the impact of human resource management practices on

Journal of Operations Management 21 (2003) 19–43

The impact of human resource management practiceson operational performance: recognizing country

and industry differences

Sohel Ahmada,∗, Roger G. Schroederb,1

a Department of Management, St. Cloud State University, 720 Fourth Avenue South, St. Cloud, MN 56301-4498, USAb Department of Operations and Management Science, Donaldson Chair in Operations Management University of Minnesota, Carlson School

of 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 thatinvestigated the impact of HRM practices on organizational performance. However, very little attention has been paid to addressthe impact of HRM practices on operations management and to generalize the findings across countries and industries. Successof some business decisions (e.g. globalization and merger and acquisition) necessitates recognition and reconciliation of thedifferences among HRM practices in different countries and industries. This study attempts to generalize the efficacy of sevenHRM 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 Pfeffer’s seven HRM practices and empirically validate an ideal-typeHRM 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 resourcesystem “. . . as a set of distinct but interrelated ac-tivities, functions, and processes that are directed atattracting, 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 firm’s human resources.” Traditionally, man-agement of this system has gained more attentionfrom service organizations than from manufacturingorganizations. However, to enhance operational per-formance, effectively managing this system is equallyimportant in both types of organizations. Needless tosay, sophisticated technologies and innovative manu-facturing practices alone can do very little to enhanceoperational performance unless the requisite humanresource management (HRM) practices are in placeto form a consistent socio-technical system. For thisreason, manufacturing organizations need to carefullyevaluate their existing HRM practices and modifythem, if needed, so that employees can effectivelycontribute to operational performance improvement.

0272-6963/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved.PII: S0272-6963(02)00056-6

20 S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 19–43

Several studies in the HR literature investigatedthe impact of HR practices on organizational perfor-mance. Although some studies related to HR practicescan be found in the operations management literature(Jayaram et al., 1999; Kathuria and Partovi, 1999;Youndt et al., 1996; Kinnie and Staughton, 1991), thisdiscipline has tended to address structural issues andanalytical questions, and has paid little attention tohuman resources issues. A review of empirical articlespublished between 1986 and 1995 in 13 OM researchoutlets revealed that less than five percent of thesearticles fell into the “HRM for operations” category(Scudder and Hill, 1998). This lack of attention is sur-prising when one considers human resources’ criticalrole in achieving superior performance in compet-itive priorities, such as low cost, quality, delivery,flexibility, and innovation.

Over the years, researchers have suggested manyHRM practices that have the potential to improve andsustain organizational performance. These practicesinclude emphasis on employee selection based on fitwith the company’s culture, emphasis on behavior,attitude, and necessary technical skills required by thejob, compensation contingent on performance, andemployee empowerment to foster team work, amongothers.Pfeffer (1998)has proposed seven HRM prac-tices that are expected to enhance organizational per-formance. The practices proposed byPfeffer (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 onorganizational performance.

5. Extensive training.6. Reduced status distinctions and barriers, including

dress, 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 studybased on these practices. First, we investigate whethermanufacturing plants’ use of these seven practicesdiffers by country or industry. Next, we assess theimpact 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 sevenpractices can form a synergistic HR bundle to repre-sent an ideal HRM system for manufacturing plantsand check the efficacy of this ideal system. Since themanufacturing plant is the unit of analysis for thisstudy, we will be testing the HRM theory at the plantor operations level of the organization.

2. Theoretical background and hypotheses

Organizations can internalize as well as externalizeemployment (Lepak and Snell, 1999). Internalizationof employment involves building an employee skillbase inside the organization, while externalizationof employment means outsourcing human resourceneeds to market-based agents (Rousseau, 1995). Eachalternative has its own costs. According to the trans-action cost theory (Williamson, 1975), the decision tointernalize or externalize a part or all of an operation’shuman resource needs should be based on the trans-actional costs involved. Arriving at a HR outsourcingdecision in such a manner is myopic as it overlooks thestrategic consequences. For example, outsourcing hu-man resource needs can minimize bureaucratic costsand complexities. However, an operation’s continueddependence on external sources may inhibit its abilityto develop core skills and capabilities vital for long-term survival in the marketplace (Lei and Hitt, 1995).

The human capital theory recognizes employeeskills, experience, and knowledge as assets with thepotential to generate economic rent (Coff, 1997). How-ever, this theory evaluates human resources throughproductivity gains. It falls short of attaching strate-gic value to causal ambiguity and tacit knowledgeembedded within an organization’s human resourcesystem. In recent years, researchers and practitionershave realized that HRM systems can be used as strate-gic levers to focus on value creation that goes beyondtraditionally emphasized cost reduction (Becker andGerhart, 1996). Drawing on a behavioral psychologyperspective, researchers have highlighted the strategicaspect of HRM practices and argued about why thesepractices can lead to competitive advantage (Schuler

S. Ahmad, R.G. Schroeder / Journal of Operations Management 21 (2003) 19–43 21

and Jackson, 1987; Schuler and MacMillan, 1984).The resource-based view of the firm further advancedthis argument by stressing that the tacit knowledgeembedded in firm-specific human resources is hard toimitate because of its attributes, such as asset speci-ficity, social complexity, path dependency, and causalambiguity (Russell, 1997; Collis and Montgomery,1995; Barney, 1991). The seven practices suggestedby Pfeffer (1998)are expected to foster such inim-itable attributes in human resources and, thereby helpan organization attain competitive advantage. Severalresearchers (Delery and Doty, 1996; Huselid, 1995;Arthur, 1994; Osterman, 1994) including Pfeffer(1998)have argued why these practices are expectedto enhance organizational performance.

The impact of HRM practices on organizationalperformance has been the subject of much attentionover the years. However, empirical validation of thefindings in operations across countries and/or indus-tries is nearly non-existent and very limited at best.Recent trends toward globalization and mergers andacquisitions in the business world make the studyof HRM practices in the context of country and in-dustry a necessity (Legare, 1998). The literature hasemphasized the need for generalizability of the rela-tionship between HRM practices and organizationalperformance. For example,Delery and Doty (1996,p. 829) raise concern that the results of their study ofHRM practices on organizational performance in thebanking industry may not be valid in other industries.These authors urge that “. . . the current findings needto be validated in other industries to rule out industryas an important contingency factor.” Similar cautionscan be made for country effects. The impact of HRMpractices on organizational performance as proposedby Pfeffer (1998)and others can be generalized acrossmanufacturing plants operating in different indus-tries and countries if we find support for the set ofhypotheses below.

H1. After controlling for the industry and country ef-fects, organizational performance will be positivelyrelated to each of the following seven HRM practices:(a) employment security (alternatively, employment in-security is negatively related to organizational perfor-mance); (b) selective hiring; (c) use of self-managedteams and decentralization; (d) use of compensationcontingent on organizational performance; (e) the ex-

tent of training; (f) reduced status distinctions and (g)sharing of information.

Much of the previous research on the relationshipbetween HRM practices and organizational perfor-mance has concentrated on a single HR practice,such as compensation, selection, etc. (Gerhart andMilkovich, 1990). However, a growing number of re-searchers have argued for instituting complementarybundles of HRM practices to enhance organizationalperformance (Ichniowski et al., 1993; Osterman,1994). “Human resource practices are said to be bun-dled when they occur in fairly complete, mutuallyreinforcing or synergistic sets” (Dyer and Reeves,1995, p. 657). Pfeffer’s seven HRM practices areinternally consistent with one another. For exam-ple, an organization promising employment securityneeds to pay close attention to selective hiring of newpersonnel. Employees cannot be retained for a longtime unless their attitudes, values, and behavior fitwith those of the organization. Therefore, identifyingthese qualities should be an integral part of the hiringprocess. Effectively operating self-managing teamsand decentralizing decision making require in-depthunderstanding of aptitudes, abilities, temperaments,idiosyncrasies, and personal traits of fellow employ-ees. Mutual understanding among employees usuallydevelops when they work together for a long timeas occurs in organizations that provide employmentsecurity. Also, when an organization institutes per-formance contingent compensation, the employeesare motivated to focus on long-term organizationalperformance rather than short-term gains if the em-ployers provide employment security. Organizationsemphasizing employment security intend to keep em-ployees longer; therefore, it makes sense to investmore in training these employees. Under a longertime horizon, training related expenditures should bestrategically evaluated and considered an investmenttoward human capital rather than merely a cost ofdoing business. Organizations need to make extraefforts to reduce status distinctions, if they intend tokeep employees loyal to them. Sharing information onorganizational strategy, goals, and performance withemployees conveys that they are trusted. Informationsharing also empowers the employees and fosters or-ganizational transparency which are crucial if the em-ployees are to have long tenures in the organization.


As explained above, “employment security” is in-ternally consistent with other HRM practices. Similararguments can be made about each of the remaining sixHRM practices. Therefore, these HRM practices areinternally consistent with one another and qualify as asynergistic set. A bundle of internally consistent prac-tices is more effective than the sum of the effects ofthe individual practices due to their mutually reinforc-ing support (MacDuffie, 1995). The resource-basedview also supports this notion by stressing that in-dividual practices have a limited ability to generatecompetitive advantage in isolation. However, in com-bination, these complementary resources can help afirm attain greater competitive advantage (Barney,1995).

Every organization differs in how much effort itputs into harnessing each of the seven HRM prac-tices. An ideal situation may be one in which each ofthese HRM practices is explored and exploited to itshighest potential, typically when an organization ex-erts the maximum effort possible to develop, institute,and implement each of these seven practices. Sucha HRM system may be termed an ideal-type HRMsystem. This ideal-type HRM system is expected toyield the highest organizational performance. Themore similar an organization’s HRM system is to theideal-type HRM system, the better the organization’sperformance. Moreover, if bundling invokes synergyamong HRM practices as previously argued, thenan organization with a HRM system similar to theideal-type HRM system will explain significantlymore variation in organizational performance thanany of the individual HRM practices or any combina-tion thereof. From the above discussion, we draw thefollowing hypothesis.

H2. After controlling for the industry and country ef-fects, the degree of dissimilarity (measured as misfit)between an organization’s existing HRM system andthe ideal-type HRM system will be negatively relatedto the organizational performance.

3. Data collection

We use world class manufacturing (WCM) projectdata to test the hypotheses. The focus of the WCMproject is to examine differences in manufacturing

Table 1Number of plants by country and industry

Electronic Machinery Automobile Total

Germany 5 10 9 24Italy 8 13 7 28Japan 13 12 14 39USA 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 thisproject was about 60%. We use a part of this project’sdatabase that addresses HRM issues; it includes 107manufacturing plants (seeTable 1) after eliminatingresponses with missing data. These plants employ1153 employees on average, including both salariedand hourly workers. The mean age of these plants isabout 37 years. The average facility size (productionand warehouse) is 160,701 ft2, with 32 product linesmanufactured on average.

Data collected from plants operating in four coun-tries and three industries are used for the empiricalanalyses. The countries are Germany, Italy, Japan, andthe USA. The four countries were selected to repre-sent the major industrial regions of the world, NorthAmerica, Asia and Europe. In each of these coun-tries, plants were randomly selected from three in-dustries: automobile, electronics, and machinery. Thethree industries were selected because the literaturesuggests that they have been implementing variousWCM 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 seekingimprovements.

Face validity of the questionnaires was insured byhaving three different researchers develop items for thescales. The three researchers then reviewed all of theitems for content validity. Whenever possible, scaleswere selected from the existing literature. The datacollection instrument was pre-tested using 10 industryexperts and academics. After the pilot testing, some ofthe items were clarified or changed to be more repre-sentative of the intended constructs. The reliability andvalidity of the constructs were formally tested usingdata from over 800 respondents in a prior round of datacollection in 43 US plants. As a result of these tests,


some of the scales were significantly revised. The USinstrument was subsequently translated into German,Italian and Japanese. The foreign language version wasthen translated back into English by another individualand compared for accuracy. Any discrepancies wereresolved.

Plant managers were contacted by a member of theWCM team and asked for their voluntary participa-tion in exchange for detailed feedback regarding theirmanufacturing practices in comparison to the indus-try. About 60% of the plants contacted participated inthe study. Interested plant managers appointed plantresearch coordinators who maintained contact withthe research team. These plant research coordinatorswere managers who had at least 3 years of experiencein the plants and were knowledgeable about the majorresponsibilities of the employees working in the plant.The research team consulted with the plant researchcoordinators to identify the right respondents in theplant who had pertinent knowledge, experience, andability to provide accurate and unbiased answers tothe questions in the survey. The questionnaires werecollected in sealed envelopes to maintain anonymityof responses. Managers, engineers, supervisors andworkers responded to these questionnaires. We usedresponses from different people for the dependent(organizational performance) and independent (HRMpractices) variables to avoid common respondentbias.

4. Measures

4.1. The seven HRM practices

Table 2summarizes 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 multiplevariables as determined by the scope of the HRMpractice and limitations of the WCM database. Fordetails on the measurement refer toAppendix A andTable 3. Most of the variables were measured usingperceptual 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 beingmeasured.

A set of Likert scales was used to measure pertinentconstructs. 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 validityof a construct was ensured through pre-testing of thequestionnaires 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 Cronbach’s alpha of 0.7or more was used as a criterion for a reliable scale(Nunnally, 1978). We removed an item if it did notcontribute strongly to the alpha value and if its contentwas not essential for the construct. After purifying ascale, we averaged all of the items in that scale, whichbecame the value of the variable representing the con-struct. Therefore, any variable measured by the scalecan range in value from one to five, where five is themost desirable value.

The remaining three variables inTable 2were mea-sured using objective measures. SeeTable 3for details.The variable INSECURE was measured as a percent-age of employees laid off during the past 5 years. Thisvariable 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 twobinary measures. The value of this variable can rangefrom 2 to 4. A value of 2 indicates that the plant doesnot 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 is4. Similarly, the variable STATDIFF is a composite offour binary measures, and its value can range from 4to 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 2lists the most desirable value for each variable.

4.2. Organizational performance

Past empirical research has mostly investigated theeffects of HRM practices on financial performance(cf. Delery and Doty, 1996) and some on efficiencyand employee turnover (cf.Huselid, 1995). However,very few studies have examined the impact of HRMpractices on operational performance measures, such


Table 2Summary of measurements of the seven HRM practices

Practice Variable Scales/measurement Description of measurements Idealprofile

Employment insecurity INSECURE Employment insecurity (The number of employees who have beenlaid off during the past 5 years/number ofemployees in the organization)×100

0

Selective hiring MFGHRFIT Manufacturing and humanresources fit

A scale of six items measuring the degree ofcooperation between manufacturing andhuman resources in designing jobdescriptions and staffing activities

5

BEHAVIOR Behavior and attitude A scale of five items measuring theimportance given to a prospective employee’sattitudes and behavior toward teamwork andproblem solving during the selection process

5

Use of teams anddecentralization

TEAMS Team activities A scale of five items used to assess theeffective use of teams on the shop floor

5

INTERACT Interaction facilitation A scale of three items that measures theextent to which supervisors encourage andfacilitate workers to work as a team

5

Compensation/incentivecontingent onperformance

CONTCOMP Contingent compensation This measure checks whether groupincentive plans (Y/N) and profit sharingplans (Y/N) are used in the organization.Y=Yes=2 and N=No=1

4

INCENTOB Incentives to meetobjectives

A scale of four items to measure whether theplant’s reward system is consistent withmanufacturing objectives and goals

5

Extensive training JOBSKILL Training on job skills A scale of three items to measure ifemployees’ on the job skills and knowledgeare being upgraded in order to maintain awork force with cutting edge skills andabilities

5

MULTFUN Training in multiplefunctions

A scale of five items to measure the extentto which employees receive cross training sothat they can perform multiple tasks or jobs

5

Status differences STATDIFF Existing status differences Four questions were asked to judge the useof symbols that indicate status differentialsamong various employees in terms of thefollowing: the use of assigned parking spots(Y/N); the use of uniforms by workers only(Y/N); access restriction to cafeteria forsome employees (Y/N); and the use ofseparate rest-rooms (Y/N) for differentemployees in the plant

4

Sharing information STRATCOM Communication of strategy A scale of three items to measure the effortsmade by management to communicate theplant’s competitive strategy to all employees

5

FEEDBACK Feedback on performance A scale of five items to measure the extentto which management provides shop floorpersonnel with information regarding theirperformance in a timely and useful manner

5


Table 3HRM 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 focusshould be on variables that have inherent meaningfor a particular context” (Becker and Gerhart, 1996,p. 791). Because the unit of analysis for this study is amanufacturing plant, we argue that HRM practiceswill impact the operational performance measuresat the plant level. Also, the strategic implications ofHRM practices make tracking intangible performancemeasures important. We, therefore, investigate theimpact 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) haveproposed a wide variety of operational performancemeasures for manufacturing facilities. These includecost, quality, delivery, and flexibility. Lately, the rateof new product introduction has also been included inthis list (Vickery et al., 1997). We performed factoranalysis to check if these five operational perfor-mance measures formed different groups. The factoranalysis revealed that all of these measures loaded on


Table 4Operational performance measures (PERFORM)

COST Unit cost of manufacturingQUALITY Quality of product conformanceDELIVERY On-time delivery performanceFLEXBLTY Flexibility to change volumeNPDSPEED Speed of new product introductionPERFORM= COST + QUALITY + DELIVERY

+ FLEXBLTY + NPDSPEED

Please circle the number which indicates your opinion about howyour plant compares to its competition in your industry. Thenumber 5: superior or better than average; 4: better than average;3: average or equal to the competition; 2: below average; 1: pooror low end of the industry.

to one factor. Additionally, the reliability analysis ofthese measures yielded a value of Cronbach’s alphaof 0.71, justifying summing up these measures toform a single performance index (PERFORM). Thiscomposite measure represents a plant’s aggregateachievement in all five areas of performance men-tioned above compared to competitors. SeeTable 4fordetails.

4.2.2. Intangible performance measureResearchers have yet to reach a consensus about

how best to define strategic HRM. However,Huselidet al. (1997, p. 172) attest that there is broad agree-ment in the literature that strategic HRM “. . . involvesdesigning and implementing a set of internally consis-tent policies and practices that ensure a firm’s humancapital contributes to the achievement of its businessobjectives.”Snell and Dean (1992)further stress thata firm invests in employees to strengthen its humancapital, but the firm does not actually own this hu-man capital. The firm has very little control over thishuman capital as employees may leave the firm or,even if they do not leave, they may not be inspired toput forward their best efforts.Snell and Dean (1992)recommend that a firm devise methods to ensurethat individuals act in the firm’s best interest overtime.

HRM practices that fail to elicit specific employeeattitudes, such as organizational commitment are lesslikely to have strategic impact (Arthur, 1994). Further-more, an employee with strong organizational com-mitment will be highly motivated to expend energyon organizational tasks (Anderson et al., 1994). Evenhighly skilled and knowledgeable employees who are

uncommitted may not contribute discretionary effortsand will thereby minimize their potential in the organi-zation. Organizational commitment is an indicator thattestifies to whether the HRM practices employed in anorganization are able to foster psychological links be-tween organizational and employee goals. This is anintangible outcome of a HRM system and is importantto retaining employees and exploiting their potentialto the fullest extent over time. We, therefore, iden-tify organizational commitment as an intangible per-formance measure and measure it using a scale. Weconducted reliability and unidimensionality analysesfor this scale. Items were dropped to obtain a reliableand unidimensional scale. The remaining items werethen averaged to obtain a score for the scale (COM-MIT) corresponding to each plant. SeeAppendix Bfor details.

4.3. Measure of misfit

In context of this paper, misfit represents the dis-similarity between an ideal HRM profile and a plant’sexisting HRM profile. We identified a theoretical idealprofile by choosing the most desirable values of thevariables representing the seven HRM practices shownin the last column ofTable 2. This profile representsthe ideal-type HRM system that has been theorized toyield the highest organizational performance. Math-ematically, misfit is the Euclidean distance betweena point defined in a multidimensional space by theideal profile (i.e. the ideal-type HRM system) and apoint representing an experimental unit. In this study,the experimental unit is a plant’s existing HRM sys-tem as measured by the variables representing theseven HRM practices shown inTable 2. Accordingly,we use the following general formula to calculateMISFIT.

MISFITi =n∑

k=1

(Xk − Xik)2 (1)

where MISFITi is the distance between the existingHRM system of a particular planti and the ideal-typeHRM system;Xik the score of thekth variable of theexisting HRM system of a particular planti; Xk thescore of thekth variable of the ideal-type HRM sys-tem;k the number of variables representing the HRM


system 1, . . . , n; for this study,k varies from 1 to 12and 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 − MULTIFUN i )

2}]2

+ STD{(4 − STATDIFFi )2}

+ [STD{(5 − STRATCOMi )2} + STD{(5 − FEEDBACKi )

2}]2

As mentioned earlier, the ranges of the variables inthe above equation are not the same. This can dis-proportionately inflate some variables’ contributionto the MISFIT calculation. We have, therefore, stan-dardized (STD) the squared differences (between theideal-type HRM system and the existing HRM systemof 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 effectsof country and industry need to be removed priorto evaluating the relationship between HRM prac-tices and organizational performance. We, therefore,included the following control variables (indicatorvariables) in the regression analyses. Three countrycontrol variables, GERMANY (Germany compared toUSA), ITALY (Italy compared to USA), and JAPAN(Japan compared to USA), are used to represent thefour countries. Similarly, two industry control vari-ables, MACHINE (machinery industry compared toelectronics industry) and AUTOMOBL (automobileindustry compared to electronics industry), are used torepresent the three industries from which the data werecollected.

5. Analyses and results

In this part of the paper, we first present thedescriptive statistics. Next, we conduct statistical

analysis to determine if the extent to which plants usethe seven HRM practices differs by country and/orindustry. Lastly, empirical analyses are performed totest the hypotheses stated earlier.

Table 5 shows means, standard deviations, andcorrelations, which allow for some interesting obser-vations. For example, high variance of the variableINSECURE indicates that plants’ employee layoffrates vary widely. Employment insecurity (INSE-CURE) is negatively related to many of the HRMpractices which implies that a plant with a high em-ployee layoff rate is less likely to foster growth in otherHRM practices listed inTable 5. The variable thatmeasures status difference (STATDIFF) shows similarresults. A higher status difference in a plant is associ-ated with lower efforts in other HRM practices. Thatis, it is unlikely that the HRM practices will flourishin a plant where high status difference exists. Further-more, a plant with high status difference is expectedto have high employment insecurity (i.e. higher em-ployee layoff rate) since STATDIFF and INSECUREare positively correlated. Positive correlations amongdifferent HRM practices show that when a plant in-creases its efforts in one of the HRM practices, it isalso 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 betweenHRM practices variables and operational performancemeasures. As suggested byHair 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 thecanonical correlation, and (3) measure of redundancyfor the percentage of variance accounted for by the twosets of variables. Only the first canonical pair was sta-tistically significant (seeTable 6). The canonical cor-relation (0.56) was moderate. The redundancy indexwas found to be 0.1128, which is quite low. Althoughthere are no guidelines about the minimum acceptablevalue for the redundancy index, generally the higherthe value of the index the better.

Traditionally, canonical pairs have been interpretedby examining the sign and the magnitude of the canon-ical weights. However, these weights are subject toconsiderable instability due to slight changes in samplesize, particularly where the variables are highly corre-lated. Canonical cross-loadings have been suggested

Table 6Results of canonical correlation analysis

Canonical correlation 0.5603Level of significance 0.0064Redundancy index 0.1128Correlations between the operational performance measures

and the first canonical variable of the HRM practicesCOST 0.3912QUALITY 0.4334DELIVERY 0.3227FLEXBLTY 0.0911NPDSPEED 0.3328

Correlations between the HRM practices variables and thefirst canonical variable of the operational performancemeasuresINSECURE −0.1183MFGHRFIT 0.3101BEHAVIOR 0.3896TEAMS 0.4121INTERACT 0.4228CONTCOMP 0.3153INCENTOB 0.4047JOBSKILL 0.4553MULTFUN 0.4625STATDIFF −0.2224STRATCOM 0.4436FEEDBACK 0.3785

as a preferable alternative to the canonical weights(Hair et al., 1998). The canonical cross-loadings showthe correlations of each of the dependent variableswith the independent canonical variate, and viceversa.Table 6shows the canonical cross-loadings forthe first canonical pair. A loading of at least 0.31 isconsidered significantly different from zero at a levelof significance of 0.05 (Graybill, 1961). According tothis 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). Onthe other hand, all independent variables (HRM prac-tices) except for employment insecurity (INSECURE)and status differences (STATDIFF) are significantlyrelated to the dependent canonical variate (canon-ical variate representing operational performancemeasures).

Researchers have argued that HRM practices candiffer across countries and/or industries for severalreasons including: cultural idiosyncrasy (Salk andBrannen, 2000), governmental regulations/policies(Morishima, 1995), competitive priorities (Boxalland Steeneveld, 1999), and adoption of managerialpractices, such as JIT and quality management (Snelland Dean, 1992). Hofstede argues that national cul-tures impact the attitudes and behaviors of employees(Hofstede, 1980). In a single company study, he foundthat cultural values varied significantly by countryand region of the world.

Most of the empirical studies related to HRM prac-tices have been conducted using data collected in asingle industry within one country (cf.Arthur, 1994).Some studies used data collected from multiple in-dustries in one country (cf.Huselid, 1995), and somestudies were conducted on data collected from a singleindustry in multiple countries (cf.MacDuffie, 1995).However, the central foci of these studies were not tocompare systematic differences that may have existedin HRM practices in the different countries and in-dustries in which the organizations operated. Empiri-cal examination of broad-based HRM practices acrossindustries and/or countries is very limited in the liter-ature (MacDuffie and Kochan, 1995; Ichniowski andShaw, 1999). Since we intend to identify generaliz-able impacts of HRM practices on organizational per-formance across countries and industries (H1), it isimportant to understand the differences that may exist


Table 7HRM 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 0.00MFGHRFIT 3.38 3.29 3.33 3.26 NS 0.25 0.86BEHAVIOR 3.17 3.10 3.67 3.23 (3, 1)∗∗ (3, 2)∗∗ (3, 4)∗∗ 21.20 0.00TEAMS 3.51 3.38 3.76 3.76 (3, 2)∗∗ (4, 2)∗ 5.41 0.00INTERACT 3.52 3.11 3.78 3.78 (1, 2)∗ (3, 2)∗∗ (4, 2)∗∗ 14.13 0.00CONTCOMP 2.83 2.32 3.79 2.56 (1, 2)∗ (3, 2)∗∗ (3, 4)∗∗ (3, 1)∗∗ 41.98 0.00INCENTOB 2.62 2.44 3.13 2.64 (3, 1)∗∗ (3, 2)∗∗ (3, 4)∗ 10.54 0.00JOBSKILL 3.20 3.25 3.62 3.55 (3, 1)∗∗ (4, 1)+ (3, 2)∗∗ 7.16 0.00MULTFUN 3.59 3.30 3.74 3.82 (1, 2)∗ (3, 2)∗∗ (4, 2)∗∗ 11.57 0.00STATDIFF 6.71 5.5 4.17 4.63 (1, 2)∗∗ (1, 3)∗∗ (1, 4)∗∗ (2, 3)∗∗ (2, 4)∗∗ 71.40 0.00STRATCOM 3.6 2.88 3.70 3.55 (1, 2)∗∗ (3, 2)∗∗ (4, 2)∗∗ 16.87 0.00FEEDBACK 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 ofP ≤ 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 differencesin HRM practices among plants operating in fourcountries. The last two columns ofTable 7show thevalues of theF-statistics and their levels of signifi-cance.F-statistics for all of the HRM practices arefound to be highly significant except for the scalerepresenting manufacturing and human resources fit(MFGHRFIT). That is, mean efforts expended byplants differed in all but one of the HRM practicesin at least two countries. Statistical insignificance oftheF-statistic for MFGHRFIT suggests that the levelof cooperation between manufacturing and humanresources in designing job descriptions and staffingactivities did not differ significantly by country.

Next, we conducted the Scheffe pairwise compar-ison tests of mean differences to better understandhow HRM practices differed between each pair ofcountries. This comparison revealed several importantaspects of HRM practices as they are used in differ-ent countries. Employment insecurity is the highestin Germany and the lowest in Japan. The well knownlifelong employment policy in Japan seems to be evi-dent in this finding. Plants in Japan emphasized someHRM 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 toTables 2 and 3, andAppendix A fordefinition and measurement of these and other HRMpractices.

Compared to other countries in this sample, plantsin Italy seem to be significantly lacking in their effortsin several HRM practices. These HRM practices in-clude team activities (TEAMS), interaction facilitation(INTERACT), training in multiple functions (MULT-FUN), communication of strategy (STRATCOM), andfeedback on performance (FEEDBACK).

The training on job skills scale (JOBSKILL) mea-sures if employees’ on-the-job skills and knowledgeare considered important and whether these are up-graded on a regular basis to maintain a work forcewith cutting edge skills and abilities. Plants in Japanput significantly more effort into training on the jobskills, while plants in Germany lagged behind othercountries in this HRM practice. We were surprisedby this observation since Germany, under a nationalindustrial and educational policy, offers apprentice-ship training to secondary school students to facilitatethe school-to-work transition (MacDuffie and Kochan,1995). Our expectation was that German plants would


Table 8HRM 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 0.21MFGHRFIT 3.39 3.29 3.29 NS 0.45 0.64BEHAVIOR 3.37 3.32 3.35 NS 0.13 0.88TEAMS 3.61 3.48 3.73 (3, 2)a,+ 2.89 0.06INTERACT 3.61 3.35 3.71 (3, 2)∗∗ 5.06 0.00CONTCOMP 2.93 2.90 3.20 NS 1.36 0.26INCENTOB 2.76 2.74 2.78 NS 0.05 0.95JOBSKILL 3.47 3.36 3.43 NS 0.53 0.59MULTFUN 3.70 3.46 3.67 (1, 2)∗ (3, 2)+ 4.43 0.01STATDIFF 4.72 5.50 5.17 (2, 1)∗ 3.99 0.02STRATCOM 3.48 3.29 3.57 NS 2.24 0.11FEEDBACK 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 ofP ≤ 0.1.+ P ≤ 0.1.∗ P ≤ 0.05.∗∗ P ≤ 0.01.

show a similar proclivity toward developing job skillsin plants. We also note that German plants exhibit thehighest status differences (STATDIFF) among all ofthe countries; Italian plants are second.

Again, we used one-way ANOVA to identifyplants’ differences in HRM practices in the threeindustries.Table 8shows that theF-statistics corre-sponding to most of the HRM practices are insignifi-cant. The Scheffe pairwise comparison tests of meandifferences revealed that plants operating in the ma-chinery industry seem to put significantly less effortinto team activities (TEAMS), interaction facilitation(INTERACT), training in multiple functions (MULT-FUN), and feedback on performance (FEEDBACK)than plants operating in the automobile industry (seeTable 8). A closer look reveals that these HRM prac-tices are often emphasized in plants that implementmanufacturing practices, such as quality managementand/or lean production. The automobile industry wasat the forefront of the quality management and JITmanufacturing revolutions in past decades (Soderquistand Motwani, 1999; Womach et al., 1990). Thiswell-known fact probably explains the difference inHRM 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 themachinery and electronic industries supports thesefindings.

In addition to conducting one-way ANOVA as dis-cussed above, the two-way interaction effects werealso tested using general linear models. Only thetwo-way interaction effect for the variable INSE-CURE was found to be statistically significant. Thisis not surprising given our earlier findings which re-vealed that this variable showed fairly high varianceacross countries and industries.

In summary, we have found that HR practices varywidely by country and to some extent by industry. Thisis 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 forthe differences we observed among the HR practicesadopted in different countries and industries.

5.1. Hypothesis 1

This hypothesis is tested by hierarchical regressionanalyses 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 enteredinto 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) ispartially supported since the variable INCENTOB isfound to be significant but the variable CONTCOMPis 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 1due to the possibility of omitted variable bias sincethe correlations between some pairs of HRM practicesare quite high.

Regression analyses show that employment insecu-rity (INSECURE) and status differences (STATDIFF)were not significant for either of the two dependentvariables. In the literature, empirical evidence showsthat employment insecurity is associated with lowerperformance (Delery and Doty, 1996). Therefore, wewere surprised that employment insecurity (INSE-CURE) was not significant. However, the correlationmatrix (Table 5) shows that employment insecurity isnegatively related to several HRM practices. TheseHRM practices have positive associations with thedependent variables. Therefore, employment insecu-rity seems to hinder the development of other HRMpractices, thereby minimizing the potential of theHRM practices as a whole. Status difference (STAT-DIFF) shows a similar relationship with other HRMpractices.

Additionally, contingent compensation (CONT-COMP) was not significant for the intangible perfor-mance measure. The literature finds mixed impact ofcontingent compensation on intangible performancemeasure, such as organizational commitment. Whilecontingent compensation can sometimes motivateworkers to put forward their best efforts (cf.Hendersonand Lee, 1992), it can sometimes de-motivate them(cf. Kohn, 1993a) because contingent compensationcan be perceived by the employees as a managementcontrol mechanism. Here, the term “control” implies

management’s attempt to ensure desired outcomes bytrying to influence employee behavior (Lawler andRhode, 1976). Therefore, the more controlling theemployees perceive the compensation system to be,the less organizational commitment it will engender(Deci, 1972; Ryan, 1982). This probably explainswhy we failed to observe a significant relationship be-tween contingent compensation (CONTCOMP) andthe intangible performance measure.

According to the behavioral approach to strategicHRM, the mechanism through which a HRM systemcontributes to operational performance is by elicitingbehaviors required to accomplish operational goals.From that standpoint, the role of an intangible perfor-mance measure (i.e. organizational commitment) asa mediating variable in HRM system’s influence onoperational performance is worth being investigated.This investigation is conducted as follows.

A variableZ is said to be a mediator of a relationshipbetween two variablesX (independent variable) andY (criterion variable), if the following are true (Baronand Kenny, 1986): (1) X significantly affectsZ, whenZis regressed onX; (2) X significantly affectsY, whenY is regressed onX; (3) Z significantly affectsY, whenY is regressed on bothX and Z. Table 11shows theresults related to mediating effects of the intangibleperformance measure (organizational commitment).According to the criteria mentioned above, organiza-tional commitment (COMMIT) acts as a mediatingvariable for MFGHRFIT, BEHAVIOR, TEAMS, IN-TERACT, INCENTOB, JOBSKILL, MULTFUN,STRATCOM, and FEEDBACK. Thus, the analysesconducted to test the direct impact of HRM prac-tices on operational performance and the subsequentanalyses for mediating effects reveal the following.INSECURE and STATDIFF seem to have no impacton operational performance, CONTCOMP influencesoperational performance directly, and the rest of theHRM practices influence operational performanceindirectly through the mediating variable COMMIT.

5.2. Hypothesis 2

The profile deviation method (Drazin and Van deVen, 1985) is used to test this hypothesis. There arethree steps in this method: (1) identifying the idealprofile; (2) calculating misfit; (3) linking misfit withorganizational performance. We completed the first


Table 11The mediating effect of organizational commitment (COMMIT)

Independentvariables

Dependent variables

COMMIT(coefficient)

PERFORM(coefficient)

PERFORM(coefficient)

INSECURE 0.00 −0.01 0.00COMMIT 2.67∗∗MFGHRFIT 0.41∗∗ 1.36∗ 0.31COMMIT 2.55∗∗BEHAVIOR 0.76∗∗ 2.22∗∗ 0.22COMMIT 2.62∗∗TEAMS 0.53∗∗ 2.04∗∗ 0.86COMMIT 2.25∗∗INTERACT 0.51∗∗ 1.41∗ 0.01COMMIT 2.73∗∗CONTCOMP 0.06 0.85+ 0.69COMMIT 2.65∗∗INCENTOB 0.49∗∗ 1.08∗ −0.40COMMIT 3.06∗∗JOBSKILL 0.68∗∗ 2.16∗∗ 0.60COMMIT 2.29∗∗MULTFUN 0.78∗∗ 2.90∗∗ 1.29COMMIT 2.07∗∗STATDIFF 0.01 −0.19 −0.23COMMIT 2.74∗∗STRATCOM 0.43∗∗ 1.90∗∗ 0.97COMMIT 2.14∗∗FEEDBACK 0.24∗∗ 1.43∗∗ 0.88+COMMIT 2.32∗∗

+ P ≤ 0.1.∗ P ≤ 0.05.∗∗ P ≤ 0.01.

Table 12Results 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 0.49F 1.97+ 4.53∗∗ 3.06∗ 16.05∗∗AdjustedR2 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 organizationalperformance according to the following regressionmodel. Hypothesis 2 will be supported by the regres-sion model below if a significant negative value ofβ6is observed.

ORG PERFi

= β0 + β1 GERMANYi + β2 ITALY i

+β3 JAPANi + β4 MACHINE i

+β5 AUTOMOBL i + β6 MISFITi + εi (2)

where ORGPERFi is the organizational perfor-mance of planti, which represents PERFORMi orCOMMITi as a dependent variable, one at a time.GERMANYi , ITALY i , and JAPANi are three indica-tor variables representing four countries. MACHINEi

and AUTOMOBLi are two indicator variables repre-senting three industries. MISFITi is the value of thevariable MISFIT for planti.

Table 12shows the results of the hierarchical re-gression analyses. The country and industry controlvariables are entered in the first step (Eq. (1)). Next,MISFIT is entered into theEq. (2). Two sets of equa-tions correspond to each dependent variable.Table 12shows thatEq. (2)for each of the two equations is sig-nificant and the coefficient of MISFIT (β6) is negativeand significant, thus providing support for Hypothe-sis 2. The variable MISFIT is found to be negatively


related to the performance measures which impliesthat as a plant’s HRM system deviates from theideal-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 systemis valid for a plant regardless of the country or in-dustry in which it operates. This finding indicatesthat management choices concerning HR practices doindeed make a difference even after accounting forcountry and industry factors.

6. Discussion

Traditionally, the focus of a HRM system has beenshort-term, and the system has been used as a bu-reaucratic control mechanism to enhance efficiency(Kalleberg and Moody, 1994). Now, practitioners andresearchers agree that human resources can be a sourceof competitive advantage and should be managedstrategically. However, organizations are discoveringthis is easier said than done. Results of the presentstudy show that differences in HRM practices exist inplants operating in different countries. Although thiswas previously implied in the literature, comparisonof a comprehensive list of HRM practices amongcountries was lacking. We obtained mixed resultswhen the HRM practices were compared across threeindustries. While the majority of HRM practices usedby plants did not differ by industry, we did find sev-eral HRM practices that differed significantly amongthe three industries. Particularly, the extent to whichsome HRM practices are used in plants operating inthe machinery industry consistently laged behind thatfound in plants operating in the automobile industry.

We find overall support for Hypothesis 1 as most ofthe relationships specified in Hypothesis 1 are foundto be significant. Hypotheses (a) and (f), however,were not supported for any of the two dependentvariables. Therefore, the proposed direct relationshipbetween employment insecurity and organizationalperformance, and between status difference and or-ganizational performance, cannot be empirically val-idated. However, as mentioned earlier, employmentinsecurity and status difference seem to hinder devel-opment of other HRM practices, and thereby influence

the work environment and minimize the potential ofHRM practices as a whole.

The mediating effect analysis revealed that mostof HRM practices impact operational performanceindirectly through organizational commitment. Thisfinding is important as it refines our understandingof the nature of relationship between HRM practicesand operational performance. Also, this finding sug-gests that a manager intending to enhance operationalperformance should create a conducive organizationalclimate that fosters employees’ commitment to theorganization.

The findings of the present study also offer impor-tant implications for several distinct trends observedin the business world today. Many organizationsare going through globalization to take advantageof proximity to suppliers, customers, and criticalresources, such as human resources. Another notice-able trend has been mergers and acquisitions amongcompanies. Several of these mergers and acquisitionsare occurring between organizations operating indifferent countries (e.g. Daimler-Benz and ChryslerCorporation) and industries (e.g. Time Warner andAmerica 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 takethis opportunity to evaluate their existing set of HRMpractices and make necessary changes to facilitatepost-merger integration. This is particularly impor-tant if organizations involved in M&A are followingdifferent HRM practices.

Our analyses show that plants operating in differentindustries and/or countries use and emphasize HRMpractices differently. Therefore, which HRM practicesshould a combined (new) organization choose whenM&A is taking place between organizations operatingin different industries and/or countries? By control-ling for country and industry in our analyses, we wereable to empirically validate those HRM practices thatare expected to yield higher performance regardlessof the country and industry in which the plant oper-ates. Therefore, one choice may be to institute theseHRM practices for the combined (new) organization,fine-tuning them according to the strategic intent of the


new organization. Thus, the findings of our study pro-vide general directions for managers to achieve betteroperational performance through HRM systems inte-gration in cross-country and/or cross-industry mergersor acquisitions.

Earlier attempts to empirically validate ideal-typeHRM systems have received mixed confirmation(Delery and Doty, 1996). Although support for Hy-pothesis 2 in our study empirically validates anideal-type HRM system, it failed to show the ex-pected level of variation explained. This is explainedas follows: by definition an HR bundle is a set of in-terrelated and internally consistent HR practices thatare expected to create mutually reinforcing and syn-ergistic impacts on performance (MacDuffie, 1995).Therefore, the variation in organizational performanceexplained by a HR bundle should be significantlygreater than that explained by an individual HR prac-tice in that bundle. However, results of our study failedto 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 plant’s HRM system deviates fromthe ideal-type HRM system, the plant’s performancedecreases, and this relationship is statistically signif-icant (the coefficient of MISFIT (β6) is negative andsignificant).

7. Limitations, future research, and conclusions

An important threat to the validity of our findingsis the distribution of the number of plants in our sam-ple. Ideally, we would have liked to use data fromthe same number of plants for each country-industrycombination. However, this was not possible due tomissing observations. Although the number of plantsdid not vary greatly among the three industries, thenumber of plants varied quite a bit among the fourcountries (seeTable 1). For example, we have usefuldata from more than twice as many plants operatingin Japan (39) than in the USA (17). Therefore, ourresults may be more representative of Japanese plantsthan American plants.

Another noteworthy concern is that we used percep-tual measures to gauge organizational performance.Although the use of perceptual measures is quiteprevalent in the literature, the use of objective mea-

sures is generally preferred. While the intangibleperformance 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 ourfindings.

We empirically showed which HRM practices areexpected to enhance performance. However, sincewe used cross-sectional data, we could suggest lit-tle regarding the process of implementation of thesepractices or the causal relationship between use ofthese HRM practices and organizational performance.Two organizations may correctly identify whichHRM practices to implement, yet only one may suc-cessfully attain higher organizational performancebecause of differences in the implementation pro-cess. Implementing these HRM practices is not aneasy task (Pfeffer, 1994); hence, a future longitudinalstudy could focus on the dynamic nature of the HRMpractices and uncover the challenges of the imple-mentation process at the plant level. A well-designedresearch study using longitudinal or panel data canalso better address the issue of causality.

Contingent compensation (CONTCOMP) wasfound to be insignificant for the intangible perfor-mance measure. Based on the literature, we specu-lated that employees might have perceived that theywere 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 theintangible performance measure. Future researchcan investigate when and why employees perceivecontingent compensation as controlling rather thanmotivating and how this ill effect can be minimized.Existing literature suggests that the level of trust andtype of relationship between superior and subordinatemay determine whether or not an incentive will beperceived as controlling by the subordinate (Kohn,1993b; Taylor, 1989).

Due to the limitations of our data, we did notinvestigate the impact of organizational strategy onthese HRM practices. Further research is needed tounderstand how an organization’s strategic contextinfluences the choice of HRM practices and its im-pact on performance. Also, whether these findings aregeneralizable across country and/or industry needs


to be investigated. Despite the compelling theoreticalargument, our study failed to show that HR practicesare synergistic.Delery and Doty (1996)have alsoreported similar results. Future study may shed somelight on this matter by theoretically deriving and em-pirically testing several context specific ideal-typeHRM systems.

Traditionally, the operations management literaturehas paid little attention to human resources issues.The present study brings some of these issues intofocus in the context of manufacturing plants operat-ing in different countries and industries. These issuescannot be resolved by isolated efforts made by oper-ations managers or human resource managers. Theircombined and synchronized efforts are needed. Ourstudy provides empirical validation for the efficacy ofthe seven HRM practices proposed byPfeffer (1998).Although this was the focal research issue, the find-ings and implications of our study go beyond justtesting the potency of Pfeffer’s seven HRM practices.

Appendix A

Scales used to measure HRM practices

Variable Scales Item questions

MFGHRFIT, α = 0.80 Manufacturing andhuman resources fit

The human resources department communicates closelywith manufacturing when writing job descriptionsJob design at this plant is closely coordinated withmanufacturingThe human resources department has a close andpositive working relationship with manufacturingStaffing, training and development of employees isclosely coordinated with manufacturingManufacturing works well with human resources staffwhen changes take place in the manufacturing processHuman resources staff knows what manufacturingconsiders important in the training of employees for newskills

BEHAVIOR, α = 0.89 Behavior and attitude We use attitude/desire to work in a team as a criterion inemployee selectionWe use problem-solving aptitude as a criterion inemployee selectionWe use work values and behavioral attitudes as acriterion in employee selectionWe select employees who can provide ideas to improvethe manufacturing process

Specifically, the present study investigates the mediat-ing effect of organizational commitment which helpsus better understand the nature of the relationship be-tween HRM practices and organizational performance.This study also evaluates HRM practices taking intoaccount country and industry contexts, thus makingthe findings generalizable across countries and indus-tries. Lastly, we empirically validate an ideal-typeHRM system for a manufacturing plant. The find-ings of this study are expected to help operations andhuman resource managers recognize the potential ofthese seven HRM practices and assist them in design-ing HRM systems at the plant level to gain superiorperformance.

Acknowledgements

The first author appreciates the faculty researchgrant provided by the St. Cloud State University.


Appendix A (Continued)


We select employees who are able to work well in smallgroups

TEAMS, α = 0.91 Team activities During problem solving sessions, we make an effort toget all team members’ opinions and ideas before makinga decisionOur plant forms teams to solve problemsIn the past 3 years, many problems have been solvedthrough small group sessionsProblem solving teams have helped improvemanufacturing processes at this plantEmployee teams are encouraged to try to solve theirproblems as much as possible

INTERACTa, α = 0.89 Interaction facilitation Supervisors encourage the persons who work for themto work as a teamSupervisors encourage people who work for them toexchange opinions and ideasSupervisors frequently hold group meetings where thepeople who work for them can really discuss thingstogether

INCENTOB, α = 0.92 Incentives to meetobjectives

Our incentive system encourages us to vigorously pursueplant objectivesThe incentive system at this plant is fair at rewardingpeople who accomplish plant objectivesOur reward system really recognizes the people whocontribute the most to our plantOur incentive system at this plant encourages us to reachplant goalsOur incentive system is at odds with our plantgoalsb

Persons (and/or teams) who achieve plant goals arerewarded the same as those who do not achieve plantgoalsb

JOBSKILL, α = 0.78 Training on job skills Our plant has a low skill level compared with ourindustryb

At this plant, some employees lack important skillsb

Plant employees receive training and development inwork-place skills on a regular basisThe management at this plant believes thatcontinual training and upgrading of employees’skills is importantEmployees at this plant have skills that are aboveaverage in this industry


Appendix A (Continued)


MULTFUN, α = 0.85 Training inmultiple functions

Employees receive training to performmultiple tasksEmployees at this plant learn how to perform avariety of tasks/jobsThe longer an employee has been at this plant, themore tasks or jobs that employee learns to performEmployees are cross trained at this plant so thatthey can fill in for others if necessaryAt this plant, employees only learn how to do onejob/taskb

At this plant, employees are encouraged to learnskills in depth, rather than develop a broad skillbaseb

STRATCOM,α = 0.92 Communication ofstrategy

In our plant, goals, objectives and strategies arecommunicated to meStrategies and goals are communicated primarilyto managersb

I know how we are planning to be competitive atthis plantI understand the long-run competitive strategy ofthis plant

FEEDBACK, α = 0.88 Feedback onperformance

Charts showing defect rates are posted onthe shop floorCharts showing schedule compliance are posted onthe shop floorCharts plotting the frequency of machinebreakdowns are posted on the shop floorI am never told whether I am doing a good jobb

Information on quality performance is readilyavailable to employeesInformation on productivity is readily available toemployeesMy manager never comments about the quality ofmy workb

α = Cronbach’s 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.


Appendix B

Intangible performance measure


COMMITa, α = 0.89 Organizationalcommitment

I am willing to put in a great deal of effort beyondthat normally expected in order to help thisorganization be successfulI talk up this organization to my friends as a greatorganization to work forI would accept almost any type of job assignmentin order to keep working for this organizationI find that my values and the organization’s valuesare very similarI am proud to tell others that I am part of thisorganizationThis organization really inspires the best in me inthe way of job performanceI am extremely glad that I chose this organizationto work for over others I was considering at thetime I joinedI really care about the fate of this organizationFor me, this is the best of all organizations forwhich to work

α = Cronbach’s alpha.a Mowday and Steers (1979).

References

Anderson, J.C., Rungtusanatham, M., Schroeder, R.G., 1994. Atheory 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 ManagementJournal 37 (3), 670–687.

Barney, J., 1991. Firm resources and sustained competitive advan-tage. Journal of Management 17, 99–120.

Barney, J., 1995. Looking inside for competitive advantage. Aca-demy of Management Executive 9 (4), 49–61.

Baron, R.M., Kenny, D.A., 1986. The moderator-mediator varia-ble distinction in social psychological research: conceptual,strategic, and statistical considerations. Journal of Personalityand Social Psychology 51 (6), 1173–1182.

Becker, B., Gerhart, B., 1996. The impact of human resourcemanagement on organizational performance: progress and pros-pect. Academy of Management Journal 39 (4), 779–801.

Boxall, P., Steeneveld, M., 1999. Human resource strategy andcompetitive advantage: a longitudinal study of engineeringconsultancies. Journal of Management Studies 36 (4), 443–444.

Coff, R.W., 1997. Human assets and management dilemmas: cop-ing with hazards on the road to resource-based theory. Academyof Management Review 22 (2), 374–402.

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-contingentrewards and controls on intrinsic motivation. OrganizationalBehavior and Human Performance 3, 217–229.

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), 802–835.

Drazin, R., Van de Ven, A.H., 1985. Alternative forms of fit incontingency theory. Administrative Science Quarterly 30, 514–539.

Dyer, L., Reeves, T., 1995. Human resource strategies and firmperformance: what do we know and where do we need togo? The International Journal of Human Resource Management6 (3), 656–670.

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 manufacturingproject. In: Fedor, D.B., Ghosh, S. (Eds.), Advances in the


Management of Organizational Quality, Vol. 1, JAI Press,Greenwich, CT, pp. 141–184.

Galpin, T.J., Herndon, M., 2000. The Complete Guide to Mergersand Acquisitions. Jossey-Bass Publishers, San Francisco, CA.

Gerhart, B., Milkovich, G.T., 1990. Organizational differences inmanagerial compensation and firm performance. Academy ofManagement Journal 33, 663–691.

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 CompetitiveEdge: Competing through Manufacturing. Wiley, New York.

Henderson, J.C., Lee, S., 1992. Managing I/S design teams: acontrol theories perspective. Management Science 38, 757–777.

Hill, T., 1989. Manufacturing Strategy Text and Cases. Irwin,Homewood, IL.

Hofstede, G., 1980. Culture’s Consequences. Sage, Beverly Hills,CA.

Huselid, M.A., 1995. The impact of human resource managementpractices on turnover, productivity, and corporate financial per-formance. Academy of Management Journal 38 (3), 635–672.

Huselid, M.A., Jackson, S.E., Schuler, R.S., 1997. Technical andstrategic human resource management effectiveness as deter-minants of firm performance. Academy of Management Journal40 (1), 171–188.

Ichniowski, C., Shaw, K., 1999. The effects of human resourcemanagement systems on economic performance: an internatio-nal comparison of US and Japanese plants. ManagementScience 45 (5), 704–721.

Ichniowski, C., Shaw, K., Prennushi, G., 1993. The effect of humanresource management practices on productivity. ColumbiaUniversity, unpublished paper.

Jayaram, J., Droge, C., Vickery, S.K., 1999. The impact of humanresource management practices on manufacturing performance.Journal of Operations Management 18, 1–20.

Kalleberg, A.L., Moody, J.W., 1994. Human resource managementand organizational performance. American Behavioral Scientist37 (7), 948–962.

Kathuria, R., Partovi, F.Y., 1999. Work force management prac-tices for manufacturing flexibility. Journal of Operations Man-agement 18, 21–39.

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), 24–40.

Kohn, A., 1993a. Why incentive plans cannot work. HarvardBusiness Review 71, 54–60.

Kohn, A., 1993b. Punished by Rewards: The Trouble with GoldStars, Incentive Plans, A’s, Praise, and Other Bribes. HoughtonMifflin, Boston, MA.

Lado, A.A., Wilson, M.C., 1994. Human resource systems andsustained competitive advantage: a competency-based pers-pective. Academy of Management Journal 19 (4), 699–727.

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), 32–41.

Lei, D., Hitt, M.A., 1995. Strategic restructuring and outsourcing:the effect of mergers and acquisitions and LBOs on buildingfirm skills and capabilities. Journal of Management 21, 835–859.

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), 31–48.

Lubatkin, M., Schweiger, D., Weber, Y., 1999. Top managementturnover in related M&A’s: an additional test of the theory ofrelative standing. Journal of Management 25 (1), 55–73.

MacDuffie, J.P., 1995. Human resource bundles and manufacturingperformance: organizational logic and flexible productionsystems in the World Auto Industry. Industrial and LaborRelations Review 48 (2), 197–221.

MacDuffie, J.P., Kochan, T.A., 1995. Do US firms invest less inhuman resources? Training in the world auto industry. IndustrialRelations 34 (2), 147–168.

Morishima, M., 1995. Embedding HRM in a social context. BritishJournal of Industrial Relations 33 (4), 617–640.

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, NewYork.

Osterman, P., 1994. How common is workplace transformation andwho adopts it? Industrial and Labor Relations Review 47 (2),173–188.

Pfeffer, J., 1994. Competitive Advantage Through People. HarvardBusiness School Press, Boston, MA.

Pfeffer, J., 1998. Seven practices of successful organizations.California Management Review 40 (2), 96–124.

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), 374–402.

Ryan, R., 1982. Control and information in the intrapersonalsphere: an extension of cognitive evaluation theory. Journal ofPersonality and Social Psychology 43, 450–461.

Salk, J.E., Brannen, M.Y., 2000. National culture, networks andindividual influence in a multinational management team.Academy of Management Journal 43 (2), 191–202.

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 strategieswith human resource management practices. Academy of Man-agement Executive 1 (3), 207–219.

Schuler, R.S., MacMillan, I.C., 1984. Gaining competitive advan-tage through human resource management practices. HumanResource Management 23, 241–255.

Scudder, G.D., Hill, C.A., 1998. A review and classification ofempirical research in operations management. Journal of Oper-ations Management 16, 91–101.


Snell, S.A., Dean Jr., J.W., 1992. Integrated manufacturing andhuman resource management: a human capital perspective.Academy of Management Journal 35 (3), 467–504.

Soderquist, K., Motwani, J., 1999. Quality issues in lean productionimplementation: a case study of a French automotive supplier.Total Quality Management 10 (8), 1107–1122.

Taylor, R.G., 1989. The role of trust in labor-management relations.Organization Development Journal, Summer, 85–89.

Taylor, J.C., Bowers, D.G., 1972. Survey of Organizations:A Machine Scored Standardized Questionnaire Instru-ment. Institute for Social Research, University of Michigan,Michigan.

Vickery, S.K., Droge, C., Markland, R.E., 1997. Dimensions ofmanufacturing strength in the furniture industry. Journal ofOperations Management 15, 317–330.

Wheelwright, S.C., 1978. Reflecting corporate strategy in manu-facturing decisions. Business Horizons 21 (1), 57–66.

Williamson, O.E., 1975. Markets and Hierarchies: Analysis andAntitrust Implications. Free Press, New York.

Womach, J.P., Jones, D., Roos, D., 1990. The Machine ThatChanged the World. Rawson-Macmillan, New York.

Youndt, M.A., Snell, S.A., Dean, J.W., Lepak, D.P., 1996. Humanresource management: manufacturing strategy and firm perfor-mance. Academy of Management Journal 39 (4), 836–866.

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