Conducting case study research in operations management

Journal of Operations Management, Elsevier

Conducting David

case study research in operations

M. McCutcheon

Department USA

239

11 (1993) 239-256

management

and Jack R. Meredith

ofOperationsManagement

and Information Systems, University of Cincinnati, Cincinnati, OH 45221-0387,

(Received 8 October 1990; accepted in revised form 3 March 1993)

Abstract Recently, there have been numerous calls for more empirical field-based research to be conducted in operations management (OM). Knowledge of how operations systems work can be enhanced significantly through contact with the “real-world” conditions that OM models seek to describe. Case study research is a primary means of exploring field conditions but is an unfamiliar methodology for many in OM. Moreover, the case study method is viewed with scepticism by those who consider it to be a weak form of research, one that lacks rigor and objectivity. Here, we offer an introduction to the case study method for OM researchers who may have little background in fieldbased research. We provide an outline of the procedure and cite some excellent sources that cover case study design, data analysis and the philosophical rationale for the methodology. We also identify some recent examples of OM case studies that illustrate our points. We then contrast the various uses for case study research and their different design and theory requirements. An appendix provides a listing of case studies that have appeared in some OM journals in recent years, classifying the studies by their research purpose. However, regardless of their purposes, case study research need to be conducted in a manner that assures maximum measurement reliability and theory validity. We describe some of the steps that must be taken to ensure that a study is as rigorous as possible. We also argue that, properly conducted, a case study is a truly scientific research approach. We conclude by pointing out some areas of OM research where case studies might be particularly valuable.

1. Introduction The need for the operations management (OM) field to pursue more empirical methods in its research has been noted in this journal (e.g., Meredith et al., 1989; Flynn et al., 1990) and elsewhere (e.g., Wood and Britney, 1989; Swamidass, 1991). A number of successful trends for managing operations have stemmed from developments in industry, such as just-in-time systems (e.g., Schonberger, 1982) and some popular quality management approaches (e.g., Crosby, 1979). These new approaches were not complex but did indicate Correspondence to: Dr. D.M. McCutcheon, Department of Operations Management and Information Systems, Carl H. Lindner Hall, University of Cincinnati, Cincinnati, OH 45221-0387, USA.

that some widely held assumptions in OM were not necessarily accurate. The gap between what academics were assuming and the real conditions of operations led to growing disparities between OM research’s prescriptive advice and workable answers for managers (e.g., McKay et al., 1988). Sensing this gap, a growing number of OM researchers have seen the need to gather better information about the realities of operations systems and to develop better, more complete theories about them. A prime means of developing well-grounded theories is through empirical, fieldbased research. One problem with making a shift to field-based research is that it is generally unfamiliar to many well-established OM researchers. Our purpose here is to foster a better understanding of one field-based empirical method, case study research.

0272-6963/93/$06.00 0 1993 Elsevier Science Publishers B. V. All rights reserved.

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We summarize the methodology’s important characteristics-the different types of case study research and their different requirements and outputs, the basic procedures for conducting them, and the central concerns in maintaining scientific rigor with the methodologyPand indicate the best sources for more complete information. To dispel the misconception that case study research lacks rigor, we point out the method’s characteristics that make it a truly scientific form of inquiry. Finally, we add to the literature by demonstrating the limited but growing acceptance of case study research in OM journals and indicate a number of areas where further case studies would be useful in our field.

2. Case study research as a method The term “case study” is commonly applied in OM to descriptions of implementations of new methods or techniques. The term is also often applied to articles based on interviews of key figures or managers. As well, the “war story”, summarizing an insider’s view ofparticular events, is frequently classified as a case study. None of these instances truly represents case study research, which is based on the observations and assessments of an objective outside party. Undoubtedly, these misapplications of the term “case study” have affected the reputation of this research method. A case study is an objective, in-depth examination of a contemporary phenomenon where the investigator has little control over events (Yin, 1989). This definition covers several significant points. First, the study typically involves one or more researchers gathering a considerable volume of data from within an organization to develop the clearest possible picture of the phenomenon. The data may come from primary sources (such as direct observation or interviews of people involved) or secondary sources (documents or records, for example). It may examine a single situation or, with multiple-case studies, several related situations. Second, distinct from historical studies, case study research generally focuses on current conditions, using historical data primarily to understand or substantiate the information

gathered about the ongoing situation. Third, the researcher usually has little or no capability of manipulating events (in contrast to uction research, where the researcher is involved as a participant and director of events in a natural setting). The case study’s purpose may be strictly to describe a situation but, more often, it is to understand how or why events occur (Yin, 1989). For this purpose, the researcher assesses the conditions surrounding the phenomenon to build a plausible explanation or discover a causal relationship that links the antecedents to the results (Benbasat et al., 1987). Conducting a case study for research purposes is very different from developing a teaching case for pedagogical use. While a single investigation might produce both, they differ in intent. The research case study requires considerable depth to allow comparisons with other study sites and may describe conditions in academic terms. The more familiar teaching case is designed primarily to expose students to managerial decision-making situations and may not allow easy comparisons to other contexts. Also, a teaching case usually provides a detailed narrative description of events and leaves the explanations as an exercise for students; the research case study will usually focus on providing explanations and use the detailed description to substantiate the researcher’s deductions. Some case studies intentionally provide only description but, unlike teaching cases, they are not designed to pose a readily solvable problem. Case research methodology is just one of many empirical approaches that aim to develop our understanding of “real world” events. The various forms of empirical investigation derive their strength from focusing the research on actual conditions. However, using an organization as the research site means that such methods also face inherent difficulties. Typically, investigating ongoing business operations does not allow conditions to be controlled or variables to be maniputo be performed) to lated (that is, “treatments” affect outcomes. This restriction eliminates the use of controlled experimentation and with it, the powerful procedures that are the bases of laboratory experiments and mathematical simulations.

D.M. McCutcheon. J.R. Meredith I Case study research in operations management

The researcher must therefore study the phenomenon by noting the states, in each case, of all the conditions that might affect outcomes. If there are a large number of cases and if the researcher knows which conditions are likely to affect outcomes, quasi-experimental methods (Campbell and Stanley, 1966) might be employed (whereby the conditions’ possible impacts can be accounted for statistically). However, with unfamiliar situations or ones for which there is little theoretical background, the researcher might not know which conditions are relevant or important; moreover, there may be very few examples to study, especially compared to the number of conditions that must be accounted for (Yin, 1989). Under these circumstances, the case study approach may be the only available means of investigating a problem. Because of its unique strengths, case study research is often used for developing new theories or for examining unfamiliar situations. As illustrated in Table 1, typical uses for case studies in the first theory development stages are to describe a hitherto unstudied situation or to explore it (Yin, 1989) probably the most common roles for this methodology. A highly influential descriptive case is Monden’s (198 la, b) expositions of Toyota’s production methods. One example of an exploratory case study is Gerwin’s examination of a single firm’s acquisition of a flexible manufacturing system (Gerwin, 198 1) when such systems were largely unfamiliar to OM researchers. The exploratory work was later expanded with Gerwin and Tarondeau’s (1982) investigation of computer-integrated manufacturing systems in four different countries, a multiple-case study that sought to explain major implementation decisions using an uncertainty-reduction theory to structure the enquiry. However, case studies may also be used to support, expand, or raise doubts about existing theories, and can do so just as effectively as other methodologies that are often viewed as more rigorous or powerful (Lee, 1989). Yin (1989) refers to case studies designed to determine “how” or “why” events occur as explanatory studies. An example of the explanatory role is a study that compared the introduction of a new technology in each of two firms which used radically different

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approaches (McLaughlin et al., 1984). Since both implementations were successful, the commonalities and differences of the two could be compared to indicate factors that did or did not appear to influence implementation success. Another example is a multi-plant longitudinal study within a single corporate division that investigated influences on productivity, as measured by a total factor productivity model (Hayes and Clark, 1985). Although this particular research project might be described as a field study, due to its size and design, the intensive investigation techniques used and the search for influencing factors are characteristic of case studies. As Table 1 indicates, each different role for case investigations has unique requirements for type and number of sites and the level of theory development. In subsequent sections, we discuss the use of case study research for description, exploration, and explanation, and outline the general requirements for making it a suitable research tool. First, though, we provide a more detailed outline of case study research in practice.

3. Conducting case studies There are some excellent sources that explain in detail how to design and conduct case study research. Here, we offer a quick outline of the methodology and indicate the primary references for more complete descriptions. 3.1. Basic procedure The case study researcher observes, firsthand if possible, the events surrounding a situation. The researcher may also try to develop an understanding of the mechanisms involved. Direct observation of events may play only a minor role. The researcher may gather information through a number of other means, primarily interviews of key individuals-the managers, workers or technical staff involved. Interviews may be structured to ensure coverage of key topics but the interview format is generally open-ended, allowing the interviewer to explore areas that come to light during the course of discussion.

For comparing competing theories, one or more cases with conditions that allow theories to be compared for their explanatory ability (Lee, 1989).

Critical case (Yin, 1989) that provides clear-cut evidence that a theory is inapplicable or incorrect. Evidence that disconfirms one or more theories designed to explain events and outcomes in the case situations.

Single or multiple cases that allow “pattern matching” (Yin, 1989), where the pattern of actual values of dependent variables vs independent variables are compared to those predicted through theory; replication by investigating other sites where theory should apply.

-Disconfirmation

Theory and perhaps operational measures of constructs are defined well enough to allow hypotheses to be proposed prior to conducting site visits.

Explanatory

Hypothesis testing.

Multiple cases that may be maximally different to highlight the commonalities and differences in the observed phenomena.

One or more cases selected to apply existing theories to situations where their applicability has not been tested.

Propositions developed, based on the observations at one or more sites. Operational constructs may be refined or developed; however, some of the measured constructs may not prove useful in the evolving theories (Eisenhardt, 1989).

May have some a priori theory that is used to select case sites and the constructs to be examined.

Exploratory

Hypothesis generation

Exemplar cases (with extreme or unique circumstances) or revelatory cases (first examination of the phenomenon for scientific purposes (Yin, 1989).

Indication of theory’s validity; may involve assessment of reliability and validity of measures.

Description of events and outcomes to allow other researchers to understand the processes and environment. May indicate the relative importance of some factors.

May be no a priori theory when events are examined; important constructs are not likely to be defined.

Descriptive

Exploration

Sites used

Confirmation

Potential output

Theory requirements

Yin’s terminology (Yin, 1989)

Research phase (Benbasat et al., 1987)

Table 1 Research design considerations

D.M. McCutcheon.

J.R. Meredith 1 Case study research in operations management

Observations and interviews may be supplemented with documents, historical records, organization charts, production statistics and other sources that either provide a clearer understanding or corroborate other data. Given enough background theory, a standardized survey might also be conducted within the case organization. An important source of information is the setting itself: much of the input that the researcher receives may come from the surroundings, the atmosphere and the myriad details that are absorbed during the interview/observation process. Case studies may be carried out by individuals or teams; teams may divide responsibilities for data collection, comparison and analysis in any number of ways. Case study investigation is inherently flexible: the research scope can be expanded as necessary, the focus shifted, or other sources sought as the study progresses. However, it should not be assumed that conducting case research is informal or casual. Properly carrying out a case study requires clearly stated goals and theoretical bases, a protocol for information gathering (e.g., interview guidelines and structure), carefully selected research sites, and the trust and cooperation of those to be studied. Probably the best source for procedural details is the book by Yin (1989); a briefer description of methods is presented by Benbasat et al. (1987). 3.2. Theory base Besides procedural preparation, case research also requires at least some theoretical development, unless its purpose is strictly descriptive. Purists may take the viewpoint of Glaser and Strauss (1967), who argue that grounded theory development should ultimately start with observation, the theory being built only after at least some initial empirical input. However, as Eisenhardt (1989) points out, a “clean theoretical slate” approach is unlikely, since the research’s purpose, site selection, and information gathering require some rationale, indicating at least some theoretical basis. However, if the research intent is to build theory, then it may be preferable to limit theoretical assumptions to a minimum. On the other hand, the theoretical background for a case study may

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in fact be very well developed, especially where the intent is to test or compare theories against empirical evidence. The necessary base may be available in the welldeveloped theories from other fields. Disciplines such as economics, organization theory, oganization behavior, and business strategy have theories that are relevant to the operations function. Many of these theories could be improved or extended by OM researchers with their understanding of operations conditions and technologies. 3.3. Design and site selection Table 1 indicates how the study’s theory base affects its design. Initially, when investigating events that may have little or no theoretical background, the researcher may select an exemplar site (Yin, 1989) that provides the best example of a phenomenon. Here, the case study may simply describe what the researcher gathered about the situation, point out factors that may be of importance or define some elements of a conceptual model. Exploratory case studies usually focus on theory development. Here, the research goals are likely more explicit (e.g., identifying the factors that appear to influence a particular process). It may be helpful to select several very different settings, through deliberate “theoretical sampling” (Glaser and Strauss, 1967), that reflect the range of conditions thought to affect outcomes. Commonalities and differences across the varied settings help to outline the patterns upon which to develop theory. In this stage, the contrasting conditions may also help in the framing of operational measures, since the measures should be applicable across all settings. Explanatory case studies involve hypothesis testing. Although case study research may not be viewed as a powerful theory-testing method, it can serve this purpose in several ways. First, testing hypotheses may involve demonstrating a theory’s applicability under circumstances not previously investigated or pointing out the theory’s inapplicability, either in specific circumstances or in general. Here, sites might be selected to provide extreme examples of outcomes (e.g., highly success-

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ful projects versus highly unsuccessful ones). Since only one well-documented contrary instance can disprove a hypothesis, a case study can be a powerful tool to delimit a theory’s generalizability or to discount it altogether. Ultimately, a single site provides a high degree of control for comparing multiple competing theories. For example, in situations where several theories each explain results on the basis of their own set of prior conditions, the presence or absence of those conditions can support or disconfirm the individual theories in the face of the identical outcome. A good example of this technique is presented by Markus (1983). 3.4. Data analysis and theory development For exploratory or explanatory studies, data analysis may be carried out concurrent with or following the site visits. Data may be strictly qualitative-verbal, descriptive, or impressionistic-or may be quantitative (e.g., demographics or scores from questionnaires). The case study method can be used to investigate problems from within a number of research paradigms: the researcher may take an interpretive approach in understanding and explaining the data or a more positivist approach, relying to some extent on objective measurement instruments. Combining more than one approach can be especially fruitful in increasing researchers’ deductive efforts (e.g., Kaplan and Duchon, 1988). Case studies intended to test theories may rely more on quantitative analysis, with well developed measures and good understanding of needed controls. Most case research generates a considerable volume of data that has to be distilled to provide a succinct picture of the events. Whether the study is exploratory or explanatory, data analysis is a critical and difficult phase. Generally, relevant details must be sorted from the mass of observations, comments, reports and interviews. Data analysis relies on two basic sets of tools. The first tools are data reduction methods. There are a number of techniques to summarize or characterize data through data arrays or reduction that can help sort out patterns from the masses of material that a case study can generate. These include using tableaux that categorize each of the important conditions at each case site, or arraying sites on two-

dimensional maps to discern groupings or clusters. A simple tableau example is the “coping mechanism” table in Raturi et al. (1990, p. 244). Miles and Huberman (1984) is a good source for qualitative data analysis methods. Perhaps the more important tools are those of logical analysis. The case researcher seeks to find logical connections among the observed events, relying on knowledge of how systems, organizations and individuals work. An interpretive approach might be taken, wherein the researcher attempts to explain results by developing an understanding of the perceptions and reactions of individuals or groups (e.g., Kaplan and Duchon, 1988; Bushe, 1988). The theory must not only be logical but must also fit the observed “facts”, at least as accurately as rival theories. Those who customarily use mathematical analysis may view this reliance on the researcher’s logical reasoning to deduce relationships as a highly subjective practice, since such deductions cannot be verified with the ease or precision afforded by mathematics. However, in advocating the use of case research for theory testing in management information systems (MIS), Lee (1989) points out that “mathematics is a subset of formal logic, not vice versa. Logical deductions in the general case do not require mathematics. “An MIS case study that performs its deductions with verbal propositions (i.e., qualitative analysis) therefore only deprives itself of the convenience of the rules of algebra; it does not deprive itself of the rules of formal logic .” (Lee, 1989; emphasis in original). The case study’s reader must judge the researcher’s reasoning, based on the provided data. In fact, this subjectivity is a property shared with virtually all forms of empirical research. However, the case’s subjective portion tends to be very obvious, while other empirical methods may have similarly subjective elements (such as a survey respondent’s interpretation of questionnaire items) that are cloaked in objectivity through their reduction to numerical data. 3.5. Expanding In some ambiguity)

a study

instances, the findings (or the data’s may need clarification or demand

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J.R. Meredith / Case study research in operations management

more sites. A case study can be expanded in an iterative fashion as theory develops and understanding increases, following the precepts of grounded theory development (Glaser and Strauss, 1967). However, the case study’s intensiveness leads to inefficiencies when the study is expanded too far, reaching “theoretical saturation” (the point at which further observations provide minimal incremental insight (Glaser and Strauss, 1967) with perhaps ten or fewer sites (Eisenhardt, 1989). The methodology, then, is characterized by design flexibility and reliance on the researcher’s ability to discern cause-and-effect relationships in complex organizational contexts. Case researchers frequently confront new concepts or situations; the researcher is often forced to rely on imaginative means to gather data about them. These conditions demand special care to ensure that measures of the investigated phenomena are as accurate as possible. The next section describes some of the major concerns in measurement and how the case study’s design must account for them.

4. Research design considerations This methodology’s flexibility, while valuable, limits the development of common standards and practices. A case study’s research contribution is largely determined by its design quality and by the researcher’s analysis (discussed in the next section). Design quality depends primarily upon the researcher’s rigor in dealing with validity and reliability issues. Problems with theory development case research often revolve around the construct and content validity of the concepts under investigation. Construct validity is the extent to which the operational measure for a construct (a concept that is rigorously defined for the purposes of scientific enquiry) (1) reflects all of the construct’s observable effects, (2) appears to describe a single construct, and (3) correlates appropriately with operational measures of other related constructs (Nunnally, 1978). Thus, construct validity is the issue of establishing the theoretical territory that goes with the defined construct and ensuring con-

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sistency between it and other recognized constructs. Content validity, on the other hand, concerns how the construct is measured rather than its theoretical basis. It is determined by how well a measure samples a specified content domain (Nunnally, 1978) to ensure that the construct is accurately reflected or, more simply, the degree to which the operational measure accurately reflects a precisely defined construct (Zaltman et al., 1982). Thus, content validity concerns whether the operational measure corresponds well with the construct it is supposed to “tap”. Content validity problems are likely to arise where constructs have broad potential domains, such as with the “technical knowledge level” of operations managers (Johnson, 1990) or “worker skill levels” within a production group. How many questions would have to be asked to properly gauge these constructs? Because many areas of OM have had limited construct development and testing, researchers are likely to confront both construct and content validity problems. Eisenhardt (1989) notes that one advantage of field-based research techniques such as case studies is that their operational measures are more likely to be measurable and usable in hypothesis testing because of their grounded nature. This makes case study research especially valuable in developing, testing and refining operational measures for constructs, a necessary precursor to theory testing. Another concern in case study research is the internal validity of the proposed relationships, that is, whether the right cause-and-effect relationships have been established (Yin, 1989). In contrast to mathematical representations or simulations where the number of variables is limited and their interactions are usually clearly specified, the fieldbased researcher may easily attribute outcomes to the wrong causes, based on spurious relationships. There are numerous common sources of problems that can mislead the researcher who attempts to detect causal relationships in natural settings. These “threats to internal validity”, and the ways to guard against them, are discussed by Cook and Campbell (1979). With case research, thorough analysis and data triangulation (use of multiple sources and meth-

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ods) can help get the most accurate picture of events. However, the case researcher may face a very complex phenomenon which, coupled with a small number of comparison sites, increases the likelihood of deducing incorrect causal relationships. One approach for validating conclusions is through “pattern matching” (Campbell, 1975), whereby the pattern of independent versus dependent variables expected through the theory are compared, case by case, against the pattern of observed characteristics. Each new case provides another independent test of the hypothesized relationships. External validity, that is, the extent to which findings drawn from studying one group are applicable to other groups or settings, is a special concern for case studies. No empirical study offers certainty that its findings are valid for other populations. Although field studies and surveys may control for some factors and thereby better define a specific population over which results might be statistically generalized, external validity (the applicability of findings beyond the group) is still an issue for them. For case studies, results can be tested and extended by replication, that is, through the investigation of other cases where results should be comparable. Yin (1989) suggests two parts to the replication procedure: literal replication, examining cases where the theory would predict similar results (e.g., similar success with an equivalent type of project) and theoretical replication, examining cases where the theory points to different but predictable results (e.g., project failure). Leonard-Barton (1990) describes a practical application of this procedure. However, note that this replication logic of using more cases is designed to extend findings to other groups or settings, not to augment the number of data points to increase the confidence of withingroup findings. A multiple-case study should not be misconstrued as a “small-sample survey”. We are accustomed to the practice of sampling from generalization” a group and using “statistical (Yin, 1989) to extend findings across the entire group but their external validity, the degree to which they apply beyond the group itself, cannot be established without testing other groups. In contrast, case study research relies on “ana-

lytical generalization” (Yin, 1989), whereby theory developed from the case is extended to other situations where the conditions appear to be similar in critical respects. Extrapolating the theory to other situations relies on logical analysis: Are similar factors present in the other situations? Do other factors not present in the cases intervene in the other situations? Generalizing a case study’s fmdings to other situations faces the same issues as generalizing those from a large-scale survey but we may have more confidence in extrapolating the case study’s results if the cases used are maximally different. If patterns are found under extreme conditions, there is greater confidence (based on logic rather than statistical evidence) that resultant theories are broadly applicable. As with any attempt to gauge real-world conditions, the case information’s reliability (the extent to which data would be duplicated if collected at another time or through another means) is a concern. Experiments or field studies that use multiitem scales can check scale reliability through statistical means (most commonly with Cronbach’s in most situations, a case alpha). However, researcher must find other ways to ensure measure reliability. Using a variety of data gathering methods is an important one. Having more than one researcher present, plus using a tape recorder, can improve the reliability of interview data. Sound judgment is also crucial. For example, does the researcher believe that a particular firm’s inventory records are as accurate for C items as for A items? Does it appear that all maintenance personnel have recorded machine downtime in the same manner? An advantage of the case study is that, if the researcher questions the reliability of some data, steps can be taken midstream to verify the suspicions and improve data-gathering procedures. Multiple measures using very different data sources can improve both validity (assuring that what is measured accurately reflects what is intended) and reliability (assuring that the data are good readings of the operational measures). Perceptions and opinions gleaned from interviews can be compared to minutes of meetings, financial records or sales figures. Information may be gathered from a firm’s suppliers and customers.

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Another means of information gathering is through unobtrusive measures (Webb et al., 1966) which rely on objective evidence that is unlikely to be affected by attitudes or perceptions. For example, employees’ use of computers might be measured by computer paper and diskette consumption. Despite accurate measurement of conditions and variables, there is no guarantee that the researcher will deduce the correct causal relationships. Usually, the only test of internal validity is logic. Having more than one researcher assessing the information is likely to help improve validity. Eisenhardt (1989) lists a number of tactics that have been used with multiple investigators: sending all team members to each site, assigning specific interview roles (such as questioner and note taker) to different team members, having multiple teams that each visit different sites, or having one or more researchers remain out of the field to play devil’s advocate for the conclusions reached by field teams. These approaches allow fresh insights to be brought into the analysis at different points. Benbasat et al. (1987) recommend that case researchers be explicit in reporting their data gathering and assessment techniques. Indication of the researcher’s thoroughness bolsters confidence in the findings or indicates shortcomings that may prompt questions about the resulting theory. However, even with a clear explication of the researcher’s methods, a case study may still be viewed by some as a questionable research approach. In particular, a case study’s apparent subjective nature, lack of controls and use of only a few sites may lead to scepticism among researchers who view case study research as lacking in objectivity, replicability and control, the supposed features of methodological rigor. However, if properly conducted, the process is in fact as “scientific” as other research methods.

5. The case study as a rigorous methodology The often negative view of the case study method is not unique to the OM field. Yin, a noted proponent of the case study, complains that it has “long

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been stereotyped as a weak sibling among social science methods” even though case studies continue to play a significant role in many research fields (Yin, 1989). For case study research to gain wider acceptance, the perception that case research lacks rigor, objectivity and other hallmarks of scientific enquiry needs to be dispelled. Most OM researchers are comfortable with the model of enquiry developed in the natural sciences (what Kuhn (1970) refers to as “normal science”) and many of our research practices emulate this model’s approach. Normal science has several precepts: - the best way to test theories is to make repeated observations under conditions that control factors which might affect outcomes; - settings should be replicable to allow multiple researchers to make independent observations, thereby eliminating potential biases; - theories should be predictive and observations that match predictions will lend support or confirmation to a theory, whereas observations that do not match the theory’s predictions disconlirm or call into question the theory’s validity; - theories may be generalized more widely when confirmed in various settings. While natural science experimental techniques (and simulation experiments) follow these precepts, case study research clearly violates the first two. However, it is not the case methodology that is weak but rather the assumption that these standards should be applied to judge the methods used to study OM problems. Lee (1989) points out that the “natural science model is primarily a model for testing theories, not formulating theories in the first place.” It would be impossible to design controlled experiments without having a clear idea of the variables that need to be controlled. Even in the natural sciences, major discoveries have often resulted from single observations or from witnessing a few related events. Once theories were developed to explain the phenomena, the natural science model methods were used to test the theories’ accuracy. OM involves complex interplays of people, technological systems, and organizational and physical processes, most of which change in their nature

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McCutcheon,

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I Case study research

over time. Attempting to test theories about this environment requires considerable knowledge about the interactions of important variables. In many areas of OM, our theoretical bases are weak; we may not fully understand all the elements that drive operations performance. Adept as we are with theory-testing methodologies that mimic the natural sciences, strict adherence to these techniques may preclude the consideration of methods, such as case study research, that are inherently better for initially developing the theories. Popper (1968) in his influential viewpoint on the nature of science, considers a field to be “scientific” according to its treatment of theory. To be scientific, a field’s theories must be: (a) falsifiable or open to refutation through substantive evidence; (b) logically consistent among themselves; (c) individually at least as predictive as any competing theory; and (d) valid under empirical tests, where such tests do not falsify them. This view stresses the use of deductive means to test theory, the basis for the natural science con1989) trolled-experiment methodology (Lee, familiar in OM research in the form of mathematical and simulation models and (less frequently) causal model testing with survey data. Such controlled empirical testing methods provide considerable power for disconfirming or lending support for theories, thus meeting the last of Popper’s criteria. However, the important criteria that distinguish a science are those concerned with the nature of the theories themselves. It is, in fact, of little consequence how a theory is generated, as long as the result is logically consistent, demonstrably predictive (being at least as accurate as rival theories) and considered open to refutation. By these standards, OM or any other discipline may be viewed as unscientific, not if it uses particular research methodologies to test theories, but if it fails to develop logically consistent, empirically testable explanatory theories relevant to its field. Case study research is needed to prevent OM from becoming unscientific by ensuring that OM

in operations management

theories are in fact testable. OM researchers using mathematical models and artificial representations of reality (such as simulations) can avoid many validity and reliability issues by being able to specify how a concept will be manifested and the variables’ exact correspondence (by definition) to their measures. Internal validity is rarely a problem as long as the researcher can control the inputs and define how they will behave. Problems arise, however, if the models’ specifications do not correspond well with reality. While conclusions drawn may be valid within the model’s context, they may be impossible to test empirically if the model lacks sufficient correspondence with the situation it supposedly represents. To maintain the status of being scientific, OM must ensure that representational models incorporate enough conditions and constraints of real-world operations to allow their content to be transferred to reality for testing. Case study research is probably the best way to determine what those real-world conditions and constraints are. With an appropriate realworld perspective, the OM researcher can use the simulation or mathematical model to test theories in a controlled environment that is rarely available in the field. Results from such experimentation may prompt further case studies to find better explanations and theories. Moreover, as pointed out in Table 1, case studies do have the power to test theories as well. Theories can be disproven by showing their inapplicability in situations where they should apply, something that case studies have been used for effectively in other fields (e.g., Markus, 1983). Unfortunately, such theory testing is not likely to develop in OM until we have more complete theories, theories that not only stipulate relationships but closely specify the contexts in which they are expected to occur.

6. Case studies in operations management journals The OM field has seen limited use of the case study methodology. Appendix 1 lists what we consider to be true case studies that have been published in some mainstream OM journals (specifically, Journal of Operations Management, OMEGA, International Journal of Operations and

D.M. McCutcheon. J.R. Meredith 1 Case study research in operations management Table 2 Breakdown by research intent~methodology Methodology

among iisted case studies in operations

management

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journals

Research intent Descriptive

Exploratory

Explanatory

n (%)

Pure case Multiple methods

7 2

33 3

1 2

41 (85.4%) 7 (14.6%)

n (%)

9 (18.8%)

36 (75.0%)

3 (6.2%)

48 (100%)

Production Management, IEEE Transactions on Engineering Management and International Journal of Production Research) in the period 19811991. Although other journals such as Harvard Business Review and Sloan Management Review also publish case studies, few such studies from this period were focused on OM topics. Several other mainstream OM journals were searched but were found to contain very few case studies. Case study research is more prevalent in the journals for some fields closely related to OM-R&D Management and Journal of Engineering and Technology management, for example-but these sources were exciuded as having insufficient mainstream OM content. The list does not include articles that we did not consider to be true case studies. Excluded articles were often descriptions of methods or systems applications in test sites, the most common form of so-called “case studies” in some journals. Note that we have classified all of the case studies from these sources, even though their topics overlap with other academic fields. Thus, we are listing case study articles in OM-related journals, rather than OM case studies in the literature in general. Appendix 1 lists our classification of each of these studies according to research intent (descriptive, exploratory/theory-building, or explanatory/theory-testing) and whether it used the case methodology alone or in conjunction with other research methods. The numbers in each classification are given in Table 2. The most common form of case study has been the exploratory or theory-building type, using the case study method alone. Explanatory or theory-testing case studies are the least common, perhaps because of the relative scarcity of testable OM theories.

Two of the three explanatory studies used multiple methods, with only one study (Meredith, 1987) using multiple case studies to confirm or reject hypotheses. Examples from each of the different categories are listed below. Descriptive, case method alone Buxey (1988) describes ten plants and their aggregate production planning situations. The article demonstrates the task’s true complexity and points out the typical misconceptions of those who develop planning models. Descriptive, case plus other methods Temple and Dale (1987) describe the use of, and problems experienced in establishing, white-collar quality circles in Britain. Eleven case studies (two of which are included in the article) were conducted in conjunction with a national questionnaire survey of firms and a survey of consultants. E.~plorator_y* case method alone Gerwin and Tarondeau (1982) investigate four implementations of innovative computer-controlled manufacturing technologies to study characteristics of decision-making at the various implementation stages. The investigation is based on a previously developed theory that a principal driver in such decision-making is uncertainty reduction and the article reports on the theory’s fit with their observations. Exploratory, case plus other methods Lawless et al. (1982) develop a normative model of organizational requirements for implementing

250

D.M. McCutcheon, J.R. Meredith 1 Case study research in operations management

Table 3 Case study publication Year

history for selected operations

Totals

journals,

1981-1991

Journala JOM

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991

management

2

OMEGA 3 2 I 4 2

IJO PM

1 I 2

I

3

I 5 5

I3

IJPR

1

I

I

IEEE TEM

I4

I I 2

2 3 I I I

3

10

8

Total 4 5 I 5 4 2 5 3 4 7 8

48

a JOM: Journal of Operations Management; IJOPM: International Journal of Operations and Production Management; IEEE TEM: IEEE Transactions on Engineering Management; IJPR: International Journal of Production Research.

OR/MS models in public service agencies. One case provides a detailed picture of an implementation; it formed the basis for a field study of 38 other OR/ MS implementations in criminal justice agencies. Explanatory, case method alone Meredith (1987) uses five case studies of advanced manufacturing technologies to test the applicability of fifteen postulates derived from the literature. Two postulates are disconfirmed, while most of the remainder are qualified to more accurately reflect observed conditions. Explanatory, case plus other methods Cohn and Turyn (1984) report that a previous survey that investigated relationships between organizational structure and innovativeness produced unexpected results. This article describes how they recast their model of the firm’s decisionmaking process, then tested the hypothesized relationships in five case studies. They further confirmed their hypotheses with survey data.

Table 3 lists the number of case study articles published in the five journals during 198 1- 199 1. It appears that only one journal (IEEE Transac-

tions on Engineering Management) has consistently published case studies throughout this period. From 1981 to 1989, an average of 3.78 case studies were published in these five journals annually but the main outlet shifted from OMEGA to Znternational Journal of Operations and Production Management and International Journal of Production Research. The shift is likely attributable to changes in editorial policies for these journals and it will be interesting to see the eventual impact of the Journal of Operations Management’s recent policy announcements regarding empirical research (Ebert, 1989). The number of case studies published has grown slowly, with the three-year moving average increasing from 3.33 articles per year in 1981-1983 to 6.33 in 1989-1991. Not evident from either Table 2 or Table 3 is the focus of their topics. Of 48 articles, 17 (35.4%) concern factory automation (e.g., implementing flexible manufacturing systems or robotics), five (10.4%) deal with information technology and four (8.3%) are concerned with manufacturing strategy. Thus, only three topic areas account for more than one-half of the published case studies. Although these particular topic areas are highly suitable for case study investigation (because of their status as emerging subfields), there were also

D.M. McCutcheon,

J.R. Meredith 1 Case study research in operations management

numerous studies that enriched our understanding of well-established OM topic areas such as materials management and aggregate planning. Arguably, OM has a definite need to use case study research more widely. The view of OM’s role within the organization has been expanding and such areas as quality management, strategy and policy, technology management and management of services will likely increase in importance (Wood and Britney, 1989). The theoretical base in some of these areas is not well developed (in fact, woefully weak) and field-based approaches are the best ways to find out about the issues, describe the problems, discover solutions and generally ground our theory in the complex, messy world of real organizations. Here are a few areas where case studies might be useful: (1) A widely used term in OM is infrastructure (e.g., Hayes and Wheelwright, 1984, p. 31; Hill, 1985; Gerwin, 1988; Meredith, 1986). It has been defined in various ways and there has been little work done to connect this concept to the organizational theory literature. (2) What are the dynamics of technology implementation that transcend the particular technology being introduced? Gerwin (1988) and LeonardBarton (1988) provide some theorizing, based on their case studies. There is considerable OM literature about the implementation problems of a few specific technologies, such as MRP, without much generalizable understanding. How applicable are these findings to situations involving radically different forms of technology? Case studies may be the best way to examine highly varied implementation situations, as Leonard-Barton has done. (3) To what extent does operations strategy affect business unit performance? To study the interplay of operations strategy and its consequences at the firm level, both constructs and linkages may have to be better understood (Swamidass and Newell, 1987; Leong et al., 1990). The case study approach is a good method for developing robust operational measures. (4) How do worker reactions to cellular manufacturing impact the technology’s effectiveness? How do workers respond to the cellular environment? Klein (1989) describes a plant workforce

251

that, having previously enjoyed a high degree of objected strongly to the changes autonomy, imposed by just-in-time and statistical process control systems. Are there particular conditions under which workers may object to a cellular manufacturing environment? Case studies can investigate the processes and reactions in various settings when cellular manufacturing techniques are introduced. (5) Bushe (1988) posits that statistical process control techniques may be rejected in some large plants because middle managers resist having information, with its resulting power, placed in the hands of workers. This contention deserves further study in other settings. (6) Many service industries manage operations markedly differently than manufacturing industries-with notable success (Heskett, 1987). Traditional OM terms and concepts developed in the manufacturing environment have commonly been applied to service sector operations, a habit that may limit our thinking and channel our research into pre-defined areas. Case study research could be used to investigate how some of the basic operational systems in service industries interact in ways that are likely quite different from those within manufacturing firms. Any number of other questions are being raised about OM issues. As economic conditions, technology and management methods evolve, our view of the real world will need updating, perhaps with increasing frequency. Case study research offers a good starting point for investigating these questions or, alternatively, an opportunity to refute some widely held assumptions.

7. Conclusions We have provided some rationale for considering the wider use of case studies in OM research and attempted to correct some common misunderstandings about the method. First, case studies can be used more broadly, within more paradigms and using more forms of data than is often assumed. Second, the method’s potential depends on the researcher’s rigor in conducting the field work and analysis. As with many research methods, it

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D.M. McCutcheon. J.R. Meredith / Case study research in operations management

is easy to perform case studies badly. However, if done properly, case study research can provide discoveries not possible through other methods. Case study research can only go so far, of course. It requires considerable time to conduct; it often entails interviewing and archival skills and an ability to see patterns amid masses of data that may be incomplete and distorted by perceptions and politics. In other words, it is not necessarily an efficient form of research. However, more efficient methods must constantly rely on such techniques as case study research to ensure that our theories, experiments and advice to managers do not become detached from reality. Case research may be an uncomfortable proposition for many OM researchers. The method is difficult to learn to do well and can involve fuzzy research goals, the need to talk to managers and trusting intuitive conclusions. However, embracing a field investigation technique such as case studies is bound to make the individual researcher, and the field in general, richer and better prepared to solve real OM problems.

References Benbasat, I., Goldstein, D.K. and Mead, M., 1987. “The case research strategy in studies of information systems”. MIS Quarterly, vol. 1 I, no. 3 (September), 3699 386. Bushe, G., 1988. “Cultural contradictions of statistical process control in American manufacturing organizations”. Journal of Management, vol. 14, no. 1 (March), 19-31. Buxey, G., 1988. “Production planning under seasonal demand: A case study perspective”. OMEGA, vol. 16, no. 54477455. Campbell, D.T., 1975. “Degrees of freedom and the case study”. Comparative Political Studies, vol. 8, no. 3, 178.. 193. Campbell, D.T. and Stanley, J.C., 1966. Experimental and Quasi-experimental Designs for Research. Rand McNally, Chicago, IL. Cohn, S.F. and Turyn, R.M., 1984. “Organizational structure, decision-making procedures, and the adoption of inEngineering novations”. IEEE Transactions on Management, vol. EM-3 1, no. 4 (November), 1544161. Cook, T.D. and Campbell, D.T., 1979. Quasi-Experimentation: Design and Analysis Issues in Field Settings. Houghton Mifflin, Boston, MA. Crosby, P.B., 1979. Quality is Free: The Art of Making Quality Certain. McGraw-Hill, New York.

Ebert, R., 1989. “Announcement on empirical/field-based methodologies in JOM”. Journal of Operations Management, vol. 8, no. 4, 294296. Eisenhardt, K.M., 1989. “Building theories from case study research”. Academy ofManagement Review, vol. 14, no. 4, 5322550. Flynn, B.B., Sakakibara, S., Schroeder, R.G., Bates, K.A. and Flynn, E.J., 1990. “Empirical research methods in operations management”. Journal of Operations Management, vol. 9, no. 2, 2544284. Gerwin, D., 1981. “Control and evaluation of the innovation process: The case of flexible manufacturing systems”. IEEE Transactions on Engineering Management, vol. EM-28, no. 3, 62-70. Gerwin, D., 1988. “A theory of innovation processes for computer-aided manufacturing technology”. IEEE Transactions on Engineering Management, vol. 35, no. 2 (May), 90@100. Gerwin, D. and Tarondeau, J.C., 1982. “Case studies of computer integrated manufacturing systems: A view of uncertainty and innovation processes”. Journal of Operations Management, vol. 2, no. 2 (February), 87-99. Glaser, B. and Strauss, A., 1967. The Discovery of Grounded Theory: Strategies in Qualitative Research. Wiedenfeld and Nicolson, London. Hayes, R.H. and Clark, K., 1985. “Explaining observed productivity differences between plants: Implications for operations research”. Interfaces, vol. 15, no. 6 (November-December), 3-14. Hayes, R.H. and Wheelwright, S.C., 1984. Restoring our

Competitive

Edge: Competing

through Manufacturing.

Wiley, New York. Heskett, J.L., 1987. “Lessons in the service sector”. Harvard Business Review, vol. 65, no. 2 (March-April), 118-126. Hill, T., 1985. Manufacturing Strategy: The Strategic Management of The Manufacturing Function. Macmillan Education, Houndsmill. Johnson, B., 1990. The Interaction of Equipment and Process Technology Knowledge and Decision-Making Methodology. Unpublished PhD Dissertation, University of Cincinnati, OH. Kaplan, B. and Duchon, D., 1988. “Combining qualitative and quantitative methods in information systems research: A case study”. MIS Quarterly, vol. 12, no. 4 (December), 571-586. Klein, J., 1989. “The human costs of manufacturing reform”. Harvard Business Review, vol. 67, no. 2 (March-April), 60-66. Kuhn, T.. 1970. The Structure qfScienttfic Revolutions. University of Chicago Press, Chocago, 11, 2nd edn. Lawless, M.W., Feinberg, A., Glassman A. and Bengtson, W.C., 1982. “Enhancing the chances of successful OR/ MS implementation: The role of the advocate”. OMEGA, vol. 10, no. 2, 1077114. Lee, AS., 1989. “A scientific methodology for MIS case studies”. MIS Quarterly, vol. 13, no. 1 (March), 33350. Leonard-Barton, D., 1988. “Implementation as mutual

D.M. McCutcheon. J.R. Meredith / Case study research in operations management adaptation of technology and organization”. Research Policy. vol. 17, no. 5 (October), 251-267. Leonard-Barton, D., 1990. “A dual methodology for case studies: Synergistic use of a longitudinal single site with replicated multiple sites”. Organizational Science, vol. I, no. 1, 248-266. Leong, G.K., Snyder, D.L. and Ward, P.T., 1990. “Research in the process and content of manufacturing strategy”. OMEGA, vol. 18, no. 2, 109%122. Markus, M.L., 1983. “Power, politics, and MIS implementation”. Communications of the ACM, vol. 26, no. 6 (June), 430-444. McKay, K.N., Safayeni, F.R. and Buzacott, J.A., 1988. “Job-shop scheduling theory: What is relevant?” Znterfaces, vol. 18, no. 4 (July-August), 84-90. McLaughlin, I., Rose, H. and Clark, J., 1985. “Managing the introduction of new technology”. OMEGA, vol. 13, no. 4, 251-262. Meredith, J., 1986. “Automation strategy must give careful attention to the firm’s ‘infrastructure’ “. Industrial Engineering, May, 68-73. Meredith, J.R., 1987. “Automating the factory: Theory vs. practice”. International Journal of Production Research, vol. 25, no. 10, 1493~1511. Meredith, J.R., Raturi, A.S., Amoako-Gyampah, K. and Kaplan, B., 1989. “Alternative research paradigms in operations”. Journal of Operations Management. vol. 8, no. 4 (October), 297-326. Miles, M. and Huberman, A.M., 1984. Qualitative Data Analysis. Sage Publications, Beverly Hills, CA. Monden, Y., 1981a. “What makes theToyota production system really tick?” Industrial Engineering, January, 36-46. Monden, Y., 1981b. “Adaptable kanban system helps Toyota maintain just-in-time system”. Industrial Engineering, May, 29-46. Nunnally, J.C., 1978. Psychometric Theory. McGraw-Hill, New York, 2nd edn. Popper, K., 1968. The Logic of Scientific Discovery. Harper, New York. Raturi, A.S., Meredith, J.R., McCutcheon, D.M. and Camm, J.D., 1990. “Coping with the build-to-forecast environment”. Journal of Operations Management, vol. 9, no. 2 (April), 230-249. Schonberger, R.J., 1982. “Some observations on the advantages and implementation issues of just-in-time production systems”. Journal of Operations Management, vol. 3, no. 1 (November), l-1 I. Swamidass, P.M., 1991. “Empirical science: The new frontier in operations management research”. Academy of Management Review, vol. 16, no. 4, 793-814. Swamidass, P.M. and Newell, W.T., 1987. “Manufacturing strategy, environmental uncertainty and performance: A path analytic model”. Management Science, vol. 33, no. 4, 509-524. Temple, A.I. and Dale, B.G., 1987. “A study of quality circles in white collar areas”. International Journal of Operations and Production Management, vol. 7, no. 6, 17-31.

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Webb, E., Campbell, D.T., Schwartz, R. and Sechrest, L., 1966. Unobtrusive Measures: Nonreactive Research in the Social Sciences. Rand McNally, Chicago, Il. Wood, A.R. and Britney, R.R., 1989. “Production operations management: Research and teaching opportunities in the 1990’s”. Operations Management Review, vol. 8, no. 3-4 (Fall-Winter), 33-43. Yin, R.K, 1989.. Case Study Research: Design and Methods. Sage Publications, Newbury Park, CA, rev. edn. Zaltman, G., Lemasters, K. and Heffring, M., 1982. “Thinker toys”. In: Theory Construction in Marketing: Some Thoughts on Thinking. Wiley, New York.

Appendix l-Case studies in selected operations management journals 1981-1991 Table A.1 lists the articles that were considered to be case studies, as they were presented in Journal of Operations Management, International Journal of Operations and Production Management, OMEGA, International Journal of Production Research and IEEE Transactions on Engineering Management. They are categorized by type according to our assessment of each paper’s intent and methods. Bibliographic information is provided in the list that follows.

Listed case studies Boddy, D. and Buchanan, D.A., 1984. “Information technology and productivity: Myths and realities”. OMEGA, vol. 12, no. 3, 233-240. Bodnar, J. and Harrison, A., 1991. “Manufacturing strategy development at Bibby Sterling Ltd.“. International Journal of Operations and Production Management, vol. 11, no. 3, 43-51. Boer, H. and Durning, W.E., 1987. “Management of process innovation-The case of FMS: A systems approach”. International Journal of Production Research, vol. 25, no. II, 1671-1682. Boer, H., Hill, M. and Krabbendam, K., 1990. “FMS implementation management Promise and performance”. International Journal of Operations and Production Management, vol. 10, no. 1, 5-20. Braun, E., Moseley, R. and Wilkinson, B., 1981. “Manufacturing innovation in the West Midlands metal forming industry”. OMEGA, vol. 9, no. 6, 563-570. Brennan, L., Finnan, F. and O’Kelly, M.E.J., 1990. “Requirements for smaller companies in integrated manufacturing”. International Journal of Operations and Production Management, vol. IO, no. 7, 57-68.

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D.M. McCutcheon, J.R. Meredith 1 Case study research in operations management

Table A. 1 Case studies in selected operations Article

management

journals

198 1~1991 Methodology

Research intent Descriptive

Boddy and Buchanan (1984) X Bodnar and Harrison (1991) Boer and Durning (1987) Boer et al. (1990) Braun et al. (1981) Brennan et al. (1990) Buchowicz (1991) x Buxey (1988) Buxey (1991) Carrie and Bannerjee (I 984) Cohn and Turyn (1984) Dale and Duncalf (1985) Dery (1981) Finch and Cox (1986) Forslin et al. (1989) Gerwin (1981) Gerwin and Tarondeau (1982) Gruhl (1982) Gudnason and Riis (1984) Hill (1985) Howson and Dale (1991) Johnson and Davidson (1982) Kinnie and Staughton (1991) Kirkwood et al. (1989) Lascelles and Dale (1990) Lawless et al. (1982) Lee (1989) Lindberg et al. (1988) Lindberg (1990) Littler and Sweeting (1983) X Lockett (1981) McCalman and Buchanan (1990) McLaughlin et al. (1985) Meredith (1987) Meredith (1988) Molet et al. (1989) Newman and Rosenberg (1985) Nonas et al. (1990) Persson (1991) Pliskin et al. (1991) Raturi et al. (1990) x Schonberger (1982) X Smilor (1987) x Temple and Dale (1987) Titus and Liberatore (1991) x Warnecke et al. (1986) White and Ghobadian (1984) Willenborg and Krabbendam (1986)

Exploratory/ theory-building

Explanatory/ theory-testing

Case

Multiple methods

x

x

x x

x

x

X

x

X

x

x

x

X X X

x

X

x

X

X

x

X

x

X

x

x

x

x

x

X

x

X

x X

x

X

x

X

X X X

X

X

X

X

X

x

x

X

X X X X

x x

X

X

X

X

D.M. McCutcheon, J.R. Meredith / Case study research in operations management Buchowicz, B.S., 1991. “A process model of make-vs.-buy decision-making: The case of manufacturing software”. IEEE Transactions on Engineering Management, vol. 38, no. 1 (February), 24432. Buxey, G., 1988. “Production planning under seasonal demand: A case study perspective”. OMEGA, vol. 16, no. 5,447F455. Buxey, G., 1991. “The nexus between CAD-CAM and quality”. International Journal of Operations and Production Management, vol. 11, no. 10, 19-32. Carrie, AS. and Bannerjee, SK., 1984. “Approaches to implementing manufacturing information systems”. OMEGA, vol. 12, no. 3, 251-259. Cohn, SF. and Turyn, R.M., 1984. “Organizational structure, decision-making procedures, and the adoption of innovations”. IEEE Transactions on Engineering Management, vol. EM-31, no. 4 (November), 1544161. Dery, D., 198 1. “The bureaucratic side of computers: Memory, evocation and management information”. OMEGA, vol. 9, no. 1, 25-32. Dale, B.G. and Duncalf, A.J., 1985. “Quality related decision making: A study in six British companies. InternaProduction tional Journal of Operations and Management, vol. 5, no. 1, 15-25. Finch, B.J. and Cox, J.F., 1986. “An examination ofjust-intime management for the small manufacturer: with an illustration”. International Journal of Production Research, vol. 24, no. 2, 329-342. Forslin, J., Thulestedt, B.-M. and Andersson, S., 1989. “Computer-aided design: A case of strategy in implementing a new technology”. IEEE Transactions on Engineering Management, vol. 36, no. 3 (August), 191-201. Gerwin, D., 1981. “Control and evaluation in the innovative process: The case of flexible manufacturing systems”. IEEE Transactions on Engineering Management. vol. EM-28, no. 3 (August), 62-70. Gerwin, D. and Tarondeau, J.C., 1982. “Case studies of computer integrated manufacturing systems: A view of uncertainty and innovation processes”. Journal of Operations Management, vol. 2, no. 2 (February), 87799. Gruhl, J., 1982. “Model credibility and independent evaluation-Three case studies”. OMEGA, vol. 10, no. 5, 525537. Gudnason, C.H. and Riis, J.O., 1984. “Manufacturing strategy”. OMEGA, vol. 12, no. 6, 5477555. Hill, M.R., 1985. “FMS management-The scope for further research”. International Journal of Operations and Production Management, vol. 5, no. 3, 5520. Howson, T.G. and Dale, B.G., 1991. “An examination of the purchasing function in a sales-oriented company”. International Journal of Operations and Production Management, vol. 11, no. 5, 71-82. Johnson, N.O. and Davidson, D.B., 1982. “Realigning an R&D organization from R-intensive to D-intensive: A case study”. IEEE Transactions on Engineering Management, vol. 29, no. 1 (February), 19-27.

255

Kinnie, N.J. and Staughton, R.V.W., 1991. “Implementing manufacturing strategy: The human resource management contribution”. International Journal of Operations and Production Management, vol. 11, no. 9, 24-40. Kirkwood, R., Smith S. and Tranfield, D., 1989. “The implementation cube for advanced manufacturing systems”. International Journal of Operations and Production Management, vol. 9, no. 8, 5-19. Lascelles, D.M. and Dale, B.G., 1990. “The key issues of a quality management process”. International Journal of Production Research, vol. 28, no. 1, 131-143. Lawless, M.W., Feinberg, A., Glassman A. and Bengtson, W.C., 1982. “Enhancing the chances of successful OR/MS implementation: The role of the advocate”. OMEGA, vol. 10, no. 2, 107-114. Lee, G.L., 1989. “Managing change with CAD and CAD/ CAM”. IEEE Transactions on Engineering Management, vol. 36, no. 3 (August), 227-233. Lindberg, P., 1990. “Strategic manufacturing management: A proactive approach”. International Journal of Operations and Production Management, vol. 10, no. 2, 944106. Lindberg, P., Linder, J. and Tunalv, C., 1988. ‘Strategic decisions in manufacturing-On the choice of investments in flexible production organizations”. International Journal of Production Research, vol. 26, no. 10, 1695-1704. Littler, D.A. and Sweeting, R.C., 1983. “New business development in mature firms”. OMEGA, vol. 11, no. 6, 537-545. Lockett, G., 1981. “The management of stocks-Some case histories”. OMEGA, vol. 9, no. 6, 595-604. McCalman, J. and Buchanan, D.A., 1990. “High performance work systems: The need for transition management”. International Journal of Operations and Production Management, vol. 10, no. 2, 10-25. McLaughlin, I., Rose, H. and Clark, J., 1985. “Managing the introduction of new technology”. OMEGA, vol. 13, no. 4, 251-262. Meredith, J.R., 1987. “Automating the factory: Theory vs. practice”. International Journal of Production Research, vol. 25, no. 10, 1493-1511. Meredith, J., 1988. “The role of manufacturing technology in competitiveness: Peerless laser processors”. IEEE Transactions on Engineering Management, vol. 35, no. 1 (February), 3- 10. Molet, H., Sautory, J.C. and van Gigch, J.P., 1989. “The complexity of introducing robots: Lessons drawn from an industrial experience”. International Journal of Production Research, vol. 27, no. 3, 529-548. Newman, M. and Rosenberg, D., 1985. ‘Systems analysts and the politics of organizational control”. OMEGA, vol. 13, no. 5, 393-406. Nonas, K., Johansson, J. and Linden, G., 1990. “Work in arc-welding stations with high technical complexity”. International Journal of Operations and Production Management, vol. 10, no. 2, 37-46. Persson, I., 1991. “Developing a manufacturing strategy

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/ Case study research

within a capital investment process-A case study”. International Journal of Operations and Production Management, vol. 11, no. 3, 32-42. Pliskin, N., Balaila, I. and Kenigshtein, I., 1991. “The knowledge contribution of engineers to software development: A case study”. IEEE Transactions on Engineering Management, vol. 38, no. 4, 3444348. Raturi, AS., Meredith, J.R., McCutcheon, D.M. and Camm, J.D., 1990. “Coping with the build-to-forecast environment”. Journal of Operations Management, vol. 9, no. 2 (April), 230-249. Schonberger, R.J., 1982. “Some observations on the advantages and implementation issues of just-in-time production systems”. Journal of Operations Management, vol. 3, no. I, l-11. Smiler, R.W., 1987. “Managing the incubator system: Critical success factors to accelerate new company development”. IEEE Transactions on Engineering Management, vol. EM-34, no. 3 (August), 1466155. Temple, A.I. and Dale, B.G., 1987. “A study of quality circles in white collar areas”. International Journal of Operations and Production Management, vol. I, no. 6, 17-31. Titus, G.J. and Liberatore, M.J., 1991. “Auditing R&D planning”. IEEE Transactions on Engineering Management, vol. 38, no. 2 (May), 171-177. Warnecke, H.J., Steinhilper, R. and Roth, H.-P., 1986. “Developments and planning for FMS-Requirements,

in operations

management

examples and experiences”. International Journal of Production Research, vol. 24, no. 4, 163-772. White, M. and Ghobadian, A., 1984. “Payment systems and technology in electrical engineering”. OMEGA, vol. 12, no. 3, 1984, 241-249. Willenborg, J.A.M. and Krabbendam, J.J., 1987. “Industrial automation requires organizational adaptations”. International Journal of Production Research, vol. 25, no. 11, 168331692. David McCutcheon is currently with the University of Victoria in British Columbia, where he teaches Operations Management. He holds an engineering degree from the Royal Military College of Canada and MBA and PhD degrees from the University of Western Ontario. He has conducted several case studies and field studies, the results of which have appeared in the Journal of Operations Management, International Journal of Operations and Production Management, Journal of Engineering and Technology Management and OMEGA. Jack Meredith is Professor of Operations Management at the University of Cincinnati. He holds degrees in mathematics and engineering from Oregon State University and the MBA and PhD degrees from the University of California at Berkeley. He is the author of numerous books and many articles. His research interests focus on technology management (particularly the areas of planning, justification, implementation and routinization) and research methodology.