Does Emotional Intelligence Influence Employees, Customers and Operational Efficiency? An Empirical Validation
Bibliographic record
Abstract
A research study was initiated to investigate the impact of emotional intelligence on employees’ satisfaction and loyalty, and how it influences operational efficiency in telecom service industry in Oman. A questionnaire-based survey was conducted and the responses received were tested with various statistical techniques. These test results were found to be in broad agreement with the assumptions widely prevalent in management literature and service industries. The findings suggest that emotional intelligence contributes significantly in improving internal performance. Employees are valuable assets and improved internal performance is due to employees’ commitment. Service industry could take care of employees, keep them satisfied to win their loyalty, which can be achieved through regular employees’ engagement and involvement. Engaged employees value customers’ expectations and build better relationship. Satisfied and loyal employees are in a position to deliver high service quality and improved productivity. The service provider shall continuously monitor service quality to maintain end users’ satisfaction. It can be sustained through employees’ continuous training and skills development that will improve operational efficiency of the company in terms of increased sales and profitability. Thus, the present study provides an empirical validation and confirmation of the propositions and hypotheses about how service providers should manage employees’ emotional intelligence for giving them satisfaction, winning their loyalty, thereby, eventually enhancing service values, operational efficiency and profitability of the company.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".