An Analysis of Credibility of CEO’s in an Organisation Linkage with Employee Engagement
Bibliographic record
Abstract
The current study examines the credibility of the respective Chief Executive Officer (CEO’s) in an organization while linking its impacts with the concurrent facets of employee engagement. The credibility of a Chief Executive Officer (from now on referred to as the CEO) can greatly influence his/her employee's behaviour and perception towards the company’s objectives and reputation. A CEO with a credible reputation can help increase the productivity of workforce, the quality and quantity of leads, secure loyal customers, and help in retaining employees. The present study surveyed 186 employees randomly selected from the top 100 companies in India using the questionnaire developed by researcher to measure credibility of CEO linkage with employee engagement. Further the quantitative questionnaires collected from 186 employees were tabulated and analysed in order to study the linkage of the credibility of a CEO in an organization with the employee engagement and its effect on the organization's success. The result shows that the CEO's credibility is positively associated with employee engagement and subsequently affects the organizational reputation and success.
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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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".