Identified Research Gaps in Employee Engagement
Bibliographic record
Abstract
This research paper sets out to investigate the research gaps in employee engagement for systematic empirical investigations, in order to substantiate future studies. A desk research has contributed to identify seven gaps in employee engagement. The first gap which is about the conceptual confusion, can be minimized by formulating a working definition of employee engagement. The nonexistence of theoretical arguments and empirical tests on the impact of the religiosity on employee engagement, in both the Sri Lankan and in the international contexts, has been identified as the second gap. The third gap has been identified to be the fact that the rapport between personal character and employee engagement being, neither theoretically argued nor empirically tested, in Sri Lankan and the international contexts. The fourth gap is the unavailability of studies in the Sri Lankan context as to how the high performance work practices (HPWPs) impact on employee engagement. The fifth gap identified is the shortage of empirical evidence regarding the link between employee engagement and organizational financial performance in the Sri Lankan context. Absence of empirical evidence on employee job performance to be an intervening variable for employee engagement and organizational financial performance is brought up as the sixth gap. The same absence is found in empirical evidence about religiosity, HPWPs, personal character, leadership and work life balance that significantly affect employee engagement in a nomological network in the Sri Lankan context as well as in the international context, which is the seventh Gap.
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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 | 0.087 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".