Integrating total quality management and management control systems: a systematic literature review and proposed integrative framework
Bibliographic record
Abstract
Purpose The purpose of this paper is to assess the status of total quality management (TQM) and management control systems (MCSs) research, identify gaps and propose directions for future research. Further, this study develops an integrative framework linking TQM principles and MCS mechanisms. Design/methodology/approach Adopting Hoque's (2014) approach, 40 articles from 25 leading accounting journals and 130 articles from 49 leading business and management journals published from 1985 to 2023 were analysed. The review covers topics, research settings, theories, methods and primary data analysis techniques. Findings Research on TQM has declined significantly since its peak until 2023. Adopting and implementing TQM as a topic and the survey research method and quantitative analysis dominated the TQM–MCS research in the review period. The TQM–MCS link remains understudied in the service and public sectors and less developed countries. The review identifies three key themes. The assumption that TQM universally enhances firm performance is challenged. Research limitations/implications The review is limited to selected accounting and business and management journals, excluding other fields. However, it provides a broad overview of TQM research published in leading journals in the respective fields. Originality/value This study highlights the need for further research into how MCS can better support TQM. The findings of this study offer practical insights for designing or improving quality performance measurement systems.
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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 | 0.046 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.044 | 0.034 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".