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RESEARCH ISSUES

SHAWNEE VICKERY, Feature Editor, Eli Broad Graduate School of Management, Michigan State University

TQM Theory: Opportunities and Strategies

by Sanjay L. Ahire, Department of Management, Western Michigan University

Over the last few years, Total Quality Management (TQM) has evolved from a topic of discussion in Operations Management (OM) into one of the central themes of operations improvement. While the research interest in TQM has grown among academicians, Kolesar (1995) voices a concern that TQM practitioners are just providing it a lip service. The increasing percentage of TQM failures has been attributed to a lack of understanding of the pairings of TQM elements that produce best results under various contexts (Cole, 1993). There is an urgent need for researchers to develop validated sets of principles that can be consistently used by practitioners for enhancing TQM effectiveness. From a methodological perspective, this means concerted efforts at "theory development and testing" are needed to make the results of TQM research more applicable to practice (Ahire et al., 1995-a; Forker, 1995; Ghosh, 1995; Meredith, 1995). Hence, the major objectives of this article are: (a) provide a framework for development and testing of TQM theory, (b) identify examples of important research projects, and (c) present guidelines to conduct individual projects in this area.

Kerlinger (1986) provides the following classic definition of theory:

    A theory is a set of interrelated constructs (concepts), definitions, and propositions that present a systematic view of phenomena by specifying relations among variables, with the purpose of explaining and predicting the phenomena. (Kerlinger, 1986)
This definition clearly specifies the elements that must be embedded in a research endeavor aiming to contribute to theory development and testing in a field. Thus, efforts of theory development and testing in TQM should attempt to explain the relationships among various implementation variables (input constructs) and outcomes (output constructs). While TQM entails sets of specific actions that can be classified into various domains of implementation (e.g., statistical process control, benchmarking, training, incentive systems, supplier management) using different perspectives (such as Baldrige categories, management level roles, functional responsibilities), there are complex interactions between these specific domains of action and overall TQM outcomes. Hence, using the "organizational scope of the investigated research problem" as a yardstick, I propose the following classification for research on TQM theory development and testing:

Class A. Integrative models linking TQM elements to plant-level effectiveness and business performance at strategic business unit (SBU) or firm-level.

Class B. Research in specific TQM domains such as TQM phases (start-Up, pilot, full-scales), roles of various functional areas, roles of various levels of management and employees in TQM, and research in specific evaluation categories of TQM from Baldrige award perspective (top management commitment, quality planning, information analysis, etc.).

Thus, research projects intending to study the overall interactions among various TQM elements and their impacts on final TQM outcomes fall in Class A. Projects that study the interactions of implementation specifics within a specific aspect of TQM fall in Class B. Early research contributions of both types are gradually appearing in the OM literature, and could be used as benchmarks or starting points for further research. For example, to measure TQM implementation constructs effectively, three survey instruments have already been developed and statistically validated (Saraph et al., 1989; Flynn et al., 1994; Ahire et al., 1995-b). Together, these provide a comprehensive set of TQM constructs for further investigation of interactions among them and for predicting TQM outcomes subject to these interactions. Some early work on TQM and allied integrative models, which analyzes linkages between quality or overall production strategies and outcome at the plant or firm-level (Benson et al., 1991; Flynn et al., 1995; Vickery et al., 1993), has also been reported. However, many more challenging research problems remain unexplored. A sample of important research problems in these classes follows.

Class A: Integrative TQM Models

  1. Effect of TQM implementation elements on measures of quality at the plant level and strategic business unit (SBU) or firm level (product quality, process efficiency, product support, etc.).
  2. Interactions among technical elements (statistical process control, benchmarking, etc.) and human elements (training, empowerment, involvement, etc.) in relation to TQM outcomes.
  3. Interactions among different management levels in TQM implementation.
  4. Interactions among different functional areas/product transformation phases (pre-production, in-production, and post-production) in TQM implementation.
  5. Linkage of various elements in TQM to competitive priorities related to cost, quality (consistency, performance), time (delivery speed, punctuality, and development speed), and flexibility (customization, volume flexibility), and the overall business performance.
  6. Linkage of TQM to other approaches to operations improvement: business process reengineering, just-in-time (JIT), and management science techniques.
Class B: Domain-Specific TQM Research
  1. Supplier quality management linkage to plant-level quality performance.
  2. Product/process design quality management linkages to plant-level quality performance.
  3. Contextual effectiveness of techniques such as SPC and benchmarking.
  4. Role of middle level managers in TQM implementation.
  5. Role of shopfloor employees in TQM implementation.
  6. Impact of production postponement strategy (make-to-inventory, make-to-order, assemble-to-order) on quality management.
  7. Impact of production volume (job-shop, batch, assembly line, continuous) on TQM implementation.
  8. Role of information systems capabilities in TQM implementation.
  9. Impact of human resource management strategies (such as training, empowerment, involvement, and incentives) on TQM under various organization cultures.
Effect of product/process technology on TQM implementation.

Empirical research (research based on actual experience, observation, or direct sense of phenomena under examination) is a prerequisite to realistic formation of hypotheses, frameworks, and typologies inherent in theory development and testing (Meredith, 1995). Hence, it must be an integral part of the research endeavors identified above. Several methods of conducting empirical research (e.g., interviews, cases, field study, action research, survey research) have been discussed in literature (Meredith et al., 1989). Researchers in TQM should use a combination of these methods to develop and test theoretical frameworks. While Forker (1995) discusses the potential for using qualitative research in theory-building, I will focus on survey research as a vehicle of empirically testing theoretical models of TQM.

As Meredith (1995) notes, survey research is generally closer to reality than the more commonly published artificial reconstruction of objective reality, and works best for theory-testing. The following steps pertain to a typical survey research project aimed at theory-testing in which data on implementation and outcomes of TQM are gathered through key respondents in firms:

  1. 1. Define the boundaries of the research problem: develop a theoretical framework consisting of various research constructs and their hypothesized relationships based on available literature, cases, and previous empirical knowledge base, and primary experiences of the researcher.
  2. 2. Determine the execution scope of study: cross-sectional versus longitudinal, plant-level versus SBU-level, single-industry versus multiple-industry, single large multi-component project versus multiple self-contained focused projects.
  3. 3. Develop one or more survey instruments to measure the identified theoretical constructs, identify the key respondent(s) in target organizations, and pre-test the instrument.
  4. 4. Execute the survey, and commensurate with the scope of study (defined in Step 2), use specific techniques to elicit maximum and reliable responses.
  5. 5. Based on usable responses, refine and validate the scales using multiple validity and refinement tests.
  6. 6. Test the hypothesized theoretical model(s) using the validated scales through rigorous methodologies.
  7. 7. Interpret and explain the results with respect to the hypothesized framework.
While all of these steps are important, space limitations preclude their detailed discussion here. I will briefly discuss Steps 5 and 6. Unless scales developed to represent various constructs are refined and validated (Step 5), they should not be used to study interrelationships among the constructs. Scale refinements can be done using indices such as Cronbach's alpha coefficients. The confirmatory factor analysis (CFA) approach to scale validation identifies and operationalizes various constructs a priori, hypothesizes their interrelationships, and empirically confirms the scales as being valid representations of these constructs (Long, 1983-a). Specifically, scales must be iteratively revised to pass various validation tests including unidimensionality, convergent validity, and discriminant validity. The overall instrument also must pass other tests of validity, such as content validity and criterion-related validity. Ahire et al. (1995-b) and Flynn et al. (1994) present two variations of this approach to scale validation in the context of TQM. An excellent review of various validity checks and broad steps to conduct these checks is provided by Dunn et al. (1994). Step 6 entails testing of the interrelationships among various constructs in the study. Choice of statistical techniques for this purpose should be driven by the goals of the study. For example, covariance structure modeling can be used to test hypothesized theoretical models (relating various constructs) involving measurement errors for the constructs and linear causal relationships among constructs with error terms (Long, 1983-b). Handfield (1993) uses this technique to test a resource dependency theory for just-in-time purchasing.

TQM theory development and testing indeed offers great opportunities to pursue a long-term research program. While individual researchers may have preferences for the topics to be pursued, the types of study, and research methodologies, the important point is that each area suggested in the two classes of TQM theory research represents such challenges and opportunities waiting to be explored. Concerted efforts from OM researchers along these dimensions will result in a much better understanding of the what, how, why, who, where and when of TQM implementation elements, thus leading to a grand theory of TQM (Ghosh, 1995). An enhanced understanding of complex implementation issues gained through this theory will result in more effective TQM implementations.

References

  • Ahire, S.L., Landeros, R., and Golhar, D.Y. Total quality management: A literature review and an agenda for future research. Production and Operations Management, 1995-a, 4(3), 277-306.
  • Ahire, S.L., Golhar, D.Y., and Waller, M.A. Development and validation of TQM implementation constructs. Decision Sciences, 1995-b (Accepted: September 1995).
  • Benson, P.G., Saraph, J.V., and Schroeder, R.G. The effects of organizational context on quality management: An empirical investigation. Management Science, 1991, 37(9), 1107-1124.
  • Cole, R.E. Introduction to the special issue on total quality management. California Management Review, 1993, 32(3), 7-11.
  • Dunn, S.C., Seaker, R.F., and Waller, M.A. Latent variables in business logistics research: Scale development and validation. Journal of Business Logistics, 1994, 15(2), 145-172.
  • Flynn, B.B., Schroeder, R.G., and Sakakibara, S. A framework for quality management research and an associated instrument. Journal of Operations Management, 1994, 11, 339-366.
  • Forker, L.B. Research on quality: Recent trends and future needs. Decision Line, 1995, 26(5), 7-9.
  • Ghosh, S. Theory development in operations management. Decision Line, 1995, 26(1), 8-9.
  • Handfield, R.B. A resource dependence perspective of just-in-time purchasing. Journal of Operations Management, 1993, 11, 289-311.
  • Kerlinger, F.N. Foundations of behavioral research, 3rd edition. New York, NY: Holt, Rinehart, Winston, 1986.
  • Kolesar, P. Partial quality management: An essay. Production and Operations Management, 1995, 4(3), 195-200.
  • Long, J.S. Confirmatory factor analysis: A preface to LISREL. Beverly Hills, CA: Sage Publications, Series no. 07-033, 1983-a.
  • Long, J.S. Covariance structure models: An introduction to LISREL. Beverly Hills, CA: Sage Publications, Series no. 07-034, 1983-b.
  • Meredith, J.R. What is empirical research? Decision Line, 1995, 26(2), 10-11.
  • Meredith, J.R., Raturi, A., and Kaplan, B. Alternative research paradigms in operations. Journal of Operations Management, 1989, 8(4), 297-326.
  • Saraph, J.V., Benson, P.G., and Schroeder, R.G. An instrument for measuring the critical factors of quality management. Decision Sciences, 1989, 20(4), 810-829.
  • Vickery, S.K., Droge, C., and Markland, R.E. Production competence and business strategy: Do they affect business performance? Decision Sciences, 1993, 24(2), 435-455. n
Sanjay L. Ahire is an assistant professor of production and operations management at Western Michigan University. He received a Ph.D. in management science from the University of Alabama in 1992. His current research interests include theory development and testing in quality management, and use of decision models in quality management. His research has been published or accepted in several journals, including Management Science, Decision Sciences, Production and Operations Management, European Journal of Operations Research, International Journal of Quality and Reliability Management, Production and Inventory Management, and Journal of Small Business Management.

Dr. Shawnee Vickery
Department of Management
College of Business
239 Eppley Center
Michigan State University
East Lansing, MI 48824
517-353-5415
fax: 517-336-1111