The Effect of Total Quality Management, Enterprise Resource Planning and the Entrepreneurial Orientation on the Organizational Performance: The Mediating Role of the Organizational Excellence --- A Proposed Research Framework
Bibliographic record
Abstract
Organizational performance and organizational excellence are the main two focuses of the management of any organization. In fact, the resources of an organization, either tangible or intangible, can distinguish the level of performance of one organization from its rivals. The question always is what are the key success factors that can enhance the organizational strategy implementation initiatives to produce value and create the competitive advantage. In today’s changing business environment, improvement and development of internal resources are very crucial to compete externally and globally. This paper aims to review the literature related to the organizational performance and explains the potential effects of TQM, ERP and EO. Given that not all the strategy implementation initiatives are fruitful, this paper attempted to establish an explanation of the mechanism through which such strategies can enhance the overall organizational performance. Based on a thorough review of the extant literature and the theoretical foundation, a research model was proposed. The proposed model is grounded by the fact that only excellent, innovative and differentiated companies and products can excite the customers and succeed in a turbulent business environment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".