Mapping the linkage between Organizational Culture and TQM
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the impact of Organisational Culture (OC) on total quality management (TQM) implementation in Indian small and medium enterprises (SMEs) in the auto component sector. Specifically; it attempts to propose a model linking OC and TQM for this sector. Design/methodology/approach – Survey method was used for data collection by targeting the whole population of 482 Indian auto component SMEs drawn from Auto Component Manufacturers Association members’ database. Out of 482, 150 completely filled questionnaires were taken for data analysis. Proposed relationships among identified dimensions of OC and TQM interventions have been validated through parameter estimation statistics and goodness-of-fit statistics using path analysis technique of structural equation modelling using AMOS 18.0. Findings – Based on the results, a culture influenced TQM model has been developed. The model demonstrates a linkage between cultural dimensions and TQM interventions, thereby suggesting that OC characterised by “openness”, “confrontation”, “trust”, “authenticity”, “proaction”, “autonomy”, “collaboration” and “experimentation” has a significant and positive impact on TQM implementation. Practical implications – The study presents many practical implications, specifically for quality managers in Indian auto component SMEs. The study has developed a culture influenced TQM model which identifies dimensions of OC that promote TQM implementation. The study also identifies various interventions of TQM in their order of significance, which can be used by SMEs in mapping the critical links between OC and TQM through this model. Thus, findings of the present study will help SMEs in this sector to move up the value chain and sustain their global competitiveness. Originality/value – The study provides a culture influenced TQM model which would assist managers in quality implementation in Indian auto component SMEs.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".