Impact of Work Engagement on Performance in Indian Higher Education System
Bibliographic record
Abstract
The moments in which employees attach themselves with their work roles are called as the moments of engagement (Kahn, 1992). The number of higher educational institutions is rapidly growing in India to cater to the increasing demand for advanced studies (KPMG, 2014). As a result, Indian academia is facing the challenge of keeping academics engaged so that academics can happily and efficiently perform a larger role. So, this study examines the influence of job resources on engagement along with how the interaction among job resources and perceived autonomy impacts performance in service delivery. We also examine the mediating role of work engagement between the job resources and service employee performance relationship. Two hundred sixty one academics elected from different Indian universities were asked to rate themselves on the support, autonomy and engagement scales. Further, 261 students were asked to rate the performance of these academics. Structural equation modeling was used to test the formulated hypotheses. The results suggest that work engagement mediates the relationship between supervisory support and service employee performance. Moreover, perceived autonomy moderates relationship between co-worker support and work engagement relationship. These findings extend the theoretical understanding of engagement enhancing the performance in service delivery as reflected in the feedback from students. Results also urge universities to make policies that enhance coworker and supervisory support which can create a culture of co-operation. Certain limitations and future research directions of this study have also been discussed in greater detail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".