The Scientific Theory-building Process

Ž .Journal of Operations Management 16 1998 321–339

The scientific theory-building process: a primer using the case ofTQM

Robert B. Handfield ), Steven A. MelnykThe Department of Marketing and Supply Chain Management, The Eli Broad Graduate School of Management, Michigan State UniÕersity,

East Lansing, MI, 48824-1122, USA

Abstract

Ž .As Operations Management OM researchers begin to undertake and publish more empirical research, there is a need tounderstand the nature of the scientific theory-building process implicit in this activity. This tutorial presents a process mapapproach to this process. We begin by defining the nature of scientific knowledge, and proceed through the stages of thetheory-building process, using illustrations from OM research in Total Quality Management. The tutorial ends with adiscussion of the criteria for OM journal reviewers to consider in evaluating theory-driven empirical research, and suggests anumber of OM topic areas that require greater theory development. q 1998 Elsevier Science B.V. All rights reserved.

Keywords: Literature review; Empirical research; Theory-building

1. Introduction

Recently introduced broad-based business prac-tices such as Lean Manufacturing, Total QualityManagement, Business Process Re-engineering, andSupply Chain Management have brought with themincreased functional integration, with managers frommultiple areas working full-time on cross-functionalimplementation teams. For researchers in Operations

Ž .Management OM , this means that we will need toparticipate and share ideas with researchers workingin areas such as organizational behavior, marketing,and strategy. To do so, however, we will need tocommunicate using the language of theory. We mustknow how to build, refine, test and evaluate theory.Theory and theory-building are critical to our contin-

) Corresponding author.

ued success, since ‘‘Nothing is so practical as aŽ .good theory’’ Simon, 1967; Van De Ven, 1989 .

Without theory, it is impossible to make meaning-ful sense of empirically-generated data, and it is notpossible to distinguish positive from negative resultsŽ .Kerlinger, 1986, p. 23 . Without theory, empiricalresearch merely becomes ‘data-dredging’. Further-more, the theory-building process serves to differen-

Ž .tiate science from common sense Reynolds, 1971 .A major objective of any research effort is to createknowledge. Knowledge is created primarily by build-ing new theories, extending old theories and discard-ing either those theories or those specific elements incurrent theories that are not able to withstand thescrutiny of empirical research. Empirical research is,after all, the most severe test of all theory andresearch. Whatever question we ask, whatever datawe collect reflects the impact of either a theory or

Ž .framework be it explicit or implicit . Whenever we

0272-6963r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved.Ž .PII S0272-6963 98 00017-5

( )R.B. Handfield, S.A. MelnykrJournal of Operations Management 16 1998 321–339322

analyze data, we are evaluating the findings in lightof these underlying theories or frameworks.

Given the increasing importance of theory, it isimperative that we have a clear and unambiguousunderstanding of what theory is and the stages in-volved in the theory-building process. Developingsuch an understanding is the primary purpose of thispaper. This primer borrows extensively from thebehavioral sciences, since the practice of theorydriven empirical research has been relatively wellestablished and many of the issues now facing OMresearchers have been previously addressed by re-searchers there.

The process of transporting this existing body ofknowledge to Operations Management is not an easytask. First of all, Operations Management is a rela-tively new field with its own unique set of needs andrequirements. This is a field strongly linked to the‘real world’. It is a field where little prior work intheory-building exists. Until recently, much of thework in OM was directed towards problem solvingrather than theory-building. Due to the nature of ourfield, most OM researchers intuitively think in termsof processes. While several prior works identify the

Žneed for theory in OM e.g., Swamidass, 1991;.Flynn et al., 1990; McCutcheon and Meredith, 1993 ,

there is no published work which specifies the actualprocess used in carrying out a theory-based empiricalstudy. Much of the existing body of knowledgepertaining to theory-building and testing has beenorganized around concepts, definitions and problemsin other fields such as marketing, strategy, sociology,and organizational behavior. As a result, there is acritical need to restate this body of knowledge into aform more consistent with the Operations Manage-ment frame of reference. This is the major objectiveof this paper.

We provide a view of theory-building andtheory-driven empirical research that is strongly pro-cess-oriented. This view of theory-building drawsheavily from an initial model developed by WallaceŽ .1971 . We begin with Wallace because he presentsone of the few models in the theory-building litera-ture that is process-based. However, it is importantto note that the theory-building model presented inthis paper draws heavily on the thoughts and contri-butions from other researchers. As such, it is aneclectic merger reflecting the contributions of many

different writers from diverse areas. Finally, giventhe application orientation of the Operations Man-agement field, we illustrate the application and powerof this model by drawing on examples from Total

Ž .Quality Management TQM . We conclude withguidelines for journal reviewers who evaluate andcriticize empirical theory-building research.

2. OM as scientific knowledge

Underlying the notion of theory-driven empiricalresearch is the view of operations management asscience. One of the major traits of a science is that itis concerned only with those phenomena that can bepublicly observed and tested. This is very relevant toOperations Management since we deal with a fieldwhich is practically oriented. Practising managers areone of the major consumers of the knowledge cre-ated by OM researchers. These managers use thisinformation to hopefully improve the performance oftheir processes. Unless we can provide these ‘con-sumers’ with knowledge pertaining to events whichare observed and tested, managers will quickly andruthlessly discredit the resulting research.

An important point to note about OM research isthat its basic aim is not to create theory, but tocreate scientific knowledge. Most people want scien-

Ž .tific knowledge to provide Reynolds, 1971, p. 4 :Ø A method of organizing and categorizing ‘things,’

Ž .a typologyØ Predictions of future eventsØ Explanations of past eventsØ A sense of understanding about what causes

events, and in some cases,Ø The potential for control of events.

The creation of knowledge, while critical, is notsufficient for success. To be successful, the researchmust be accepted and applied by other researchersand managers in the field. To gain such acceptance,the research must improve understanding of the find-

Ž .ings Reynolds, 1971; Wallace, 1971 and it mustachieve one or more of the five above objectives ofknowledge. Finally, it must pass the test of the realworld. An untested idea is simply one researcher’sview of the phenomenon—it is an educated opinionŽ .nothing more . It is for this reason that empiricalresearch is the cornerstone for scientific progress,

( )R.B. Handfield, S.A. MelnykrJournal of Operations Management 16 1998 321–339 323

especially in a field such as Operations Managementwhere research results may be put to the test bymanagers on a regular basis.

A good example of how a great idea can laterbecome accepted can be illustrated by the earlybeginnings of TQM. In the 1920s, two Bell System

Ž Ž .and Western Electric employees, Shewhart 1931.and George Edwards , in the inspection engineering

department, began noting certain characteristics ofproblems associated with defects in their products.Based on these observations, Edwards came up withthe idea that quality was not just a technical, butrather an organizational phenomenon. This conceptwas considered novel at the time, but generallyirrelevant even in the booming post-war marketŽ .Stratton, 1996 . Quality assurance was simply anidea. Its impact had yet to be extensively tested inthe real world; that task would fall to Deming, Juranand their disciples in post-war Japan. At this point inhistory, however, few researchers and practitionerswere aware of the importance of Quality and QualityAssurance.

Clearly, one cannot specify how OM researchersshould go about creating knowledge. However, aswe will show, theory is the vehicle that links data toknowledge. This is the process that we will focus onin the next section.

3. The scientific theory-building process

How are theories developed? Researchers havenoted over the years that there exists no commonseries of events that unfold in the scientific process.However, several leading philosophy of sciencescholars have identified a number of common themeswithin the scientific process. The most common of

Ž . Ž .these was stated by Bergmann 1957 p. 31 , andŽreiterated over the years by others Popper, 1961;

Bohm, 1957; Kaplan, 1964; Stinchcombe, 1968;.Blalock, 1969; Greer, 1969 : ‘The three pillars on

which science is built are observation, induction, anddeduction’. This school of thought was later summa-rized into a series of elements and first mapped by

Ž . Ž .Wallace 1971 see Fig. 1 . The map provides auseful reference in identifying the different stagesthat must occur in the scientific process.

Fig. 1. The Principal Information Components, MethodologicalControls, and Information Transformations of the Scientific Pro-

Ž .cess Wallace, 1971, p. 18 .

Due to the cyclical nature of the process, there isreally no unique starting point at which to beginwithin this map. However, it makes sense to begin at

Ž . Žthe lower section, with Step 1. Wallace 1971 p..17 summarized his mapping as follows:

Individual observations are highly specific and es-sentially unique items of information whose synthe-sis into the more general form denoted by empiricalgeneralizations is accomplished by measurement,sample summarization, and parameter estimation.Empirical generalizations, in turn, are items of infor-mation that can be synthesized into a theory viaconcept formation, proposition formation, and propo-sition arrangement. A theory, the most general typeof information, is transformable into new hypothesesthrough the method of logical deduction. An empiri-cal hypothesis is an information item that becomestransformed into new observations via interpretationof the hypothesis into observables, instrumentation,scaling, and sampling. These new observations aretransformable into new empirical generalizations,Žagain, via measurement, sample summarization, and

.parameter estimation , and the hypothesis that occa-sioned their construction may then be tested forconformity to them. Such tests may result in a newinformational outcome: namely, a decision to accept

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Table 1Match research strategy with theory-building activities

Purpose Research question Research structure Examples of data collection tech- Examples of data analysis proce-niques dures

1a. DiscoÕery ØWhat is going on here? Ø In-depth case studies ØObservation Ø InsightØUncover areas for research Ø Is there something interesting ØUnfocused, longitudinal field Ø Interviews ØCategorizationand theory development enough to justify research? study

ØDocuments ØExpert OpinionØElite Interviewing ØDescriptions

Ž1b. Description ØWhat is there? Ø In-depth case studies ØObservation Interviews group Ø Insight.or individual

ØExplore territory ØWhat are the key issues? ØUnfocused, longitudinal field ØDocuments ØCategorizationstudy

ØWhat is happening? ØElite Interviewing ØExpert OpinionØCritical Incident ØDescriptionsØTechnique ØContent Analysis

2. Mapping ØWhat are the key variables? ØFew focused case studies ØObservation ØVerbal ProtocolØ Identifyrdescribe key vari- ØWhat are the salientrcritical Ø In-depth field studies Ø In-depth interviews ØAnalysisables themes, patterns, categories?ØDraw maps of the territory ØMulti-site case studies ØDiaries Survey questionnaires ØCognitive Mapping

ØBest-in-class case studies ØHistory ØRepertory grid techniqueØUnobtrusive measures ØEffects Matrix

ØContent Analysis

3. Relationship Building ØWhat are the patterns or link- ØFew focused case studies ØObservation ØVerbal Protocolages between variables?

Ø Improve maps by identify- ØCan an order in the relation- Ø In-depth field studies Ø In-depth interviews ØAnalysising the linkages between ships be identified?variablesØ Identify the ‘why’ underly- ØWhy should these relationships ØMulti-site case studies ØDiaries Survey questionnaires ØCognitive Mappinging these relationships exist?

ØBest-in-class case studies ØHistory ØRepertory grid techniqueØUnobtrusive measures ØEffects Matrix

ØContent AnalysisØFactor AnalysisØMultidimensionalØScalingØCorrelation analysisØNonparametric statistics

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4. Theory Validation ØAre the theories we have gen- ØExperiment ØStructured Interviews ØTriangulationerated able to survive the test ofempirical data?

ØTest the theories developed ØDid we get the behavior that ØQuasi-experiment ØDocuments ØAnalysis of variancein the previous stages was predicted by the theory or

did we observe another unantici-pated behavior?

ØPredict future outcomes ØLarge scale sample of popula- ØOpen and closed-ended ques- ØRegressiontion tionnaires

ØLab experiments ØAnalysisØField experiments ØPath AnalysisØQuasi-experiments ØSurvival AnalysisØSurveys ØMultiple comparison proce-

duresØNonparametric statistics

5. Theory Extensionr ØHow widely applicablergener- ØExperiment ØStructured Interviews ØTriangulationRefinement alizable are the theories that we

have developed?ØTo expand the map of the ØWhere do these theories apply? ØQuasi-experiment ØDocuments ØAnalysis of variancetheoryØTo better structure the the- ØWhere don’t these theories ap- ØLarge scale sample of popula- ØOpen and closed-ended ques- ØRegressionories in light of the observed ply? tion tionnairesresults

ØLab experiments ØAnalysisØField experiments ØPath AnalysisØQuasi-experiments ØSurvival AnalysisØSurveys ØMultiple comparison proce-

duresØDocumentation ØNonparametric statisticsØArchival Research ØMeta Analysis


or reject the truth of the tested hypothesis. Finally, itis inferred that the latter gives confirmation, modifi-cation, or rejection of the theory.

Once again, note that there is no distinct patternfor the manner in which this process unfolds. Thespeed of the events, the extent of formalization andrigor, the roles of different scientists, and the actualoccurrence of the events themselves will vary con-siderably in any given situation. However, the modelprovides a useful way of conceptualizing the primarythemes that take place. The model also provides aninitial template for OM researchers interested intheory-driven empirical research. Moving throughthe different stages requires a series of trials. Thesetrials are initially often ambiguous and broadlystaged, and may undergo several revisions beforebeing explicitly formalized and carried out.

The left half of the model represents what ismeant by the inductiÕe construction of theory fromobservations. The right half represents the deductiÕeapplication of theory to observations. Similarly, thetop half of the model represents what is often re-ferred to as theorizing, via the use of inductive anddeductive logic as method. The bottom half repre-sents what is commonly known as doing empiricalresearch, with the aid of prescribed research meth-ods. The transformational line up the middle repre-sents the closely related claims that tests of congru-ence between hypotheses and empirical generaliza-tions depend on the deductive as well as the induc-tive side of scientific work, and that the decision toaccept or reject hypotheses forms a bridge betweenconstructing and applying theory, and between theo-

Žrizing and doing empirical research Platt, 1964 Mer-.ton, 1957 . With this model in mind, we can now

proceed to each quadrant of the model and illustratethe processes using the unfolding field of TQM as areference point to illustrate each process.

3.1. Step 1: ObserÕation

Observation is a part of our daily lives, and is alsothe starting point for the scientific process. As NagelŽ . Ž .1961 p. 79 points out:

Scientific thought takes its ultimate point of depar-ture from problems suggested by observing things

and events encountered in common experience; itaims to understand these observable things by dis-covering some systematic order in them; and its finaltest for the laws that serve as instruments of explana-tion and prediction is their concordance with suchobservations.

Observation, however, is shaped by the observer’sprior experiences and background, including priorscientific training, culture, and system of beliefs.Likewise, observations are interpreted through scal-ing, among which certain specified relations areconventionally defined as legitimate. In this manner,observations can be compared and manipulated. Theassignment of a scale to an observation is by defini-tion a classificatory generalization. Summarizing asample of individual observations into ‘averages’,‘rates’, and ‘scores’ is by definition dependent onthe sample. A biased sample will surely affect theway that observations are interpreted, and will there-fore also affect parameter estimation. The transfor-mation of observations into empirical generalizationsis therefore affected by the choice of measures,sample, and parameter estimation techniques em-ployed.

Ž .This problem was noted by Kaplan 1964 in hisparadox of sampling. This paradox states that thesample is of no use if it is not truly representative ofits population. However, it is only representativewhen we know the characteristics of the populationŽ .in which case we have no need of a sample! . Thispresents a dilemma, since samples are supposed tobe a random representation of a population. Al-though the paradox of sampling can never be com-pletely resolved, OM researchers need to carefullyconsider the attributes of their population in general-izing observations. Specifically, researchers mustconsider the possible effects of industry, organizationsize, manufacturing processes, and inter-organiza-tional effects in setting boundary assumptions ontheir observations. Such precautions taken early inthe theory development process will result in greaterrewards later in the theory testing phase, and willenhance the power of the proposed relationships.

The underlying purpose and set of techniquesassociated with different types of observations aresummarized in the first two rows of Table 1, whichcan be better appreciated if the nature of the columns


Table 2Recasting Table 1 from a Process and TQM Perspective

Process step Purpose Illustrative TQM references

Step 1a: Observation Discovery Juran, 1974; Deming, 1981, 1982, 1986; Shewhart, 1931Step 1b: Observation Description Business Week, 1979; Crosby, 1979Step 2: Empirical Generalizations Mapping Hayes, 1981; Wheelwright, 1981; Tribus, 1984Step 3: Theories Relationship Building Greene, 1993; Handfield and Ghosh, 1994; Sirota and Alper, 1993Step 4a, 4b: Hypothesis Testing Theory Validation Ahire et al., 1996; Black and Porter, 1996; Flynn et al., 1994;

Hendricks and Singhal, 1998; Misterek, 1995; Powell, 1995Step 5: Logical Deduction Theory ExtensionrRefinement Anderson et al., 1994; Sitkin et al., 1994; Spencer, 1994

is first understood. The first column, Purpose, de-scribes the goals driving research at each stage; theResearch Questions column lays out some of thetypical questions that a researcher might be inter-ested in answering at each stage of the process; theResearch Structure column deals with the design ofthe study; Data Collection Techniques presents someof the procedures that a researcher might draw on incollecting material for analysis; Data Analysis Proce-dures summarizes some of the methods we might useto summarize and study the results of the data col-lected. The techniques and procedures presented inthe last two columns are not intended to be exhaus-tive or comprehensive; rather they are intended to beillustrative. Finally, we have also provided someillustrative examples of studies from the TQM litera-ture that are representative of each process stage inTable 2.

3.2. Step 2: Empirical generalization

An empirical generalization is ‘an isolated propo-sition summarizing observed uniformities of relation-

Žships between two or more variables’ Merton, 1957:.95 . This is different from a ‘scientific law’, which is

‘a statement of invariance deriÕable from a theory’Ž .Merton, 1957: 96 . A theory, on the other hand, canbe defined as a statement of relationship betweenunits observed or approximated in the empirical worldŽBacharach, 1989; Coehn, 1980; Dubin, 1969; Nagel,

.1961 . Moreover, empirical generalizations do nothave explanatory theories to explain them. The trans-formation from an ‘idea’r‘fantasy’ into ‘understand-ing’r‘law’ is an important part of the inductivetheory-building process, but is somewhat difficult todescribe precisely. There are a variety of differentperspectives regarding whether theories are induced

Žfrom facts or from simple thought experiments see,for example, Nagel, 1961; Popper, 1961; Hempel,

.1965, 1952; Watson, 1938 .Some of the techniques for transforming observa-

tions into empirical generalizations are summarizedin the first three rows of Table 1. ‘Discovery’ createsawareness of a problem or an event which must beexamined or explained. In a sense, discovery uncov-ers those situations or events which are mystifying,and leads to further inquiry. Another form of obser-vation is ‘Description,’ wherein one tries to explainwhat is happening in those situations identified in theDiscovery phase. Description is often concerned withinformation gathering and identifying key issues.There are two major types of descriptions: tax-onomies and typologies. Taxonomies deal with a

Žcategorical analysis of the data i.e., ‘What are the.phenomena?’ . In contrast, a typology tries to de-

scribe what is the most important aspect of thephenomena or activity under consideration. The goalin each case is to provide a thorough and usefuldescription of the event being studied. With thisdescription completed, we can now proceed to Map-ping, where one attempts to identify the key vari-ables and issues involved, without specifying theactual structure of the problem. In essence, one istrying to generalize from a set of observations on avery broad level. Specific problem structures aredeveloped later during the relationship building

Žphase, which occurs through concept formation in.the theory-building process . Thus, discovery ex-

pands boundaries; description provides a portrait ofthe new events or problems; mapping identifies thefactors that are important; and, relationship buildingprovides structure.

Returning to our TQM example, we know that asŽ .early as 1941 Deming and Geoffrey 1941 , while


working for the Bureau of the Census, had shownthat the introduction of quality control in clericaloperations saved the bureau several thousand dollars.In 1946, the American Society for Quality Controlwas formed. At this point, America was entering thepost-war boom, and production issues tended tooverride concerns of quality control. As a result,

Žquality was relegated second-class status Handfield,.1989 . It was for this reason that Deming, along with

Juran, traveled to Japan to teach and implementmethods of quality management. The Japanese rec-ognized the importance of quality control in rebuild-ing their industries and their economy.

By the late 1970s, the American public becameaware of the difference in quality between Japaneseand American-made products. Business Week pub-lished a major article recognizing the need for Amer-ican manufacturers to adopt a top–down quality

Ž .management changeover Business Week, 1979 . Theauthors broke down the components of quality as

Ž . Ž .defined by Juran 1974 , Crosby 1979 and Deming,and also noted how the Japanese had implementedthese methods. The article identified the importanceof quality as a strategy: ‘‘ . . . product quality can be apivotal, strategic weapon—sometimes even morecrucial than price—in the worldwide battle for mar-

Ž .ket share’’ 1979: 32 . The article also laid the fullblame on top management, not the worker. It wasalso noted how the Japanese stressed defect preven-tion as opposed to defect detection, which in turnrelates to careful product design. These discoveriesbegan to lead to a major empirical generalization.

Empirical generalization 1: Japanese companies em-ploy quality assurance techniques, produce highquality products, and haÕe penetrated a number ofAmerican markets. American companies do not em-ploy quality assurance techniques and are losingmarket share in seÕeral industries to the Japanese.

This generalization began to suggest some of thekey variables, and even hinted at a relationship, butdid not go the next step in answering ‘why’?

3.3. Step 3: Turning empirical generalizations intotheories

The creation of theories from empirical observa-Žtions is a process of ‘disciplined imagination’ Weick,

.1989 , involving a series of thought trials establish-ing conditions and imaginary outcomes in hypotheti-cal situations. Once a problem has been identified,the researcher develops a set of conjectures in theform of ‘If–Then statements’. In general, a greaternumber of diverse conjectures will produce bettertheories than a few homogeneous ones. The conjec-tures are then chosen according to the researcher’sselection criteria, which should include judgments ofwhether the relationship is interesting, plausible,consistent, or appropriate. The researcher must becareful at this stage to maintain consistency in crite-ria when evaluating different conjectures. Examplesof selection criteria include the following:

Ž .1. ‘That’s Interesting’ Davis, 1971 —Is the rela-tionship not obvious at first?

Ž .2. ‘That’s Connected’ Crovitz, 1970 —Are theevents related when others have assumed they areunrelated?

Ž .3. ‘That’s Believable’ Polanyi, 1989 —Is the rela-tionship convincing?

Ž .4. ‘That’s Real’ Campbell, 1986; Whetten, 1989—Is the relationship useful to managers?The theory-building process at this stage should

not be constrained by issues of testability, validity,or problem solving. When theorizing is equated withproblem-solving at this stage, the activity becomesdominated by the question.

The process of taking an empirical generalizationand developing it into a theory produces conceptsand propositions that specify relationships. Theoriesemerge when the terms and relationships in empiri-cal generalizations are made more abstract by intro-ducing terms that refer to non-observable constructs.The researcher may employ ‘heightened idealization’,by either dropping error terms that are usually ex-plicit in empirical generalization, or relegating them

Ž .to an implicit status in the theory Wallace, 1971 . Indeveloping and building relationships between con-structs, the researcher is seeking to achieve twomajor objectives. The first is to generalize the nature

Žof relationships between key variables often in the.form: x´y . These relationships address the ‘how’

component of theory. Second, we also try to explainthe reasons for these relationship. In other words, we

Ž .provide the ‘why’ Whetten, 1989 . Theory mustcontain both how and why. It must also both predictand explain known empirical generalizations.


Returning to the TQM example, Empirical Gener-alization 1 proposed a link between quality andchange in market share in the 1970s and early 1980s.Initially academics and practitioners conducted casestudies of Japanese manufacturers, thereby generat-ing another set of empirical generalizations thathelped explain the relationship between quality andfinancial performance in greater detail. One of the

Ž .first was Hayes 1981 . He described the typicalJapanese factory as being clean and orderly, with

Žsmall inventories neatly piled up in boxes as op-posed to the large work-in-process inventories wit-

.nessed in comparable American facilities . He notedthat Japanese workers were extensively involved inpreventive maintenance of machines and equipmentmonitoring on a daily basis. Hayes also noted thatJapanese plants had defect rates of about 0.1%, ascompared to around 5% in most U.S. plants. Thiswas achieved by ‘thinking quality’ into the productthrough product design, worker training, quality cir-cles, and screening of materials and suppliers. Fromthese observations, Hayes surmised that a philosoph-ical change in Japanese organizations had taken placedue to Deming’s teachings.

ŽOther researchers e.g., Wheelwright, 1981;.Tribus, 1984 in the 1980s began to notice another

attribute of successful Japanese corporations: topmanagement responsibility for quality. An associa-tion between quality, flexibility, cost, dependabilityand overall corporate performance was also recog-nized. The positive nature of these relationships led

Žto the notion of simultaneity where improving qual-.ity also simultaneously reduces costs . These and

other observations made by researchers during the1980–1985 period regarding quality management canbe summarized in the following theoretical state-ment.

Proposition 1: Quality management practices driÕenby top management leads to fewer defects, reducedcosts, and higher customer satisfaction, which inturn leads to lower oÕerhead costs, higher marketshare, and improÕed financial performance.

Although this proposition partially helps explainwhy Japanese companies outperformed Americancompanies, it fails to explain the ‘how’ behind theconcept of TQM. Although American executivesbelieved quality was important, they were still un-

sure about the methods and procedures to implementŽTQM within their companies Wall St. Journal,

.1985 . During this period, Deming returned to theU.S. from Japan and began to work with Americancompanies to achieve this objective.

The most famous set of prescriptions to emergefrom Deming’s work were the Fourteen Points and

Ž .the Seven Deadly Sins Deming, 1981, 1982, 1986 .The emphasis of these points is essentially about theattitudes that should exist and the nature of therelationships among people in successful organiza-

Ž .tions Stratton, 1996 . From the Fourteen Points andtheir own observations, a number of researchersbegan to develop concepts and theoretical relation-ships between them that specify the relationshipbetween TQM to financial performance in an in-creasingly abstract manner.

One method frequently used in OM to describeand explore an area when there is no a priori theory

Žis the case study Eisenhardt, 1989; McCutcheon and.Meredith, 1993 . This type of theory-building relies

on direct observations with the objects or partici-pants involved in the theory and its developmentŽGlaser and Strauss, 1967; Yin and Heald, 1975;

.Miles and Huberman, 1994 . The potential output isa description of events and outcomes that allow otherresearchers to understand the processes and environ-

Žment with the focus often being on exemplary orŽ ..revelatory cases Yin and Heald, 1975 .

ŽSeveral researchers e.g., Sirota and Alper, 1993;.Greene, 1993; Handfield and Ghosh, 1994 have

used direct observation from case studies and devel-oped theoretical statements about TQM. In thesestudies, the focus of attention shifted from the prod-uct and process to the corporate culture. For exam-

Ž .ple, Sirota and Alper 1993 after interviewing em-ployees at 30 companies, proposed that the greatestimpact of TQM occurred when companies switchedfrom a ‘detection’ to a ‘prevention’ culture. In a setof interviews conducted with 14 North American andEuropean Fortune 500 quality managers, Handfield

Ž .and Ghosh 1994 found a relationship betweenchanges in the corporate culture and performance.They proposed the existence of a transformation

Žprocess that consisted of a series of stages Aware-ness, Process Improvement, Process Ownership, and

.Quality Culture . Financial performance improved asthe firms progressed through the stages. Based on


these and other studies, we might modify our priortheoretical proposition as follows.

Proposition 1a: Visionary leadership driÕes the inte-gration of continuous improÕement and Õariationreduction processes into organizational culture,thereby enabling firms to eliminate defects, reducecosts, and improÕe customer satisfaction, which inturn leads to reduced oÕerhead costs, higher marketshare, and improÕed financial performance.

Here again, we predict a relationship betweenŽobservablernonobservable constructs continuous

improvement, variation reduction processes, organi-zational culture, visionary leadership, customer satis-

. Žfaction and direct observables defects, costs, mar-.ket share, and financial performance. At this stage,

as shown in Fig. 2, the construction of theory fromobservation ends and the application of theory toobservations begins.

3.4. Step 4a: Hypothesis generation

By this stage, we have in place all the elements ofa theory. We have presented the what, how, and whydemanded of a theory. Now, the researcher mustbegin to compare the theory to determine its relative

Ž .applicability to observations Wallace, 1971 . Thereare three types of comparisons that may be used todetermine the extent to which a given theory pro-vides useful symbolic representations of actual andpossible observations. First, internal comparisonsmay be made, whereby some parts of the theory arecompared with other parts in order to test whether itis internally consistent and non-tautological. Tauto-logical theories cannot, by definition, be falsified.For instance, suppose we hypothesize that ‘Reducingmaterial defects leads to better quality’. If we findthat quality is defined by the number of materialdefects, then the relationship is tautological.

Secondly, the theory may be compared with othertheories to test whether, all other things being equal,it has a higher level of abstraction, is more parsimo-nious, or is more flexible. This may involve anassessment of the ‘nomological net’, which consistsof the underlying groundwork of related theory whichexists in a particular field. The net is establishedthrough a literature review that establishes the frame-

work within which the new theory is embedded orŽ .framed Cook and Campbell, 1979 . The primary

purpose of this net is often to place one constructrelative to other constructs. While the development

Ž .of a nomological net with its boxes and arrowsserves to answer the ‘what’ and ‘how’ questions as

Ž .posed by Whetten 1989 , the nomological net is nottheory because the ‘why’ question and the boundaryconditions are often not specified.

For instance, TQM has been examined in relationto the mechanistic, organismic, and cultural models

Žof organization which exist in the literature Spencer,.1994 . The author found that many of the new ideas

regarding TQM are associated with organismic con-cepts, whereas Deming’s work seems to graft mecha-nistic and organismic concepts into a coherent whole.The cultural model also taps into the philosophicalcomponents of TQM and is useful for evaluating thedeployment of the practice. This assessment seems toprovide reasonable support for Proposition 1a.

Finally, the theory may be compared with empiri-cal facts by comparing its predictions or low-levelhypotheses with appropriate empirical generaliza-

Ž .tions to test the truth of the theory Wallace, 1971 .For instance, we could partially compare our theoret-ical statement with the experiences of firms such asXerox, Ford, and Motorola, which have successfullyemployed TQM to respond to their various problemsŽ .Greene, 1993 . A very succinct empirical general-ization regarding quality was made by the CEO of alarge US global multi-national company: ‘‘Quality

Ž .equals survival’’ Bonsignore, 1992 . Such general-izations also lend support to Proposition 1a.

The real test of a theory begins, however, whenhypotheses are deduced from the theory. Once aparticular set of conjectures or propositions has beenselected, the researcher must now put them intoempirically testable form. A key requirement of thisform is that the researcher must be able to reject

Ž .them based on empirical data Popper, 1961 . Hy-potheses act as the vehicle by which the researcherdiscards old variables and relationships which havenot been able to pass through the screen of falsifica-tion and replaces them with new variables and rela-

Ž .tionships which are again subject to evaluation .With this approach, all hypotheses are essentially

tentative. They are always in the process of eitherbeing developed or being refuted. Recalling that a


theory is a ‘statement of relations among conceptswithin a set of boundary assumptions and con-straints’, the hypothesis development involves anexplicit translation of concepts into measures. Be-cause many of the concepts used in OM have nospecific metrics, OM researchers often find that asystem of constructs and Õariables will have to becreated before testing can proceed.

Constructs are approximated units, which by theirvery nature, cannot be observed directly. ConstructsŽ .otherwise referred to as latent variables are createdfor the explicit purpose of testing relationships be-

Žtween associated concepts. Variables or manifest.variables , on the other hand, are observed units

which are operationalized empirically by measure-ment. ‘The raison d’etre of a variable is to provideˆan operational referent for a phenomenon described

Ž .on a more abstract level’ Bacharach, 1989: p. 502 .In testing a theory, the researcher is testing a state-ment of a predicted relationship between units ob-served or approximated in the real world. Thus,constructs are related to each other by propositions,while variables are related by hypotheses. The wholesystem is bounded by the researcher’s assumptions.

Since OM is a relatively new field, researchersmay need at times to borrow measures from other

Žmore developed fields such as marketing and orga-.nizational behavior while in other cases developing

new detailed measures of their own. If newly devel-oped multiple measures are needed to accuratelyassess a construct, the variables used to define theconstructs in the proposed relationship must be co-herent, and must pass a series of tests which addresstheir measurement properties. These include tests ofcontent and face validity, convergent, discriminant,construct, and external validity, reliability, and statis-

Ž .tical conclusion validity Flynn et al., 1990 . Thesefactors will in turn depend on the choice of instru-

Ž .mentation case studiesrinterviews vs. surveys ,Ž .scaling nominal, ordinal, or ratio , and sampling

Ždefining the population and the relative power of.the test .

Returning to our TQM theory, we find that Propo-sition 1a consists of a number of rather broad con-structs. Several researchers have undertaken the taskof developing a set of constructs and measures re-lated to the concepts proposed in our theoretical

Ž .statement. Powell 1995 developed a number of

constructs and measures related to continuous im-Žprovement quality training, process improvement,

.executive commitment and organizational cultureŽopenness, employee empowerment, and closer rela-

.tionships with suppliers and customers . Others suchŽ . Ž .as Flynn et al. 1994 , Black and Porter 1996 , and

Ž .Ahire et al. 1996 have also developed measures forconstructs commonly associated with TQM by theBaldrige Award, including top management commit-ment, customer focus, supplier quality management,employee involvement, employee empowerment,employee training, process improvement, teamwork,SPC usage, and others. Suppose that among thedifferent types of process variation techniques, welimit our analysis to the implementation of processcapability in manufacturing. This involves the cross-functional cooperation between design and manufac-turing personnel to ensure that product specificationsare wider than process specifications, in order toensure that natural variations in the process do notresult in product defects. Let us now suppose that weare also interested in the effect of this form of TQMpractice on new product success. We can then spec-ify the following hypothesis.

Hypothesis 1: Companies that employ process capa-bility studies in new product deÕelopment haÕe prod-ucts that are positiÕely associated with improÕedmarket performance.

This hypothesis embodies certain important traits.First, causality is proposed, which assumes a stricttime precedence—the introduction of process capa-bility in new product development must precedeproduct introduction and change in market perfor-mance. Second, the theory specifies a relationshipbetween two constructs: the use of process capabilitystudies and market performance. Third, on a moredetailed level, we have now begun to operationalizeour constructs through bounding assumptions, andhave limited the level of abstraction first from TQMin general to variation reduction practices, and fi-nally to the use of process capability studies. At thispoint, we can identify measurable or manifest vari-ables associated with each of the constructs withinthe hypothesis. For instance, we could measure theuse of process capability through direct measuresŽe.g., C or C to measure process capability, per-p pk


.cent market share for market performance , or indi-Ž .rect measures multiple Likert scales . We would

then need to set up conditions for testing the effectof one variable on another, and apply the appropriateset of tests to determine whether the occurrence wasnot just mere chance.

In testing our hypothesis, we may have severalŽoptions available as shown in the ‘Theory Valida-

.tion’ portion of Table 1 . We might begin by con-ducting a set of single or multiple case studies usingstructured interviews that allow ‘pattern matching’Ž .Yin, 1989 , where the pattern of actual values ofproduct market share versus their comparable Cpk

ratings in the product development process are com-pared. If the constructs and measures are relativelywell-developed, we might conduct a survey of auto-motive part manufacturers that elicits multiple re-sponses from design and development engineers,assessing the extent to which process capability stud-ies are carried out on a routine basis. We may needto develop indirect measures for new constructs,such as routine product development team meetings,information system implementation, training of de-sign engineers in process capability methods, etc. Inturn, this could lead us to develop a set of statistical

Ž .tests e.g., regression or structural equations specify-ing the impact of these elements on new productmarket success, determined by the percent of satis-fied customers called at random, number of warrantycalls, number of complaints, ratings in Ward’s AutoWorld, etc. When such a study has been carried out,and the statistical results summarized, we are readyfor the next step.

3.5. Step 4b: Hypothesis testing

When we have finished with the summary of theŽ .empirical data, we return to ‘observations’ Fig. 1 .

At this point a set of findings has been constructed tocorrespond logically to a given theoretically-deducedhypothesis. We are now interested in internal validityor the extent to which the variables identified by thehypotheses are actually linked or related in the way

Ždescribed by the hypothesis Hedrick et al., 1993, p..39 . The hypothesis is highly testable if and when it

can be shown to be false by any of a large number oflogically possible empirical findings and when onlyone or a few such findings can confirm it. In other

words, the researcher is concerned with whether xindeed affects y in the way that we predicted basedon the initial theory and hypotheses developed.

The decision to accept or reject a hypothesis isŽ . Žnot always straightforward. Popper 1961 pp. 109–

.110 suggests that the test procedure is like a trial byjury. All of the elements in the theory-building pro-cess are put ‘on trial’, including the originatingtheory, its prior support, the steps of deducing hy-potheses, and the interpretation, scaling, instrumenta-tion, and sampling steps involved.

3.6. Step 5: Logical deduction

Next, we close the gap between theory and theempirical results. Logical deduction requires that wereturn to our original research question, and askourselves if the results make sense or at least con-tribute to the theory from on a more specific level. Ingeneral, there are three possible outcomes at this

Ž .point: 1 ‘end confirmation to’ the theory by notŽ .disconfirming it; 2 ‘modify’ the theory by discon-

Ž .firming it, but not at a crucial point; or 3 ‘over-throw’ the theory by disconfirming it at a crucialpoint in its logical structure, in its history of competi-

Ž .tion with rival theories Wallace, 1971 . Irrespectiveof the outcome, the theory is affected to some extent.

Suppose that we have generated reasonable empir-ical support for our hypothesis. What does this tell usabout our theory outlined in Proposition 1a? Whensupport for the hypothesis is found, the researchermay proceed to theory extensionrrefinement. Thisset of activities associated with the theory-buildingprocess focuses on external Õalidity, or the ‘extent towhich it is possible to generalize from the data andcontext of the research study to broader populations

Žand settings especially those specified in the state-. Žment of the original problemrissue ’ Hedrick et al.,

.1993, p. 40 . As shown in the last section of Table 1,theory extensionrrefinement involves applying thetheory and the hypotheses in different environmentsto assess the extent to which the results and out-comes indicated by the hypotheses are still realized.If we had tested our hypothesis using a sample ofdomestic automotive parts manufacturers, then onemight argue that the same hypotheses on process

Žcapability studies and by association, process varia-.tion practices be tested both in other industrial


Žsettings e.g., electronics manufacturers, machine tool.manufacturers, hard disk manufacturers, etc. and

Žother countries e.g., Japan, Germany, Brazil and.Malaysia . Generally speaking, OM focuses at the

level of individuals, groups, processes and plants.The greater the range of settings in which a theorycan be successfully applied, the more general thetheory and the more powerful it is.

The strongest statement possible from our ‘imag-inary study’ is that there is statistical evidence tosupport the fact that process capability studies canimprove performance. Because of the path taken indeveloping this hypothesis, there is also support forthe theory that variation reduction practices can re-duce product defects, and by extension, improvefinancial performance. The researcher will now prob-ably seek to publish this result in an academicjournal.

4. Evaluating empirical theory-driven research

As an increasing number of OM researchers sub-mit empirically-based articles, reviewers are faced bythe challenge of evaluating these manuscripts. Injudging the contribution of a theory-buildingrtest-ingrextension study, reviewers should pay attention

Žto four major criteria as identified by researchersŽ . Ž .such as Blalock 1970 , Wallace 1971 , Whetten

Ž . Ž .. Ž . Ž .1989 and Simon 1967 : 1 ‘not wrong,’ 2Ž . Ž .falsifiability, 3 utility, and 4 parsimony.

4.1. ‘Not wrong’

The criterion of ‘not wrong’ is a test applied tothe overall approach of the paper and the proceduresused by the researchers. This is a simple test inwhich we, as reviewers, examine the study to ensurethat the research carried out and described has beenexecuted correctly. This test begins by looking atwhether the research methodology used within thestudy is appropriate given the nature of the researchproblem stated. If the research problem is essentiallyexploratory in nature, then using a statistical proce-

Ždure such as linear regression which is more appro-.priate for evaluating well developed hypotheses

should raise concerns.The ‘not wrong’ criterion also focuses on whether

the constructs defined by the researcher are consis-

tent with the manner in which they are implemented.For example, if the researcher uses a single indicatorto measure or implement a multi-trait construct, thenthis should raise a ‘red flag’ in the mind of thereviewer. It is not appropriate to measure a complexconstruct such as quality with a simple, single indica-tor such as ‘Number of defects per million parts’.

Next, the ‘not wrong’ criterion forces the re-viewer to assess whether the researcher has used theresearch methodology correctly. This assessment em-braces a number of different issues. On the lowestlevel, it forces the reviewer to determine if theresearch project or the data set violated any of themajor assumptions on which the procedures beingused are based. It also forces the reviewer to deter-mine if the data is reported correctly. In some cases,this may mean that the reviewer must ensure thatthey accept the ‘correctness’ of such indicators asdegrees of freedom or the p-statistic or the standarderrors. This criterion also requires that the researcherprovide sufficient data so that the reviewer can judgeindependently the ‘correctness’ of the results. Forexample, if the researcher were to analyze a data set

Ž . Žusing Structural Equation Modeling SEM see.Bollen, 1989 , then it would be useful for the re-

viewer to have either the variancercovariance matrixor the correlation matrix. At the highest level, wemust determine if the researcher is using the mostpowerful or suitable tool or whether the researcher isinvolved in ‘fashion statistics’. That is, in our fieldas in others, there emerge new statistical techniquesthat suddenly attract a great deal of attraction. Sud-denly, it seems, nearly every paper uses this tech-nique, even though it may not be appropriate.

Finally, this criterion forces the reviewer to deter-mine whether the question being posed is ‘post hoc’Ž . Ž .after the fact or ‘ad hoc’ before the fact . That is,the reviewer must assess the extent to which thetheory and questions are driving the resulting analy-sis, or whether the data and its subsequent analysisare driving the theory and question. The latter ap-proach, indicative of statistically driven data fitting,is highly inappropriate within any theory driven em-pirical research framework.

4.2. Falsifiability

Falsifiabilty requires that the proposed theory becoherent enough to be refuted, and that it specify a


relationship among concepts within a set of bound-Ž .ary assumptions and constraints Bacharach, 1989 .

Anytime we propose a relationship between twoŽconcepts often specified by arrows between ‘boxes’

.in a diagram we are faced with the task of demon-strating causality. In the case of OM, most re-searchers are interested in identifying what the effectof various managerial decisions are on system orfirm performance. While the notion of cause andeffect is fairly simple to understand, demonstratingcausality for these types of situations is by no meansan easy undertaking.

Causality represents the ‘holy grail’ since re-searchers have always striven to achieve it yet haveacknowledged that it can never be irrefutably‘proven’. The role of causality in science has its

Žorigins in the work of Hume see Cook and Camp-.bell, 1979 who stressed three conditions for infer-

Ž .ring that a cause X has an effect Y: 1 both theproposed cause and effect must be concurrent and

Ž . Žcontiguous, 2 there is temporal precedence, the.cause always occurs before the effect in real time ,

Ž .and 3 there must be demonstrated conjunction be-tween the two. Hume’s approach challenged the

Žearly positivist views of causality that correlation. Žimplied causation , and later essentialist views that

causation referred to variables which were necessary.and sufficient for the effect to occur .

Ž .The work of Mill 1843 has had the greatestinfluence on current paradigms of causation, andposits three conditions for establishing causation:

ŽØ Cause precedes effect in time temporal prece-.dence

Ž .Ø Cause and effect have to be related simultaneityŽØ Other explanations have to be eliminated exclu-

.sivity .Of these three requirements, the third is often the

most difficult to achieve in an OM setting, for itimplies control over alternative explanations. Whilesources of variation and simultaneity can be estab-lished in simulation and mathematical modeling,these factors are not so easily controlled for inempirical research designs. Most OM studies takeplace in field settings which are subject to a greatnumber of possible sources of variation. An empiri-cal OM researcher must strive to develop criteriawithin the research design which provide some evi-dence that the conditions for causality are being met,

by ruling out those variables that are possible ‘causes’of the effects under study.

Ž .Blalock 1970 notes two primary problems thatempirical researchers encounter:

One is in the area of measurement, and in particularthe problem of inferring indirectly what is going on

Ž .behind the scenes e.g., in people’s minds on thebasis of measured indicators of the variables inwhich we are really interested. The second areawhere precise guidelines are difficult to lay down isone that involves the linking of descriptive factsŽ .whether quantitative or not with our causal inter-pretations or theories as to the mechanisms that haveproduced these facts.

Statistical methods are important in proving thatthere is sufficient evident that a relationship betweentheoretical constructs exists. However they are notsufficient by themselves. In many cases, the reviewer

Žmust look beyond the tables and numbers Blalock,.1970 . The reviewer should judge the validity of the

proposed relationship based on the soundness of thelogic used in measuring the constructs, and shouldalso look for supporting evidence of the relationship,even if it is anecdotal. One area which should re-ceive special attention is that of the ‘quality’ of themeasures being used. Theory-driven research is verysensitive to this aspect. Unlike studies where the goalis prediction, the intent is that of explaining. Toensure that this objective is met, the researcher mustrecognize the threats to validity posed by the poten-tial presence of missing variables, common methods,errors in variables and other similar problems andmust have controlled for them within the study.

Authors can strengthen their argument by employ-ing data ‘triangulation’. That is, they should be ableto supplement their statistical results with case stud-ies, quotes, or even personal insights that may helpto portray the results in a vivid way and provideadditional insights. Research that draws on differentkinds of data that converge to support a relationshipcertainly provides a stronger case for inferringcausality. For instance, perhaps the OM researchercan interview members of a process improvementteam, or even check with the plant’s customers. Thecombination of methodologies to study the samephenomenon borrows from the navigation and mili-tary strategy that use multiple reference points to


Žlocate an object’s exact point Smith, 1975; Jick,.1983 .

One problem that OM researchers often encounteris that managers want to be represented in the bestpossible light. Triangulation can be an important toolto combat this problem. For instance, a manager maycircle the ‘Strongly Agree’ response on a Likertscale survey question asking whether ‘We use TQMin our plant’. However, if the researcher conductsinterviews in the plant and finds that few workersagree with the philosophy of TQM and even fewerunderstand or use the tools of Statistical ProcessControl, then considerable doubt is cast on the valid-ity of the survey question.

Finally, researchers should look for multiplemethods to verify statements deduced from priorempirical studies. What may be appropriate in onesetting may not be appropriate in another.

4.3. Utility

The third attribute is more commonly referred toas usefulness. Using the basic questions and practical

Ž .styles of a journalist, Whetten 1989 suggests thatthe essential ingredients of a value-added theoreticalcontribution are explicit treatments of Who?, What?,Where?, When?, Why?, and How? These criteria arenot enough however. A theory that is complete,comprehensive and exhaustive in its analysis butwhich addresses an issue seldom if ever encounteredin the field is not useful. In OM, research fundingoften comes from private industries that are impa-tient in realizing the practical significance and utilityof abstract theories. Our research should be appliedand the outputs potentially applicable to the OMenvironment. Therefore, useful theories should havethe following traits:Ø The theory must deal with a problem of ‘real

importance’.Ø The theory must point to relationships or uncover

potentially important variables overlooked in priorstudies.

Ø The theory must direct the researcher to issuesŽand problems not previously examined but which

.are still of interest .Ø The theory must explain or provide insight into

behavior or events occurring in other areas.Ø The theory must be operationalized.

Ø The theory and its output must be interesting.Of these traits, the last one requires further expla-

Ž .nation. First advocated by Davis 1971 , interestingtheories were ones which caused the readers to ‘sit

Ž .up and take notice’ Davis, 1971, p. 310 . To beinteresting, theories had to present an attack on anassumption which was taken for granted by thereaders. Interesting theories present one of two types

Ž .of arguments Davis, 1971, p. 311 :Ø What is seen as non-X is really X, or,Ø What is accepted as X is actually non-X.

The assumptions attacked by interesting theoriescannot be ones strongly held by the readers. Paperspresenting such arguments are examples of That’sAbsurd and are often summarily dismissed by thereaders, not to mention reviewers and discussants!For example, if we were to read a paper arguing thatthere is no linkage between corporate strategy, cor-porate performance and manufacturing capabilities,our initial reaction would be to dismiss the paperout-of-hand without reading it any further. Why?Because we see economic performance as beingstrongly influenced by manufacturing capabilities.This view has been shaped by a long line of researchgoing back to Adam Smith!

Second, interesting theories must consider bothŽthe theoretical and the practical dimensions Simon,

.1967 . They must be seen as being of real practicalsignificance to the audience. This significance mightlie in directing research into new directions; it couldindicate new research methodologies. If the practicalconsequences of a theory are not immediately appar-ent, the theory will be rejected. Theories lackingsuch practical significance are examples of the cate-gory of Who Cares?

Third, interesting theories must challenge. Theo-ries which merely confirm views, assumptions orframeworks already accepted by the audience are notinteresting. Such theories represent the That’s ObÕi-ous category. As can be seen from this discussion, togenerate theories that are interesting, the writersmust identify and understand their audience. Whatmay be obvious to one audience may be absurd toanother and interesting to a third.

It should be noted here that the discipline of OMis one in which practical knowledge, accumulatedfrom years of experience, has surpassed scientificknowledge built upon theories that have withstood


many empirical attempts at falsification. To establishOM as a scientific field, it may therefore be neces-sary in some cases to relax the stringent condition of‘That’s Obvious’. What is obvious in OM is oftenbased on anecdotal evidence with few formally con-structed statements of conceptual relationships.Therefore, in the early stages of our field’s emer-gence as a scientific discipline, perhaps it is all right,if not imperative, that such ‘obvious’ theories begenerated and empirically examined.

4.4. Parsimony

In addition to ‘not wrong,’ causality and utility, aŽfourth trait of good theory is parsimony Reynolds,

.1971 . A good theory should be rich enough tocapture the fewest yet most important variables andinteractions required to explain the events or out-comes of interest. Why is parsimony so important?Because the power of any theory is inversely propor-tional to the number of variables and relationshipsthat it contains. The theory should be free of redun-dancy, and if it could do as well or better without agiven element of form or content, that element is anunnecessary complexity and should be discardedŽ . Ž . Ž .Wallace, 1971 . As Popper 1961 p. 142 noted,‘‘Simple statements . . . are to be prized more highlythan less simple ones because they tell us more;

because their empirical content is greater; becausethey are better testable.’’

Researchers should be aware that the need to beparsimonious introduces its own set of challenges.By excluding certain dimensions to focus on othermore important dimensions, the researcher runs therisk of potentially overlooking or omitting importantfactors in the development of a theory. Importantextensions to current theories are often uncovered byresearchers who examine those factors which areeither omitted or treated in a very superficial orsimplified manner. In deciding whether a theorycontains extraneous elements, the reviewer shouldconsider whether it adds to our overall understandingof the phenomenon, or whether it can be simplifiedto its essential elements. The 80r20 rule may againapply in such cases!

5. Conclusion

Theory development is a dynamic process withtheories essentially being ‘work-in-process’. At anypoint in time, some segments of the theory are beingtested for internal or external validity and othersegments being discovered, described or mapped.Each stage in this process is driven by different typesof research questions and has different objectives. As

Table 3Stages of research areas in operations management

Theory-building Discoveryrdescription Mappingr Theory validationrstage relationship-building extensionrrefinement

Research Area ØComputer Integrated ØOrder Entry and Ø Inventory TheoryManufacturing Release Systems

ØEnvironmentally-friendly Ø Just-in-Time ØManufacturingManufacturingrDesign Planning and Control

ØSupply Chain Mgmt. ØTotal Quality Mgmt. Ø Jobshop SchedulingØGlobal Manufacturing ØTime-based Competition ØLotsizingØ ‘Extended Enterprises’ ØLean Manufacturing ØStatistical Process

ControlØLearning Organizations ØWorld Class Mfg. ØProject ManagementØ Inter-Organizational ØConcurrent Engineering ØFocused Factories

Information SystemsØManufacturing Strategy ØProduction

CompetenceØMass CustomizationØCross-functional Teaming


a result, there is a need to apply different sets ofresearch methodologies as one undertakes variousactivities. What may be highly appropriate for onestage of theory-building may be inappropriate foranother. These relationships between stage in theorydevelopment and research methodologies are sum-marized in Table 1.

Although the process of theory-building is notalways strongly sequential, it often begins with dis-covery and ultimately culminates with theory valida-tion, extension, and refinement. However, re-searchers can enter at any stage in the theory-build-ing process. The stage at which a researcher enters isoften influenced by their academic research trainingand skills, and the degree to which he or she feelscomfortable in dealing with the methodologies typi-cally employed at each stage. Working at the earlystages of theory-building requires that the OM re-searcher be out in the field and in close contact with

Žthe environments being studied. In later stages e.g.,.theory validation , a large portion of our knowledge

and frameworks now come from previously com-Ž .pleted research published and private . In these

stages, the researcher can choose to maintain dis-tance from the situation being studied by drawingdata from large scale mailings of survey question-naires, or employing computer simulation models.

We are fortunate to find ourselves in a compara-tively young field with many areas ripe for theorydevelopment. Some of these areas have been classi-fied according to their stage of theory developmentin Table 3. This table is not intended to be anexhaustive list of topics in OM, but is intended toprovoke discussion. Areas such as inventory theory,job shop scheduling, and manufacturing planning arecomparatively well-developed. Much of the currentresearch here involves extending and refining exist-ing theories. Other concepts such as Time-basedcompetition, Total Quality Management, Lean Man-ufacturing, and Cross-functional Teaming have beenaround for a number of years, but are still in theMapping and Relationship Building stage from atheoretical context. While the concepts are fairlywell-defined, there remains considerable work to bedone in establishing the critical implementation suc-cess factors within organizations that lead to im-proved performance. Finally, a number of emergingareas in operations are still in the embryonic stage,

including Environmentally-Conscious Manufactur-ing, Supply Chain Management, and the VirtualOrganization.

This article proposes a road map for OM re-searchers to follow in developing theory-driven re-search, and has also outlined a number of key at-tributes for evaluating this research. From this point,we need to assess the critical areas for theory devel-opment through a number of different streams ofactivity. First, theory-building needs to be empha-sized in our research methods seminars for doctoralstudents. Second, we should encourage cross-fertili-zation of our doctoral students in other fields tobroaden our theoretical foundations. Finally, profes-sional development seminars at conferences shouldbe developed in order to ‘re-tool’ academics inter-ested in theory-building. It is hoped that OM re-searchers can employ the material presented to guidethem in building better and more consistent theories,and progress towards a better understanding of theradical change taking place in the field.

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The Scientific Theory-building Process

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