The effect of TQM on firms’ performance: the mediating role of environmental management practices and green innovation
Bibliographic record
Abstract
Purpose The current study aims to investigate the relationship between total quality management (TQM) practices, environmental management practices (EMP), green innovation (GI), green competitiveness and firms’ financial (FP) and environmental performance (EP) in a developing country by considering both EMP and GI as mediator variables of the above relationships. Design/methodology/approach The authors collected data from 203 manufacturing companies through a survey questionnaire while also using partial least square structural equation modeling to analyze the collected data and validate the research hypotheses. Findings The study findings indicate that implementation of TQM practices encourages the adoption of EMP and GI. Furthermore, it was found that EMP significantly (partially) mediates the relationship between TQM and GI and that GI fully mediates the relationship between TQM practices, green competitiveness and firms’ financial and environmental performance. Research limitations/implications This study has some limitations, as it was conducted uniquely on Tunisian manufacturing firms, and therefore, its findings may not be generalized to other sectors or countries. Moreover, the study findings can provide important insights for top and middle managers of manufacturing companies in Tunisia by highlighting the critical role of GI in enhancing green competitiveness and financial and environmental performance. Originality/value This study presents an integrated framework that combines contemporary concepts such as TQM, environmental (sustainability) practices, GI, green competitiveness and financial and environmental performance, which have rarely been highlighted in previous studies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".