The Relationship between Enterprise Resource Planning, Total Quality Management, Organizational Excellence, and Organizational Performance-the Mediating Role of Total Quality Management and Organizational Excellence
Bibliographic record
Abstract
Purpose: This study was set up to examine the mediating effect of TQM and organizational excellence between ERP and organizational performance.Design/methodology/approach: To examine the model of the study, design of survey questionnaire was employed through data collected from Dubai Police Departments. Out of 565 questionnaires, 320 only usable returned. Partial Least Square (PLS) structural equation modelling was employed to analyze the data.Findings: Based on statistical results, the effect of ERP on TQM, organizational excellence, and organizational performance were confirmed. In addition, the effect of TQM and organizational excellence on organizational performance was also confirmed. Moreover, TQM was found to partially mediate the effect of ERP on organizational performance, whereas organizational excellence was found to fully mediate the effect on the same relationship.Practical implications: The results of this study have several practical implications. This study will help managers and decision makers to take the proper decision when implementing ERP system. Due to that, TQM and organizational excellence are the most important practices to ease the ERP implementation. Originality/value: This study is considered as the only empirical study that examines the collective effect of ERP, TQM, and organizational excellence on organizational performance.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".