The Impact of High Quality Relationship on Innovative Work Behavior of Employees through Psychological Wellbeing: A Case of Pharmaceutical Sector in Pakistan
Bibliographic record
Abstract
The impact of qualities and experiences of high quality relationship is of great value to the companies and field of human resource. Pakistan is facing critical situation regarding qualities and experiences of relationship of employees. Innovative work behavior of employees has not been studied in developing countries like Pakistan. In order to assist the business in pharmaceutical sector this study is aimed at investigating the effect of experiences and qualities of high quality relationship on innovative work behavior of employees. Pharmaceutical sector was considered for this study as this sector has significant contribution in Gross Domestic Product (GDP) of Pakistan. This research was quantitative in nature in which data was collected from managerial and non-managerial employees of pharmaceutical sector. Total 310 questionnaires were completely filled and entered in SPSS for analysis. Correlation analysis was performed in SPSS to show relationship between the variables. Model was tested through structural equation modeling in AMOS and goodness of fit indices were estimated using Hu and Bentler (2010) criteria and all the values were found to show good fit model. To test the mediation among variables through regression the SOBEL test was used as a supplemental test. All the direct and mediational hypotheses were accepted. The results reveal that the psychological wellbeing mediates the relationship between experiences, qualities of high quality relationship and innovative work behavior. Limitation of the study and managerial implications are also discussed along with guideline for future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".