An investigation of the total quality management survey based research published between 1989 and 2000
Bibliographic record
Abstract
There has been a plethora of published research related to total quality management (TQM) in the last few decades. However, very few studies focused on cataloging critical factors of TQM. One of the objectives of this literature review was to investigate the state of TQM by examining and listing various TQM factors identified based on survey studies conducted in different countries and published in a variety of journals over the past decade. An examination of 76 survey studies that used an integrated approach to TQM showed that the TQM factors could be grouped under 25 categories. An analysis of the 347 survey based research articles published between 1989 and 2000 using these 25 factors as a framework revealed the most frequently covered TQM factors in the literature. Another goal of the paper was to analyse the objectives of these articles by year and type of journal they were published in to determine the trends in TQM survey based studies and recommend future direction for research. The analysis showed that the objectives of the 347 studies could be grouped under six categories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".