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
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
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
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
Class B: Domain-Specific TQM Research
- 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.).
Interactions among technical elements (statistical process
control, benchmarking, etc.) and human elements (training,
empowerment, involvement, etc.) in relation to TQM outcomes.
Interactions among different management levels in TQM
Interactions among different functional areas/product
transformation phases (pre-production, in-production, and
post-production) in TQM implementation.
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
Linkage of TQM to other approaches to operations improvement:
business process reengineering, just-in-time (JIT), and management
Effect of product/process technology on TQM implementation.
- Supplier quality management linkage to plant-level quality
Product/process design quality management linkages to
plant-level quality performance.
Contextual effectiveness of techniques such as SPC and
Role of middle level managers in TQM implementation.
Role of shopfloor employees in TQM implementation.
Impact of production postponement strategy (make-to-inventory,
make-to-order, assemble-to-order) on quality management.
Impact of production volume (job-shop, batch, assembly line,
continuous) on TQM implementation.
Role of information systems capabilities in TQM implementation.
Impact of human resource management strategies (such as
training, empowerment, involvement, and incentives) on TQM under
various organization cultures.
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:
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.
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. 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. 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. Execute the survey, and commensurate with the scope of study
(defined in Step 2), use specific techniques to elicit maximum and
5. Based on usable responses, refine and validate the scales using
multiple validity and refinement tests.
6. Test the hypothesized theoretical model(s) using the validated
scales through rigorous methodologies.
7. Interpret and explain the results with respect to the
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.
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.
- 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,
Meredith, J.R., Raturi, A., and Kaplan, B. Alternative research
paradigms in operations. Journal of Operations Management, 1989,
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
Dr. Shawnee Vickery
Department of Management
College of Business
239 Eppley Center
Michigan State University
East Lansing, MI 48824