"Evaluating The Factors Influencing Employee Engagement In The Life Insurance Corporation"
Bibliographic record
Abstract
Employee engagement is a vital driver of organizational success, especially in the competitive landscape of the life insurance industry. This study evaluates the factors influencing employee engagement within the Life Insurance Corporation (LIC), focusing on intrinsic and extrinsic motivators. By analyzing data from Administrative and Marketing Employees, the study identifies key areas impacting engagement, including job satisfaction, recognition, career growth, compensation, work-life balance, and leadership support. The findings reveal that Administrative Employees generally report higher job satisfaction compared to their Marketing counterparts, who exhibit more dissatisfaction with their roles. Recognition is highly valued across both groups, though Marketing Employees place a higher emphasis on it. Career growth opportunities and compensation perceptions show significant differences, with Marketing Employees rating these aspects more favorably than Administrative Employees. Both groups report moderate support for work-life balance and engagement levels. The study also highlights that while engagement is perceived to significantly impact productivity and customer satisfaction, there is room for improvement in engagement initiatives. The Chi-Square test results suggest no statistically significant relationship between intrinsic motivators and engagement in the sample. To enhance employee engagement, LIC should consider strengthening recognition programs, improving career growth opportunities, revising compensation packages, and expanding support for work-life balance. These measures will help create a more motivated and productive workforce, ultimately driving LIC's success in a competitive market.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".