Exploring the antecedents of employee engagement
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the relationship between employee engagement and general management, performance management, reward management and transformational leadership. Design/methodology/approach A survey was distributed to a mid-sized energy company based in North America. A two-stage hierarchical multiple regression was performed. Employee engagement was the dependent variable, and the control variables of age and education were entered at stage one. In stage two, the four variables of general management, performance management, reward management and transformational leadership were included. Findings The findings revealed that the factors most predictive of employee engagement were reward management, followed by performance management, general management and transformational leadership. The only control variable predictive of engagement was age, where older employees reported greater engagement. Practical implications The study can offer practitioners more insight into employee engagement which in turn can help with employee related decision-making in their own individual workplaces. Originality/value The study contributes to the existing literature on human resource management by providing insights into the factors that contribute to employee engagement and corroboration that age is a contributing factor.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".