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: [email protected] (S. Ahmad),

[email protected] (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.

P I I : S 0 2 7 2 - 6 9 6 3 ( 0 2 ) 0 0 0 5 6 - 6


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20 S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 1943

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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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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16/25


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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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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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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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)


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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Appendix A (Continued)


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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Appendix B

Intangible performance measure


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).

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