The influence of balanced scorecard dimension on total quality management and sustainable performance as a mediating variable: An empirical study in KSA services projects ,
Bibliographic record
Abstract
This study aims to analyze the influence of the Balanced Scorecard (BSC) dimensions (financial, customer, internal processes, and learning and growth) on Total Quality Management (TQM), with sustainability performance as a mediating variable. The study utilized a cross-sectional survey method, distributing 400 questionnaires to employees in the service projects in KSA. Out of these, 340 questionnaires were deemed valid for final analysis. Data analysis was conducted using the SmartPls program. The study found that all BSC dimensions, except for the learning and growth dimension, had a negative direct effect on TQM, and the BSC dimensions through sustainability performance positively affected TQM, except for the learning and growth dimension. The main contribution of the research is to identify the BSC dimensions that best predict TQM in service projects in Saudi Arabia. The extended model test shows that sustainability performance is a good mediator in the causal relationship between the BSC dimensions and TQM. Future research can validate these findings in projects such as capital construction and industrial projects using a longitudinal survey design. Organizations can apply these research findings by leveraging the Balanced Scorecard as a management framework for predicting and improving TQM.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".