the impact of human resource management practices on manufacturing performance

20
Ž . Journal of Operations Management 18 1999 1–20 www.elsevier.comrlocaterdsw The impact of human resource management practices on manufacturing performance Jayanth Jayaram a, ) , Cornelia Droge b,1 , Shawnee K. Vickery b,2 a Department of Decision Sciences, Charles H. Lundquist College of Business, UniÕersity of Oregon, Eugene, OR 97403, USA b Department of Marketing and Supply Chain Management, N370 North Business Complex, Eli Broad Graduate School of Management, Michigan State UniÕersity, East Lansing, MI 48824-1046, USA Received 2 January 1997; accepted 13 April 1999 Abstract Ž . A human resource management HRM analysis framework is proposed and tested using data from first tier suppliers to the Big 3 in North America. Relationships among underlying dimensions of human resource management practices and manufacturing performance are examined. The study found support for the proposed framework, suggesting that human resource management practices can be grouped into five distinct factors, four of which are associated with specific Ž . manufacturing competitive dimensions quality, flexibility, cost and time . The remaining HRM factor is generic. The four priority-specific HRM factors are strongly related to their respectiÕe manufacturing performance dimensions. q 1999 Elsevier Science B.V. All rights reserved. Keywords: Automotive supplier industry; Human resource management; Manufacturing performance; Factor score regression 1. Introduction Global competition, shorter product life cycles, and volatile product and market environments have contributed to the complexity faced by businesses and industries as the new millennium approaches. Traditional competitive mechanisms have become less effective as competitors meet or copy each Ž . other’s corporate initiatives Ulrich, 1987 . In re- sponse, firms constantly search for newer sources of ) Corresponding author. Tel. q1-541-346-3407; fax: q1-541- 346-3341; e-mail: [email protected] 1 Tel.: q1-517-353-6381; fax: q1-517-432-1112. 2 Tel.: q1-517-353-6381; fax: q1-517-432-1112. competitive advantage, one of the most important Ž being human resource management HRM Schuler . and MacMillan, 1984 . Recent conceptual and empir- ical articles have examined the impact of human resource management on the oÕerall competitive Ž . performance of a firm Arthur, 1994; Huselid, 1995 . However, given the importance and complexities of human resource decisions, the existing body of work still falls short of comprehensively examining key research questions in human resource management Ž . Becker and Gerhart, 1996 . Despite claims that innovative human resource practices can boost firm-level performance and na- tional competitiveness, few studies have been able to confirm this relationship empirically, and still fewer have been able to systematically describe the manner 0272-6963r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. Ž . PII: S0272-6963 99 00013-3

Upload: jayanth-jayaram

Post on 03-Jul-2016

217 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: The impact of human resource management practices on manufacturing performance

Ž .Journal of Operations Management 18 1999 1–20www.elsevier.comrlocaterdsw

The impact of human resource management practices onmanufacturing performance

Jayanth Jayaram a,), Cornelia Droge b,1, Shawnee K. Vickery b,2

a Department of Decision Sciences, Charles H. Lundquist College of Business, UniÕersity of Oregon, Eugene, OR 97403, USAb Department of Marketing and Supply Chain Management, N370 North Business Complex, Eli Broad Graduate School of Management,

Michigan State UniÕersity, East Lansing, MI 48824-1046, USA

Received 2 January 1997; accepted 13 April 1999

Abstract

Ž .A human resource management HRM analysis framework is proposed and tested using data from first tier suppliers tothe Big 3 in North America. Relationships among underlying dimensions of human resource management practices andmanufacturing performance are examined. The study found support for the proposed framework, suggesting that humanresource management practices can be grouped into five distinct factors, four of which are associated with specific

Ž .manufacturing competitive dimensions quality, flexibility, cost and time . The remaining HRM factor is generic. The fourpriority-specific HRM factors are strongly related to their respectiÕe manufacturing performance dimensions. q 1999Elsevier Science B.V. All rights reserved.

Keywords: Automotive supplier industry; Human resource management; Manufacturing performance; Factor score regression

1. Introduction

Global competition, shorter product life cycles,and volatile product and market environments havecontributed to the complexity faced by businessesand industries as the new millennium approaches.Traditional competitive mechanisms have becomeless effective as competitors meet or copy each

Ž .other’s corporate initiatives Ulrich, 1987 . In re-sponse, firms constantly search for newer sources of

) Corresponding author. Tel. q1-541-346-3407; fax: q1-541-346-3341; e-mail: [email protected]

1 Tel.: q1-517-353-6381; fax: q1-517-432-1112.2 Tel.: q1-517-353-6381; fax: q1-517-432-1112.

competitive advantage, one of the most importantŽ . Žbeing human resource management HRM Schuler

.and MacMillan, 1984 . Recent conceptual and empir-ical articles have examined the impact of humanresource management on the oÕerall competitive

Ž .performance of a firm Arthur, 1994; Huselid, 1995 .However, given the importance and complexities ofhuman resource decisions, the existing body of workstill falls short of comprehensively examining keyresearch questions in human resource managementŽ .Becker and Gerhart, 1996 .

Despite claims that innovative human resourcepractices can boost firm-level performance and na-tional competitiveness, few studies have been able toconfirm this relationship empirically, and still fewerhave been able to systematically describe the manner

0272-6963r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved.Ž .PII: S0272-6963 99 00013-3

Page 2: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–202

in which human resource practices influence perfor-mance. Innovative HR practices are often studied ina vacuum with more attention paid to isolating theeffects of individual practices than to understandinghow different HR practices interact to reinforce oneanother or how they are linked to business and

Ž .functional strategies MacDuffie, 1995 . Moreover,prior work has shown that examining the impact ofindividual practices on performance is misleadingbecause individual practices obviate the effect of agroup of HR variables that comprise the systemŽ .Ichniowski et al., 1997 . Other researchers havesuggested that a ‘bundle’ of inter-related, overlap-ping HR practices provides several non-exclusive

Žmodes of influencing performance Hackman, 1985;.MacDuffie, 1995 .

We examine the impact of sets or ‘bundles’ ofhuman resource practices on strategic dimensions ofmanufacturing performance. The purpose of the re-search is three-fold. First, we identify key dimen-

Ž .sions of human resource management HRM prac-tices from the literature and propose a conceptualmodel for analyzing the deployment of HRM prac-tices within firms. Second, we examine the effects ofindiÕidual HRM items on individual manufacturing

Žperformance dimensions i.e., cost, quality, flexibil-. Žity, and time . The unit of analysis is at the firm or.business unit level, and thus it is appropriate to

select these four competitive priorities because theyhave been described in the literature as key mea-sures. While there is merit in investigating the im-pact of HRM items on finer details of manufacturing

Žperformance such as, conformance quality, design.quality, and durability instead of overall quality , we

have chosen not to do so because our research intentis to determine what affects strategic dimensions ofmanufacturing performance. Finally, we test our con-ceptual model and examine linkages between HRM

Ždimensions or ‘bundles’ i.e., groups of inter-related.HRM items and manufacturing performance.

This paper is organized as follows. First, theoperations management and HRM literatures are re-viewed to identify key manufacturing performancedimensions and to specify a set of human resourcemanagement practices that should impact manufac-turing performance. Five major categories of HRMpractices are identified. Propositions are introducedthat focus on the relationship between individual

HRM practices and manufacturing performance, thedeployment pattern of HRM practices within firms,and the relationship between groups or ‘bundles’ ofHRM practices and manufacturing performance. Theresearch methodology is described next, includingthe sampling procedure and measurement issues. Re-lationships between individual HRM items and man-ufacturing performance dimensions are explored us-ing correlation analysis. Factor analysis is then usedto reduce to underlying dimensions or ‘bundles’ thevarious HRM practices identified from the literature.Next, factor score regression analyses are used toexamine the relationships between HRM factors andmanufacturing performance. Last, the results of thestudy are discussed and their managerial implicationsare explored.

2. Literature review: identification of key con-structs

2.1. Manufacturing performance dimensions

The number of dimensions comprising manufac-turing performance has been the subject of much

Ž .debate over the years. Skinner 1974 described sev-eral, including short delivery cycles, superior qualityand reliability, dependable deliveries, fast new prod-uct development, flexibility in volume changes, and

Ž .low cost. Wheelwright 1978 focused on efficiency,dependability, quality, and flexibility, and later,

Ž .Hayes and Wheelwright 1984 changed efficiencyto cost. Three years later, Krajewski and RitzmanŽ .1987 identified five manufacturing competitive di-mensions: cost, high performance design, consistentquality, on-time delivery, product flexibility, and

Ž .volume flexibility. In a related vein, Hill 1989outlined a set of ‘‘order-winning criteria’’ that fallunder the broad auspices of manufacturing. These

Žcriteria included: cost, product quality conformance.to specifications and reliability, delivery speed, de-

Žlivery reliability, and volume flexibility ability to.respond to increases in demand .

In a comprehensive review of the literature, LeongŽ .et al. 1990 contended that five dimensions are the

most critical: quality, delivery, cost, flexibility andinnovativeness. Around the same time, Ferdows and

Ž .DeMeyer 1990 focused on four generic manufac-

Page 3: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–20 3

turing capabilities, namely, cost efficiency, quality,dependability and flexibility in one of the earliestempirical studies of manufacturing competitive di-

Žmensions. In two recent studies by the same au-. Ž . Ž .thors , Ward et al. 1995 and Ward et al. 1998

factor analyzed items relating to manufacturing com-petitive priorities into the four dimensions of cost,quality, time and flexibility. Even more recently,

Ž .Vickery et al. 1996 found that manufacturing per-formance in the furniture industry consisted of four

Ž .distinct dimensions: delivery, value qualityrcost ,flexibility, and innovation. However, a related studyalso indicated that while manufacturing had the lion’sshare of responsibility for delivery, quality, cost, andflexibility, it had a much smaller degree of responsi-

Ž .bility for innovation Droge et al., 1994 . Vokurka etŽ .al. 1998 also empirically investigated the impact of

different manufacturing improvement techniques onthe competitive performance capabilities of cost,quality, delivery flexibility and time. Delivery flexi-bility was meant to tap the firm’s capability ofmeeting promised delivery dates, which is a functionof timing and variety. In this study, time capturedmanufacturing throughput time or speed. In the hu-

Ž .man resources literature, Youndt et al. 1996 opera-tionalized dimensions of manufacturing strategy per-formance as cost, quality, delivery flexibility, andscope flexibility. Delivery flexibility was defined interms of the timing performance of releasing newproducts and making on-time deliveries. Scope flexi-bility was defined in terms of variety, such as adjust-ing product mix, handling non-standard items andmaking products in small lots to allow for highervariety.

The purpose of this research is not to delineate orexamine every conceivable dimension of manufac-turing performance. Rather, we have focused on theones most strongly supported by the literature,namely, the two traditional measures of cost andquality, as well as flexibility and time. It may benoted that this is a single-industry study and in orderto provide comparable data on manufacturing perfor-mance, respondents were asked to provide a rating oftheir firm’s performance relative to its major com-petitors for each of the four measures of manufactur-ing performance. In the literature review on humanresource management practices that follows, we fo-cus particular attention on those HRM practices that

impact cost, quality, flexibility, and time perfor-mance.

2.2. Human resource management practices

Several human resource management practiceshave been touted as key factors affecting both manu-facturing performance and competitive advantage.

Ž .Our research focuses on: 1 top management com-Ž . Ž .mitment; 2 communication of goals; 3 employeeŽ . Ž .training; 4 cross functional teams; 5 cross train-

Ž . Ž .ing; 6 employee autonomy; 7 employee impact;Ž . Ž . Ž .8 broad jobs; 9 open organizations; and 10effective labor management relations. There is con-siderable consensus in the HRM literature for identi-

Žfying most of these items as ‘best practices’ Freundand Epstein, 1984; Delaney et al., 1989; Arthur,1994; Pfeffer, 1994; Huselid, 1995; and MacDuffie,

.1995 . Note that the first four are closely linked withŽspecific manufacturing performance goals e.g., top

management commitment to flexibility; employee.training for quality , while the other HRM practices

listed above are less tightly linked with specificŽperformance objectives e.g., broad jobs, labor man-

.agement relations . This critical distinction framesthe literature review that follows.

2.2.1. HRM practices linked to specific manufactur-ing performance goals

2.2.1.1. Top management commitment. MacDuffieŽ .1995 observed that firms with flexible productionplants consistently outperformed firms with standardmass production plants on the measures of productiv-ity and quality performance. This suggests that a topmanagement commitment to flexibility affects multi-ple dimensions of manufacturing performance. Simi-larly, top management commitment to quality was

Žsignificantly related to quality performance Powell,.1995 . In a meta analysis of 70 management by

Ž .objectives MBO studies, Rodgers and HunterŽ .1991 reported that the most essential success factorfor implementing MBO programs was top manage-ment commitment. The results showed that when topmanagement commitment to specific performanceobjectives was high, firms experienced an averagegain in productivity of 56%. When top management

Page 4: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–204

commitment to organizational objectives was low,the average gain in productivity was only 6%. AnsariŽ .1986 found that top management commitment toJIT purchasing implementation was critical for JITpurchasing success. Finally, Cooper and Klein-

Ž .schmidt 1995 found that top management commit-ment to new products was significantly related tonew product lead time performance.

2.2.1.2. Communication of goals. In a case study ofŽ .Dow Corning, Seward 1992 identified communica-

tion of quality goals as critical for the successfulŽ .implementation of total quality management TQM .

Ž .Zhu et al. 1994 conducted a critical review ofpublished studies of key success factors in JIT im-plementation and found that communication of JIT-related goals was included in several articles. In thenew product environment, Rosenthal and TatikondaŽ .1993 reported that clear communication ofproject-related goals significantly reduced productdevelopment lead times.

Ž .2.2.1.3. Employee training. Bartel 1994 establisheda link between the use of training programs andproductivity growth. In a randomized cross sectionalsurvey of 747 managers of manufacturing firms,

Ž .Dreyfus and Vineyard 1996 found that employeetraining and education was significantly related to

Ž .product quality performance. Magnan et al. 1995reported that employee training was significantlyrelated to flexibility performance in the furnitureindustry. Employee training in the form of JIT train-ing was one of the critical factors of JIT program

Ž .success Im et al., 1994 . Kinnie and StaughtonŽ .1991 examined the role of HRM in implementingmanufacturing strategies in 7 batch manufacturing

Žfirms. They found that employee training e.g., edu-cational programs, technical training and role change

.training was one of the three critical HRM practicesthat significantly contributed to success in imple-menting manufacturing strategy. Quality relatedtraining has been emphasized in the literature as akey human resource element of total quality manage-

Ž .ment TQM and also facilitates the effective use ofŽadvanced manufacturing technologies Snell and

.Dean, 1992 . Similarly, The Malcolm Baldrige Awardcategory of ‘‘human resource deployment and man-agement’’, emphasizes employee education and

training in quality as a critical enabler of qualityŽ .success Award Criteria, 1994 .

2.2.1.4. Cross functional teams. High performancework teams were significantly related to quality per-formance as measured by defect rates even after

Žcontrolling for the learning effect Banker et al.,.1995 . While the use of quality circles is most often

associated with quality improvement, Katz et al.Ž .1983 found that the use of cross functional teamsin the form of quality circles increased productivity.Cross functional teams were also found to be signifi-cantly related to flexibility performance and new

Žproduct performance Cooper and Kleinschmidt,.1995; Magnan et al., 1995 . In a recent exploratory

study of 15 software package companies, all thesurvey respondents reported that cross-functionalteams were important for cycle time reductionŽ .Carmel, 1995 . Northern Telecom’s use of cross-functional product teams to improve new productintroduction and development times helped achieve20–50% reduction in new product introduction time,

Ždepending upon the division and product Merrills,.1989 .

2.2.2. Other HRM practicesSeveral authors have suggested that cross training

contributes to a variety of strategic goals. In a studyŽ .of flexible manufacturing system FMS implementa-

tion in eight companies, Graham and RosenthalŽ .1986 found that cross training of workers is animportant factor in successful FMS implementation.

Ž .Polakoff 1991 suggested that cross training of em-ployees improves manufacturing performance via re-duction in cycle times, elimination of non-productivelabor, and reduction in inventory costs. Cross train-ing of workers was also important for improving

Ž .quality performance Moras et al., 1994 . In a reviewŽ .of published JIT studies, Zhu et al. 1994 found that

over half of the cases reported cross training as a keyelement of JIT implementation.

Employee autonomy and employee impact havealso been examined in the literature. Dreyfus and

Ž .Vineyard 1996 found that employee autonomy andemployee impact were significantly related to prod-uct quality performance. Similarly, in the furniture

Ž .industry, Magnan et al. 1995 found that employeeautonomy and employee impact were significantly

Page 5: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–20 5

related to flexibility performance. In a recent empiri-cal study, employee autonomy and employee impactwere shown to be underlying dimensions of em-

Ž .ployee empowerment Spreitzer, 1995 . Furthermore,Ž .Powell 1995 found that employee empowerment

was significantly related to both TQM performanceŽ .and overall firm performance. MacDuffie 1995 alsoŽfound that participative work systems which in-

cluded items such as employee involvement, em-.ployee suggestions, and employee empowerment

were significantly related to both quality perfor-mance and productivity.

Some authors have examined the role of structuralvariables such as broad jobs and open organizationson competitive performance. In a survey of manufac-

Ž .turing firms, Powell 1995 found that broad jobsŽand open organizations as indicated by an open

.culture were significantly related to quality perfor-mance. Similarly, in an empirical study of plantperformance in the auto industry, Keefe and KatzŽ . Ž1990 found that broad jobs as indicated by a

.combination of job classifications was significantlyrelated to quality performance. Finally, Cooper and

Ž .Kleinschmidt 1995 found that an entrepreneurialclimate, which is often associated with open organi-zations, was significantly related to new productperformance.

Several authors have examined the impact oflabor–management relations on performance. BusheŽ .1988 conducted a longitudinal study of five manu-facturing plants and found that in two of the plantsan improvement in labor–management relations im-

Ž .proved product quality performance. Ansari 1986found that effective labor–management relations wasone of the critical operational factors for JIT pur-

Ž .chasing success. Cutcher-Gershenfield 1991 re-ported that firms adopting ‘‘transformational’’ laborrelations — those emphasizing cooperation and dis-pute resolution — had lower costs, less scrap andhigher productivity than did firms using ‘‘traditional’’adversarial labor relations practices. Finally, on aglobal scale, labor–management relations was one ofthe five major activities that contributed to success inproductivity measures in Japanese firms in the auto-

Ž .motive industry Otis, 1993 .In summary, the more general human resource

management practices examined herein are crosstraining, employee autonomy, employee impact,

broad jobs, open organizations and effective labor–management relations. They were chosen becausethe literature identifies them to be critical determi-nants of various aspects of manufacturing perfor-mance, even though they are not as tightly linked tospecific manufacturing performance dimensions asare top management commitment, communication ofgoals, employee training, and cross functional teams.In view of the literature pertaining to both categoriesof HRM items, we propose the following:

Proposition 1: There are positive relationships be-tween individual HRM practices and manufacturingperformance.

3. A proposed human resource management ini-tiative framework

The review of the literature suggests that thebroad array of human resource management prac-tices affecting manufacturing performance can be

Ž .grouped into five major categories: 1 Top manage-Ž . Ž .ment commitment; 2 Communication of goals; 3Ž .Employee training; 4 Cross functional teams; and

Ž .5 General HRM practices. This grouping suggests aconceptual scheme as highlighted in Fig. 1. Thisfigure implies that human resource managementpractices can be analyzed using the five broad group-ings of practices for realizing four broad strategic

Ž .dimensions cost, quality, flexibility, and time . Notethat we do not disaggregate these strategic dimen-sions further into their respective sub-dimensions;for instance, we do not measure volume flexibilityversus mix flexibility but rather oÕerall flexibility.The strategic SBU goal is flexibility which in partic-ular plants may mean volume or mix flexibility, orboth. Our focus is on overall HRM initiatives andoverall strategic SBU goals.

Based on the human resource analysis frameworkin Fig. 1, three orientations for deploying humanresource management practices can be identified:

Ž .row-wise, column-wise and cell-wise or cellular . Inthe row-wise orientation, firms use multiple humanresource management practices for achieving spe-cific strategic dimensions. For example, firms desir-ing to improve quality performance may choose touse cross functional teams for quality and top man-

Page 6: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–206

Fig. 1. Conceptual model.

agement commitment to quality. On the other hand,in the column-wise orientation, firms use specifichuman resource management practices for achievingmultiple strategic dimensions. For instance, crossfunctional teams may be used for multiple purposessuch as flexibility, quality improvement and lowercosts. Finally, in the cellular approach, specific hu-man resource management practices are geared to-wards achieving specific strategic dimensions. Forexample, firms seeking to improve overall qualityperformance would deploy cross functional teams for

Ž .quality i.e., to support quality goals .The review of the literature suggests that the

cellular approach has not been examined in a com-prehensive manner in any single study. As there canbe a huge number of possible cells, the cellularapproach offers little help in terms of reducing thebroad array of human resource management prac-tices into a manageable set. Thus, the question be-comes whether these HRM practices are organized in

Ža manner driven by the practice itself i.e., column-.wise or by the strategic manufacturing goal behind

Ž .the HRM practices i.e., row-wise . It may be notedthat this proposition, as illustrated in Fig. 1 does not

Ž .specify item-to-factor s correspondence, as wouldbe necessary for the confirmatory approach. Indeed,it is premature to conduct a confirmatory analysisand accordingly, we have termed our expectations aspropositions as opposed to hypotheses.

Our research focus is on whether the split will berow or column-wise as shown in Fig. 1. We suggestthat human resource management practices are bestgrouped around strategic manufacturing goals. Thisreflects the pervasive theme in the strategic manage-ment literature that ‘‘strategy precedes structure’’ŽMintzberg, 1979; Miller, 1981; Drazin and Van deVan, 1985; Ginsberg and Venkatraman, 1985; Miller

.and Droge, 1986; Venkatraman, 1989 . If we accept¨the classic ordering of ‘‘strategy and then structure’’,

Žit follows that HRM practices seen as the structur-.ing of human resources should factor analyze along

strategic underlying dimensions rather than alongdimensions that describe the type of HRM initiative.Thus:

Proposition 2: Human resources initiatives can begrouped according to the manufacturing performancepriority they are meant to support.

The above proposition suggests that human re-source management practices can be grouped intoHRM-Cost, HRM-Quality, HRM-Flexibility, andHRM-Time. These should be interpreted as ‘‘humanresource management practices to support cost re-duction goals,’’ ‘‘human resource management prac-tices to support quality strategic goals,’’ and so on.Our final proposition focuses on these HRM factors:We propose that these underlying factors will also berelated to manufacturing performance. In particular,

Page 7: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–20 7

Ž .we wish to determine if HRM-Cost for example isrelated to manufacturing cost performance. Thus:

Proposition 3: There is a positive relationship be-Ž .tween each HRM factor e.g., HRM-Cost and mea-

Žsures of manufacturing performance e.g., cost per-.formance ; i.e., the HRM factors found from the

factor analysis are significant predictors of manufac-turing performance.

From the literature cited above, two things can beŽ .observed: 1 HRM practices can exhibit a signifi-

cant, positive relationship with more than one di-Ž .mension of manufacturing performance; and 2

HRM practices are usually discussed individually.One goal in this research is to determine whether

Žsets or ‘bundles’ of HRM initiatives from factor.analysis related to Proposition 2 are related to one

or more dimensions of manufacturing performanceŽ .Proposition 3 . We also examine the traditionalHRM-item to manufacturing performance measurerelationship. Proposition 1 examines the HRM initia-tives one by one and thus to some extent replicatespast work.

4. Research methodology

4.1. The sampling procedure and sample

The study focused on first tier suppliers to the‘‘Big Three’’ in North America. The populationframe consisted of the top 150 first tier suppliers interms of annual sales. The list of companies wasprovided by industry experts from the Automotive

Ž .Industry Action Group AIAG . AIAG is a profes-sional association with over 1000 members includingthe Big Three North American automobile manufac-

Ž .turers Actionline, 1995 .The research questionnaire, accompanied by an

informational letter, was mailed to the CEOs of allfirms included in the population frame. The letterstated the purpose of the project and that a memberof the research team would be calling soon. CEOs of

Ž .strategic business units SBUs or individual firmswere instructed to complete the survey for their SBUor firm. CEOs of multiple business units were in-structed to select one of their SBUs to participate inthe study and to forward the research questionnaire

to the CEO of that unit. It may be noted that onlyupper management can evaluate the firm-wide fac-tors of interest: the tools and practices are broad-based initiatives that are not defined with referenceto a certain project or plant.

Repeated telephone calls were made to obtaindefinitive responses from CEOs regarding their par-ticipation. In the survey, respondents were asked tofill out their titles and contact addresses to receive acopy of the summary results. All respondents filledout this information and almost all of them wereeither a CEO or member of the top managementteam. We also called back the respondent to com-plete missing values and to confirm those responseswhich seemed to be questionable or illegible.

The final sample for the study consisted of 57firms. The response rate was approximately 39 per-cent. The response rate is high when compared toother empirical studies in operations management.Mean sales for the sample was US$501,516,415 witha standard deviation of US$637.46 million. The meannumber of employees was 2810.09 with a standarddeviation of 3431.07.

An examination of firms comprising the first tierof automobile suppliers reveals the diversity of prod-ucts manufactured. Firms in the sample ranged frommanufacturers of seating systems to manufacturers ofanti-lock braking systems. The manufacturing envi-ronments of the responding companies was also di-verse on average, 32% of the sample’s productionvolume was characterized by a production depart-

Žment organization or batch production similarequipment is organized into departments, and pro-duction flows in batches from department to depart-

.ment ; 30% was characterized as work cells, 24%was characterized as assembly lines; and 14% wascharacterized as continuous flow production. Thediversity of products and manufacturing environ-ments represented in our sample contributed to thegeneralizability of our research results.

4.2. Measurement issues

4.2.1. Validation of the research questionnaireThe unit of analysis was the individual firm or

Ž .strategic business unit SBU involved in manufac-turing and selling automotive systemsrcomponentsto North American OEMs. A panel of experts from

Page 8: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–208

AIAG assisted in ensuring completeness and clarityof meanings for the items. To engender a commonunderstanding of the questions, all items appearing inthe survey instrument were defined. Finally, theexpert panel assisted in the pilot testing of the surveyinstrument.

4.2.2. Interrater reliabilityTo assess the reliability of our measures, we

faxed a second, very abbreviated questionnaire to ouroriginal respondents. We asked them to have astrategically knowledgeable individual within theirfirms fill out the short-form questionnaire. To lessenthe burden on this second respondent, only a fewselected items from our original research question-naire appeared in this brief survey. However, all of

Žour department variables i.e., the overall measures.of manufacturing performance were represented.

4.2.3. Manufacturing performance measurementFour aspects of manufacturing performance were

measured in this study: cost reduction, quality im-provement, flexibility, and time reduction. The re-spondents were asked to provide a seven-point ratingof the firm’s performance relative to its major com-petitors for each item, where 1 represented ‘‘Poor’’

Ž .and 7 represented ‘‘Excellent’’ see Appendix A .ŽDescriptive statistics and correlations with p-val-

.ues of the manufacturing performance items areprovided in Table 1. Note that only time and flexibil-ity performance are significantly correlated.

As noted above, we collected data to assess theinterrater reliability of these four measures of overallmanufacturing performance. 25 of the original re-sponding companies participated in this follow-upsurvey. The results were extremely encouraging. Foroverall quality, overall flexibility, overall cost, and

Žoverall time, the correlations and p-values one-tail. Ž . Ž .tests were as follows: 0.451 0.009 ; 0.270 0.087 ;

Ž . Ž .0.359 0.033 and 0.379 0.025 . All correlationswere positive and all except one was significant at alevel of significance less than 0.05. However, eventhe exception was marginally significant at less than0.10.

Respondents were also asked to rate the impor-tance of each of the four dimensions of manufactur-ing performance using a seven-point scale with end-

Ž .points labelled ‘‘Least Important’’ s1 and ‘‘Ex-Ž .tremely Important’’ s7 . Results indicate that CEOs

judged quality and cost to be the most importantdimensions of manufacturing performance. Themeans were: 6.351 for quality, 6.280 for cost, 5.228for flexibility and 5.017 for time. These four impor-tance scores are uncorrelated with sales or the num-

Žber of employees all p-values are greater than.0.10 , indicating that firm size is unrelated to the

importance of these four performance dimensions.

Table 1Descriptive statistics and correlations of manufacturing performance items

Manufacturing performance items Mean Std. Dev. Correlations

1 2 3 4

1. Overall cost performance 5.053 1.245 rs 1nsps

2. Overall quality performance 5.772 0.982 rs 0.171 1ns 57ps 0.205

3. Overall flexibility performance 5.035 1.101 rs 0.142 0.090 1ns 57 57ps 0.293 0.505

UU4. Overall time performance 4.912 1.005 rs 0.089 0.106 0.261 1ns 57 57 57ps 0.509 0.433 0.050

Manufacturing performance items are on a 1 to 7 scale with 1s‘Poor’ and 7s‘Excellent’.

Page 9: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–20 9

4.2.4. Measurement of ‘extent of use’ of humanresource management practices

The survey instrument measured the extent of useof 22 human resource management practices. Re-spondents were asked to indicate the extent to whicheach of these practices was used by the firm to

Žsupport its overall business strategy see Appendix.A . If a practice was not used by a firm or SBU, the

respondent was asked to circle ‘‘Not Used.’’ Theextent of use scale was a seven-point scale withendpoints labelled ‘‘Extremely Low Use of Initia-

Ž .tive’’ s1 and ‘‘Extremely High Use of Initiative’’Ž .s7 . Means and standard deviations for the extentof use ratings for the 22 human resource manage-ment practices items are presented in Table 2.

Note the order of the first 16 items in the ap-pendix: they are organized by HRM practice and notby strategic goal. That is, the HRM practices for

Ž .cost reduction for example are not listed togetherbecause we wanted to avoid having the design of thequestionnaire encourage respondents to lump all thecost reduction items together.

5. Results and discussion

5.1. IndiÕidual HRM practices and manufacturing( )performance Proposition 1

Ž .The correlations and one-tailed p-values ofHRM items with each of the manufacturing perfor-mance measures are reported in Table 3. Several

Ž .HRM items indicated in boldface in Table 3 weresignificantly and positively associated with measuresof manufacturing performance at the ps0.10 levelor less. Twenty-one of the 22 HRM items had signif-

Table 2Descriptive statistics of human resource management practice items

Human resource management practices items Sample size Mean Std. Dev.

Top Level Management Commitment to Cost Reduction 57 5.947 1.025Top Level Management Commitment to Total Quality Management 57 5.895 1.160Top Level Management Commitment to Flexibility 57 4.930 0.998Top Level Management Commitment to Time-based Competition 56 5.018 1.228

Communication of Goals Relative to Cost Reduction 57 5.983 0.991Communication of Goals Relative to Total Quality Management 57 5.614 1.177Communication of Goals Relative to Flexibility 57 4.526 1.283Communication of Goals Relative to Time-based Competition 57 4.456 1.364

Formal Employee Training to support Cost Reduction 57 4.947 1.288Formal Employee Training to support Total Quality Management 57 5.614 1.373Formal Employee Training to support Flexibility 57 4.070 1.237Formal Employee Training to support Time-based Competition 57 3.983 1.482

Cross functional teams to support Cost Reduction 57 5.649 1.173Cross functional teams to support Total Quality Management 57 6.000 1.086Cross functional teams to support Flexibility 57 4.404 1.425Cross functional teams to support Time-based Competition 57 4.246 1.491

Broad Jobs 56 4.875 1.192Cross training 57 4.983 1.217Employee autonomy 54 4.722 1.352Employee impact 57 5.123 1.053Labor management relations 55 5.436 1.167Open organizations 57 5.544 1.196

Human resource management practice items are on a 1 to 7 scale with 1s‘Extremely Low Use of Initiative’ and 7s‘Extremely High Useof Initiative’.

Page 10: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–2010

Table 3Correlatios of HRM items with manufacturing performance items

Ž .Sample sizes50. Significant correlations alpha-10; one-tailed test are in bold.

Overall cost Overall quality Overall flexibility Overall time-basedperformance performance performance performance

Top Level Management Commitment 0.436 0.105 y0.164 0.154to Cost Reduction ps0.000 ps0.235 ps0.127 ps0.142Top Level Management Commitment 0.074 0.203 y0.121 0.125to Total Quality Management ps0.152 ps0.078 ps0.202 ps0.193Top Level Management Commitment 0.197 0.112 0.397 0.149to Flexibility ps0.042 ps0.220 ps0.002 ps0.151Top Level Management Commitment 0.107 0.251 0.336 0.413to Time-based Competition ps0.115 ps0.039 ps0.008 ps0.001Communication of Goals Relative 0.313 y0.023 I0.226 0.060to Cost Reduction ps0.007 ps0.437 ps0.057 ps0.339Communication of Goals Relative 0.122 0.223 I0.309 y0.049to Total Quality Management ps0.200 ps0.006 ps0.014 ps0.368Communication of Goals Relative 0.236 0.074 0.373 0.246to Flexibility ps0.025 ps0.306 ps0.004 ps0.042Communication of Goals Relative 0.093 0.040 0.249 0.286to Time-based Competition ps0.130 ps0.391 ps0.040 ps0.022Formal Employee Training to support 0.404 0.029 0.117 0.197Cost Reduction ps0.001 ps0.421 ps0.209 ps0.085Formal Employee Training to support y0.029 0.271 y0.113 0.045Total Quality Management ps0.420 ps0.028 ps0.217 ps0.378Formal Employee Training to support 0.236 0.145 0.383 0.230Flexibility ps0.049 ps0.157 ps0.003 ps0.054Formal Employee Training to support y0.068 y0.086 0.319 0.401Time-based Competition ps0.319 ps0.277 ps0.012 ps0.002Cross functional teams to support 0.420 y0.062 y0.057 y0.071Cost Reduction ps0.001 ps0.334 ps0.347 ps0.313Cross functional teams to support 0.193 0.183 y0.152 0.035Total Quality Management ps0.089 ps0.101 ps0.146 ps0.405Cross functional teams to support 0.343 y0.021 0.259 0.048Flexibility ps0.007 ps0.441 ps0.035 ps0.371Cross functional teams to support y0.004 0.017 0.277 0.358Time-based Competition ps0.489 ps0.453 ps0.025 ps0.005Broad Jobs 0.029 0.044 0.178 0.299

ps0.421 ps0.380 ps0.108 ps0.017Cross training 0.097 0.105 0.019 0.281

ps0.251 ps0.234 ps0.449 ps0.024Employee autonomy 0.026 0.092 0.144 0.355

ps0.428 ps0.262 ps0.159 ps0.005Employee impact y0.017 0.168 0.116 0.156

ps0.454 ps0.122 ps0.211 ps0.140Labor management relations 0.078 0.260 y0.002 0.212

ps0.295 ps0.034 ps0.499 ps0.070Open organizations 0.136 0.170 0.186 0.290

ps0.173 ps0.119 ps0.098 ps0.020

icant correlations with at least one dimension ofmanufacturing performance. The exception was em-ployee impact. Twelve items had significant correla-

tions with two manufacturing performance measures,Žand three items top management commitment to

time, communication of goals related to flexibility,

Page 11: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–20 11

.and employee training for flexibility were signifi-cantly related to three manufacturing performancemeasures. Thus, Proposition 1 was strongly sup-ported.

If we examine the correlations in Table 3, certainpatterns become clear. First, observe the pattern

Ž .under ‘‘cost performance’’ first column in the table .ŽAll cost-specific HRM items i.e., top management

commitment to cost reduction, communication ofcost-related goals, employee training for cost reduc-

.tion, and cross functional teams for cost reductionare positively correlated with cost performance.These correlations are among the highest in thecolumn. However, all flexibility-specific HRM itemsare also positively correlated with cost performance.

ŽSecond, the pattern for quality performance second.column suggests that it is primarily quality-specific

HRM items that determine quality performance. Allfour quality-specific HRM items are positively corre-lated with quality performance, but little else. Third,

the pattern in the next column suggests that flexi-bility-specific HRM items are related to flexibility

Ž .performance all four are significant and thattime-specific HRM items are related to flexibility

Ž .performance all of these four are also significant .Finally, an analysis of the fourth column shows that:Ž .1 all four time-specific HRM items are signifi-

Ž .cantly correlated with time performance; 2 flexibil-ity-specific HRM items play a lesser role in time

Žperformance particularly in communication of goals. Ž .and employee training ; and 3 the generic HRM

items at the bottom of the column are generallyrelated to time-based performance but not so exten-sively to any of the other three performance dimen-sions.

5.2. Dimensions of human resource management( )practices Proposition 2

As stated earlier in the discussion of the concep-tual model in Fig. 1, the literature is not clear as to

Table 4Rotated factor loadings for the five HRM factors

Variables Factor 1 Factor 2 Factor 3 Factor 4 Factor 5Ž . Ž . Ž . Ž . Ž .Cost Quality Flexibility Time Generic

Top Level Management Commitment to Cost Reduction 0.774 0.189 0.176 y0.116 0.121Communication of Goals Relative to Cost Reduction 0.798 0.131 y0.073 0.052 y0.058Formal Employee Training to support Cost Reduction 0.648 0.028 0.052 0.336 0.317Cross functional teams to support Cost Reduction 0.693 0.213 0.152 0.039 0.026Top Level Management Commitment to Total Quality Management 0.100 0.883 0.127 y0.015 0.191Communication of Goals Relative to Total Quality Management 0.264 0.863 y0.089 0.080 0.067Formal Employee Training to support Total Quality Management 0.067 0.749 y0.077 0.261 0.169Cross functional teams to support Total Quality Management 0.228 0.851 0.095 y0.006 0.206Top Level Management Commitment to Flexibility 0.113 y0.015 0.852 0.230 y0.060Communication of Goals Relative to Flexibility 0.184 y0.083 0.759 0.416 0.021Formal Employee Training to support Flexibility 0.026 y0.040 0.687 0.389 0.289Cross functional teams to support Flexibility 0.010 0.148 0.821 0.139 0.052Top Level Management Commitment to Time-based Competition 0.044 0.160 0.264 0.756 0.208Communication of Goals Relative to Time-based Competition 0.117 0.039 0.281 0.824 0.152Formal Employee Training to support Time-based Competition 0.001 y0.058 0.175 0.835 0.279Cross functional teams to support Time-based Competition 0.021 0.182 0.187 0.647 0.103Broad Jobs 0.067 0.088 0.099 0.143 0.698Cross training 0.055 0.270 y0.070 0.109 0.854Employee autonomy 0.159 0.037 0.171 0.350 0.715Employee impact y0.169 0.256 y0.033 0.326 0.715Labor management relations 0.102 0.203 y0.082 0.148 0.766Open organizations 0.124 y0.055 0.434 y0.097 0.666Eigenvalue 6.823 3.387 2.432 1.647 1.338Percentage of variance explained 31.0% 15.4% 11.1% 7.5% 6.1%Cumulative percentage of total variance explained 31.0% 46.4% 57.5% 64.9% 71.0%

Page 12: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–2012

whether deployment of human resource managementpractices is competitive priority-holistic. That is, arehuman resource management practices grouped ac-cording to the strategic dimensions they are meant tosupport? The 22 HRM items were subject to princi-pal components factor analysis with varimax rota-tion. The factor analysis revealed a stable five-factorsolution with each of the factors having eigenvaluesexceeding one. The cumulative percentage of totalvariance explained due to these five factors was71%. Table 4 presents the results of the factor analy-sis. It can be seen from Table 4 that there was a high

Ždegree of convergence within each factor the lowest.factor loading within a factor was 0.647 . Also there

was a high degree of divergence across factors as

indicated by the lack of cross loading of any item onmore than one factor. Clearly, the nature of the itemsthat load on each factor suggests that the five factorsbe named HRM-Cost, HRM-Quality, HRM-Flexibil-ity, HRM-Time and HRM-Generic. Proposition 2 issupported.

It is interesting to note that the six items constitut-ing generic HRM items loaded separately on a singlefactor. While the remaining sixteen items are specificto a competitive manufacturing priority, the six itemsunder HRM-Generic factor can be construed as itemsthat are diffused in their impact on several dimen-sions. For example, cross training is frequently men-tioned in the JIT literature. JIT implementationstypically have objectives that transcend several com-

Table 5

Factor items Corrected Cronbach’s Cronbach’s alpha Sample sizeitem-total alpha if item is deletedcorrelation

Cost HRM Factor 0.766 57Top Level Management Commitment to Cost Reduction 0.620 0.685Communication of Goals Relative to Cost Reduction 0.558 0.717Formal Employee Training to support Cost Reduction 0.570 0.714Cross functional teams to support Cost Reduction 0.539 0.725

Quality HRM Factor 0.878 57Top Level Management Commitment to Total Quality Management 0.798 0.821Communication of Goals Relative to Total Quality Management 0.787 0.825Formal Employee Training to support Total Quality Management 0.648 0.888Cross functional teams to support Total Quality Management 0.745 0.843

Flexibility HRM Factor 0.849 57Top Level Management Commitment to Flexibility 0.727 0.803Communication of Goals Relative to Flexibility 0.743 0.783Formal Employee Training to support Flexibility 0.711 0.798Cross functional teams to support Flexibility 0.614 0.849

Time HRM Factor 0.878Top Level Management Commitment to Time-based Competition 0.770 0.833Communication of Goals Relative to Time-based Competition 0.744 0.841Formal Employee Training to support Time-based Competition 0.767 0.831Cross functional teams to support Time-based Competition 0.678 0.868

Generic HRM Factor 0.867 51Broad Jobs 0.645 0.848Cross training 0.779 0.824Employee autonomy 0.718 0.836Employee impact 0.689 0.842Labor management relations 0.660 0.846Open organizations 0.514 0.870

Page 13: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–20 13

Žpetitive manufacturing dimensions e.g., cycle timereduction, reduction of waste and quality improve-

.ment . Similarly, broad jobs and open organizationsrepresent structural interventions to ensure fluidresponsibilities and informal employee relations.Effective labor–management relations may be morecritical in unionized rather than non-unionized envi-ronments. Finally, employee autonomy and em-ployee impact constitute the desirability of empower-ing employees to demonstrate ‘visibility’ in deci-sion-making which was traditionally the prerogativeof managers.

The items forming each of the HRM factors werethen tested for internal consistency using Cronbach’s

Ž .alpha Cronbach, 1951 . As can be seen from Table5, the scales for each of the HRM factors wereinternally consistent and the constructs were reliable,with Cronbach’s alphas ranging from 0.76 to 0.88.

Ž .Churchill 1979 suggests that items with correcteditem–total correlations which are less than 0.45should be eliminated from the scale. As can be seenfrom the first column in Table 5, no item had acorrected item–total correlation less than 0.45 andthus no item was eliminated. The third column inTable 5 indicates that for each of the items includedin the final instrument, the non-inclusion of that item

Žresults in a reduction of internal consistency as can.be seen from the reduction in alpha values . Overall,

the analyses indicated that the constructs were unidi-mensional and reliable, and thus the factor scoreditems were taken as the units for further analyses fortesting Proposition 3.

5.3. HRM factors and manufacturing performance( )Proposition 3

Ž .The correlations and p-values of the fiveHRM-factors with the four manufacturing perfor-mance items are presented in Table 6. For cost,flexibility and time, each HRM factor was consis-tently related to performance on its respectiÕe per-formance dimension. For example, HRM-Cost was

Žsignificantly related to overall cost performance p.s0.000 . Furthermore, HRM-Quality just missed

one-tailed significance at 0.05 on the quality perfor-mance dimension. The HRM-Generic factor was onlyrelated to time-based performance.

Two HRM factors were related to multiple manu-facturing performance measures. The HRM-Flexibil-ity factor was a significant predictor of flexibilityand cost performance. The HRM-Time factor was asignificant predictor of time and flexibility perfor-

Žmance. These correlation results which are equiva-lent to standardized beta regression results since the

.independent variables are orthogonal present anoverall picture that is essentially the same as the one

Table 6Correlations of HRM factors and manufacturing performance items

Variables Correlations

HRM-Cost HRM-Quality HRM-Flexibility HRM-Time HRM-GenericUUU UU1. Cost performance rs 0.480 0.032 0.276 y0.073 0.036

ns 50 50 50 50 50ps 0.000 0.823 0.052 0.616 0.803

U2. Quality performance rs y0.043 0.234 0.083 0.002 0.140ns 50 50 50 50 50ps 0.765 0.102 0.564 0.987 0.332

UUU UU3. Flexibility performance rs y0.167 y0.225 0.362 0.285 0.100ns 50 50 50 50 50ps 0.247 0.116 0.010 0.045 0.492

UU UU4. Time performance rs 0.064 y0.059 0.098 0.324 0.286ns 50 50 50 50 50ps 0.659 0.684 0.497 0.022 0.044

U Ž .Significant at alpha of 0.10 two-tailed .UU Ž .Significant at alpha of 0.05 two-tailed .UUU Ž .Significant at alpha of 0.01 two-tailed .

Page 14: The impact of human resource management practices on manufacturing performance

()

J.Jayaramet

al.rJournalof

Operations

Managem

ent181999

1–

2014

Table 7Regression analyses of the five HRM factors and size versus the four manufacturing performance measures

2 Ž .Manufacturing performance Model Adj. R Beta coefficient controlling for size with p-values in parenthesesŽ .item dependent variable p-value

HRM cost HRM quality HRM flexibility HRM time HRM generic Sizefactor factor factor factor factor

UUU UŽ . Ž . Ž . Ž . Ž . Ž .Overall cost performance 0.011 0.215 0.474 0.001 0.019 0.882 0.240 0.069 y0.090 0.486 0.002 0.988 0.178 0.178U UŽ . Ž . Ž . Ž . Ž . Ž .Overall quality performance 0.314 0.027 y0.067 0.641 0.265 0.071 0.074 0.608 0.027 0.850 0.151 0.298 y0.268 0.071

U UU UUŽ . Ž . Ž . Ž . Ž . Ž .Overall flexibility performance 0.017 0.196 y0.176 0.183 y0.232 0.080 0.333 0.014 0.281 0.036 0.080 0.542 0.078 0.556UU UUŽ . Ž . Ž . Ž . Ž . Ž .Overall time performance 0.101 0.103 0.080 0.560 y0.065 0.637 0.117 0.398 0.321 0.024 0.295 0.038 0.034 0.809

U Ž .Significant at alpha of 0.10 two-tailed .UU Ž .Significant at alpha of 0.05 two-tailed .UUU Ž .Significant at alpha of 0.01 two-tailed .

Page 15: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–20 15

derived from the analysis of the patterns of correla-tions per individual HRM item.

For our final set of analyses, we performed re-gressions with all five HRM factors and firm size asindependent variables and each of the four manufac-turing performance measures as dependent variableŽ .Table 7 . The number of employees is used as aproxy for firm size, and the inclusion of this sizevariable ensures that the betas for the HRM factorsare calculated controlled for size. In Table 7, foreach of the four measures of overall manufacturingperformance, the final model p-value, the adjusted

2 Ž .R , the regression coefficients b controlling forsize, and the p-values for the independent variablesare listed. The inclusion of size as a control variableparticularly affected the regression in which overallquality performance was the dependent variable. In

Žthis regression, the number of employees a proxy.for size entered as a significant negative predictor

suggesting that smaller companies achieved higherquality performance.

These analyses revealed that a particular HRMfactor was always significantly related to perfor-mance on the corresponding manufacturing perfor-mance measure. For example, the HRM-Cost factor

Ž .was significantly related ps0.000 to cost perfor-mance. Similarly, HRM-Quality, HRM-Flexibility,and HRM-Time were significantly related to theirrespective manufacturing performance measures.

After controlling for size, HRM factors entered assignificant predictors in more than one model. TheHRM-Quality factor appeared as a significant predic-tor of two manufacturing performance measures —

Ž .quality and flexibility in the latter case inversely .HRM-Flexibility factor predicts both flexibility andcost, while HRM-Time predicts both time and flexi-bility performance. The generic HRM factor wassignificantly related to only one measure of manufac-turing performance, i.e., time performance. Based onthe results of correlations and the regression analy-ses, we can conclude that, overall, Proposition 3 wassupported.

6. Discussion and conclusions

Until recently, the importance of human resourcemanagement practices was supported primarily by

case studies and anecdotal evidence rather than bylarge-scale studies. The issue of selecting the appro-priate set of HRM practices from a widely available‘menu’ of practices was troublesome and many ef-forts have failed. We examined a comprehensive setof 22 HRM practices and four manufacturing perfor-mance measures. We proposed that there is an under-lying pattern to deploying HRM practices in a strate-gic manufacturing environment, and found that HRMpractices can be classified empirically into five fac-tors. Four of these factors are specific to particularmanufacturing strategic dimensions of cost, quality,flexibility and time, while the remaining factor isgeneric. These five factors were shown to be relatedto manufacturing performance, where performancewas measured in four areas: cost, quality, flexibilityand time. Nevertheless, additional research is neededto examine the robustness of the findings, and gener-alizations should be interpreted with caution.

6.1. Summary of the results

Ž .The results show that 1 cost performance isengendered by HRM-Cost and, to a lesser extent, by

Ž .HRM-Flexibility initiatives; 2 quality performanceŽ .is associated with HRM-Quality initiatives; 3 flexi-

bility performance is engendered by HRM-Flexibil-Ž .ity and HRM time initiatives positively followed

Ž . Ž .by HRM-Quality initiatives inversely ; and 4 timeperformance is related to HRM-Time and HRM-Generic initiatives. Finally, only flexibility per-formance and time performance are significantlycorrelated with one another. This last result is notsurprising since each aspect of flexibility defined inthe literature has a range measure and a timing

Ž .measure associated with it see Gerwin, 1993 .The determinants of overall flexibility perfor-

mance warrant further discussion. While HRM-Flexibility and HRM-Time were significantly andpositively related to flexibility performance, HRM-Quality displayed a significant negatiÕe relationshipwith flexibility performance. If we examine the fouritems that constitute the HRM Quality factor, it isapparent that all four correlations with flexibilityperformance have negative signs, although only the‘‘communication of goals’’ item is statistically sig-

Ž .nificantly negative correlated with flexibility per-Ž .formance Table 3 . To understand why an emphasis

Page 16: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–2016

on quality in HRM could actually hinder perfor-mance in flexibility it is useful to think of qualityefforts as reducing variance while flexibility effortsas accommodating variance. For example, if humanresources are focused on achieving very high confor-

Žmance to specifications an important aspect of over-.all quality in this industry , it can be more difficult to

achieve performance on flexibility dimensions suchŽ .as: 1 rapidly accommodating a sudden increase in

Ž . Ž .demand volume flexibility ; 2 quick implementa-Ž .tion of design changes modification flexibility ; or

Ž .3 frequent or rapid changes in the product mixŽ .changeover flexibility . Accommodating thesesources of variation in a timely fashion can be moredifficult when conformance standards are higher andworkers are highly focused on achieving them ascompared to when quality standards and focus areless stringent.

6.2. Implications

The implications of the findings of this study inthe automotive supplier industry are several. First,our results suggest that HRM ‘bundles’ are impor-tant predictors of manufacturing performance. Thefocus on manufacturing performance as opposed tooverall firm performance offers new perspectives onhuman resource management in this industry. Sec-ond, the focus on four different aspects of manufac-turing performance — cost, quality, flexibility andtime — presents actionable guidelines for managers.

To a large extent, trends in the automotive sup-plier industry are dictated by trends in the parentindustry of original equipment manufacturersŽ .OEMs . Therefore, the pattern of competition in theOEMs industry offers an interesting reference pointfor interpreting the results of this study. Tradition-ally, this industry has been subject to intense globalcompetition, especially from Japan. Advances in

ŽJapanese manufacturing techniques e.g., JIT, kaizen.etc. have attracted the attention of the North Ameri-

can car manufacturers. In one of the most compre-hensive research projects in the car industry, Wom-

Ž .ack et al. 1990 found that a large portion ofJapanese competitive advantage was at the factorylevel. Therefore, manufacturing performance, as ex-amined herein, may be the key indicator of overallcompetitiveness. However, the adoption of Japanese

manufacturing techniques requires a careful attentionto human resource management issues. In this re-gard, our results suggest that a focused emphasis ona strategy-specific HRM ‘bundle’ has a high impacton its corresponding dimension of manufacturingperformance and that the typical prescriptions focus-ing on what we have termed ‘generic’ HRM initia-tives are less effective for achieving strategicallytargeted performance improvement. For example, theflexibility literature touts the use of empowermentŽ .i.e., employee impact, employee autonomy , broadjobs, and cross training as important for obtaining

Žflexibility success Adler, 1988; Parthasarathy and.Sethi, 1992; Upton, 1995 . Our study suggests that a

top management commitment to flexibility, commu-nication of flexibility goals, employee training forflexibility, and the use of cross-functional teams forflexibility could have a higher impact on flexibilityperformance than a ‘bundle’ of such generic HRMpractices.

6.3. Limitations and directions for future research

Certain limitations of this research delimit theinterpretation of our findings. First, the data used inthis study were self reported by the respondents.However, the survey instrument contained items for

Žwhich objective performance data on ROI, ROA,.and market share for example were also requested

Ž .and a good portion of our sample 28 firms com-plied. For 6 overall firm performance measures, 5 ofthem had positive correlations between subjectiveand objective data that were statistically significantat a 0.05 level of significance and one of them had apositive correlation that was marginally significantŽ . Žp-0.10 . The substantial concordance high corre-

.lations with significant p-values between the objec-tive and subjective performance measures for theseitems increased our confidence in using the subjec-tive performance measures examined for sub-classesherein. It should also be noted that many priorstudies have used self report data to make inferences

Žbetween practices and performance see for example,Choi and Hartley, 1996; Williams et al., 1995; Ward

.et al., 1995 . Nevertheless, one area of future re-search is the use of objective measures for capturingdimensions of manufacturing performance. For ex-

Ž .ample, parts per million defective PPMs might be

Page 17: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–20 17

used as a proxy for overall quality in the automotiveindustry.

Second, we operationalized manufacturing perfor-mance to consist of four separate macro-level as-pects, i.e., quality, flexibility, cost and time. Whilethere is considerable support for this in the literature,there is also support for a second order factor struc-ture for manufacturing performance in which each ofthese four aspects is comprised of subdimensions.For example, subdimensions of quality may includeconformance quality, product durability, product reli-ability and design quality. It is possible that thehuman resource factors considered in this researchdifferentially affect various subdimensions. Futureresearch could include a detailed operationalizationof manufacturing performance as a second orderfactor structure.

Third, other measures of manufacturing perfor-mance were not considered here. For example, previ-ous research has examined the impact of humanresource management practices on productivityŽ .Huselid, 1995 , which can also be considered as animportant aspect of manufacturing performance.However, a lack of proper definitions of terms andmeasurement schemes makes it a troublesome vari-able to investigate. For example, productivity can beconstrued as labor productivity or asset productivity,the former being less complex to measure. Futureresearch should consider other measures of manufac-turing performance such as manufacturing asset pro-ductivity, agility, customer responsiveness and inno-

Žvation all measures in which manufacturing has an.influence, albeit, an indirect responsibility .

Fourth, the choice of human resource items in ourstudy were driven by our conceptual model andpopular practice within the automotive supplier in-dustry as suggested by our expert panel. Thus, cer-

Žtain HRM items for example, contingent compensa-.tion were not included in our study even though the

literature indicates that they influence manufacturingperformance. Future research can be directed at ameta-analysis of key determinants of manufacturingperformance.

Finally, our study used CEOs as the respondentsfor data collection. As is well known, there aretrade-offs involving using CEOs as respondents. Onthe one hand, they are most knowledgeable on ‘‘sys-temic’’ issues and strategic decisions. However,

CEOs are also very busy people. Due to constraintsof time, it is conceivable that they hurriedly re-sponded to the research questionnaires, possiblycompromising the quality of data. Nevertheless, thisconcern was substantially ameliorated by our consis-tent practice of calling back respondents to check onmissing values, questionable responses andror toobtain objective performance data. Our excellent in-ter-rater reliability results also serves to amelioratethis concern. In all but one case, we found that therespondents had devoted time and effort to accu-rately complete the research questionnaires. In theone case where accuracy was questionable, the corre-sponding data was not included in our analysis.

6.4. Conclusion

In summary, our study offers several contribu-tions to the HRM and operations strategy literatures.First, the link between individual human resourcemanagement practices and four dimensions of manu-facturing performance was examined. Second, ourstudy suggested that deploying strategy-specific bun-dles of human resource management practices has asignificant influence on manufacturing performance,specifically, corresponding dimensions of perfor-mance. This has major implications for crafting over-all, coordinated HRM strategies and linking thesestrategies to competitive goals of manufacturing.Third, we have shown that there is merit in lookingat manufacturing performance dimensions as keyindicators for measuring the effects of HRM prac-tices. In some cases, the percentage explanation ofperformance variation attributed to HRM practiceswas in the 20% range. Finally, the impact of ourHRM-Generic bundle of items on manufacturingperformance reveals new insights. The HRM-Genericbundle had direct impact on time performance mea-sures only. One interpretation of this finding is thatHRM-Generic items as a set might be supplementaryto the strategy-driven HRM factors in achievinghigher performance on some manufacturing mea-sures.

An alternative possibility might be that the word-ings for the items constituting this factor caused theformation of this factor. Some items were linked by

Ž .common words cost, for example and some ofthese items did load together. However, this did notvitiate the robustness of our factor structure, as it is

Page 18: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–2018

equally true that other items sharing common wordsŽ .such as ‘‘cross functional teams’’ did not loadtogether. For example, the item ‘‘open organization’’included the word ‘‘communications’’ in its defini-tion; however, it did not load with other itemscontaining the word ‘‘communication.’’ The fourother items containing the word ‘‘communication’’Ž . Žsee Table 2 did not even load with each other see

.Table 4 even though they were listed together onthe questionnaire. Note also that six items containedthe word ‘‘employee’’ but only two of these itemsloaded together. It is true, however, that wordingmight have had a role in the formation of the‘‘generic’’ factor. Furthermore, we acknowledge thepossibility that the HRM practices associated with aspecific competitive priority could be linked together

Ž .in name of the priority only. These limitations areacknowledged and future research could focus moreclearly on these issues.

Appendix A. Operationalizationrrrrrdefinition of in-dependent variables

A.1. Operationalization

Summary of Instruction: ‘‘Rate the degree toŽwhich the following items Top Level Management

Commitment, Communication of Goals to Employ-ees, Formal Employee Training and Cross-Func-

.tional Teams were utilized by your SBU to supportthe strategic objectives:’’Ø Top Level Management Commitment to Cost Re-

ductionØ Top Level Management Commitment to Total

Quality ManagementØ Top Level Management Commitment to Flexibil-

ityØ Top Level Management Commitment to Time-

Based CompetitionØ Communication of Goals Relative to Cost Reduc-

tionØ Communication of Goals Relative to Total Qual-

ity ManagementØ Communication of Goals Relative to FlexibilityØ Communication of Goals Relative to Time-Based

CompetitionØ Formal Employee Training to Support Cost Re-

duction

Ø Formal Employee Training to Support TotalQuality Management

Ø Formal Employee Training to Support FlexibilityØ Formal Employee Training to Support Time-

Based CompetitionØ Cross-Functional Teams to Support Cost Reduc-

tionØ Cross-Functional Teams to Support Total Quality

ManagementØ Cross-Functional Teams to Support FlexibilityØ Cross-Functional Teams to Support Time-Based

CompetitionNote: 7-point scale was used with endpoints, 1s

Extremely Low Use of Item to Support StrategicObjective, and 7sExtremely High Use of Item toSupport Strategic Objective.

Summary of Instruction: ‘‘Rate the degree towhich the following initiatives were utilized by yourSBU to support your overall business strategy:’’Ø Broad Jobs: Job design that permits employees to

do many different things at work, using a varietyof skills and talents.

Ø Cross TrainingrJob Rotation: Training employ-ees to do more than one job to enable job rota-tion.

Ø Employee Autonomy: Allowing employees to de-cide on their own how to go about doing theirwork.

Ø Employee Impact: Ensuring that action is takenon employee input or suggestions.

Ø Labor–Management Relations: A set of practicesto foster a long-term cooperative labor–manage-ment relationship that permits things such as flex-ible job assignments.

Ø Open Organizations: Lean staff, open horizontalcommunications, and a relaxation of traditionalhierarchy.Note: 7-point scale was used with end points,

1sExtremely Low Use of Initiative, and 7sExtremely High Use of Initiative; a ‘‘Not Used’’option was also provided.

A.2. Operationalizations of dependent Õariables

Summary of Instruction: ‘‘Rate the oÕerall per-formance of your SBU relative to major competitorsfor each competitive objective:’’

QUALITYFLEXIBILITY

Page 19: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–20 19

TIME-BASED COMPETITIONCOST REDUCTIONNote: 7-point scale was used with endpoints, 1s

Poor and 7sExcellent.

References

Actionline, 1995. Strength in Numbers. July, 8–9.Adler, P.S., 1988. Managing flexible automation. California Man-

Ž .agement Review 30 3 , 34–56.Ansari, A., 1986. Strategies for the implementation of JIT pur-

chasing. International Journal of Purchasing and MaterialsŽ .Management 16 7 , 5–12.

Arthur, J.B., 1994. Effects of human resource systems on manu-facturing performance and turnover. Academy of ManagementJournal 37, 670–687.

Award Criteria, Malcolm Baldrige National Quality Award, 1994.Department of Commerce, National Institute of Standards andTechnology, Washington, DC, United States.

Banker, R.D., Field, J.M., Schroeder, R.G., Sinha, K.K., 1995.The impact of work teams and their life cycle phases onmanufacturing quality: a field study. Proceedings of the 1995Annual Decision Sciences Institute Conference, 1335–1337.

Bartel, A.P., 1994. Productivity gains from the implementation ofemployee training programs. Industrial Relations 33, 411–425.

Becker, B., Gerhart, B., 1996. The impact of human resourcemanagement on organizational performance: progress and

Ž .prospects. Academy of Management Journal 39 4 , 779–801.Bushe, G.R., 1988. Developing cooperative labor–management

relations in unionized factories: a multiple case study ofŽ .quality. Journal of Applied Behavioral Science 24 2 , 129–

150.Carmel, E., 1995. Cycle time in packaged software firms. Journal

Ž .of Product Innovation Management 12 2 , 110–123.Choi, T.Y., Hartley, J.L., 1996. An exploration of supplier prac-

tices across the supply chain. Journal of Operations Manage-Ž .ment 14 4 , 333–343.

Churchill, G.A., 1979. A paradigm for developing better measuresof marketing constructs. Journal of Marketing Research 16,64–73.

Cooper, R.G., Kleinschmidt, E.J., 1995. Benchmarking the firm’scritical success factors in new product development. Journal of

Ž .Product Innovation Management 12 5 , 374–391.Cronbach, L.J., 1951. Coefficient alpha and the internal structure

of tests. Psychometrika 16, 297–334.Cutcher-Gershenfield, J., 1991. The impact of economic perfor-

mance of a transformation in industrial relations. Industrialand Labor Relations Review 44, 5–33.

Drazin, R., Van de Van, H., 1985. Alternative forms of fit inŽ .contingency theory. Administrative Science Quarterly 30 4 ,

514–539.Delaney, J.T., Lewin, D., Ichniowski, C., 1989. Human Resource

Policies and Practices in American Firms. U.S. Department ofLabor, Washington, DC.

Dreyfus, P.L., Vineyard, M.L., 1996. Impact of employee rela-

tions on quality of products in a manufacturing environment.Proceedings of the 1996 Annual Decision Sciences InstituteConference, 1365–1366.

Droge, C., Vickery, S.K., Markland, R.E., 1994. Sources andoutcomes of competitive advantage: an exploratory study in

Ž .the furniture industry. Decision Sciences 25 5r6 , 669–699.Ferdows, K., DeMeyer, A., 1990. Lasting improvements in manu-

facturing performance: in search of new theory. Journal ofŽ .Operations Management 9 2 , 168–184.

Freund, W., Epstein, E., 1984. People and Productivity, DowJones-Irwin, New York.

Gerwin, D., 1993. Manufacturing flexibility: a strategic perspec-Ž .tive. Management Science 39 4 , 394–410.

Ginsberg, A., Venkatraman, N., 1985. Contingencies perspectivesof organizational strategy: a critical review of empirical re-search. Academy of Management Review 10, 421–434.

Graham, M.B.W., Rosenthal, S.R., 1986. Flexible manufacturingsystems require flexible people. Human Systems ManagementŽ .6 3 , 211–222.

Hackman, J.R., 1985. Doing research that makes a difference. In:Ž .Lawler, Edward, III et al. Eds. , Doing Research that is

Useful for Theory and Practice. Jossey-Bass, San Fransisco.Hayes, R.H., Wheelwright, S.C., 1984. Restoring our Competitive

Edge: Competing Through Manufacturing, Wiley, New York.Hill, T., 1989. Manufacturing Strategy Text and Cases, Home-

wood, Irwin, IL.Huselid, 1995. The impact of human resource management prac-

tices on turnover, productivity, and corporate financial perfor-mance. Academy of Management Journal 38, 635–672.

Ichniowski, C., Shaw, K., Prennushi, G., 1997. The effect ofhuman resource management on productivity. The American

Ž .Economic Review 87 3 , 291–313.Im, J.H., Hartman, S.J., Bondi, P.J., 1994. How do JIT systems

affect human resource management?. Production and Inven-Ž .tory Management Journal 35 1 , 1–4.

Katz, H.C., Kochan, T.A., Keefe, J.H., 1983. Industrial Relationsand Productivity in the U.S. Automobile Industry. BrookingsInstitution, Washington, DC.

Keefe, J.H., Katz, H.C., 1990. Job classifications and plant perfor-Ž .mance in the auto industry. Industrial Relations 29 1 , 111–

118.Kinnie, N.J., Staughton, R.V.W., 1991. Implementing manufactur-

ing strategy: the human resource contribution. InternationalŽ .Journal of Operations and Production Management 11 9 ,

24–40.Krajewski, L.J., Ritzman, L.P., 1987. Operations Management,

Strategy and Analysis, Reading, Addison-Wesley Publishing,MA.

Leong, G.K., Snyder, D.L., Ward, P.T., 1990. Research in theprocess and content of manufacturing strategy. OMEGA 18Ž .2 , 109–122.

MacDuffie, J.P., 1995. Human resource bundles and manufactur-ing performance: organizational logic and flexible productionsystems in the world auto industry. Industrial and LaborRelations Review 48, 197–221.

Magnan, G.M., Vickery, S.K. Droge, C. 1995. The use of humanresource strategies to support manufacturing flexibility, Pro-

Page 20: The impact of human resource management practices on manufacturing performance

( )J. Jayaram et al.rJournal of Operations Management 18 1999 1–2020

ceedings of the 1995 Annual Decision Sciences Institute Con-ference 1332–1334.

Merrills, R., 1989. How Northern Telecom competes on time.Harvard Business Review 67, 109–114.

Miller, D., 1981. Toward a new contingency approach: the searchfor organizational gestalts. Journal of Management Studies 18Ž .1 , 1–26.

Miller, D., Droge, C., 1986. Psychological and traditional determi-¨nants of structure. Administrative Science Quarterly 31, De-cember, 539–560.

Mintzberg, H., 1979. The Structuring of Organizations. Prentice-Hall, Englewood Cliffs, NJ.

Moras, R.G., Sanchez, C.M., Ford, R.G., 1994. Quality successstories in San Antonio industry. Production and Inventory

Ž .Management Journal 35 4 , 36–42.Otis, I., 1993. The Japanese automotive industry: a lesson for

Ž .American managers. Industrial Engineering 25 10 , 56–60.Parthasarathy, R., Sethi, S.P., 1992. The impact of flexible au-

tomation on business strategy and organizational structure.Academy of Management Review 17, 86–111.

Pfeffer, J., 1994. Competitive advantage through people. HarvardBusiness School Press, Boston.

Powell, T.C., 1995. Total quality management as competitiveadvantage: a review and empirical study. Strategic Manage-

Ž .ment Journal 16 1 , 15–37.Polakoff, J.C., 1991. Reducing manufacturing costs by reducing

Ž .cycle time. Corporate Controller 4 2 , 62–64.Rodgers, R., Hunter, J.E., 1991. Impact of management of objec-

tives on organizational productivity. Journal of Applied Psy-Ž .chology 76 2 , 322–336.

Rosenthal, S.R., Tatikonda, M.V., 1993. Time management innew product development: case study findings. Engineering

Ž .Management Review 21 3 , 13–20.Schuler, R.S., MacMillan, I.C., 1984. Gaining competitive advan-

tage through human resource management practices. HumanŽ .Resource Management 23 3 , 241–255.

Seward, E., 1992. Quality in R&D: it all began with a customer’sŽ .request. Research-Technology Management 35 5 , 28–34.

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

Spreitzer, G.M., 1995. Psychological empowerment in the work-

place: dimensions, measurement, and validation. Academy ofŽ .Management Journal 38 5 , 1442–1465.

Skinner, W., 1974. The focused factory. Harvard Business Review52, 113–121.

Ulrich, D., 1987. Organizational capability as a competitive ad-vantage: human resource professionals as strategic partners.

Ž .Human Resource Planning 10 4 , 169–184.Upton, D.M., 1995. What makes factories flexible?. Harvard

Ž .Business Review 73 4 , 74–84.Venkatraman, N., 1989. The Concept of fit in strategy research:

toward verbal and statistical correspondence. Academy ofŽ .Management Review 14 3 , 423–444.

Vickery, S.K., Droge, C., Markland, R.E., 1996. Dimensions ofcompetitive strength in manufacturing: an analysis of competi-tive priorities in the furniture industry. Journal of Operations

Ž .Management 15 January , 317–330.Vokurka, R.J., O’Leary-Kelly, S., Flores, B., 1998. Approaches to

manufacturing improvement: use and performance implica-Ž .tions. Production and Inventory Management Journal 39 2 ,

42–48.Ward, P.T., Duray, R., Leong, G.K., Sum, C.C., 1995. Business

environment, operations strategy, and performance: an empiri-cal study of Singapore manufacturers. Journal of Operations

Ž .Management 13 2 , 99–116.Ward, P.T., McCreery, J.K., Ritzman, L.P., Sharma, D., 1998.

Competitive priorities in operations management. DecisionŽ .Sciences 29 4 , 1037–1048.

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

Williams, F.P., D Souza, D.E., Rosenfeldt, M.E., Kassaee, M.,1995. Manufacturing strategy, business strategy and firm per-formance in a mature industry. Journal of Operations Manage-

Ž .ment 13 1 , 19–33.Womack, J., Jones, D., Roos, D., 1990. The Machine that Changed

the World, Rawson Associates, New York.Youndt, M.A., Snell, S.A., Dean, J.W., Lepak, D.P., 1996. Hu-

man resource management, manufacturing strategy and firmŽ .performance. Academy of Management Journal 39 4 , 836–

866.Zhu, Z., Meredith, P.H., Makboonprasith, S., 1994. Defining

critical elements in JIT implementation: a survey. IndustrialŽ .Management and Data Systems 94 5 , 3–10.