Quality Management as a Strategic Tool to Enhance the Relationship between Leaders’ Behavior and Lecturers’ Job Satisfaction
Bibliographic record
Abstract
From last two decades, the Higher Education institutions of the developing countries have also realized the importance of better quality services and leadership behavior. Previous studies investigated that better quality services and leadership behavior are fundamental tools for the enhancement of satisfaction level of the university lecturers. Like other business organizations, the education sector also required to adopt the new approaches and techniques for effective leadership. Pakistan is also trying to improve as an education hub amongst all the countries in this region and focusing to render the quality of services according to the perception and expectations of the staff. The objective of this paper is to determine the intervening role of service quality management on the relationship between leadership behavior and job satisfaction. This research study based on quantitative in its nature. The lecturers of public universities in Punjab, Pakistan were a population of the study. Total 396 public university lecturers were selected as sample for the delimitation of the population from public universities and stratified random sampling technique was adopted for collection data. 15% proportionate was adopted to select the sample of the study. A survey method was used to collect the data from the respondents by questionnaire. Data was entered in sheets for analysis using smart PLS-SEM 3 (Partial Least Square). The findings of the study show that leader’s behavior has significance effect on job satisfaction whereas, service quality management has significance-mediating role on the relationship between leadership behavior and job satisfaction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".