by affective organizational commitment (Hypothesis 3a) andnegatively influenced by continuance organizational commitment (Hypothesis 3b). Our structural equation
results provided strong support for these predictions. Specifically, we found that job quality improvement wassignificantly and positively affected by affective commitment (β=.68, p<.001), but negatively affected bycontinuance commitment (β=-.14, p<.05). These findings suggest that affective commitment will help
motivate an employee to improve his or her job quality, while an employee’s motivation to improve job
quality will decrease once he or she holds a high level of continuance commitment. These findings give
support to Hypotheses 3a and 3b of the present study.
In order to further analyze the mediating role that P-O fit plays between HRM practices and the two21

forms of organizational commitment, we examined an additional structural model that subsumes two extra
paths in Model A initiating from (1) HRM practices to affective commitment and (2) HRM practices tocontinuance commitment. This additional estimated structural model has led us to further identify that P-O fit
mediates the relations between HRM practices and the two forms of commitment. First, the positive effect of
HRM practices on P-O fit was still very strong and statistically significant, even when we simultaneously
estimated the direct paths between HRM practices to two latent variables of organizational commitment. The
magnitude of HRM effects on P-O fit was almost equivalent when estimated in this additional model (β=.24,
p<.001) and Model A (β=.25, p<.001). This indicates that adding the direct paths from HRM to the two formsof commitment to Model A has almost nothing to do with the HRM and P-O fit relationship. Moreover, thedirect paths from HRM to continuance and affective commitment were found to be either insignificant orvery low (β=-.05, n.s. for the HRM-continuance path, and β=.07, p<.05 for the HRM-affective path). Finally,
the effects of P-O fit on affective and continuance commitment were found to remain statistically significant
with ample sizes of beta coefficients being presented, even in this additional model (β=.83, p<.001 and β=.66,
p<.001, respectively) Again, the magnitudes of these effects in this model remained almost unchanged fromthose of the respective effects in Model A (β=.85, p<.001 and β=.65, p<.001, respectively). The above extra
evidence gives further support to the mediation hypothesis depicted in Hypotheses 1a and 1b of the study.
To summarize, all the coefficients of the predictive paths included in Model A were found to bestatistically significant, and thus Hypotheses 1a, 1b, 2a, 2b, 3a, and 3b were supported. Nevertheless, three
individual hypotheses that assumed the presence of significant paths initiating from HRM practices to P-J fit
and job involvement (H-1c), from job involvement to turnover intention (H-2c), and from job involvement tojob quality improvement (H-3c) were unsupported due to the poor fit of the hypothesized models B and C.
Insert Figure 2 about here

Hypothesis Testing based on HLM
To further test the cross-level effects involved in Hypotheses 1a to 1c, namely the effect of
establishment-level HRM practices on employees’ P-O fit and P-J fit perceptions, we administered the HLM
22

for the 37 establishment-level and 876 individual-level data simultaneously. First, in order for the above
cross-level hypotheses to be supported, there should be significant between-establishment variances in P-O fit
and P-J fit perceptions. Non-statistically significant variances in the individual-level dependent variables (i.e.,
P-O and P-J fit) indicate that a large proportion of P-O or P-J fit variances can be explained by otherwithin-establishment (i.e., individual-level) variances, and hence the effect of organizational HRM practices
on an employee’s P-O and P-J fit can not be warranted. To check this basic condition, we first estimated a nullmodel in which no predictors were specified either in the level 1 (individual-level) or the level 2(establishment-level) function.
The findings of two null model estimations for both P-O fit and P-J fit indicated that a significant
level of the level 2 residual variance was found only in P-O fit (τoo=.04, p<.001), not in P-J fit (τoo=.01, p>.10).
This suggests that establishment-level predictors including HRM practices cannot explain employees’
perceptions of P-J fit, which is consistent with the findings obtained from the above SEM analysis. As such,
Hypothesis 1c was rejected again. There is a possibility, however, that employees’ P-O fit perceptions can bedirectly influenced by establishment-level HRM practices, given the significant level 2 residual variance in
P-O fit. The ICC(1) value that was calculated for P-O fit was found to be .12, indicating that 12 percent of thevariance in employees’ perceptions of P-O fit lay between establishments, and 88 percent of it lay withinestablishments.
In the next step, we estimated the model that incorporated both level 1 and level 2 predictors to test
the cross-level effects of organizational HRM practices on employees’ perceptions of fit in their organization.
Adding to the five individual-level control variables, as well as one establishment-level one, theestablishment-level HRM practices bundle measure was put into the level 2 equation. Table 2 displays theresults of the HLM for predicting P-O fit perceptions.
As can be seen in Model 1 of Table 2, establishment-level HRM practices were found to have asignificant and positive effect on the employees’ perceptions of P-O fit (γ=.50, p<.001), indicating that
establishment-level HRM practices, when implemented as an overall bundle, directly enhanced members’
perceptions of P-O fit. This finding provides additional support for Hypotheses 1a and 1b of the present study,
23which assumed a direct and positive association between organizational HRM practices and the resulting
level of members’ P-O fit.
An additional HLM model was built and tested for the purpose of contributing to both theconfigurational theory and the HRM bundle arguments. As seen in Model 2 of Table 2, an appropriate staffing
policy had a significant and positive impact on employees’ perceptions of P-O fit (γ=.39, p<.05), while noneof the other three HRM practices had any meaningful effects on the employees’ attitudinal variable. Moreover,magnitude of the effect of the individual HRM practice (i.e., staffing practice) seems to be rather weak, withstaffing practice generating a statistical significance level of p<.05. These findings may be consistent with thepenetrating debate on configurational theory, which asserted the importance of the synergistic impacts ofinternally aligned HRM policies and practices in eliciting positive and desirable behavioral and organizational
outcomes.
Insert Table 2 about here

DISCUSSION
A possible contribution that our study makes is that it provides partial but significant evidence tofuel the on-going debate on the black box issue of the SHRM field. Although empirical studies of SHRM
consistently reported a positive relationship between HRM practices and performance at the organizational
level, the mechanisms by which organizational HRM practices stimulate its members’ attitudes and behaviors
have yet to be explored. Our findings may partly answer the fundamental question as to what is inside theblack box. Our tentative answer would be that the use of a high performance HRM practices bundle seems toincrease employees’ sense of P-O fit, which then elicits their affective commitment to an organization and its
resulting level of their retention and job quality-enhancing behaviors. Future studies should further test, adjust,and modify our mediating model of HRM effectiveness, accumulating solid evidence to penetrate deep inside
the black box of an HRM-performance relationship.
Another important contribution is that our study of HRM effectiveness has shed light on the
person-environment fit studies and connected them to the extant work of the strategic HRM field. Issues of24

P-E fit have often been treated with respect to the selection and training activities of firms. With such abackground, the sample target and/or the scope of general P-E fit studies have mostly been applicants(potential employees) or new hires. Very few studies have involved organizational employees as a whole inthe sample or scope of studies. Our study is one of the few that has demonstrated that P-O fit can be a
strategically important concept for both human resource researchers and practitioners in order to help translatea firm’s HRM activities into the retention and quality-enhancing behaviors of its employees.
Despite these possible contributions, this study has several limitations that necessitate caution wheninterpreting the results. First, our study relied on the use of self-reported measures in P-O fit and job quality
improvement behaviors. In particular, it is well known that P-O fit can be measured from two different meansyielding virtually entirely different concepts: subjective fit and objective fit (Hoffman & Woehr, 2006; Kristof,
1996). Hoffman and Woehr (2006) illustrated from their meta-analysis of past P-O fit studies that thecorrelational levels of subjective fit and objective fit with behavioral outcome variables were slightly different.
Correlations of subjective fit with turnover behavior and job performance were found to be slightly lower thanthose of objective fit. Although there did not seem to be a large difference between subjective fit and objective
fit measures in terms of their correlates with outcome variables, the findings might have been somewhat
different if we had used the objective fit index in the study. However, the subjective fit measurement method
is also a well-accepted method and is widely used in contemporary P-O fit studies for its conciseness andsimplicity (Cable & Parsons, 2001).
Second, we adopted a cross-sectional survey method by which various HRM activities and theemployees’ attitudinal and behavioral data were collected at the same time. While we presented and tested themodel in which causal directions are assumed to initiate from HRM practices, followed by P-E fit, workcommitment, and employees’ behaviors, there is the possibility that the causal flow may occur in the reverse
order. In other words, more committed and loyal employees may actively adjust themselves to organizations
that implement high performance HRM practices in firms. Adequate time between the assessments of thesedifferent groups of variables would strengthen the causal inferences that can be derived from data similar toours. A longitudinal study needs to be carefully conducted with ample time intervals between the25measurements of the different variables, as well as their sequencing.

REFERENCES
Appelbaum, E., Bailey, T., Berg, P., & Kalleberg, A. 2000. Manufacturing advantage: Why highperformance work systems pay off. Ithaca, NY: ILR Press.
Arthur, J. B. 1994. Effects of human resource systems on manufacturing performance and turnover.
Academy of Management Journal, 37: 670-687.
Brislin, R. W., Lonner, W. J., & Thorndike, R. M. 1973. Cross-cultural research methods. New York: Wiley.
Bretz, R. D. Jr., Rynes, S. L., & Gerhart, B. 1993. Recruiter perceptions of applicant fit: Implications forindividual career preparation and job search behavior. Journal of Vocational Behavior, 43: 310-327.
Brown, S. P. 1996. A meta-analysis and review of organizational research on job involvement. Psychological
Bulletin, 120: 235-255.
Cable, D. M., & Judge, T. A. 1996. Person-organization fit, job choice decisions, and organizational entry.
Organizational Behavior and Human Decision Processes, 67: 294-311.
Cable, D. M., & Parsons, C. K. 2001. Socialization tactics and person-organization fit. Personnel Psychology,
54: 1-23.
Chang, E. 2005. Employees’ overall perception of HRM effectiveness. Human Relations, 58: 523-544.
Chen, Z. X., & Francesco, A. M. 2003. The relationship between the three components of commitment andemployee performance in China. Journal of Vocational Behavior, 62: 490-510.
Cohen, A. 1999. Relationships among five forms of commitment: An empirical examination. Journal of
Organizational Behavior, 20: 285-308.
Datta, D. K., Guthrie, J. P., & Wright, P. M. 2005. Human resource management and labor productivity: Does
industry matter? Academy of Management Journal, 48: 135-145.
Delery, J. E., & Doty, D. H. 1996. Modes of theorizing in strategic human resource management: Tests ofuniversalistic, contingency, and configurational performance procedures. Academy of Management
Journal, 39: 802-835.
26

Diefendorff, J. M., Brown, D. J., Kamin, A. M., & Lord, R. G.. 2002. Examining the roles of job involvement
and work centrality in predicting organizational citizenship behaviors and job performance. Journal of
Organizational Behavior, 23: 93-108.
Edwards, J. R. 1991. Person-job fit: A conceptual integration, literature review and methodological critique.
International Review of Industrial/Organizational Psychology, 6: 283-357.
Gaertner, K. N., & Nollen, S. D. 1989. Career experiences, perceptions of employment practices, andpsychological commitment to the organization. Human Relations, 42: 975-991.
Gerhart, B., Wright, P. M., McMahan, G. C., & Snell, S. A. 2000. Measurement error in research on humanresources and firm performance: How much error is there and how does it influence effect size
estimates? Personnel Psychology, 53: 803-834.
Glick, W. H. 1985. Conceptualizing and measuring organizational and psychological climate: Pitfalls in
multilevel research. Academy of Management Review, 10: 601-616.
Guest, D. E. 1997. Human resource management and performance: A review and research agenda.
International Journal of Human Resource Management, 8: 263-276.
Hoffman, B. J., & Woehr, D. J. 2006. A quantitative review of the relationship between person-organization fit
and behavioral outcomes. Journal of Vocational Behavior, 68: 389-399.
Hofmannm, D. A., Griffin, M. A., & Gavin, M. B. 2000. The application of hierarchical linear modeling toorganizational research. In K.J. Klein & S.W. J. Kozlowski (Eds.) Multilevel theory, research, andmethods in organizations: Foundations, extensions, and new directions: 467-511. San Francisco:
Jossey-Bass.
Huselid, M. A. 1995. The impact of human resource management practices on turnover, productivity, andcorporate financial performance. Academy of Management Journal, 38: 635-672.
Huselid, M. & Becker, B. E. 2000. Comment on measurement error in research on human resources and firm
performance: How much error is there and how does it influence effect size estimates? by Gerhart,
Wright, McMahan and Snell. Personnel Psychology, 53: 835-854.
Japan Ministry of Health, Labor and Welfare. 2004. The 2004 annual survey report on healthcare service
27

units and establishments in Japan. (in Japanese).
Judge, T. A., & Ferris, G. R. 1992. The erusive criterion of fit in human resources staffing decisions. Human
Resource Planning, 14: 47-67.
Kanungo, R. N. 1982. Measurement of job and work involvement. Journal of Applied Psychology, 67:
341-349.
Klein, J. H., & Weaver, N. A. 2000. The effectiveness of an organizational-level orientation training programin the socialization of new hires. Personnel Psychology, 53: 47-66.
Konovsky, M. A., & Cropanzano, R. 1991. Perceived fairness of employee drug testing as a predictor of
employee attitudes and job performance. Journal of Applied Psychology, 76: 698-707.
Kristof, A. L. 1996. Person-organization fit: An integrative review of its conceptualizations, measurements,
and implications. Personnel Psychology, 49: 1-49.
Lauver, K. J., & Kristof-Brown, A. 2001. Distinguishing between employees’ perceptions of person-job andperson-organization fit. Journal of Vocational Behavior, 59: 454-470.
Lawler, E. E., & Hall, D. T. 1970. Relationship of job characteristics to job involvement, satisfaction andintrinsic motivation. Journal of Applied Psychology, 54: 305-312.
Liao, H., & Chuang, A. 2004. A multilevel investigation of factors influencing employee service performanceand customer outcomes. Academy of Management Journal, 47: 41-58.
Lodhal, T. M., & Kejner, M. 1965. The definition and measurement of job involvement. Journal of Applied
Psychology, 49: 24-33.
London, M. 1983. Toward a theory of career motivation. Academy of Management Review, 8: 620-630.
MacDuffie, J. P. 1995. Human resource bundles and manufacturing performance: Organizational logic andflexible production systems in the world auto industry. Industrial and Labor Relations Review, 48:
197-221.
Meyer, J. P., & Allen, N. J. 1997. Commitment in the workplace: Theory, research, and application.
Thousand Oaks, CA: Sage.
Meyer, J. P., Allen, N. J., & Smith, C. A. 1993. Commitment to organizations and occupations: Extension and28

test of a three-component conceptualization. Journal of Applied Psychology, 78: 538-551.
Meyer, J. P., Herscovitch, S. L., & Topolnytsky, L. 2002. Affective, continuance, and normative commitment
to the organization: A meta-analysis of antecedents, correlations, and consequences. Journal of
Vocational Behavior, 61: 20-52.
Meyer, J. P., & Smith, C. A. 2000. HRM practices and organizational commitment: Test of a mediation model.
Canadian Journal of Administrative Sciences, 17: 319-331.
Morrow, P. C. 1993. The theory and measurement of work commitment. Greenwich, CT: JAI.
Morrow, P. C., & McElroy, J. C. 1987. Work commitment and job satisfaction over three career stages.
Journal of Vocational Behavior, 30: 330-346.
Ogilvie, J. R. 1986. The role of human resource management practices in predicting organizational
commitment. Group & Organizational Studies, 11: 335-359.
O’Reilly, C. A., Chatman, J., & Caldwell, D. F. 1991. People and organizational culture: A profile comparison
approach to assessing person-organization fit. Academy of Management Journal, 34: 487-516.
Podsakoff, P. M., & Organ, D. W. 1986. Self-reports in organizational research: Problems and prospects.
Journal of Management, 12: 531-544.
Saks, A. M., & Ashforth, B. 1997. A longitudinal investigation of the relationship between job information
sources, applicant perceptions of fit, and work outcomes. Personnel Psychology, 50: 395-426.
Sekiguchi, T. 2006. How organizations promote person-environment fit: Using the case of Japanese firms toillustrate institutional and cultural influences. Asia Pacific Journal of Management, 23: 27-69.
Shore, L. M., Newton, L. A., & Thornton, G. C. 1990. Job and organizational attitudes in relation to employee
behavioral intentions. Journal of Organizational Behavior, 11: 57-67.
Shrout, P. E. & Fleiss, J. L. 1979. Intraclass correlations: Uses in assessing rater reliability. Psychological
Bulletin, 86: 420-428.
Somers, M. J., & Birnbaum, D. 1998. Work-related commitment and job performance: It’s also the nature of
the performance. Journal of Organizational Behavior, 19: 621-634.
Takeuchi, N., Wakabayashi, M., & Chen, Z. 2003. The strategic HRM configuration for competitive29

advantage: Evidence from Japanese firms in China and Taiwan. Asia Pacific Journal of Management,
20: 447-480.
Verquer, M. L., Beehr, T. A., & Wagner, S. H. 2003. A meta-analysis of relations between person-organization
fit and work attitudes. Journal of Vocational Behavior, 63: 473-489.
Wall, T. D., & Wood, S. J. 2005. The romance of human resource management and business performance,
and the case for big science. Human Relations, 58: 429-462.
Way, S. A. 2002. High performance work systems and intermediate indicators of firm performance within the
US small business sector. Journal of Management, 28: 765-785.
Whitener, E. M. 2001. Do “high commitment” human resource practices affect employee commitment? A
cross-level analysis using hierarchical linear modeling. Journal of Management, 27: 515-536.
Wright, P. M., Gardner, T. M., & Moynihan, L. M. 2003. The impact of HR practices on the performance ofbusiness units, Human Resource Management Journal, 13: 21-36.
Wright, P. M., Gardner, T. M., Moynihan, L. M., & Allen, M. R. 2005. The relationship between HR practices
and firm performance: Examining causal order. Personnel Psychology, 58: 409-446.
Wood, S. J., & de Menezes, L. 1998. High commitment management in the U.K.: Evidence from the
Workplace Industrial Relations Survey and Employers’ Manpower and Skills Practices Survey.
Human Relations, 51: 485-515.
Youndt, M. A, Snell, S. A., Dean, J. W., & Lepak, D. P. 1996. Human resource management, manufacturing
strategy, and firm performance. Academy of Management Journal, 39: 836-866.

30

FIGURE 1
Three SEM Analytical Models Tested in the Study
Model A: Organizational Commitment Model

HRM
Practices

AffectiveOC

TurnoverIntention

ContinuanceOC

Job Quality
Improvement

P-O
Fit

Model B: Job Involvement Model
TurnoverIntention

HRM
Practices

Job
Involvement

P-J
Fit

Job Quality
Improvement

Model C: Combined Model
AffectiveOC
TurnoverIntention

P-O
Fit
ContinuanceOC

HRM
Practices

Job
Involvement

P-J
Fit

31

Job Quality
Improvement

TABLE 1
Results of Model Fit in the Three Different Models
Model

χ

2

df

χ /df

2

IFI

TLI

CFI

RMSEA

Model A:
Organizational
commitment model

770.02

163

4.72

.92

.90

.91

.07

Model B: Job
Involvement model

849.55

115

7.39

.89

.87

.89

.09

Model C: Combined
model

1988.96

313

6.36

.84

.82

.84

.08

TABLE 2
Hiererchical Linear Modeling Results on HRM Practice Effects on Employees' P-O Fit
Perceptions
Variables

MODEL 1

MODEL 2

γ

(SE)

p

γ

(SE)

p

(Intercept)

3.35

(.04)

***

3.34

(.04)

***

Gender

-.11

(.06)

†

-.11

(.06)

†

Marital status

-.28

(.05)

***

-.28

(.05)

***

Years of service

.02

(.01)

.02

(.01)

Job change experience

.02

(.04)

.02

(.04)

Educational background

-.02

(.02)

-.02

(.02)

Company size

-.01

(.01)

*

.00

(.00)

*

HRM practices bundle

.50

(.09)

***

Appropriate staffing and selection

.39

(.18)

*

Fair performance appraisal

-.01

(.12)

Comprehensive training and development

.18

(.19)

Level 1

Level 2Competitive compensation
Level 1 residual variance (σ²)
Level 2 residual variance (τ00)
Model deviance.49-.12 (.19).49.011652.00.01
1650.23† p<.10* p<.05** p<.01*** p<.001

32

FIGURE 2
Results of a Path Analysis for Model A
e8

e9

e10

e11

AOC1 AOC2 AOC3 AOC4
.80 .75

e5
P-O1

e1

Staffing

e2Appraisale3

Training

e4Compensation

.88
.71
.87
.79

e6
P-O2

.66 .68 .76

HRM
Practices.25***.69 .57-.54***

Affective
OC

e7

d4
TurnoverIntention

Turnovere12.82

Turnover2

e13

.57

JQI1

e14

.62

JQI2

e15

.74
.66

JQI3

e16

JQI4

e17

P-O3

.85***

d2-.34***.65***

d3.68***

P-O
Fit
d1
ContinuanceOC.72* p<.05** p<.01*** p<.001

.76

-.14*

.41

.65

COC1

COC2

COC3

e12

e13

e14

33

Job Quality
Improvementd5

APPENDIX A
Mean, standard deviation, and correlation coefficient among variables used in the present study a
Variables
Mean
(SD)
1
2
3
4
5
6
7
8
9
10
11
1 Gender
.75
(.43)
1.00
2 Marital status
.50
(.50)
-.02 1.00
3 Years of service
3.85(3.36).13-.17 1.00
4 Job change experience
.64
(.48)
-.02
-.24 -.04 1.00
5 Educational background2.90(1.07) -.16
.14-.06 -.14 1.00
6 Company size
86.92 (44.93) -.16
.20
.04
-.28
.19
1.00
7 HRM practices bundle
2.90
(.32)
-.36
-.38 -.39.12.05-.09 1.00
8 Appropriate staffing and selection
3.08
(.40)
-.24
-.33 -.37
.16
.06
.01
.88
1.00
9 Fair performance appraisal
2.72
(.43)
-.32
-.32 -.24 -.05
.04
-.12
.83
.54
1.00
10 Comprehensive training and development 3.18
(.34)
-.27
-.32 -.50
.24
.05
-.12
.89
.79
.63
1.00
11 Competitive compensation
2.61
(.33)
-.41
-.34 -.27
.10
.01
-.10
.86
.69
.69
.64
1.00
12 P-O fit 1
3.24
(.93)
-.08
-.21
.08
.08
-.06
-.04
.58
.58
.47
.56
.37
13 P-O fit 2
3.58
(.95)
-.09
-.13 -.03
.06
.00
-.02
.43
.49
.23
.55
.25
14 P-O fit 3
3.02
(.81)
-.02
-.16
.07
.06
-.02
-.17
.68
.75
.44
.69
.47
15 P-J fit 1
3.08
(.95)
.00
-.14
.16
.04
-.05
-.17
.02
.08
-.02
.02
.00
16 P-J fit 2
3.49
(.97)
-.02
-.15
.04
.09
-.17
-.01
.24
.33
.07
.38
.04
17 P-J fit 3
3.24
(.98)
-.02
-.14
.14
.02
-.07
-.19
.07
.14
-.05
.12
.07
18 P-J fit 4
3.27
(.91)
-.08
-.15
.10
.03
-.09
.05
.30
.48
.01
.41
.17
19 Affective organizational commitment 1
3.53(1.05) -.08
-.20
.08
.13
.01
-.21
.52
.49
.34
.53
.46
20 Affective organizational commitment 2
3.01(1.10) -.11
-.17
.06
.08
-.03
-.31
.52
.55
.27
.59
.40
21 Affective organizational commitment 3
3.46(1.07) -.17
-.10
.04
.06
.04
-.15
.48
.50
.31
.44
.43
22 Affective organizational commitment 4
3.15(1.05) -.15
-.07
.04
.07
.07
-.05
.54
.47
.46
.42
.49
23 Continuance organizational commitment 1 2.50
(1.22) -.07
-.13
.07
.07
-.07
-.01
.04
.12
.10
.02
-.15
24 Continuance organizational commitment 2 3.81
(1.11) -.08
.06
.01
-.08
.05
.15
.33
.37
.25
.18
.33
25 Continuance organizational commitment 3 3.21
(1.34) -.06
-.05
.08
.02
-.06
-.01
.04
.12
.10
.02
-.15
26 Job involvement 1
3.08(1.09)
.00
-.18
.10
.09
-.15
-.09
.43
.42
.45
.37
.23
27 Job involvement 2
3.72
(.94)
-.07
-.08 -.03
.08
.03
-.07
.54
.64
.30
.40
.54
28 Job involvement 3
3.20(1.12) -.03
-.19
.07
.09
-.15
-.24
.30
.20
.39
.31
.13
29 Turnover 1
2.54(1.12).07.22-.03 -.09.05.11-.41 -.45
-.27
-.43
-.29
30 Turnover 2
2.34(1.14)
.06
.19
.00
-.08
.05
.26
-.53 -.47
-.46
-.52
-.37
31 Job quality improvement 1
4.04
(.80)
.01
-.13
.08
.02
-.01
-.06
.32
.38
.25
.21
.24
32 Job quality improvement 2
3.38
(.86)
-.03
-.10
.02
-.02
.02
.08
.43
.62
.07
.56
.27
33 Job quality improvement 3
3.68
(.80)
-.11
-.14
.02
.05
-.07
-.25
.38
.34
.24
.42
.33
34 Job quality improvement 4
3.81
(.83)
-.13
-.14
.08
.07
-.04
-.02
.24
.13
.26
.20
.25

12

13

14

15

16

17

1.00
.47
.53
.23
.50
.28
.38
.39
.36
.37
.26
.32
.14
.22
.46
.31
.40
-.39
-.43
.17
.23
.33
.26

1.00
.52
.14
.32
.20
.27
.44
.37
.38
.23
.30
.17
.27
.35
.29
.29
-.52
-.55
.16
.18
.27
.22

1.00
.26
.38
.30
.35
.50
.48
.42
.33
.36
.10
.21
.42
.30
.37
-.42
-.47
.23
.29
.34
.27

1.00
.40
.66
.49
.19
.23
.20
.20
.08
.05
-.05
.25
.27
.23
-.10
-.21
.38
.39
.41
.29

1.00
.48
.54
.30
.31
.31
.23
.25
.08
.12
.47
.35
.40
-.25
-.34
.33
.32
.47
.31

1.00
.62
.20
.22
.17
.20
.11
.08
-.02
.34
.29
.33
-.11
-.23
.37
.37
.42
.27

a. n =37 for inter-correlations among company size and HRM practices, and cross-level correlations between company size, HRM practices and aggregated employee attitudes and behaviors; n =876 for intercorrelations among individual-level employee attitudes and behaviors.

34

APPENDIX A (continued)
Variables

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

1 Gender2 Marital status
3 Years of service
4 Job change experience
5 Educational background
6 Company size
7 HRM practices bundle
8 Appropriate staffing and selection
9 Fair performance appraisal
10 Comprehensive training and development
11 Competitive compensation
12 P-O fit 1
13 P-O fit 2
14 P-O fit 3
15 P-J fit 1
16 P-J fit 2
17 P-J fit 3
18 P-J fit 4
19 Affective organizational commitment 1
20 Affective organizational commitment 2
21 Affective organizational commitment 3
22 Affective organizational commitment 4
23 Continuance organizational commitment 1
24 Continuance organizational commitment 2
25 Continuance organizational commitment 3
26 Job involvement 1
27 Job involvement 2
28 Job involvement 3
29 Turnover 1
30 Turnover 2
31 Job quality improvement 1
32 Job quality improvement 2
33 Job quality improvement 3
34 Job quality improvement 4

1.00
.29
.31
.29
.25
.23
.11
.08
.48
.36
.38
-.21
-.32
.34
.37
.42
.28

1.00
.66
.52
.45
.40
.16
.25
.40
.41
.39
-.44
-.50
.19
.30
.30
.25

1.00
.52
.41
.41
.14
.23
.36
.37
.32
-.38
-.39
.16
.29
.29
.26

1.00
.46
.38
.23
.26
.39
.40
.35
-.34
-.39
.24
.32
.34
.28

1.00
.35
.16
.22
.32
.38
.36
-.28
-.31
.23
.33
.28
.29

1.00
.27
.47
.43
.24
.36
-.36
-.36
.03
.16
.21
.14

1.00
.36
.12
.14
.07
-.15
-.19
.08
.10
.07
.14

1.00
.21
.13
.19
-.33
-.33
-.02
.03
.09
.07

1.00
.43
.71
-.36
-.42
.25
.32
.37
.23

1.00
.39
-.28
-.29
.32
.36
.39
.32

1.00
-.32
-.38
.21
.31
.35
.24

1.00
.63
-.12
-.20
-.27
-.20

1.00
-.13
-.22
-.29
-.21

1.00
.39
.39
.40

1.00
.49
.35

35