Alternative Quality Improvement Practices and Organization Performance

7/27/2019 Alternative Quality Improvement Practices and Organization Performance

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JOURNAL FOPERATIONS

MANAGEMENTELSEVTER Journal of Operations Management 12 (1994) 27-44

Alternative quality improvement practices and organizationperformance

Everett E. Adam, Jr.Management Department, College of Business and Public Administration, University of Missouri-Columbia, Columbia, MO 65211, USA

Received 20 September 1993; accepted in revised form 5 June 1994

AbstractQuality improvement is a highly desired objective in the fiercely competitive international business world, yet it

remains elusive to many US organizations. This study relates alternative quality improvement approaches to actualoperating and financial performance. Productivity improvement approaches are also investigated and related toperformance to define better the relationship between quality and productivity. In this study, multiple quality andproductivity approaches are correlated to eight quality, three operating, and three financial performance measures for187 US business firms. Results indicate a strong relationship between a quality improvement approach and performancequality. The relationship between a quality improvement approach and operating or financial performance is weaker, butsignificant. Productivity improvement approaches also help predict quality, operating, and financial performance -often similarly to quality improvement approaches. This study suggests that the profile of quality and productivityimprovement approach should vary, depending upon whether the firm is most interested in performance quality,operating improvement, or financial performance.

Competitiveness is a driving force behind the re-evaluation of North American business. Duringthe recession in the early 1980s chief executiveofficers seemingly awoke to the realities that noteverything made or every service offered ~ nomatter how well-developed or shoddy - couldbe sold. This realization resulted in a genuineinterest in quality improvement, an interest height-ened by the economic, downturn of the early 1990s.

This paper addresses the issue of competitivenessand the necessary ingredients to remain or becomea world-class competitor, focusing on the quality ofthe organizations product or service. More spe-cifically, this study identiJies alternative approachesto quality improvement practiced in the UnitedStates and then relates quality improvement prac-tice to actual quality, quality costs, operating per-

formance, andjinancial performance. The strengthof the correlations between quality improvementalternatives and actual performance should con-tribute to our understanding and guide qualityimprovement practices in the future.

1. Literature reviewImproved quality is commonly thought to

reduce cost, as waste is eliminated by doingthings correctly the first time (Crosby, 1979, 1984;Deming, 1986; Juran, 1982, 1989). An approachtoward improvement, total quality management(TQM), that is popular today has its roots in elimi-nating waste, reducing variations, and continuallyimproving. Although Feigenbaum (1983) outlined

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28 E.E. A dam, Jr./Journal of Operati ons M anagement 12 (1994) 27-44

this approach in the 1970s perhaps the Japaneseexperience popularized these cornerstones, as wellas some of the techniques, for improvement (see,for example, (Ishikawa, 1976)).

But what is the empirical evidence that qualityimprovement leads to improved business perfor-mance? The evidence is limited at best, and tothat we now turn.Wor ld-wide quali ty pr act i ces

US manufacturing strategy in the 1990s reflectsthe continuing challenges from the 1980s - theneed for continuous improvement in quality,costs, and product development. During the1980s United States priorities were more similarto Europe than Japan. Japan, having achievedcompetitive advantages in manufacturing quality,seemed to focus more than the United States onproduct development speed and cost reduction.This focus can be seen in Boston Universitysannual survey of manufacturing executives in theUnited States, Europe, and Japan (Miller and Kim,1990). During 1985-1987, US quality was the mostrapidly improving of seven primary manufacturingcore performance measures, increasing at morethan a 6 percent annual rate. In 1990-1992,inventory turns increased the fastest, about 7.5percent annually, while overall quality clusteredwith five other performance measures, each atabout a 6 percent annual improvement. Qualitysposition in the manufacturing strategy portfoliochanged substantially from 1984 to 1992. In 1984,quality improvement was not yet a top-fivestrategy. By 1986, the top three strategies were allquality related: implementing SPC, introducingzero defects programs, and involving vendors inquality efforts. Vendor quality and SPC remainedthe top two manufacturing strategies in 1988, whileimproving conformance quality and vendor qualitywere the top two strategies in 1990. The 1992study indicates that the top five most importantcompetitive capabilities of US manufacturersare, in order of importance (Kim and Miller,1992, p. 1):_ conformance quality,- product reliability,_ on-time delivery,

- performance quality, and- price.It is noteworthy that three of the top four capa-bilities reflect quality.

Further support for a quality emphasis in theUnited States comes from Carl Thor, the presidentof the American Productivity and Quality Center(Thor, 1990). To remain competitive, he suggeststhe United States need to do the right things,regardless of what its competitors/partners do. Inparticular, he suggest (p. 40) that US companieswill have to take their own initiatives to improveproductivity and quality significantly. This alonedoes not guarantee success, but it is the partcorporate executives can control.

A comprehensive study conducted jointly by theAmerican Quality Foundation and the publicaccounting firm Ernest & Young investigated bestquality management practices (1992). The studywas quite broad, examining 945 managementpractices in more than 580 organizations in fourindustries on three continents. The study hadtwo objectives: to asses the impact of individualmanagement practices on profitability, productiv-ity, and quality and to structure a causal model tounderstand better the interaction of practices thatcreate the critical path for improvement. Resultswere presented in a format that suggested a firmcould position itself as low-, medium-, or high-performing and then, based on similar firms,understand the characteristics (actions) requiredto improve. Dos and don% were suggestedfor each of three broad categories: people,process, and strategy/technology. The tonewas clearly managerial and focused on summariz-ing results rather than presenting the data andanalysis.

From the report emerged two important pointsfor this study. First, only three managementpractices reportedly have significant impact onperformance, regardless of industry, country, orstarting positions. Those are process improvementmethods, strategic plan deployment, and suppliercertification programs. Process improvement(reflected by practices such as process value analy-sis, process simplification, and process cycle timeanalysis) significantly impact profitability, produc-tivity, and quality ~ but especially so productivity.


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E.E. Adam, Jr./Journal of Operations Management 12 (1994) 27-44 29

Strategic plan deployment significantly affectsall three performances measures, and middlemanagers must understand strategies for the bestresults. Supplier certification programs relate moreclosely to increased quality and productivity. Thesecond point important to this study is that thedata and analysis were not publicly shared. Anappendix of assessment areas was provided - alisting of many of the 945 management practices- but the report did not provide the scores forthese measures nor correlations to actual profit-ability, productivity, and quality. A causal modewas not provided. In short, one could not replicatethis study, but presuming the report was preparedas a guide for management, replication might nothave been the reports intent.US quality experiences

Experienced executives understand the relation-ship between product (and service) quality andmarket share. The Boston Consulting Groupand Harvard Business School faculty, for instance,have developed the widely quoted Profit Impact ofMarketing Strategy (PIMS) database. Althoughthe database is not readily available as a part ofthe public domain, it has been cited for some 10years as one source supporting market share aspositively and strongly related to perceived qualityof a firms products (Buzzell and Wiersema, 1981;Leonard and Sasser, 1982; Garvin, 1984; Maani,1988; Craig and Douglas, 1982; Phillips et al.,1983).

The Malcom Baldrige National Quality Award,patterned after the Deming Prize in Japan, was firstawarded in 1988 and is becoming highly valued inthe United States. What practices do Baldridgeaward winners follow, and what are their results?A fairly recent US General Accounting Office(1991) study provides some answers. In the GAOstudy of 1988 and 1989 Baldrige finalists andwinners, evaluation focused on employees, oper-ating achievements, customer satisfaction, andfinancial performance. For these companies -among the best in the Unites States - the primaryemployee responses were increased suggestions(15 percent average annual improvement) and low-ered turnover (6 percent lower rate annually).

Operating gains were most noticeable in averageannual improvements in order-processing time(12 percent), reliability (12 percent), errors ordefect reduction (10 percent), and cost of qualitydecreases (9 percent). Customer satisfaction wasreflected by decreased complaints (12 percent peryear) and small increases in overall satisfaction andcustomer retention. Financial performance acrossall finalists and winners indicated increased annualmarket share (14 percent) and sales per employee (9percent), but only a small increase was found inreturn-on-assets (2 percent) and return on sales(1 percent).Empirical studies relating quality improvement andoperations management

The production and operations managementliterature has identified quality as a core contentvariable that has strategic significance within theoperations function and for the firm (Adamand Swamidass, 1989; Miller and Roth, 1988;Schroeder et al., 1986; Skinner, 1978; Wheel-wright, 1984), with profitability, to some extent,driven by quality (Adam et al., 1986; Heyl, 1987;Sluti, 1992).

Sluti (1992), in the most complete empiricalstudy on quality, utilized structural equationsmodeling to study 184 manufacturing firms inNew Zealand. Quality was found to have mixedresults when related to performance. For many ofthe measured direct relationships between qualityand business financial performance, results werenot significant, yet, the relationship betweenquality and production/operations outcomeswas significant. Quality had significant positiveimpacts on performance measures for processutilization, process output, production costs, work-in-process inventory levels, and on-time delivery.Alternative views exist on how quality should bemanaged in organizations. Practicing managersseem to favour one quality expert (guru) overanother, while the empirical studies indicate noclear directions. To illustrate, Benson et al. (1991)have proposed a system-structure model of qualitymanagement that relates organization context,actual quality management, ideal quality manage-ment, and quality performance. Their results


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30 E.E. Adam, Jr./Journal ofOperations anagement 12 (1994) 27-44suggest that organizational context influencesmanagers perceptions of both ideal and actualquality management. Important contextual vari-ables are corporate support for quality, pastquality performance, managerial knowledge, andthe extent of external quality demands.

With the practice of quality improvement litera-ture as background, it is the empirical literaturethat provides the clearest direction for this study.Specifically, the work of Benson et al. (1991) isextended to include a wider array of contextualvariables, an alternative set of quality improve-ment practices or approaches, and a full spectrumof performance variables (Sluti, 1992) - bothoperational and financial performance measures.

2. Research question, experimental design, andprocedureResearch question

Alternative approaches to quality improvementdo exist. For example, current total quality man-agement (TQM) infers that the entire organizationis involved (total). TQM suggests customer focus,top-management leadership, statistical thinking,continuous improvement, problem solving, andworkforce training (Evans and Lindsay, 1993, pp.32-33). It often includes variation reduction andemployee empowerment as key TQM attributesas well. If one desires to achieve improvements inthese attributes, what approaches are available?There are many, including behavioural interven-tions, differing management practices, alternativeproblem-solving methods, and statistical processcontrol.

Similar to quality, a range of productivityimprovement approaches also exist. Approachesinclude traditional cost reduction, industrial engi-neering work and process analysis, wage incen-tives, and management practices. Contemporaryproduction/operations managers have availableinventory reduction (via just-in-time (JIT) ormaterial requirements planning (MRP)), increasedspeed of product/process design, and flexiblemanufacturing, to name but a few alternatives.

The choice of an underlying theory against

which to test hypotheses is a complex issue. Whatis the prevailing quality improvement theory? Is it thetotal cost curve approach and the minimization ofthe costs as proposed by the Lundvall-Juranmodel (Juran, 1974; Fine, 1986)? Is it statisticalprocess control and the application of samplingand statistical inference in control charting(Western Electric, 1956)? Or, is appropriate theoryfrom problem solving and the application of thescientific method (Ishikawa, 1976)? Is appropriatetheory grounded in employee empowerment,small-group behaviour, and leadership in thebehavioral science literature? The difficulty inspecifying an underlying quality improvementtheory is quite real.

The quality outcomes in this study are best pre-dicted by the Lundvall-Juran model, while theitems thought to influence these outcomes arefound in the statistical process control (SPC) andbehavioral components of total quality manage-ment (TQM). The operating and financial out-comes evaluated in this study are derived fromthe production/operations management theoryinvolving the technology of resource transfor-mation. The items thought to influence these out-comes are found in traditional industrialengineering/operations research cost minimizationtheory.The primary interest in this study is qualityimprovement, proponents suggesting that throughquality improvement, operating and financialperformance are enhanced as costs are reduced.This study also suggests that when design qualityimproves, revenues and market share increase(Deming, 1986; Garvin, 1988). These linkageslead to questions as to whether overall perfor-mance is always improved through qualityimprovement and whether productivity improve-ment techniques are utilized in tandem withquality improvement techniques to achieve theperformance gains.

What combination of quality improvement andproductivity improvement techniques lead to thehighest organization performance? How is perfor-mance defined? In this study, the interest is inidenttfying a quality/productivity technique profilethat can predict quality, operational, and financialperformance. Here quality is distinguished from


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overall operating performance. Quality includesactual quality data expressed as error rates, costof quality components, and customer satisfaction.Operating performance is employee turnover,employee satisfaction, and productivity expressedas net profit as a percent of sales. Financial per-formance includes return-on-assets (ROA) andannual sales growth.

More specifically, the following hypotheses areaddressed in this study:1. A companys approach to quality and pro-

ductivity improvement correlates to productand service quality, operating performance,and financial performance.

2. A companys approach to quality improvementcorrelates to product and service quality.

3. A companys approach to quality improvementcorrelates to operating and financial performance.

4. A companys approach to productivity improve-ment correlates to operating and financial per-formance.

Experimental designThe first hypothesis examines interactions

between quality improvement interventions, prod-uctivity improvement interventions, and thecompanys actual performance. Actual perfor-Table 1Independent variables and levels

mance is broadly defined here as it includesquality, operating, and financial performance. Aprofile of high performance versus low perfor-mance companies is sought, and details unfold asHypotheses 2-4 are explained.Testing the second hypothesis requires adelineation of quality improvement approachesand measures for quality. Table 1 outlines thequality improvement approaches: no formalapproach, statistical process control, behavioral,a customer focus, projects emphasized, or a focuson design, conformance, or both. Projects empha-sized refers to Crosby (1979) and Jurans (1982)suggestion of quality teams assigned improvementprojects. Table 2 sets forth the quality measures -percent defective, the cost of quality, and customersatisfaction.

To test the third hypothesis, the approachesto quality improvement (six levels, Table 1)are related to operating and financial performancemeasures set forth in Table 2. Operatingperformance is expressed as net profit as apercent of sales, annual employee turnover, andemployee satisfaction. Financial performancemeasures include return-on-assets for the pastyear and for the average of the past three years,as well as sales growth, as an average of the pastthree years.

Independent variable Level

Productivity improvement approach

Quality improvement approach 1. No formal approach2. Statistical process control3. Behavioral; operative employee or managerial

a. reward focusb. degree of training

4. A customer focus5. Projects emphasized6. Design, conformance, or both1. Traditional industrial engineering

such as process analysis and work measurement2. Inventory reduction3. Improving quality4. Employee selection - pre-employment testing

and biographical data5. Decentralizing decision making6. Providing objective feedback on performance


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Table 2Dependent variables

E.E. Adam, Jr./Journal of Operations Management 12 (1994) 27-44

Dependent variable Measure

Performance quality Average % items defectiveCost of quality (as % of sales)scrapreworkinspectiontraining & developmentreturns and warrantytotal cost of quality

Customer satisfactionOperating performance Net profit as % of sales, past year

Annual employee turnover rateEmployee satisfaction

Financial performance Return on assets, past yearReturn on assets, average past three yearsSales growth, average past three years

The operating performance measures are surro-gates for actual productivity, the total output ofgoods and services divided by all resources usedto provide that output. Traditional accountingtechniques do not readily support total factorproductivity measurement, so the cost of goodssold as a percent of sales was considered.This cost of goods sold related to sales wascaptured as net profit, the difference in sales andtotal costs.

The research literature concerning employeeturnover suggests a wide array of contributors toturnover and relationships that are not alwaysstraightforward. For this study, the general beliefthat low turnover is better than high and that lowturnover contributes to higher operating perfor-mance is accepted. Employee satisfaction doesnot directly relate to productivity. Neither doesthe literature show that employee satisfactionrelates directly to quality. Employee satisfactionis placed as a performance measure because astronger case can be made for that relationshipthan as a quality or financial indicator. It couldbe argued that employee satisfaction should notbe included at all, as it is not an outcome ofthe organization, but rather a condition thatleads to outcomes.

operating and financial performance (discussedabove and in Table 2). Productivity improvementapproaches include traditional industrial engi-neering, inventory reduction, and improvingquality. Also included are managerial/behavioralapproaches: employee selection, decentralizing/low-level decision making, and providing objec-tive feedback. Kopelman (1986) provides anexcellent summary of practical, behavioral inter-ventions that enhance productivity as demonstra-tive in the management and behavioral researchliterature. These managerial/behavioral inter-ventions reflect his top composite interventions(Kopelman, 1986, p. 290) and are incorporated asalternatives in this study.

Table 3 summarizes the experimental design.Two independent variables - quality and pro-ductivity improvement interventions - are inde-pendently and jointly related to three dependentvariables - actual quality, operating perfor-mance, and financial performance. Multiple levelsof the independent variables and multiple measureswill exist, as indicated in Table 3 and the previousdiscussion.Procedure

The fourth, and final, hypothesis relates the A survey was conducted of practicing manu-productivity improvement approach (Table 1) to facturing firms. The primary source of companies


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Table 3Experimental design


Independent variable

Quality improvement approachesProductivity improvement approaches

Dependent variables (measures)

Actual quality (8)Operating performance (3)Financial performance (3)

was SIC and geographical listings where the com-panys address and telephone number were avail-able. A secondary source was membership in theOperations Management Association, an asso-ciation of production/operations managementacademics and executives. Only the executiveswere sampled concerning their company.

The general procedure was to telephone the com-pany and ask for the chief production or opera-tions officer. Provided was a brief statementabout the person and university conducting theresearch and an explanation of this study. Thepotential respondent was then asked to partici-pate, and if he or she agreed, a questionnaire wasmailed to that person. Firms were systematicallyselected; up to four telephone calls were made tosolicit participation and one follow-up call wasmade to those who agreed to participate but didnot return the questionnaire. Willingness to parti-cipate from those reached by telephone was quitehigh. Questionnaires were returned in pre-addressed, postage-paid envelopes.

For the primary source, a systematic randomsampling procedure was used. Responding andnonresponding companies were similar industrialtypes (SIC) and had similar geographical disper-sion. No differences were detected in the twogroups. For the telephoned group, the return ratewas 67.1 percent: 163 usable returns from 243 ques-tionnaires mailed. For the secondary source, adirect mailing was used with no telephone con-tact. For this group, the return rate was 14.4 per-cent: 24 usable returns from 166 questionnairesmailed. No differences were detected in this groupbetween respondents and nonrespondents. A totalof 187 usable responses from 409 questionnairesmailed resulted in a 45.7 overall return rate.Thirty-four percent of the respondents indicatedthey would like a summary of the survey results.

Questionnaire. The questionnaire content

reflected the research design and was thus basedon the quality (and productivity) improvementresearch literature. A primary source of questionswas (Benson et al., 1991) who utilized the instru-ment designed, verified, and validated by Saraph etal. (1989). In their 1991 study, the 26 measurementitems for organizational quality context wereassessed (Benson et al., 1991, Appendix A).Accepting their results, this study utilized itemsrelating to corporate support for quality, manage-rial knowledge, past quality performance, andmarketplace. Respectively, these sets accountedfor 40, 19, 10 and 9 percent of the variance fromall questions in the Benson study. That is, 78 per-cent of the variance was captured by items in thesecategories. Saraph et al. (1989) had 111 items in thequestionnaire they developed and tested. Benson etal. (1991) benefited from that work, as did thisstudy, by selecting items that accounted for ahigh percentage of the variance.The 1991 Malcolm Baldridge National QualityAward (US Department of Commerce, 1991) iden-tified seven categories of quality and under theseven categories evaluated a total of 32 items.Based on previously cited research literature, 12of those items became candidates for this study.The 12 items were taken from Baldridge categorieshuman resource utilized, quality assurance ofproducts and services, leadership, quality results,and customer satisfaction.

Kopelmans (1986) survey of the behavioural/managerial literature provided a summary ofitems concerning productivity interventions.Items from that survey included here wereemployee biographical data in selection, placingdecision making at lower levels, providing objec-tive feedback on performance, and employee satis-faction. The survey instrument also includedquestions that summarized traditional productiv-ity improvement techniques such as industrial


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34 E.E. Adam, Jr./Journal of Operations Management 12 (1994) 27-44

engineering, effective inventory planning and con-trol, and improving quality. These items were sta-ted in a style consistent with the overall survey,which was primarily a Likert seven-point scaling.Example questions are included as Fig. 1.Demographic charact eri sti cs of t he part icipant s.Respondents tended to be from a wide distri-bution of firm sizes. From the 187 respondents,the average number of firm employees is3,770 and average annual sales are $914 million.These data are skewed upward considerablyby a few large firms. The median number ofemployees is 500, and the median annual sales is$10 million.

For the most part, the firms have been inbusiness for some time, the mean 40.7 years. Only15.7 percent of the firms have been in businessfewer than 10 years. An average of 50.3 percentof present employees are involved in qualityimprovement within participating firms. The vastmajority of firms, 8 1 percent, have a quality depart-ment. Firms have had a formal approach to qualityimprovement and to productivity improvement forsome time, averaging 7.1 and 9.7 years respectively.Respondents are primarily in general management- presidents or manufacturing vice-presidents(15 percent), general managers (8 percent), andplant-works managers (32 percent). Secondarily,

Strongly Neither Agree StronglyDisagree Nor Disagree Agree

QUALITY IMPROVEMENT at this companyis best described as...

Applying no formal approach . 1 2 3 4 5 6 7

Statistical control (SPC)rocess . 1 2 3 4 5 6 7Involving employees; each employees

responsibility; behavioral in nature . 1 2 3 4 5 6 7Quality products and services depend upon the degree to which a company(1) understands and specifies customer requirements (design) and (2) producesand services these requirements (conformance). AT MY COMPANY...

Customers opinions and views regardingtheir needs are actively sought throughdirect contact; sales calls, focus groups,and so forth . . . 1 2 3 4 5 6 7

Customers regularly and formally receivecustomer satisfaction questionnaires 1 2 3 4 5 6 7

Senior executives (responsible for companyprofit and loss) create and sustainclear and visible quality values . 1 2 3 4 5 6 7

Productivity can be improved by introducing behavioral, engineering, and accountingpractices. At my company PRODUCTIVITY IS IMPROVED BY...

Traditional industrial engineeringapplications: process flow analyses,work measurement, standards, layouts.and so forth 1 2 3 4 5 6 7

Effective inventory planning and control 1 2 3 4 5 6 7

Fig. 1. Example measurement items for quality and productivity improvement.


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respondents were the quality managers - adirector or manager of the quality function (36percent). A few more were owners or othermanagers with a major resource responsibilities (9percent).Overall, the sample represents a mature crosssection of US manufacturing. Respondents havemajor responsibilities for quality within the firm,and they view themselves as quite knowledgeableabout quality and its practices.Anal ysis procedures

Data included responses to Likert scale questions(circles 1 through 7), ordinal scaled numbers, andessay responses. The essay responses are not codedand reported here. The responses for each of 187respondents were displayed in various forms - theraw information, frequency distributions, graphs,means, and standard deviations. The data werecarefully examined and some entry errors werediscovered and corrected.

Independent variable constructs for quality arenot commonly defined and accepted in the researchliterature. Therefore, factor analysis was necessaryto define these constructs. Factor analysis was con-ducted on the quality and productivity improve-ment responses to determine which items wereanswered similarly. The SAS System factor analy-sis routine was selected and a principal componentsanalysis performed, including an orthogonaltransformation with a varimax rotation. Factorscores were the average of the items with factorloadings exceeding 0.400. After the factor analy-sis, a step-wise multiple regression was conductedto test the hypotheses. The independent variables,expressed as factor scores condensed from theitem responses, were regressed against the depend-ent variables quality, operating, and financialperformance.

A decision was made to use factor analysis as anexploratory method for finding the minimumnumber of factors to account for the observedcovariation. Consideration was given to confirma-tory analysis, where factors are pre-determined andthen tested to see if the items actually did fall inthose groups (Kim and Mueller, 1976). Conserva-tive factor analysis procedures use a respondent/

items ratio of about 10. With the 20 items thesample size would need to be about 200 respond-ents. The 187 respondents of this study fall slightlyshort of that.

3. ResultsQual i t y andproducti v i t y

Table 4 identifies the 20 items that were factoranalyzed, 13 that are quality improvement indi-cators and 7 that are productivity improvementindicators. The factors with eigenvalues greaterthan 1 resulted in five factors that captured all ofthe variance. Factors 1, 2, and 3 explained cumu-lative variances of 39, 57, and 72 percent, respec-tively. The orthogonal transformation with avarimax rotation resulted in factors nearly ortho-gonal, but not totally so. In Table 4, the itemquality training addressing employee needsremained in three factors, while the itemsimproving quality and applying a formalapproach remained in two factors.

Factor 1 is broadly defined by nine of the 20items. Quality items that involve managementand employee behaviors have strong factorscores: involving employees, management involve-ment, project teams, and quality training to meetemployee needs. Somewhat similarly, productivityitems that are behavioral have strong factor scores:employee satisfaction, objective feedback, anddecision making at lower levels. Nonbehavioralproductivity items that carry high scores includeeffective inventory planning and control andimproving quality.

Factor 2 captures five items that reflect con-formance and design: a conformance emphasis,actively seeking customers views, a designemphasis, improving quality, and quality training.Customers views could be sought regarding whatthey desire (reflected in design) and what theyreceive (reflected in conformance). Factor 3 couldbe viewed as knowledge: the desire to expand knowl-edge, skills, and training. Factor 4 captures rewardsand statistical process control (SPC), while Factor 5reflects traditional engineering items. In summary,Factors l-5 will be referred to respectively as


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Table 4E.E. Adam, Jr./Journal of Operations Management I2 (1994) 27-44

Quality and productivity factor analysisItems Factor pattern

Quality (or Productivity) improved at my company by_ involving employees- providing objective feedback on performance_ employees satisfied with the company_ managements involvement & responsibility_ not crisis based; attention other than after a failure_ effective inventory planning & control- placing decision making at lower levels_ identifying & resolving improvement projects_ improving quality~ quality training addressing employee needs_ applying a formal approach- quality practice reflecting a conformance emphasis_ actively seeking customers views- quality practice reflecting a design emphasis_ attempting to expand knowledge in quality area- being reward focused; pay for quality performance- utilizing statistical process control (SPC)~ employee biographical data used in selection~ traditional industrial engineering_ inspectors trying to assure conformance to specs

Factor 10.761150.728030.691610.690480.670180.6493 10.637070.624710.559240.445320.44117

Factor 2

0.551550.414630.827380.554810.46267

Factor 3 Factor 4 Factor 5

0.430450.63121

0.829930.753940.715340.52639

0.743970.73818

behav i oral , conformance and design, know ledge,rewards and SPC, and tr adit ional engineeri ng.

The crux of this study is explaining how thesequality and productivity improvement factorsrelated to actual quality, operating, and financialperformance. The stepwise regression summarizedin Table 5 defines or explains the dependentvariable in terms of the independent variables(Factors l-5). Only the statistically significantvariables at a level of significance less than 0.05are reported. Dependent variables where norelationship was found include annual employeeturnover, past years net profit, and sales growth.

Most dependent variables are predicted by asignificant regression. The R*s appear low, exceptfor the two opinion-based items - customersatisfaction (R* = 0.1759, F ratio 14.73) andemployee satisfaction (R* = 0.5214, F ratio 75.16).Six of 14 regressions are significant at p < 0.01, fiveatp < 0.05, and four nonsignificant. Each regressionis a test of a separate model.

The total cost of quality is important because ofthe broad scope of the measure. It emerges as animportant dependent variable as it is significantly

(p < 0.05) related to Factors 2 and 5, with an R* of0.1463. By examining Factors 2 and 5, we see thatfocusing on conformance and design (F2) andtraditional engineering (F5) are the highest itemloadings in these factors. One component of totalcost of quality deserves attention: training anddevelopment (p < 0.01, R* = 0.1632, F = 5.59).It is explained by Factors 1, 4 and 5: the broadbehavioral factor, rewards and SPC, and tradi-tional engineering.

The operating and financial results indicate somepromise. It is noteworthy that last years return-on-assets can be explained (or predicted) at thep < 0.05 level. Note that Factor 1 is the primarydeterminant. Factor 1 reflects behavior and loadsmost heavily on involving employees and pro-viding objective feedback on performance. Moreattention in industry is being placed on under-standing employee and management behavior.Perhaps these data reflect that.Performance quality

An exploratory investigation was next con-


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Table 5Quality and productivity regression summaryStepwise regressionprocedure

Dependent variables

Performance qualityTotal costof quality

Wasteandscrap

Returnsandwarranty

Inspection Rework Traininganddevelopment

InterceptRegression component(parameter estimate)

RR2F ratio

28.842 7.199 7.746 7.270 4.979 -2.295F2(-5.317) F2(- 1.206) Fl(-1.156) F2(- 1.508) F2(-2.426) Fl(0.725)

F5(3.026) F5(0.606) F5( 1.037) F3(1.333) F4(-0.291)F5(0.828) F5(0.316)

0.3824b 0.2716a 0.2847b 0.3165b 0.3261a 0.4043b0.1463 0.0738 0.0811 0.1002 0.1064 0.16325.66 4.11 8.12 5.12 3.85 5.59

Stepwise regressionprocedure

Dependent variablesPerformance quality (cont.) Operating FinancialAverage percentof itemsdefective

Customersatisfaction

Employeesatisfaction

Last yearsreturn-on-assets

InterceptRegression component(parameter estimate)RR2F ratioa p < 0.05bp < 0.01

6.269 4.273 1.764 -6.950Fl(-1.696) Fl(0.546) Fl(l.184) Fl(3.500)

F3(1.067) F3(-0.229) F3(-0.516)0.2357a 0.4194b 0.7220b 0.2774=0.0556 0.1759 0.5214 0.07703.18 14.73 75.16 4.17

ducted on a set of quality items that represented thedetails of alternative quality improvementtechniques. Deleted were items relating to produc-tivity improvement. The quality improvementitems included represented details concerning:customer assessment, design, conformance, seniorexecutive involvement, markets and quality,resources and training, and employee involve-ment. Selected quality relationships are presentedin Table 6. Note that the independent variablesin the regression, the factors, are representedby FQ to denote they are not the factors inTable 4. Factor FQl, which explains employee

knowledge, training, and customer involvement,is very broad, with nine items loading into thefactor as 0.400 or higher. Factor FQ2 reflectssenior executive involvement and encompassessix items. Taken together, FQl and FQ2 reflectbehavioral items that affect quality. FactorFQ3 loads clearly on quality practice, reflectingboth design and conformance (with three itemsat 0.4000 or higher). The final factor, FQ4,encompasses items on employee conformance tospecifications and inspectors seeking conformanceas well.

In summary, the relationships in Table 6 are


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38 E.E. Adam, Jr./Journal of Operations Management I2 (1994) 27-44Table 6Quality regression: selected relationshipsStepwise regression Dependent variablesprocedure

Performance quality Operating FinancialTotal cost Inspection Waste Training Employee Past years Past 3 yearsof quality and and satisfaction return-on- return-on-

scrap development assets assetsIntercept -0.831 3.057 4.018 -0.172 1.075 2.476 3.171Regression component FQ4(3.434) FQ3(-0.717) FQ3(-0.606) FQl(0.327) FQ2(0.458) FQ3(2.849) FQ2(2.735)(parameter estimate) FQ4( 1.029) FQ4(0.585) FQ3(0.336) FQ4(-1.833) FQ4(-1.783)R 0.3765b 0.3464b 0.2679a 0.2080 0.6204b 0.3277a 0.3742aR= 0.1418 0.1200 0.0718 0.0433 0.3850 0.1074 0.1401F ratio 12.22 6.89 4.41 4.43 44.13 3.79 4.89ap < 0.05b&I < 0.01

similar to those in Table 5. That is, quality alone(Table 6) provides (explains) relationships that aresignificant, as does quality and productivitytogether (Table 5). Quality factor relationships tothe dependent variables are significant at bothp < 0.01 and p < 0.05, and the R2s are somewhatlow.

Not all significant dependent variables areshown in Table 6. For the seven selected, totalcost of quality (p < 0.01, R2 = 0.1418) inspection(p < 0.01, R2 = 0.1200);and employee satisfaction(p < 0.01, R2 = 0.3850) all provide clear relation-ships. The weakest quality relationship is trainingand development, with a low R2 and but one pre-dictive variable, FQl. It should be noted that pastyears ROA measure relates similarly to the datafor quality alone as for quality and productivitycombined (Table 5). Yet, for the quality alonedata, the past three years ROA was significant aswell.Productivity

Productivity improvement items alone werefactor analyzed and the results regressed againstthe dependent variables. Results are less signifi-cant overall than the combined quality and prod-uctivity regression (Table 5).

Validation of quality itemsThe quality improvement items utilized from

Benson et al. (1991) were evaluated in two differ-ent ways to determine if that study and this one arein general agreement. First, the Benson items wereorganized by their factor analysis results (Bensonet al., 1991) and regressed upon the dependentvariables in this study. Second, as an alteration,the Benson items were factor analyzed with thecurrent study data and the factors were regressedagainst the dependent variables. Both analysesyielded similar results. In both approaches, onlyfive of the 14 dependent variables had significantregressions, substantially fewer than the reportedanalyses for this study (Table 5), where 11 of the 14dependent variables were significant. With theBenson items, significant regressions were foundfor returns and warranty costs, training anddevelopment costs, employee satisfaction, and bothpast years and the past three years return-on-assets (ROA) measures. In the study reportedhere, the first three of these regressions are signifi-cant at p < 0.01 and the last two at p < 0.05(Table 5).

The Benson items load into factors adequately,but taken alone they do not relate to a high numberof dependent variables. This replication provides


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E.E. Adam, Jr./Journal of Operations Managemenl 12 (1994) 27-44 39

general validity to the current study. However, incomparing results to the current study, as repre-sented in Tables 5 and 6, we can see the broadernature of the current investigation.

A direct comparison of the Benson factorsand those in this study suggest similarity on themajority of factors. Bensons corporate supportfor quality is similar to the broad behavioral fac-tor (Factor 1, Table 4) in this study. Managerialquality knowledge is clearly a factor in bothstudies. In the Benson study extent of externalquality demands is similar to the confrormanceand design factor (Factor 2, Table 4) of this study- customer imposed practices. The Benson factorpast quality performance does not correspond toany factor in this study.

4. Discussion and conclusionsA set of hypotheses were suggested as research

questions. Did they hold based on the regressions?Could performance be predicted, based on thisstudy?Quali ty andproducti vit y improvement

Hypothesis 1 reflected an interest in examininginteractions between quality improvement approach,productivity improvement approach, and perfor-mance. Performance is captured by quality, operat-ing, and financial measures. Table 5 presents theresults of this analysis.

Reviewing the results, we first conclude thatfactors reflecting approach to improvement couldbe identified for performance quality (p < 0.01)operating (p < O.Ol), and financial results(p < 0.05). Second, we can observe that for mostdependent variables (columns of Table 5) theregression used multiple factors to explain therelationship. This is saying that factors - whichare groupings of quality improvement approaches(items) - are required to explain quality, pro-ductivity, and financial performance. Stated other-wise, an individual item, expressed as one approachto quality or productivity improvement, is notsufficient to explain performance significantly(statistically). Relationships are multiple and com-

plex. Finally, it is clear that quality improvementinfluences a broad set of actual quality outcomesbut only one operating and one financial outcome.

What is the profile that would best improve per-formance? From Table 5, i fperformance qualit y isones objective, the quality measures might betotal cost of quality, return and warranty costs,inspection costs, and training and developmentcosts as a percent of sales (all p < 0.01) as well ascustomer satisfaction results (p < 0.01). Improve-ment should utilize Fl, F2, F4, and F5. Thesefactors encompass improvement approaches thatare behavioral (Fl), reflect design and con-formance (F2), emphasize rewards and SPC (F4),and are traditional engineering (F5). The broadinternal failure quality measures were significantat p < 0.05: average percent of items defective,waste and scrap, and rework.

If operat ing im provement is ones object iv e, onlyemployee satisfaction (p < 0.01) was found tobe important. Factors that influence employeesatisfaction include Fl and F3. Employee turn-over and net profit were insignificantly related tothese quality and productivity improvementapproaches (p > 0.15).

Finally, if j in ancial perf ormance i s ones empha-sis, then measuring past years return-on-assets(ROA) is useful (p < 0.05). Annual sales growthcould not be explained by these improvementfactors (p = 0.14), nor could the past three yearsROA (p = 0.07). The factor that influenced ROAmost was Fl, suggesting a broad set of behavioralitems as an improvement scheme.

Again, the quality and productivity approachesstudied here seem to have a greater impact onquality than on operating and financial perfor-mance. We could also conclude that a wide arrayof improvement approaches (items, factors) impactquality rather than one or two items.Qual it y im provement approaches

An important finding regarding quality andproductivity improvement, as well as qualityimprovement, is that respondents to a great degreeperceived most improvement techniques similarly.The quality and productivity factor analysis (Table4) demonstrates this by the larger number of items


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40 E.E. Adam, Jr./Journal ofOperations anagement 12 (1994) 27-44(nine) loading into factor 1. Recall that 39 percentof the variance was in Factor 1, and that was thebehavioral factor.

For quality, this is represented by FQl, alsoencompassing nine of the items the researchliterature suggested should influence quality.Items loaded into FQl at 0.400 or greater andFQl accounted for 36 percent of the overallvariance in the factor analysis. Quality factorsFQl and FQ2, the behavioral factors, accountedfor 67 percent of the variance.

What does this mean? One interpretation is thatquality improvement is a broad construct, noteasily divisible into components such as involvingemployees, management involvement and respon-sibility, identifying and resolving improvementprojects, and so forth. Rather, respondents sawmany items as similar - and generally agreed orstrongly agreed they were used at their company.But do all these items, in Fl, FQl, and FQ2, forexample, significantly correlate to the outcomevariables quality, performance, and financial per-formance? The answer is no, not always, as Table 6and the following indicate.

Hypothesis 2 suggested that a companysapproach to quality would correlate to actualquality. This was found to be true, as Table 6demonstrates. Quality as measured by total costof quality and inspection costs was explained by

quality improvement approaches captured in FQ3and FQ4. Total cost of quality (p < 0.01) could beexplained by inspection and specifications beingemphasized (FQ4), while inspection (p < 0.01)was explained by specifications emphasized aswell as design and conformance (FQ3, FQ4).Although not reported in Table 6, quality itemswere also significantly related to returns andwarranties (p < 0.05) rework (p < 0.05), andcustomer satisfaction (p < 0.01). For all the actualquality measures, only the average percent ofdefective items was insignificantly related toquality improvement approaches, no improve-ment approaches significant at the p < 0.15 level.

As we see from an analysis of the actualfrequency tables for the approaches to qualityimprovement, companies favored employee involve-ment, management involvement and responsibilityfor quality, and quality improvement projects toguide improvement. Fig. 2 presents means forthese and several other items of particular interest.Surprisingly, statistical process control (SPC) wasnot a strongly favored quality improvement tech-nique, although it was reported as useful by parti-cipants. It was no surprise, based on experience,but a continued disappointment based on theresearch literature, that reward-focused pay forquality performance techniques was not widelyused as a technique to improve quality.

cl1 :02:03:Q4:05:06:Q7:

0 1 2 3 4 5 6 7Average

Applying no formal approachStatistical process control (SPC)Involving employees; each employees responsibility; behavorial in natureManagements involvement and responsibilityIdentifying and resolving improvement projectsCrisis based: attention and improvement primarily after a failureReward focused; pay for quality performance

Fig. 2. Quality improvement item means.


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Hypothesis 3 asked if the quality improvement To some extent it is pleasing to obtain statisticalapproach correlates to operating and financial per- significant, even though the explained variance (R2)formance. Productivity and financial performance is not as great as desired. In summary, it is sug-were measured as described in Table 2, and the gested that these statistical results are reasonablerelationship to quality improvement approaches for this experimental design and methodology.explained in Table 6. Operating performance as However, regardless of arguments, the readermeasured by employee satisfaction was explained must demand the level of significance andby quality improvement approaches captured in explained variance he or she desires. The followingFQ2 and FQ3. section provides an alternative view.

Financial performance as measured by return-on-assets (ROA) was explained by quality improvementapproaches captured in FQ2, FQ3 and FQ4. FQ2captured senior executive involvement items (six),FQ3 reflected design and performance items (three),and FQ4 encompassed items on inspection andseeking conformance to specifications. An interest-ing finding of this study was that financial per-formance could be somewhat explained byquality improvement techniques; these results aresignificant at p < 0.05 and with R2s of 0.1074 and0.1401. The F ratios were good, 3.79 and 4.89, aswell.

There were no significant relationships atp < 0.15 between sales growth and quality improve-ment approach. Overall, these operating and financialresults support Slutis (1992) findings and are new tothe quality improvement literature.

The case for TMQ as a failure. This section hasbeen added for balance to reflect the position thatsome readers might hold, that R2s this low demon-strate no accountability for the vast majority of thevariability. To illustrate, consider training anddevelopment expenditures as a percent of sales(Table 5, R2 = 0.1632). Unaccounted-for varianceis 1 - 0.1632 = 0.8368. This suggests that 83 percentof the variability is not explained by this model.Regardless of significance (p < 0.05 or p < 0.01)there is simply too much unexplained variance.Further, this example was for the model with thehighest R2, other than the customer and employeesatisfaction models. Strengthening the case, recallthat for three models there was no significance -for employee turnover, past years net profit, andsales growth.

Explained variance. At this point in thediscussion, we should note that R2s vary consider-ably throughout the results. They tend to be lowerfor the continuous, ordinal scaled operating andfinancial data (R2 from 0.04 to 0.14) and higherfor the respondent opinion data (R2 from 0.17 to0.53). Several points are in order. First, in themanagement literature R2, the explained variance,is generally much higher for attitude or opinionmeasures than for more exact operating and finan-cial measures. This was true here as well. Second,this is a cross-sectional rather than continuousstudy. The study is at a point in time, rather thanfrom repeated measures over time. The R2 scoresare generally much lower for cross-sectionalregressions as opposed to many continuous regres-sions found in research in economics, finance, andproduction/operations. Finally, another reason forlower R2s is that many items besides quality orproductivity improvement techniques affectoperating and financial performance.

What does this mean? Simply that for threemodels there are no relationships and for theother nine models these factors, and the itemsthat comprise them, do not explain the varianceadequately. The items are directly from the qualityimprovement research literature, from what TQMproponents suggest will influence quality, operat-ing performance, and profitability, yet the factorscomprising the items explain little, if anything.These R2s therefore, suggest that TQM andother commonly promoted practices have littlepractical influence on the performance variablesreported in this study, Significant p values simplydistinguish a set of factors that are statisticallydifferent; they do not explain the unaccounted-forvariance. A case can be made from this study thatTQM is a failure.Productivity improvement approaches

The fourth and final hypothesis asked whether acompanys approach to productivity actually


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42 E.E. Adam, Jr./Journal of Operations Management 12 (1994) 27-44

related to productivity and financial performance.The factor analysis and regressions conducted forproductivity improvement techniques alone are notreported here, due to length restrictions. Overall,regressions were significant and can explain areasonable amount of variation. Findings were(1) no productivity improvement techniques(factors) explained the total cost of quality,although some quality cost components and per-cent of the items defective could be explained byproductivity factors; (2) turnover, net profit, andemployee satisfaction all regressed strongly to pro-ductivity improvement factors (with strong R2scores as well); and (3) financial performance asexpressed by ROA and sales growth was notsignificantly explained by the approach to prod-uctivity. In summary, the approach to produc-tivity improvement - as expressed in factorgroupings of items - related to operating per-formance measures, related somewhat to per-formance quality measures, and did not relateto financial measures. These results are consistentwith the American Quality Foundation andErnest & Young (1992) study in which product-ivity improvement practices strongly related toperformance.

Fig. 3 presents the item means for a few produc-tivity improvement approaches. Inventory reduc-tion (Ql and Q3) and quality improvement (Q2)were viewed as improving productivity by respond-ents at these companies. These items were loaded

Q :02:03:04:05:

0 1 2 3 4 5 6 7Average

Effective inventory planning and controlImproving qualityImplementing just-in-time productionOutput-based individual incentive plansGroup incentive plans like gain-sharing and profit sharing

All data were self-report. They were both anexpression of opinion and beliefs (Likert-scaled)and actual performance. For the actual perfor-mance data, respondents were asked to obtainwithin the firm percent defective, various costs asa percent of sales, return-on-assets, and so forth. Itwould be better to collect these from independentobservation or public records such as financialreports. Some data were missing. Among the low-est response items was the financial information.The lowest response came from past three yearsreturn-on-assets, with but 68 of 187 respondentsproviding answers. Another low response rate wasthe calculated total cost of quality, again withbut 68 of 187 respondents answering al l compo-nents of the total cost calculation. Componentsresponses, such as training and developmentexpenditures as a percent of sales, rangedfrom 112 to 129 of the 187 respondents answering.On the other hand, the majority of all questionshad more than 180 of the 187 respondentsanswering.

Fig. 3. Productivity improvement item means. The issue of an adequate theoretical base for

heavily in the factor loadings (not reportedhere). Incentive systems were not widely thoughtto impact productivity at these companiesthough the research literature suggests theyshould.Fig. 4 presents a summary of the four hypothesesas a model of significant relationships between theapproach to performance improvement and per-formance. One would expect research literature-based quality improvement approaches to impactquality and productivity improvement approachesto impact operating performance. That is shown tobe so (H2 and H4). Interestingly, quality andproductivity improvement (Hi) and qualityimprovement (H,) significantly relate tofinancial performance, especially return-on-assets. This expands our understanding of qualityimprovement.

Research shortcomings. Several deficiencies inthis study might well be addressed in futurestudies. Among them are (1) the self-report natureof all data, (2) missing data, (3) the shortcomings inconducting empirical research without a strongtheoretical basis, and (4) the issue of adequate R*values.


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E.E. Adam, Jr./Jour nal of Operations Management 12 (1994) 27-44 43

IMPROVEMENT PERFORMANCE

QUALITY ANDPRODUCTIVITYIMPROVEMENT- FINANCIALPERFORMANCE

PRODUCTIVITYIMPROVEMENT

OPERATINGPERFORMANCE

Fig. 4. Model of significant relationships between improvement approach and performance.

quality improvement research was reviewed earlier.Perhaps studies such as this will illustrate how the-ories can be tested and revised. The issueof an adequate R* value is left to the reader. Somany factors influence quality, productivity, andfinancial performance that low R*s are almostinevitable in a study such as this. Still, lowR*s are a deficiency and should be addressedin subsequent research. The American QualityFoundation and Ernest & Young (1992) studyinvestigated 945 management practices and foundonly three that broadly and consistently impactperformance. The problem persists for others aswell.

ConclusionsIn conclusion, this study confirms and extends

the work of Benson et al. (1991). Factors wereidentified that capture approach to qualityimprovement (Benson et al., 1991) and to prod-uctivity improvement. A profile emerges for theorganization as to what improvement techniquesmight be most useful if the objective is to improvequality, operating, and/or jinancial performance.The approach to improvement also will varydepending upon the selected measure of quality,operating, and financial performance.

This study is in agreement with reports fromthe PIMS database (Maani, 1988), the Baldrigefinalists (U.S. General Accounting Office, 1991), asystem-structure model of quality management(Benson et al., 1991), and the internationalAmerican Quality Foundation and Ernest &Young report (1992). Yet the results of thisstudy compare most closely to those of Sluti(1992), where he related quality improvementto operating and financial performance for 184manufacturing firms in New Zealand. The 187US firms participating in this study provided thebasis of support necessary to add to a small bodyof empirical knowledge on how best to improveperformance quality in the firm. Results arepromising. Additional knowledge is needed toguide both quality improvement practice andresearch.

AcknowledgementsThe author wishes to acknowledge the assistance

of S. Thomas Foster, Jr., Boise State University forhis assistance in data collection while a doctoratecandidate at the University of Missouri-Columbia,and the assistance of Ron Howren, senior analyst,University of Missouri-Columbia for his assistancein data analysis.


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44 E.E. A dam, Jr./Journal of Operati ons M anagement 12 (1994) 27-44

References

Adam, E.E., Jr., Hershauer, J.C. and Ruth, W.A. 1986. Produc-ti vi ty and Quali ty : Measurement as a Basisor Improvement.Research Center, College of Business and Public Adminis-tration, University of Missouri, Columbia, MO.

Adam, E.E., Jr. and Swamidass, P.M., 1989. Assessingoperations management from a strategic perspective. J.M anagement, vol. 15, no. 2, 181-203.

American Quality Foundation and Ernest & Young, 1992. Theinternational quality study best practices report: An analysisof management practices that impact performance. 49 pp.

Benson, P.G., Saraph, J.V. and Schroeder, R.G., 1991. Theeffects of organizational context on quality management:An empirical investigation. M anagement Science, vol . 31,no. 9, 1107-l 124.

Buzzell, R.D. and Wiersema, F.D., 1981. Successful share-building strategies. Harvard Business Rev., 135-145.

Craig, C.S. and Douglas, S.P., 1982. Strategic factors asso-ciated with market and financial performance. QuarterlyRev. Economi cs and Busi ness, vol. 22, no. 2, 101-l 12.

Crosby, P.B., 1979. Qual it y is Free. New American Library,New York.

Crosby, P.B., 1984. Qual i t y W ithout Tears. McGraw-Hill, NewYork.

Deming, W.E., 1986. Out of t he Crisis. Center for AdvancedEngineering Study, Cambridge, MA.

Evans, J.R. and Lindsay, W.M., 1993, The M anagement andControl of Qual i t y. West Publishing Company, Minneapolis.

Feigenbaum, A.V., 1983. Total Qual i ty Control (3rd ed.).McGraw-Hill, New York.

Fine, C.H., 1986. Quality improvement and learning inproduct systems. M anagement Science, vol. 32, no. 10,1301l1305.

Garvin, D.A., 1984. What does product quality reallymean?. Sloan M anagement Rev., vol. 26, no. 1, 25-43.

Garvin, D.A., 1988. Managing Qual i ty, The Free Press, NewYork.

Heyl, J.E., 1987. The strategic role of quality and productivity:Building competitive strength through operations. In: J.E.Hey1 (Ed.), Proceedings of Fourth Annual OM A Conference,Pheonix, Arizona, 1987.

Ishikawa, K., 1976. Guide to Qual i t y Control . Nordica Inter-national Limited for the Asian Productivity Organization,Hong Kong.

Juran, J.M., 1974. Qual i t y Control Handbook. McGraw-Hill,New York.Juran, J.M., 1982. Juran on Qualit y Improvement. Juran

Institute, New York.

Juran, J.M., 1989. Juran on Leadership for Quali ty . JuranInstitute, New York.

Kim, J.O. and Mueller, C.W., 1976. Int roducti on to FactorAnalysis. Sage Publications, Beverly Hills.

Kim, J.S. and Miller, J.G., 1992. Bui ldi ng the Value Factor y: AProgress Report or U.S. M anufactur ing, A Research Reportof the Boston University School of Management Manu-facturing Roundtable, Boston, MA.

Kopelman, R.E., 1986. M anaging Product ivi ty in Organizat ions,McGraw-Hill, New York.

Leonard, F.S. and Sasser, W.E., 1982. The incline of quality.Har vard Business Rev.

Maani, K., 1988. Quality and productivity: Are they reallycompatible?. Proc. The ORSAjTIM S Joint NationalM eeti ng, Denver, CO.

Miller, J.G. and Kim, J.S., 1990. Beyond the Quali ty Revolut ion:U.S. M anufacturi ng Strat egy in the 1990s. A ResearchReport of the Boston University School of ManagementRoundtable, Boston, MA.

Miller, J.G. and Roth, A.V., 1988. M anufactur ing Str ategies.Executi ve Summary of the 1988 M anufactur ing FuturesSurvey (Manufacturing Roundtable Research Report).Boston University, Boston.

Phillips, L.W., Chang, D.R. and Buzzell, R.D., 1983. Productquality, cost position, and business performance: A test ofsome key hypotheses. J. M ark eti ng, vol. 46, 26643.

Saraph, J.V., Benson, P.G. and Schroeder, R.G., 1989. Aninstrument for measuring the critical factors of qualitymeasurement. Decisi on Sciences, vol. 20, no. 4, 810-829.

Schroeder, R.G., Anderson, J.C. and Cleveland, G., 1986. Thecontent of manufacturing strategy: An empirical study. J.Operati ons M anagement, vol. 6, no. 3,405-416.

Skinner, W., 1978. M anufacturi ng in the Corporate Strategy,Wiley, New York.Sluti, D.G., 1992. Li nki ng Process Quali ty w it h Performance:

An Empiri cal Study of New Zealand M anufacturi ng Plants.Ph.D. Dissertation, The University of Auckland, Auckland,NZ.

Thor, C.G., 1990. Perspectives. Research Report of theAmerican Productivity and Quality Center, Houston, Texas.

U.S. Department of Commerce, 1991. 1991 Application Guide-l ines M alcolm Baldr ige Nat ional Q ual i ty Aw ard. 43 pp.

U.S. General Accounting Office, 1991. Management practices:U.S. companies improve performance through qualityefforts. GAOINSIAD-91-190.

Western Electric, 1956. Stat i st ical Q ual i ty Control Handbook.Delmar Printing Co., Charlotte, NC. Tenth printing: May1984.

Wheelwright, SC., 1984. Manufacturing strategy: defining themissing link. Stra tegic Management J., vol. 5, 77-91.

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