Business sustainability of manufacturing companies: The role of eco supply chain management (ESCM) and total quality management (TQM)
Bibliographic record
Abstract
The proposed study of this paper investigates the effects of total quality management (TQM) and eco supply chain management on business sustainability. The type of research used in this research is quantitative research and uses an online questionnaire as a tool to collect data from respondents. Research data was obtained by distributing online questionnaires to 568 Manufacturing company managers who were determined using the simple random sampling method. The questionnaire was designed to contain statement items and the Likert scale used in this research. The data analysis method used in this research was structural equation modelling partial least squares (PLS-SEM) with data processing tools, namely SmartPLS 4.0 software. The findings of this research specify that TQM and eco supply chain management had significant effects on business sustainability. The results of this research indicate that TQM and eco supply chain implementation were manifested in the company's ability to produce high product quality directly affecting the business sustainability of manufacturing companies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".