Total Quality Management Implementation: The "Core" Strategy
Bibliographic record
Abstract
ABSTRACT This research presents an empirical investigation of total quality management (TQM) implementation in small- to medium-sized manufacturing firms. The study introduces a new TQM implementation strategy: the Core approach and tests the efficacy of a five-element quality management model. Factor analysis, cluster analysis, and ANOVA are used to test relationships among implementation, resulting practices, and performance. Results suggest TQM implementation transcends industry type and is most successful when viewed as a holistic process rather than either selective or contingent. INTRODUCTION Most American and European businesses have deployed some type of quality initiative in their operations (Silvestro, 2001). Yet, many firms have seen little to no benefit from their quality management efforts. Research has attributed many of these disappointments to improper quality management program implementation (Belohav, 1993; Cole, Bacdayan, & White, 1993; Smith, Tranfield, Foster, & Whittle, 1994; Hackman & Wageman, 1995; Douglas & Judge, 2001; Yusof & Aspinwall, 2002). Indeed, recent work suggests that the high failure rate of quality management initiatives results from a mismatch between these processes and critical problems in their respective environments; in short, that quality management should be seen and properly executed as a contingent process (Melcher, Khouja, & Booth, 2002; Das, Handfield, Clalantone, & Ghosh, 2000; Claycomb, Droge, & Germain, 2002; Wang, 2004). While there is a growing body of literature studying the linkage between quality management practice and performance, most research is not empirically-based and centers on large manufacturing companies (Rahman, 2001). Furthermore, Ingle (2000) noted that little discussion has focused on total quality management (TQM) implementation methodologies and that further work in the area is called for. It is these gaps that this research will address by investigating the relationships among implementation practices and performance in small-to-medium manufacturing businesses. This research will show that, for these firms, quality management implementation transcends industry type and is most successful when viewed as a holistic process, as opposed to either a step-wise or contingent process. The next section of the paper features a review of the literature relevant to the current study. We follow with the operational definition of TQM upon which our research is based. Research methodology is then presented, followed by an analysis of the demographics of firms included in the study. Empirical results are then shown. A final discussion of results and implications is presented in the conclusion section. EXECUTION, CONTINGENCY THEORY, AND IMPLEMENTATION Powell (1995) hypothesized that TQM firms outperform those without quality management programs in a survey of CEOs and quality executives in the Northeastern U.S. Powell utilized financial performance as a dependent variable and evaluated it on the basis of profits, sales growth, and overall financial performance, reported subjectively by the senior manager responding to the survey. He found that certain behavioral aspects of TQM result in improved performance, and concluded that firms with a formal quality management program outperform those without a TQM program. Ahire (1996) studied the impact of TQM programs centering on the following question: Is TQM a long campaign, one taking several years before desired results are seen? He surveyed a total of 499 U.S. and Canadian plant managers and found that successful firms see measurable benefits of the quality management efforts in 2-3 years. In addition, he found that higher levels of top management commitment, customer focus, supplier relations, design quality, training, use of quality management tool s, and employee involvement were associated with better operational results. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".