journal of operations management (a. mehta)
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Journal of Operations Management 21 (2003) 1943
The impact of human resource management practiceson operational performance: recognizing country
and industry differences
Sohel Ahmad a,, Roger G. Schroeder b,1
a Department of Management, St. Cloud State University, 720 Fourth Avenue South, St. Cloud, MN 56301-4498, USA
b Department of Operations and Management Science, Donaldson Chair in Operations Management University of Minnesota, Carlson Schoolof Management, 3-140 CarlSMgmt Building, 321-19th Avenue South, Minneapolis, MN 55455, USA
Received 17 August 2000; accepted 14 January 2002
Abstract
The interest in strategic human resource management (HRM) has spawned a number of empirical research studies that
investigated theimpactof HRMpracticeson organizational performance. However, very little attention hasbeen paid to address
the impact of HRM practices on operations management and to generalize the findings across countries and industries. Success
of some business decisions (e.g. globalization and merger and acquisition) necessitates recognition and reconciliation of the
differences among HRM practices in different countries and industries. This study attempts to generalize the efficacy of seven
HRM practices proposed by Pfeffer in the context of country and industry, focusing primarily on the effects of these practiceson operations. The findings provide overall support for Pfeffers seven HRM practices and empirically validate an ideal-type
HRM system for manufacturing plants.
2002 Elsevier Science B.V. All rights reserved.
Keywords: Human resource/OM interface; Strategic human resource management; Staffing; Operational performance improvement
1. Introduction
Human resources are considered the most impor-
tant asset of an organization, but very few organi-
zations are able to fully harness its potential. Ladoand Wilson (1994, p. 701) define a human resource
system . . . as a set of distinct but interrelated ac-
tivities, functions, and processes that are directed at
attracting, developing, and maintaining (or disposing
Corresponding author. Tel.: +1-320-255-2994;
fax: +1-320-255-3986.
E-mail addresses: ahmad@stcloudstate.edu (S. Ahmad),
rschroeder@csom.umn.edu (R.G. Schroeder).1 Tel: +1-612-624-9544; fax: +1-612-624-8804.
of) a firms human resources. Traditionally, man-
agement of this system has gained more attention
from service organizations than from manufacturing
organizations. However, to enhance operational per-
formance, effectively managing this system is equallyimportant in both types of organizations. Needless to
say, sophisticated technologies and innovative manu-
facturing practices alone can do very little to enhance
operational performance unless the requisite human
resource management (HRM) practices are in place
to form a consistent socio-technical system. For this
reason, manufacturing organizations need to carefully
evaluate their existing HRM practices and modify
them, if needed, so that employees can effectively
contribute to operational performance improvement.
0272-6963/02/$ see front matter 2002 Elsevier Science B.V. All rights reserved.
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Several studies in the HR literature investigated
the impact of HR practices on organizational perfor-
mance. Although some studies related to HR practices
can be found in the operations management literature(Jayaram et al., 1999; Kathuria and Partovi, 1999;
Youndt et al., 1996; Kinnie and Staughton, 1991), this
discipline has tended to address structural issues and
analytical questions, and has paid little attention to
human resources issues. A review of empirical articles
published between 1986 and 1995 in 13 OM research
outlets revealed that less than five percent of these
articles fell into the HRM for operations category
(Scudder and Hill, 1998). This lack of attention is sur-
prising when one considers human resources critical
role in achieving superior performance in compet-
itive priorities, such as low cost, quality, delivery,
flexibility, and innovation.
Over the years, researchers have suggested many
HRM practices that have the potential to improve and
sustain organizational performance. These practices
include emphasis on employee selection based on fit
with the companys culture, emphasis on behavior,
attitude, and necessary technical skills required by the
job, compensation contingent on performance, and
employee empowerment to foster team work, among
others. Pfeffer (1998) has proposed seven HRM prac-
tices that are expected to enhance organizational per-formance. The practices proposed by Pfeffer (1998,
p. 96) are:
1. Employment security.
2. Selective hiring of new personnel.
3. Self-managed teams and decentralization of deci-
sion making as the basic principles of organiza-
tional design.
4. Comparatively high compensation contingent on
organizational performance.
5. Extensive training.
6. Reduced status distinctions and barriers, includingdress, language, office arrangements, and wage dif-
ferences across levels.
7. Extensive sharing of financial and performance in-
formation throughout the organization.
There are several objectives of the present study
based on these practices. First, we investigate whether
manufacturing plants use of these seven practices
differs by country or industry. Next, we assess the
impact of each of these practices on organizational
performance which includes (1) operational perfor-
mance measures: unit cost, quality, delivery, flexibil-
ity, and speed of new product introduction and (2)
an intangible performance measure: organizationalcommitment. Lastly, we examine whether these seven
practices can form a synergistic HR bundle to repre-
sent an ideal HRM system for manufacturing plants
and check the efficacy of this ideal system. Since the
manufacturing plant is the unit of analysis for this
study, we will be testing the HRM theory at the plant
or operations level of the organization.
2. Theoretical background and hypotheses
Organizations can internalize as well as externalize
employment (Lepak and Snell, 1999). Internalization
of employment involves building an employee skill
base inside the organization, while externalization
of employment means outsourcing human resource
needs to market-based agents (Rousseau, 1995). Each
alternative has its own costs. According to the trans-
action cost theory (Williamson, 1975), the decision to
internalize or externalize a part or all of an operations
human resource needs should be based on the trans-
actional costs involved. Arriving at a HR outsourcing
decision in such a manner is myopic as it overlooks thestrategic consequences. For example, outsourcing hu-
man resource needs can minimize bureaucratic costs
and complexities. However, an operations continued
dependence on external sources may inhibit its ability
to develop core skills and capabilities vital for long-
term survival in the marketplace (Lei and Hitt, 1995).
The human capital theory recognizes employee
skills, experience, and knowledge as assets with the
potential to generate economic rent (Coff, 1997). How-
ever, this theory evaluates human resources through
productivity gains. It falls short of attaching strate-gic value to causal ambiguity and tacit knowledge
embedded within an organizations human resource
system. In recent years, researchers and practitioners
have realized that HRM systems can be used as strate-
gic levers to focus on value creation that goes beyond
traditionally emphasized cost reduction (Becker and
Gerhart, 1996). Drawing on a behavioral psychology
perspective, researchers have highlighted the strategic
aspect of HRM practices and argued about why these
practices can lead to competitive advantage (Schuler
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As explained above, employment security is in-
ternally consistent with other HRM practices. Similar
arguments can be made about each of the remaining six
HRM practices. Therefore, these HRM practices areinternally consistent with one another and qualify as a
synergistic set. A bundle of internally consistent prac-
tices is more effective than the sum of the effects of
the individual practices due to their mutually reinforc-
ing support (MacDuffie, 1995). The resource-based
view also supports this notion by stressing that in-
dividual practices have a limited ability to generate
competitive advantage in isolation. However, in com-
bination, these complementary resources can help a
firm attain greater competitive advantage (Barney,
1995).
Every organization differs in how much effort it
puts into harnessing each of the seven HRM prac-
tices. An ideal situation may be one in which each of
these HRM practices is explored and exploited to its
highest potential, typically when an organization ex-
erts the maximum effort possible to develop, institute,
and implement each of these seven practices. Such
a HRM system may be termed an ideal-type HRM
system. This ideal-type HRM system is expected to
yield the highest organizational performance. The
more similar an organizations HRM system is to the
ideal-type HRM system, the better the organizationsperformance. Moreover, if bundling invokes synergy
among HRM practices as previously argued, then
an organization with a HRM system similar to the
ideal-type HRM system will explain significantly
more variation in organizational performance than
any of the individual HRM practices or any combina-
tion thereof. From the above discussion, we draw the
following hypothesis.
H2. After controlling for the industry and country ef-
fects, the degree of dissimilarity (measured as misfit)between an organizations existing HRM system and
the ideal-type HRM system will be negatively related
to the organizational performance.
3. Data collection
We use world class manufacturing (WCM) project
data to test the hypotheses. The focus of the WCM
project is to examine differences in manufacturing
Table 1
Number of plants by country and industry
Electronic Machinery Automobile Total
Germany 5 10 9 24Italy 8 13 7 28
Japan 13 12 14 39
USA 6 5 5 17
Total 32 40 35 107
practices across plants in different countries and in-
dustries (Flynn et al., 1996). The response rate for this
project was about 60%. We use a part of this projects
database that addresses HRM issues; it includes 107
manufacturing plants (see Table 1) after eliminating
responses with missing data. These plants employ
1153 employees on average, including both salaried
and hourly workers. The mean age of these plants is
about 37 years. The average facility size (production
and warehouse) is 160,701 ft2, with 32 product lines
manufactured on average.
Data collected from plants operating in four coun-
tries and three industries are used for the empirical
analyses. The countries are Germany, Italy, Japan, and
the USA. The four countries were selected to repre-
sent the major industrial regions of the world, North
America, Asia and Europe. In each of these coun-tries, plants were randomly selected from three in-
dustries: automobile, electronics, and machinery. The
three industries were selected because the literature
suggests that they have been implementing various
WCM approaches, such as total quality management
(TQM), just-in-time (JIT), and employee involvement
(EI). We wanted industries that had been threat-
ened by global competition and thus were seeking
improvements.
Face validity of the questionnaires was insured by
having three different researchers develop items for thescales. The three researchers then reviewed all of the
items for content validity. Whenever possible, scales
were selected from the existing literature. The data
collection instrument was pre-tested using 10 industry
experts and academics. After the pilot testing, some of
the items were clarified or changed to be more repre-
sentative of the intended constructs. The reliability and
validity of the constructs were formally tested using
data from over 800 respondents in a prior round of data
collection in 43 US plants. As a result of these tests,
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S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 1943 23
some of the scales were significantly revised. The US
instrument was subsequently translated into German,
Italian and Japanese. The foreign language version was
then translated back into English by another individualand compared for accuracy. Any discrepancies were
resolved.
Plant managers were contacted by a member of the
WCM team and asked for their voluntary participa-
tion in exchange for detailed feedback regarding their
manufacturing practices in comparison to the indus-
try. About 60% of the plants contacted participated in
the study. Interested plant managers appointed plant
research coordinators who maintained contact with
the research team. These plant research coordinators
were managers who had at least 3 years of experience
in the plants and were knowledgeable about the major
responsibilities of the employees working in the plant.
The research team consulted with the plant research
coordinators to identify the right respondents in the
plant who had pertinent knowledge, experience, and
ability to provide accurate and unbiased answers to
the questions in the survey. The questionnaires were
collected in sealed envelopes to maintain anonymity
of responses. Managers, engineers, supervisors and
workers responded to these questionnaires. We used
responses from different people for the dependent
(organizational performance) and independent (HRMpractices) variables to avoid common respondent
bias.
4. Measures
4.1. The seven HRM practices
Table 2 summarizes the variables used and the meth-
ods employed to measure the seven HRM practices.
While most of these HRM practices are measuredusing one variable, some are measured using multiple
variables as determined by the scope of the HRM
practice and limitations of the WCM database. For
details on the measurement refer to Appendix A and
Table 3. Most of the variables were measured using
perceptual scales with a few exceptions where objec-
tive measures were used. The list of scales includes:
MFGHRFIT, BEHAVIOR, TEAMS, INTERACT,
INCENTOB, JOBSKILL, MULTFUN, STRATCOM,
and FEEDBACK. These scales closely approxi-
mate the definition of the seven HR practices being
measured.
A set of Likert scales was used to measure pertinent
constructs. Each item of a construct was answered us-ing the following five-point scale: strongly agree (5),
agree (4), neutral (3), disagree (2), and strongly dis-
agree (1). As mentioned earlier, the content validity
of a construct was ensured through pre-testing of the
questionnaires and structured interviews with the man-
agers and academic experts in the field.
Each scale was evaluated for its reliability and uni-
dimensionality. A value of Cronbachs alpha of 0.7
or more was used as a criterion for a reliable scale
(Nunnally, 1978). We removed an item if it did not
contribute strongly to the alpha value and if its content
was not essential for the construct. After purifying a
scale, we averaged all of the items in that scale, which
became the value of the variable representing the con-
struct. Therefore, any variable measured by the scale
can range in value from one to five, where five is the
most desirable value.
The remaining three variables in Table 2 were mea-
sured using objective measures. See Table 3 for details.
The variable INSECURE was measured as a percent-
age of employees laid off during the past 5 years. This
variable measures job insecurity rather than job secu-
rity; as such, the most desirable value of this variableis 0. The variable CONTCOMP is a composite of two
binary measures. The value of this variable can range
from 2 to 4. A value of 2 indicates that the plant does
not use any group incentive or profit sharing plans,
while a value of 4 indicates that the plant uses both.
Therefore, the most desirable value for this variable is
4. Similarly, the variable STATDIFF is a composite of
four binary measures, and its value can range from 4
to 8. The higher the value of the variable STATDIFF,
the higher the status differences. Hence, the most de-
sirable value for this variable is 4. The last column ofTable 2 lists the most desirable value for each variable.
4.2. Organizational performance
Past empirical research has mostly investigated the
effects of HRM practices on financial performance
(cf. Delery and Doty, 1996) and some on efficiency
and employee turnover (cf. Huselid, 1995). However,
very few studies have examined the impact of HRM
practices on operational performance measures, such
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Table 2
Summary of measurements of the seven HRM practices
Practice Variable Scales/measurement Description of measurements Ideal
profileEmployment insecurity INSECURE Employment insecurity (The number of employees who have been
laid off during the past 5 years/number of
employees in the organization)100
0
Selective hiring MFGHRFIT Manufacturing and human
resources fit
A scale of six items measuring the degree of
cooperation between manufacturing and
human resources in designing job
descriptions and staffing activities
5
BEHAVIOR Behavior and attitude A scale of five items measuring the
importance given to a prospective employees
attitudes and behavior toward teamwork and
problem solving during the selection process
5
Use of teams and
decentralization
TEAMS Team activities A scale of five items used to assess the
effective use of teams on the shop floor
5
INTERACT Interaction facilitation A scale of three items that measures the
extent to which supervisors encourage and
facilitate workers to work as a team
5
Compensation/incentive
contingent on
performance
CONTCOMP Contingent compensation This measure checks whether group
incentive plans (Y/N) and profit sharing
plans (Y/N) are used in the organization.
Y=Yes=2 and N=No=1
4
INCENTOB Incentives to meet
objectives
A scale of four items to measure whether the
plants reward system is consistent with
manufacturing objectives and goals
5
Extensive training JOBSKILL Training on job skills A scale of three items to measure if
employees on the job skills and knowledge
are being upgraded in order to maintain awork force with cutting edge skills and
abilities
5
MULTFUN Training in multiple
functions
A scale of five items to measure the extent
to which employees receive cross training so
that they can perform multiple tasks or jobs
5
Status differences STATDIFF Existing status differences Four questions were asked to judge the use
of symbols that indicate status differentials
among various employees in terms of the
following: the use of assigned parking spots
(Y/N); the use of uniforms by workers only
(Y/N); access restriction to cafeteria for
some employees (Y/N); and the use of
separate rest-rooms (Y/N) for differentemployees in the plant
4
Sharing information STRATCOM Communication of strategy A scale of three items to measure the efforts
made by management to communicate the
plants competitive strategy to all employees
5
FEEDBACK Feedback on performance A scale of five items to measure the extent
to which management provides shop floor
personnel with information regarding their
performance in a timely and useful manner
5
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Table 3
HRM practices measured using objective measures
as quality, cost or delivery (cf. MacDuffie, 1995)
or intangible performance measures, such as orga-
nizational commitment (cf. Kalleberg and Moody,
1994). The appropriate dependent variable will varywith the level of analysis, but in each case the focus
should be on variables that have inherent meaning
for a particular context (Becker and Gerhart, 1996,
p. 791). Because the unit of analysis for this study is a
manufacturing plant, we argue that HRM practices
will impact the operational performance measures
at the plant level. Also, the strategic implications of
HRM practices make tracking intangible performance
measures important. We, therefore, investigate the
impact of HRM practices on operational performance
measures as well as the intangible performance mea-
sure defined below.
4.2.1. Operational performance measuresResearchers (Wheelwright, 1978; Schmenner,
1981; Hayes and Wheelwright, 1984; Hill, 1989) have
proposed a wide variety of operational performance
measures for manufacturing facilities. These include
cost, quality, delivery, and flexibility. Lately, the rate
of new product introduction has also been included in
this list (Vickery et al., 1997). We performed factor
analysis to check if these five operational perfor-
mance measures formed different groups. The factor
analysis revealed that all of these measures loaded on
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Table 4
Operational performance measures (PERFORM)
COST Unit cost of manufacturing
QUALITY Quality of product conformanceDELIVERY On-time delivery performance
FLEXBLTY Flexibility to change volume
NPDSPEED Speed of new product introduction
PERFORM = COST + QUALITY + DELIVERY
+ FLEXBLTY + NPDSPEED
Please circle the number which indicates your opinion about how
your plant compares to its competition in your industry. The
number 5: superior or better than average; 4: better than average;
3: average or equal to the competition; 2: below average; 1: poor
or low end of the industry.
to one factor. Additionally, the reliability analysis of
these measures yielded a value of Cronbachs alpha
of 0.71, justifying summing up these measures to
form a single performance index (PERFORM). This
composite measure represents a plants aggregate
achievement in all five areas of performance men-
tioned above compared to competitors. See Table 4 for
details.
4.2.2. Intangible performance measure
Researchers have yet to reach a consensus about
how best to define strategic HRM. However, Huselid
et al. (1997, p. 172) attest that there is broad agree-ment in the literature that strategic HRM . . . involves
designing and implementing a set of internally consis-
tent policies and practices that ensure a firms human
capital contributes to the achievement of its business
objectives. Snell and Dean (1992) further stress that
a firm invests in employees to strengthen its human
capital, but the firm does not actually own this hu-
man capital. The firm has very little control over this
human capital as employees may leave the firm or,
even if they do not leave, they may not be inspired to
put forward their best efforts. Snell and Dean (1992)recommend that a firm devise methods to ensure
that individuals act in the firms best interest over
time.
HRM practices that fail to elicit specific employee
attitudes, such as organizational commitment are less
likely to have strategic impact (Arthur, 1994). Further-
more, an employee with strong organizational com-
mitment will be highly motivated to expend energy
on organizational tasks (Anderson et al., 1994). Even
highly skilled and knowledgeable employees who are
uncommitted may not contribute discretionary efforts
and will thereby minimize their potential in the organi-
zation. Organizational commitment is an indicator that
testifies to whether the HRM practices employed in anorganization are able to foster psychological links be-
tween organizational and employee goals. This is an
intangible outcome of a HRM system and is important
to retaining employees and exploiting their potential
to the fullest extent over time. We, therefore, iden-
tify organizational commitment as an intangible per-
formance measure and measure it using a scale. We
conducted reliability and unidimensionality analyses
for this scale. Items were dropped to obtain a reliable
and unidimensional scale. The remaining items were
then averaged to obtain a score for the scale (COM-
MIT) corresponding to each plant. See Appendix B
for details.
4.3. Measure of misfit
In context of this paper, misfit represents the dis-
similarity between an ideal HRM profile and a plants
existing HRM profile. We identified a theoretical ideal
profile by choosing the most desirable values of the
variables representing the seven HRM practices shown
in the last column of Table 2. This profile representsthe ideal-type HRM system that has been theorized to
yield the highest organizational performance. Math-
ematically, misfit is the Euclidean distance between
a point defined in a multidimensional space by the
ideal profile (i.e. the ideal-type HRM system) and a
point representing an experimental unit. In this study,
the experimental unit is a plants existing HRM sys-
tem as measured by the variables representing the
seven HRM practices shown in Table 2. Accordingly,
we use the following general formula to calculate
MISFIT.
MISFITi =
n
k=1
(Xk Xik)2 (1)
where MISFITi is the distance between the existing
HRM system of a particular plant i and the ideal-type
HRM system; Xik the score of the kth variable of the
existing HRM system of a particular plant i; Xk the
score of the kth variable of the ideal-type HRM sys-
tem; k the number of variables representing the HRM
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system 1, . . . , n; for this study, k varies from 1 to 12
and i varies from 1 to 107.
More specifically, for this study, MISFIT is calcu-
lated as follows:
MISFITi = STD{(0 INSECUREi)2}
+[STD{(5 MFGHRFITi)
2} + STD{(5 BEHAVIORi)2}]
2
+[STD{(5 TEAMSi)
2} + STD{(5 INTERACTi)2}]
2
+[STD{(4 CONTCOMPi)
2} + STD{(5 INCENTOBi)2}]
2
+[STD{(5 JOBSKILLi)
2 + STD{(5 MULTIFUNi)2}]
2
+STD{(4 STATDIFFi)2}
+[STD{(5 STRATCOMi)
2} + STD{(5 FEEDBACKi)2}]
2
As mentioned earlier, the ranges of the variables in
the above equation are not the same. This can dis-
proportionately inflate some variables contribution
to the MISFIT calculation. We have, therefore, stan-
dardized (STD) the squared differences (between the
ideal-type HRM system and the existing HRM system
of a plant) before adding them together to avoid thisproblem.
4.4. Control variables
Since we intend to identify impacts of HRM prac-
tices on organizational performance that can be gen-
eralized across countries and industries, the effects
of country and industry need to be removed prior
to evaluating the relationship between HRM prac-
tices and organizational performance. We, therefore,
included the following control variables (indicatorvariables) in the regression analyses. Three country
control variables, GERMANY (Germany compared to
USA), ITALY (Italy compared to USA), and JAPAN
(Japan compared to USA), are used to represent the
four countries. Similarly, two industry control vari-
ables, MACHINE (machinery industry compared to
electronics industry) and AUTOMOBL (automobile
industry compared to electronics industry), are used to
represent the three industries from which the data were
collected.
5. Analyses and results
In this part of the paper, we first present the
descriptive statistics. Next, we conduct statistical
analysis to determine if the extent to which plants use
the seven HRM practices differs by country and/or
industry. Lastly, empirical analyses are performed to
test the hypotheses stated earlier.
Table 5 shows means, standard deviations, and
correlations, which allow for some interesting obser-
vations. For example, high variance of the variableINSECURE indicates that plants employee layoff
rates vary widely. Employment insecurity (INSE-
CURE) is negatively related to many of the HRM
practices which implies that a plant with a high em-
ployee layoff rate is less likely to foster growth in other
HRM practices listed in Table 5. The variable that
measures status difference (STATDIFF) shows similar
results. A higher status difference in a plant is associ-
ated with lower efforts in other HRM practices. That
is, it is unlikely that the HRM practices will flourish
in a plant where high status difference exists. Further-more, a plant with high status difference is expected
to have high employment insecurity (i.e. higher em-
ployee layoff rate) since STATDIFF and INSECURE
are positively correlated. Positive correlations among
different HRM practices show that when a plant in-
creases its efforts in one of the HRM practices, it is
also more likely to increase efforts in other practices.
Canonical correlation analysis is often used to in-
vestigate the relationship between two sets of vari-
ables. This analysis is primarily descriptive, although
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it can be used for predictive purposes. We, therefore,
use this method to identify the relationship between
HRM practices variables and operational performance
measures. As suggested by Hair et al. (1998), threecriteria were considered when determining the num-
ber of important canonical pairs: (1) level of statisti-
cal significance of the function, (2) magnitude of the
canonical correlation, and (3) measure of redundancy
for the percentage of variance accounted for by the two
sets of variables. Only the first canonical pair was sta-
tistically significant (see Table 6). The canonical cor-
relation (0.56) was moderate. The redundancy index
was found to be 0.1128, which is quite low. Although
there are no guidelines about the minimum acceptable
value for the redundancy index, generally the higher
the value of the index the better.
Traditionally, canonical pairs have been interpreted
by examining the sign and the magnitude of the canon-
ical weights. However, these weights are subject to
considerable instability due to slight changes in sample
size, particularly where the variables are highly corre-
lated. Canonical cross-loadings have been suggested
Table 6
Results of canonical correlation analysis
Canonical correlation 0.5603Level of significance 0.0064
Redundancy index 0.1128
Correlations between the operational performance measures
and the first canonical variable of the HRM practices
COST 0.3912
QUALITY 0.4334
DELIVERY 0.3227
FLEXBLTY 0.0911
NPDSPEED 0.3328
Correlations between the HRM practices variables and the
first canonical variable of the operational performance
measures
INSECURE 0.1183MFGHRFIT 0.3101
BEHAVIOR 0.3896
TEAMS 0.4121
INTERACT 0.4228
CONTCOMP 0.3153
INCENTOB 0.4047
JOBSKILL 0.4553
MULTFUN 0.4625
STATDIFF 0.2224
STRATCOM 0.4436
FEEDBACK 0.3785
as a preferable alternative to the canonical weights
(Hair et al., 1998). The canonical cross-loadings show
the correlations of each of the dependent variables
with the independent canonical variate, and viceversa. Table 6 shows the canonical cross-loadings for
the first canonical pair. A loading of at least 0.31 is
considered significantly different from zero at a level
of significance of 0.05 (Graybill, 1961). According to
this criterion, except for flexibility to change volume
(FLEXBLTY), each of the dependent variables is sig-
nificantly related to the independent canonical variate
(canonical variate representing HRM practices). On
the other hand, all independent variables (HRM prac-
tices) except for employment insecurity (INSECURE)
and status differences (STATDIFF) are significantly
related to the dependent canonical variate (canon-
ical variate representing operational performance
measures).
Researchers have argued that HRM practices can
differ across countries and/or industries for several
reasons including: cultural idiosyncrasy (Salk and
Brannen, 2000), governmental regulations/policies
(Morishima, 1995), competitive priorities (Boxall
and Steeneveld, 1999), and adoption of managerial
practices, such as JIT and quality management (Snell
and Dean, 1992). Hofstede argues that national cul-
tures impact the attitudes and behaviors of employees(Hofstede, 1980). In a single company study, he found
that cultural values varied significantly by country
and region of the world.
Most of the empirical studies related to HRM prac-
tices have been conducted using data collected in a
single industry within one country (cf. Arthur, 1994).
Some studies used data collected from multiple in-
dustries in one country (cf. Huselid, 1995), and some
studies were conducted on data collected from a single
industry in multiple countries (cf. MacDuffie, 1995).
However, the central foci of these studies were not tocompare systematic differences that may have existed
in HRM practices in the different countries and in-
dustries in which the organizations operated. Empiri-
cal examination of broad-based HRM practices across
industries and/or countries is very limited in the liter-
ature (MacDuffie and Kochan, 1995; Ichniowski and
Shaw, 1999). Since we intend to identify generaliz-
able impacts of HRM practices on organizational per-
formance across countries and industries (H1), it is
important to understand the differences that may exist
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30 S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 1943
Table 7
HRM practices across countries
Practice Countries Pairwise differences F-value Significance
GER (1) ITL (2) JPN (3) USA (4)
INSECURE 42.09 1.41 0.40 20.97 (1, 2)a, (1, 3) 7.77 0.00
MFGHRFIT 3.38 3.29 3.33 3.26 NS 0.25 0.86
BEHAVIOR 3.17 3.10 3.67 3.23 (3, 1) (3, 2) (3, 4) 21.20 0.00
TEAMS 3.51 3.38 3.76 3.76 (3, 2) (4, 2) 5.41 0.00
INTERACT 3.52 3.11 3.78 3.78 (1, 2) (3, 2) (4, 2) 14.13 0.00
CONTCOMP 2.83 2.32 3.79 2.56 (1, 2) (3, 2) (3, 4) (3, 1) 41.98 0.00
INCENTOB 2.62 2.44 3.13 2.64 (3, 1) (3, 2) (3, 4) 10.54 0.00
JOBSKILL 3.20 3.25 3.62 3.55 (3, 1) (4, 1)+ (3, 2) 7.16 0.00
MULTFUN 3.59 3.30 3.74 3.82 (1, 2) (3, 2) (4, 2) 11.57 0.00
STATDIFF 6.71 5.5 4.17 4.63 (1, 2) (1, 3) (1, 4) (2, 3) (2, 4) 71.40 0.00
STRATCOM 3.6 2.88 3.70 3.55 (1, 2) (3, 2) (4, 2) 16.87 0.00
FEEDBACK 3.22 2.70 3.65 3.36 (1, 2) (3, 1)+ (3, 2) (4, 2) 13.19 0.00
NS: not significant; GER: German plants; ITL: Italian plants; JPN: Japanese plants; USA: American plants.a The average percentages of employees laid off in the past 5 years from the plants in Germany and Italy differ at a level of statistical
significance of P 0.01.+ P 0.1. P 0.05. P 0.01.
in HRM practices in various countries and industries.
We investigate these differences below.
We use one-way ANOVA to identify differences
in HRM practices among plants operating in four
countries. The last two columns of Table 7 show thevalues of the F-statistics and their levels of signifi-
cance. F-statistics for all of the HRM practices are
found to be highly significant except for the scale
representing manufacturing and human resources fit
(MFGHRFIT). That is, mean efforts expended by
plants differed in all but one of the HRM practices
in at least two countries. Statistical insignificance of
the F-statistic for MFGHRFIT suggests that the level
of cooperation between manufacturing and human
resources in designing job descriptions and staffing
activities did not differ significantly by country.
Next, we conducted the Scheffe pairwise compar-
ison tests of mean differences to better understand
how HRM practices differed between each pair of
countries. This comparison revealed several important
aspects of HRM practices as they are used in differ-
ent countries. Employment insecurity is the highest
in Germany and the lowest in Japan. The well known
lifelong employment policy in Japan seems to be evi-
dent in this finding. Plants in Japan emphasized some
HRM practices significantly more than plants in other
countries. These practices are: behavior and attitude
(BEHAVIOR), contingent compensation (CONT-
COMP), and incentives to meet objectives (INCEN-
TOB). Refer to Tables 2 and 3, and Appendix A for
definition and measurement of these and other HRMpractices.
Compared to other countries in this sample, plants
in Italy seem to be significantly lacking in their efforts
in several HRM practices. These HRM practices in-
clude team activities (TEAMS), interaction facilitation
(INTERACT), training in multiple functions (MULT-
FUN), communication of strategy (STRATCOM), and
feedback on performance (FEEDBACK).
The training on job skills scale (JOBSKILL) mea-
sures if employees on-the-job skills and knowledge
are considered important and whether these are up-
graded on a regular basis to maintain a work force
with cutting edge skills and abilities. Plants in Japan
put significantly more effort into training on the job
skills, while plants in Germany lagged behind other
countries in this HRM practice. We were surprised
by this observation since Germany, under a national
industrial and educational policy, offers apprentice-
ship training to secondary school students to facilitate
the school-to-work transition (MacDuffie and Kochan,
1995). Our expectation was that German plants would
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Table 8
HRM practices across industries
Practice Industries Pairwise differences F-value Significance
ELEC (1) MACH (2) AUTO (3)
INSECURE 22.90 11.57 5.85 NS 1.58 0.21
MFGHRFIT 3.39 3.29 3.29 NS 0.45 0.64
BEHAVIOR 3.37 3.32 3.35 NS 0.13 0.88
TEAMS 3.61 3.48 3.73 (3, 2)a,+ 2.89 0.06
INTERACT 3.61 3.35 3.71 (3, 2) 5.06 0.00
CONTCOMP 2.93 2.90 3.20 NS 1.36 0.26
INCENTOB 2.76 2.74 2.78 NS 0.05 0.95
JOBSKILL 3.47 3.36 3.43 NS 0.53 0.59
MULTFUN 3.70 3.46 3.67 (1, 2) (3, 2)+ 4.43 0.01
STATDIFF 4.72 5.50 5.17 (2, 1) 3.99 0.02
STRATCOM 3.48 3.29 3.57 NS 2.24 0.11
FEEDBACK 3.27 2.96 3.59 (3, 2) 8.23 0.00
NS: not significant; ELEC: electronics industry; MACH: machinery industry; AUTO: automobile industry.a The average levels of effort put in team activities (TEAMS) by the automobile and machinery industries differ at a level of statistical
significance of P 0.1.+ P 0.1. P 0.05. P 0.01.
show a similar proclivity toward developing job skills
in plants. We also note that German plants exhibit the
highest status differences (STATDIFF) among all of
the countries; Italian plants are second.
Again, we used one-way ANOVA to identifyplants differences in HRM practices in the three
industries. Table 8 shows that the F-statistics corre-
sponding to most of the HRM practices are insignifi-
cant. The Scheffe pairwise comparison tests of mean
differences revealed that plants operating in the ma-
chinery industry seem to put significantly less effort
into team activities (TEAMS), interaction facilitation
(INTERACT), training in multiple functions (MULT-
FUN), and feedback on performance (FEEDBACK)
than plants operating in the automobile industry (see
Table 8). A closer look reveals that these HRM prac-tices are often emphasized in plants that implement
manufacturing practices, such as quality management
and/or lean production. The automobile industry was
at the forefront of the quality management and JIT
manufacturing revolutions in past decades (Soderquist
and Motwani, 1999; Womach et al., 1990). This
well-known fact probably explains the difference in
HRM practices. Also, the plants in the machinery in-
dustry exhibited significantly higher status differences
(STATDIFF) than those in the electronics industry.
The general perception of work environments in the
machinery and electronic industries supports these
findings.
In addition to conducting one-way ANOVA as dis-
cussed above, the two-way interaction effects werealso tested using general linear models. Only the
two-way interaction effect for the variable INSE-
CURE was found to be statistically significant. This
is not surprising given our earlier findings which re-
vealed that this variable showed fairly high variance
across countries and industries.
In summary, we have found that HR practices vary
widely by country and to some extent by industry. This
is consistent with institutional theory when the insti-
tutions are taken to be country or industry. These in-
stitutions exhibit an important and pervasive influenceon the HR practices employed. National culture, in-
dustry competition and other factors may account for
the differences we observed among the HR practices
adopted in different countries and industries.
5.1. Hypothesis 1
This hypothesis is tested by hierarchical regression
analyses using PERFORM (Table 9) and COMMIT
(Table 10) as dependent variables. First, the country
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and industry control variables (GERMANY, ITALY,
JAPAN, MACHINE, and AUTOMOBL) were entered.
Next, the HRM practices were independently entered
into the equation. For each of the dependent variables,the results show that most of the HRM practices ex-
plain a significant incremental level of the variance,
providing overall support for this hypothesis. Specif-
ically, for the dependent variable PERFORM, all hy-
potheses are supported except for hypotheses (a) and
(f). For the dependent variable COMMIT, hypotheses
(b), (c), (e), and (g) are supported and hypothesis (d) is
partially supported since the variable INCENTOB is
found to be significant but the variable CONTCOMP
is not. In order for hypothesis (d) to be fully supported,
both INCENTOB and CONTCOMP had to be signif-
icant. We, however, ask the reader to exercise cau-
tion while interpreting results related to Hypothesis 1
due to the possibility of omitted variable bias since
the correlations between some pairs of HRM practices
are quite high.
Regression analyses show that employment insecu-
rity (INSECURE) and status differences (STATDIFF)
were not significant for either of the two dependent
variables. In the literature, empirical evidence shows
that employment insecurity is associated with lower
performance (Delery and Doty, 1996). Therefore, we
were surprised that employment insecurity (INSE-CURE) was not significant. However, the correlation
matrix (Table 5) shows that employment insecurity is
negatively related to several HRM practices. These
HRM practices have positive associations with the
dependent variables. Therefore, employment insecu-
rity seems to hinder the development of other HRM
practices, thereby minimizing the potential of the
HRM practices as a whole. Status difference (STAT-
DIFF) shows a similar relationship with other HRM
practices.
Additionally, contingent compensation (CONT-COMP) was not significant for the intangible perfor-
mance measure. The literature finds mixed impact of
contingent compensation on intangible performance
measure, such as organizational commitment. While
contingent compensation can sometimes motivate
workers to put forward their best efforts (cf. Henderson
and Lee, 1992), it can sometimes de-motivate them
(cf. Kohn, 1993a) because contingent compensation
can be perceived by the employees as a management
control mechanism. Here, the term control implies
managements attempt to ensure desired outcomes by
trying to influence employee behavior (Lawler and
Rhode, 1976). Therefore, the more controlling the
employees perceive the compensation system to be,the less organizational commitment it will engender
(Deci, 1972; Ryan, 1982). This probably explains
why we failed to observe a significant relationship be-
tween contingent compensation (CONTCOMP) and
the intangible performance measure.
According to the behavioral approach to strategic
HRM, the mechanism through which a HRM system
contributes to operational performance is by eliciting
behaviors required to accomplish operational goals.
From that standpoint, the role of an intangible perfor-
mance measure (i.e. organizational commitment) as
a mediating variable in HRM systems influence on
operational performance is worth being investigated.
This investigation is conducted as follows.
A variableZis said to be a mediator of a relationship
between two variables X (independent variable) and
Y (criterion variable), if the following are true (Baron
and Kenny, 1986): (1) Xsignificantly affects Z, when Z
is regressed on X; (2) X significantly affects Y, whenYis regressed on X; (3) Zsignificantly affects Y, whenY is regressed on both X and Z. Table 11 shows the
results related to mediating effects of the intangible
performance measure (organizational commitment).According to the criteria mentioned above, organiza-
tional commitment (COMMIT) acts as a mediating
variable for MFGHRFIT, BEHAVIOR, TEAMS, IN-
TERACT, INCENTOB, JOBSKILL, MULTFUN,
STRATCOM, and FEEDBACK. Thus, the analyses
conducted to test the direct impact of HRM prac-
tices on operational performance and the subsequent
analyses for mediating effects reveal the following.
INSECURE and STATDIFF seem to have no impact
on operational performance, CONTCOMP influences
operational performance directly, and the rest of theHRM practices influence operational performance
indirectly through the mediating variable COMMIT.
5.2. Hypothesis 2
The profile deviation method (Drazin and Van de
Ven, 1985) is used to test this hypothesis. There are
three steps in this method: (1) identifying the ideal
profile; (2) calculating misfit; (3) linking misfit with
organizational performance. We completed the first
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Table 11
The mediating effect of organizational commitment (COMMIT)
Independent
variables
Dependent variables
COMMIT
(coefficient)
PERFORM
(coefficient)
PERFORM
(coefficient)
INSECURE 0.00 0.01 0.00
COMMIT 2.67
MFGHRFIT 0.41 1.36 0.31
COMMIT 2.55
BEHAVIOR 0.76 2.22 0.22
COMMIT 2.62
TEAMS 0.53 2.04 0.86
COMMIT 2.25
INTERACT 0.51 1.41 0.01
COMMIT 2.73
CONTCOMP 0.06 0.85+ 0.69
COMMIT 2.65INCENTOB 0.49 1.08 0.40
COMMIT 3.06
JOBSKILL 0.68 2.16 0.60
COMMIT 2.29
MULTFUN 0.78 2.90 1.29
COMMIT 2.07
STATDIFF 0.01 0.19 0.23
COMMIT 2.74
STRATCOM 0.43 1.90 0.97
COMMIT 2.14
FEEDBACK 0.24 1.43 0.88+
COMMIT 2.32
+ P 0.1. P 0.05. P 0.01.
Table 12
Results of hierarchical regression analysis of MISFIT on PERFORM and COMMIT
Variables PERFORM COMMIT
Eq. (1) (coefficient) Eq. (2) (coefficient) Eq. (1) (coefficient) Eq. (2) (coefficient)
Constant 17.88 17.49 3.58 3.48
GERMANY 0.07 1.14 0.21 0.06ITALY 0.71 0.56 0.04 0.36
JAPAN 1.17 0.47 0.30 0.47
MACHINE 0.34 0.02 0.06 0.14+
AUTOMOBL 0.52 0.38 0.17+ 0.13+
MISFIT 0.33 0.08
R2 0.09 0.21 0.13 0.49
F 1.97+ 4.53 3.06 16.05
Adjusted R2 0.04 0.17 0.09 0.46
+ P 0.1. P 0.05. P 0.01.
and second steps earlier, and the third step is com-
pleted here by linking MISFIT with organizational
performance according to the following regression
model. Hypothesis 2 will be supported by the regres-sion model below if a significant negative value of6is observed.
ORG PERFi
= 0 + 1 GERMANYi + 2 ITALYi
+3 JAPANi + 4 MACHINEi
+5 AUTOMOBLi + 6 MISFITi + i (2)
where ORG PERFi is the organizational perfor-
mance of plant i, which represents PERFORMi or
COMMITi as a dependent variable, one at a time.GERMANYi , ITALYi , and JAPANi are three indica-
tor variables representing four countries. MACHINEiand AUTOMOBLi are two indicator variables repre-
senting three industries. MISFITi is the value of the
variable MISFIT for plant i.
Table 12 shows the results of the hierarchical re-
gression analyses. The country and industry control
variables are entered in the first step (Eq. (1)). Next,
MISFIT is entered into the Eq. (2). Two sets of equa-
tions correspond to each dependent variable. Table 12
shows that Eq. (2) for each of the two equations is sig-
nificant and the coefficient of MISFIT (6) is negative
and significant, thus providing support for Hypothe-
sis 2. The variable MISFIT is found to be negatively
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36 S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 1943
related to the performance measures which implies
that as a plants HRM system deviates from the
ideal-type HRM system its performance suffers. Ad-
ditionally, this relationship between MISFIT andorganizational performance was observed after con-
trolling for country and industry effects. We can,
therefore, conclude that this ideal-type HRM system
is valid for a plant regardless of the country or in-
dustry in which it operates. This finding indicates
that management choices concerning HR practices do
indeed make a difference even after accounting for
country and industry factors.
6. Discussion
Traditionally, the focus of a HRM system has been
short-term, and the system has been used as a bu-
reaucratic control mechanism to enhance efficiency
(Kalleberg and Moody, 1994). Now, practitioners and
researchers agree that human resources can be a source
of competitive advantage and should be managed
strategically. However, organizations are discovering
this is easier said than done. Results of the present
study show that differences in HRM practices exist in
plants operating in different countries. Although this
was previously implied in the literature, comparisonof a comprehensive list of HRM practices among
countries was lacking. We obtained mixed results
when the HRM practices were compared across three
industries. While the majority of HRM practices used
by plants did not differ by industry, we did find sev-
eral HRM practices that differed significantly among
the three industries. Particularly, the extent to which
some HRM practices are used in plants operating in
the machinery industry consistently laged behind that
found in plants operating in the automobile industry.
We find overall support for Hypothesis 1 as most ofthe relationships specified in Hypothesis 1 are found
to be significant. Hypotheses (a) and (f), however,
were not supported for any of the two dependent
variables. Therefore, the proposed direct relationship
between employment insecurity and organizational
performance, and between status difference and or-
ganizational performance, cannot be empirically val-
idated. However, as mentioned earlier, employment
insecurity and status difference seem to hinder devel-
opment of other HRM practices, and thereby influence
the work environment and minimize the potential of
HRM practices as a whole.
The mediating effect analysis revealed that most
of HRM practices impact operational performanceindirectly through organizational commitment. This
finding is important as it refines our understanding
of the nature of relationship between HRM practices
and operational performance. Also, this finding sug-
gests that a manager intending to enhance operational
performance should create a conducive organizational
climate that fosters employees commitment to the
organization.
The findings of the present study also offer impor-
tant implications for several distinct trends observed
in the business world today. Many organizations
are going through globalization to take advantage
of proximity to suppliers, customers, and critical
resources, such as human resources. Another notice-
able trend has been mergers and acquisitions among
companies. Several of these mergers and acquisitions
are occurring between organizations operating in
different countries (e.g. Daimler-Benz and Chrysler
Corporation) and industries (e.g. Time Warner and
America Online). These trends pose a unique chal-
lenge for HRM (Legare, 1998; Lubatkin et al., 1999).
Researchers and practitioners have strongly empha-
sized that M&A provide a window of opportunity forrestructuring HRM practices in the combined (new)
organization (Galpin and Herndon, 2000). Organiza-
tions involved in mergers and acquisitions should take
this opportunity to evaluate their existing set of HRM
practices and make necessary changes to facilitate
post-merger integration. This is particularly impor-
tant if organizations involved in M&A are following
different HRM practices.
Our analyses show that plants operating in different
industries and/or countries use and emphasize HRM
practices differently. Therefore, which HRM practicesshould a combined (new) organization choose when
M&A is taking place between organizations operating
in different industries and/or countries? By control-
ling for country and industry in our analyses, we were
able to empirically validate those HRM practices that
are expected to yield higher performance regardless
of the country and industry in which the plant oper-
ates. Therefore, one choice may be to institute these
HRM practices for the combined (new) organization,
fine-tuning them according to the strategic intent of the
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S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 1943 37
new organization. Thus, the findings of our study pro-
vide general directions for managers to achieve better
operational performance through HRM systems inte-
gration in cross-country and/or cross-industry mergersor acquisitions.
Earlier attempts to empirically validate ideal-type
HRM systems have received mixed confirmation
(Delery and Doty, 1996). Although support for Hy-
pothesis 2 in our study empirically validates an
ideal-type HRM system, it failed to show the ex-
pected level of variation explained. This is explained
as follows: by definition an HR bundle is a set of in-
terrelated and internally consistent HR practices that
are expected to create mutually reinforcing and syn-
ergistic impacts on performance (MacDuffie, 1995).
Therefore, the variation in organizational performance
explained by a HR bundle should be significantly
greater than that explained by an individual HR prac-
tice in that bundle. However, results of our study failed
to show significant increments in variation explained
(R2) for the HR bundle. Nonetheless, our results em-
pirically validate the proposed ideal-type HRM sys-
tem because as a plants HRM system deviates from
the ideal-type HRM system, the plants performance
decreases, and this relationship is statistically signif-
icant (the coefficient of MISFIT (6) is negative and
significant).
7. Limitations, future research, and conclusions
An important threat to the validity of our findings
is the distribution of the number of plants in our sam-
ple. Ideally, we would have liked to use data from
the same number of plants for each country-industry
combination. However, this was not possible due to
missing observations. Although the number of plants
did not vary greatly among the three industries, thenumber of plants varied quite a bit among the four
countries (see Table 1). For example, we have useful
data from more than twice as many plants operating
in Japan (39) than in the USA (17). Therefore, our
results may be more representative of Japanese plants
than American plants.
Another noteworthy concern is that we used percep-
tual measures to gauge organizational performance.
Although the use of perceptual measures is quite
prevalent in the literature, the use of objective mea-
sures is generally preferred. While the intangible
performance measure (COMMIT) is inherently per-
ceptual, the operational performance measure (PER-
FORM) could be measured using objective data.Future studies can use objective performance mea-
sures at the plant level to check the robustness of our
findings.
We empirically showed which HRM practices are
expected to enhance performance. However, since
we used cross-sectional data, we could suggest lit-
tle regarding the process of implementation of these
practices or the causal relationship between use of
these HRM practices and organizational performance.
Two organizations may correctly identify which
HRM practices to implement, yet only one may suc-
cessfully attain higher organizational performance
because of differences in the implementation pro-
cess. Implementing these HRM practices is not an
easy task (Pfeffer, 1994); hence, a future longitudinal
study could focus on the dynamic nature of the HRM
practices and uncover the challenges of the imple-
mentation process at the plant level. A well-designed
research study using longitudinal or panel data can
also better address the issue of causality.
Contingent compensation (CONTCOMP) was
found to be insignificant for the intangible perfor-
mance measure. Based on the literature, we specu-lated that employees might have perceived that they
were being controlled by this HRM practice. As a re-
sult, while contingent compensation (CONTCOMP)
was found to be significant for the operational per-
formance measure, it was not significant for the
intangible performance measure. Future research
can investigate when and why employees perceive
contingent compensation as controlling rather than
motivating and how this ill effect can be minimized.
Existing literature suggests that the level of trust and
type of relationship between superior and subordinatemay determine whether or not an incentive will be
perceived as controlling by the subordinate (Kohn,
1993b; Taylor, 1989).
Due to the limitations of our data, we did not
investigate the impact of organizational strategy on
these HRM practices. Further research is needed to
understand how an organizations strategic context
influences the choice of HRM practices and its im-
pact on performance. Also, whether these findings are
generalizable across country and/or industry needs
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38 S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 1943
to be investigated. Despite the compelling theoretical
argument, our study failed to show that HR practices
are synergistic. Delery and Doty (1996) have also
reported similar results. Future study may shed somelight on this matter by theoretically deriving and em-
pirically testing several context specific ideal-type
HRM systems.
Traditionally, the operations management literature
has paid little attention to human resources issues.
The present study brings some of these issues into
focus in the context of manufacturing plants operat-
ing in different countries and industries. These issues
cannot be resolved by isolated efforts made by oper-
ations managers or human resource managers. Their
combined and synchronized efforts are needed. Our
study provides empirical validation for the efficacy of
the seven HRM practices proposed by Pfeffer (1998).
Although this was the focal research issue, the find-
ings and implications of our study go beyond just
testing the potency of Pfeffers seven HRM practices.
Appendix A
Scales used to measure HRM practices
Variable Scales Item questions
MFGHRFIT, = 0.80 Manufacturing andhuman resources fit
The human resources department communicates closelywith manufacturing when writing job descriptions
Job design at this plant is closely coordinated with
manufacturing
The human resources department has a close and
positive working relationship with manufacturing
Staffing, training and development of employees is
closely coordinated with manufacturing
Manufacturing works well with human resources staff
when changes take place in the manufacturing process
Human resources staff knows what manufacturing
considers important in the training of employees for newskills
BEHAVIOR, = 0.89 Behavior and attitude We use attitude/desire to work in a team as a criterion in
employee selection
We use problem-solving aptitude as a criterion in
employee selection
We use work values and behavioral attitudes as a
criterion in employee selection
We select employees who can provide ideas to improve
the manufacturing process
Specifically, the present study investigates the mediat-
ing effect of organizational commitment which helps
us better understand the nature of the relationship be-
tween HRM practices and organizational performance.This study also evaluates HRM practices taking into
account country and industry contexts, thus making
the findings generalizable across countries and indus-
tries. Lastly, we empirically validate an ideal-type
HRM system for a manufacturing plant. The find-
ings of this study are expected to help operations and
human resource managers recognize the potential of
these seven HRM practices and assist them in design-
ing HRM systems at the plant level to gain superior
performance.
Acknowledgements
The first author appreciates the faculty research
grant provided by the St. Cloud State University.
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Appendix A (Continued)
Variable Scales Item questions
We select employees who are able to work well in smallgroups
TEAMS, = 0.91 Team activities During problem solving sessions, we make an effort to
get all team members opinions and ideas before making
a decision
Our plant forms teams to solve problems
In the past 3 years, many problems have been solved
through small group sessions
Problem solving teams have helped improve
manufacturing processes at this plant
Employee teams are encouraged to try to solve their
problems as much as possible
INTERACTa, = 0.89 Interaction facilitation Supervisors encourage the persons who work for them
to work as a team
Supervisors encourage people who work for them to
exchange opinions and ideas
Supervisors frequently hold group meetings where the
people who work for them can really discuss things
together
INCENTOB, = 0.92 Incentives to meet
objectives
Our incentive system encourages us to vigorously pursue
plant objectives
The incentive system at this plant is fair at rewardingpeople who accomplish plant objectives
Our reward system really recognizes the people who
contribute the most to our plant
Our incentive system at this plant encourages us to reach
plant goals
Our incentive system is at odds with our plant
goalsb
Persons (and/or teams) who achieve plant goals are
rewarded the same as those who do not achieve plant
goalsb
JOBSKILL, = 0.78 Training on job skills Our plant has a low skill level compared with our
industryb
At this plant, some employees lack important skillsb
Plant employees receive training and development in
work-place skills on a regular basis
The management at this plant believes that
continual training and upgrading of employees
skills is important
Employees at this plant have skills that are above
average in this industry
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40 S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 1943
Appendix A (Continued)
Variable Scales Item questions
MULTFUN, = 0.85 Training inmultiple functions
Employees receive training to performmultiple tasks
Employees at this plant learn how to perform a
variety of tasks/jobs
The longer an employee has been at this plant, the
more tasks or jobs that employee learns to perform
Employees are cross trained at this plant so that
they can fill in for others if necessary
At this plant, employees only learn how to do one
job/taskb
At this plant, employees are encouraged to learn
skills in depth, rather than develop a broad skillbaseb
STRATCOM, = 0.92 Communication of
strategy
In our plant, goals, objectives and strategies are
communicated to me
Strategies and goals are communicated primarily
to managersb
I know how we are planning to be competitive at
this plant
I understand the long-run competitive strategy of
this plant
FEEDBACK, = 0.88 Feedback on
performance
Charts showing defect rates are posted on
the shop floor
Charts showing schedule compliance are posted on
the shop floor
Charts plotting the frequency of machine
breakdowns are posted on the shop floor
I am never told whether I am doing a good jobb
Information on quality performance is readily
available to employees
Information on productivity is readily available to
employees
My manager never comments about the quality of
my workb
= Cronbachs alpha.a Taylor and Bowers (1972).b Indicates a reversed scale question. All scale questions use a five-point Likert response scale, where 1: I
strongly disagree and 5: I strongly agree.
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S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 1943 41
Appendix B
Intangible performance measure
Variable Scales Item questions
COMMITa, = 0.89 Organizational
commitment
I am willing to put in a great deal of effort beyond
that normally expected in order to help this
organization be successful
I talk up this organization to my friends as a great
organization to work for
I would accept almost any type of job assignment
in order to keep working for this organization
I find that my values and the organizations values
are very similar
I am proud to tell others that I am part of this
organizationThis organization really inspires the best in me in
the way of job performance
I am extremely glad that I chose this organization
to work for over others I was considering at the
time I joined
I really care about the fate of this organization
For me, this is the best of all organizations for
which to work
= Cronbachs alpha.a Mowday and Steers (1979).
References
Anderson, J.C., Rungtusanatham, M., Schroeder, R.G., 1994. A
theory of quality management underlying the Deming manage-
ment method. Academy of Management Review 19 (3), 472
509.
Arthur, J.B., 1994. Effects of human resource systems on manufac-
turing performance and turnover. Academy of Management
Journal 37 (3), 670687.
Barney, J., 1991. Firm resources and sustained competitive advan-
tage. Journal of Management 17, 99120.
Barney, J., 1995. Looking inside for competitive advantage. Aca-demy of Management Executive 9 (4), 4961.
Baron, R.M., Kenny, D.A., 1986. The moderator-mediator varia-
ble distinction in social psychological research: conceptual,
strategic, and statistical considerations. Journal of Personality
and Social Psychology 51 (6), 11731182.
Becker, B., Gerhart, B., 1996. The impact of human resource
management on organizational performance: progress and pros-
pect. Academy of Management Journal 39 (4), 779801.
Boxall, P., Steeneveld, M., 1999. Human resource strategy and
competitive advantage: a longitudinal study of engineering
consultancies. Journal of Management Studies 36 (4), 443444.
Coff, R.W., 1997. Human assets and management dilemmas: cop-
ing with hazards on the road to resource-based theory. Academy
of Management Review 22 (2), 374402.
Collis, D.J., Montgomery, C.A., 1995. Competing on resources:
strategy for the 1990s. Harvard Business Review 73 (4), 118
128.
Deci, E.L., 1972. The effects of contingent and non-contingent
rewards and controls on intrinsic motivation. Organizational
Behavior and Human Performance 3, 217229.
Delery, J.E., Doty, D.H., 1996. Modes of theorizing in stra-
tegic human resource management: tests of universalistic,
contingency, and configurational performance predictions. Aca-demy of Management Journal 39 (4), 802835.
Drazin, R., Van de Ven, A.H., 1985. Alternative forms of fit in
contingency theory. Administrative Science Quarterly 30, 514
539.
Dyer, L., Reeves, T., 1995. Human resource strategies and firm
performance: what do we know and where do we need to
go? The International Journal of Human Resource Management
6 (3), 656670.
Flynn, B.B., Schroeder, R.G., Sakakibara, S., 1996. The relation-
ship between quality management practices and performance:
synthesis of findings from the world class manufacturing
project. In: Fedor, D.B., Ghosh, S. (Eds.), Advances in the
-
7/28/2019 Journal of Operations Management (a. Mehta)
24/25
42 S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 1943
Management of Organizational Quality, Vol. 1, JAI Press,
Greenwich, CT, pp. 141184.
Galpin, T.J., Herndon, M., 2000. The Complete Guide to Mergers
and Acquisitions. Jossey-Bass Publishers, San Francisco, CA.
Gerhart, B., Milkovich, G.T., 1990. Organizational differences inmanagerial compensation and firm performance. Academy of
Management Journal 33, 663691.
Graybill, F.A., 1961. An Introduction to Linear Statistical Models,
Vol. 1. McGraw-Hill, New York.
Jr. Hair, J.F, Anderson, R.E., Tatham, R.L., Black, W.C., 1998.
Multivariate Data Analysis. Prentice Hall, Upper Saddle River,
NJ.
Hayes, R.H., Wheelwright, S.C., 1984. Restoring Our Competitive
Edge: Competing through Manufacturing. Wiley, New York.
Henderson, J.C., Lee, S., 1992. Managing I/S design teams: a
control theories perspective. Management Science 38, 757777.
Hill, T., 1989. Manufacturing Strategy Text and Cases. Irwin,
Homewood, IL.
Hofstede, G., 1980. Cultures Consequences. Sage, Beverly Hills,CA.
Huselid, M.A., 1995. The impact of human resource management
practices on turnover, productivity, and corporate financial per-
formance. Academy of Management Journal 38 (3), 635672.
Huselid, M.A., Jackson, S.E., Schuler, R.S., 1997. Technical and
strategic human resource management effectiveness as deter-
minants of firm performance. Academy of Management Journal
40 (1), 171188.
Ichniowski, C., Shaw, K., 1999. The effects of human resource
management systems on economic performance: an internatio-
nal comparison of US and Japanese plants. Management
Science 45 (5), 704721.
Ichniowski, C., Shaw, K., Prennushi, G., 1993. The effect of human
resource management practices on productivity. Columbia
University, unpublished paper.
Jayaram, J., Droge, C., Vickery, S.K., 1999. The impact of human
resource management practices on manufacturing performance.
Journal of Operations Management 18, 120.
Kalleberg, A.L., Moody, J.W., 1994. Human resource management
and organizational performance. American Behavioral Scientist
37 (7), 948962.
Kathuria, R., Partovi, F.Y., 1999. Work force management prac-
tices for manufacturing flexibility. Journal of Operations Man-
agement 18, 2139.
Kinnie, N.J., Staughton, R.V.W., 1991. Implementing manufactur-
ing strategy: the human resource management contribution.
International Journal of Operations and Production Management11 (9), 2440.
Kohn, A., 1993a. Why incentive plans cannot work. Harvard
Business Review 71, 5460.
Kohn, A., 1993b. Punished by Rewards: The Trouble with Gold
Stars, Incentive Plans, As, Praise, and Other Bribes. Houghton
Mifflin, Boston, MA.
Lado, A.A., Wilson, M.C., 1994. Human resource systems and
sustained competitive advantage: a competency-based pers-
pective. Academy of Management Journal 19 (4), 699727.
Lawler, E.E., Rhode, J.G., 1976. Information and Control in Orga-
nizations. Goodyear Publishing Company, Pacific Palisades,
CA.
Legare, T.L., 1998. The human side of mergers and acquisitions.
Human Resource Planning 21 (1), 3241.
Lei, D., Hitt, M.A., 1995. Strategic restructuring and outsourcing:
the effect of mergers and acquisitions and LBOs on building
firm skills and capabilities. Journal of Management 21, 835859.
Lepak, D.P., Snell, S.A., 1999. The human resource architecture:
toward a theory of human capital allocation and development.
Academy of Management Review 24 (1), 3148.
Lubatkin, M., Schweiger, D., Weber, Y., 1999. Top management
turnover in related M&As: an additional test of the theory of
relative standing. Journal of Management 25 (1), 5573.
MacDuffie, J.P., 1995. Human resource bundles and manufacturing
performance: organizational logic and flexible production
systems in the World Auto Industry. Industrial and Labor
Relations Review 48 (2), 197221.
MacDuffie, J.P., Kochan, T.A., 1995. Do US firms invest less in
human resources? Training in the world auto industry. IndustrialRelations 34 (2), 147168.
Morishima, M., 1995. Embedding HRM in a social context. British
Journal of Industrial Relations 33 (4), 617640.
Mowday, R.T., Steers, R.M., 1979. The measurement of organi-
zational commitment. Journal of Vocational Behavior 14, 224
247.
Nunnally, J.C., 1978. Psychometric Theory. MacGraw-Hill, New
York.
Osterman, P., 1994. How common is workplace transformation and
who adopts it? Industrial and Labor Relations Review 47 (2),
173188.
Pfeffer, J., 1994. Competitive Advantage Through People. Harvard
Business School Press, Boston, MA.
Pfeffer, J., 1998. Seven practices of successful organizations.California Management Review 40 (2), 96124.
Rousseau, D.M., 1995. Psychological Contracts in Organizations:
Understanding Written and Unwritten Agreements. Sage, Thou-
sand Oaks, CA.
Russell, W.C., 1997. Human assets and management dilemmas:
coping with hazards on the road to resource-based theory.
Academy of Management Review 22 (2), 374402.
Ryan, R., 1982. Control and information in the intrapersonal
sphere: an extension of cognitive evaluation theory. Journal of
Personality and Social Psychology 43, 450461.
Salk, J.E., Brannen, M.Y., 2000. National culture, networks and
individual influence in a multinational management team.
Academy of Management Journal 43 (2), 191202.
Schmenner, R.W., 1981. Production/Operations Management,
Concepts and Situations, 1st Edition. Science Research Asso-
ciates, Chicago, IL.
Schuler, R.S., Jackson, S.E., 1987. Linking competitive strategies
with human resource management practices. Academy of Man-
agement Executive 1 (3), 207219.
Schuler, R.S., MacMillan, I.C., 1984. Gaining competitive advan-
tage through human resource management practices. Human
Resource Management 23, 241255.
Scudder, G.D., Hill, C.A., 1998. A review and classification of
empirical research in operations management. Journal of Oper-
ations Management 16, 91101.
-
7/28/2019 Journal of Operations Management (a. Mehta)
25/25
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