The mediating effect of TQM on the relationship between market orientation and organizational performanc
Bibliographic record
Abstract
The purpose of the present paper was to explore whether total quality management (TQM) mediated the influence of market orientation (MO) on organizational performance (OP). The study was conducted using a quantitative way of investigation with the help of the survey instrument as the primary tool for data collection. Firm ICT managers completed the survey, and the data collected was used to explore the pathways proposed. After having been collected, the data were screened using the SPSS 26 version, and the hypothesized correlations were tested using the same data. The results indicate that both MO and TQM had significant impacts on OP. Such a connection was also impacted by the implementation of TQM. The information and communication technology (ICT) industry is popular among developing countries and is considered as one of the most employers and tools that fasten economic progress. The results provide new insights into the relationships and impacts of MO and TQM on organizational performance to owners/managers, practitioners, and academicians in the ICT sector. The owners and managers can serve as the guidelines to make better decisions in implementing MO with TQM standards to outperform in all aspects and sustain market competition. The owners and managers are advised to develop better strategies to adopt MO and TQM plans into more effective and performance-oriented approaches. This study is the first to do empirical research on the connections between overall quality management, market orientation, and organizational performance in the ICT sector of an Arab developing country.
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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 | 0.005 | 0.016 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".