The mediating role of competitive advantage in the relationship between total quality management, entrepreneurial orientation, organizational innovation, and organizational performance
Bibliographic record
Abstract
The study aimed to know the positive impact of TQM, EO, and OI on OP, in addition to the indirect impact of TQM, EO, and OI on OP when using CA as an intermediary variable. The study sample consisted of all (307) employees in senior and middle departments working in total quality. Random sampling was the sampling technique strategy used in this study. For the measurements in this investigation, a closed questionnaire from previous research was used. The primary data analysis technique in this study was to evaluate the measurement model and structural model using SmartPLS (4.01). The discriminant and convergent validity of the measurement model was assessed. The results of the study show that in addition to having a direct and positive effect on OP, TQM, EO and OI also have an indirect and positive effect on OP when CA is used as a mediating variable. According to the study, OI, TQM, and EO should receive equal attention from major local industrial enterprises operating in the Kingdom of Saudi Arabia. As one of the most important management strategies for advancing management thought, large local Saudi industrial companies should also implement and ensure the success of a management program. Comprehensive quality and pioneering orientation. These strategies have been proven to be successful in helping organizations solve problems related to productivity or service quality.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".