Effect of emotional labor on customer satisfaction: The study of multiple moderators in hospitality industry of Pakistan
Bibliographic record
Abstract
Emotional labor is the foremost focus for both academicians and practitioners of human resource management from the last two decades. It has become mandatory job for service workers to create, sustain and construct progressive emotional state with customer throughout service encounter process. Different emotional labor strategies execute by service worker capitulate diverse outcomes, since customer may perceive them as fake, robotic or sincere. This study is discussing the relationship between emotional labor and customer satisfaction in the context of hospitality industry in Pakistan. The contemporary research portrays the effect of emotional labor on customer satisfaction by the role of three key moderators which are self-monitoring, co-worker support and customer satisfaction. The selected context is hotel industry of Pakistan which incorporates all five star hotels representing different provinces of country. Accumulated sample size comprises 185 respondents exemplifying front office department of all five star hotels of country. Multivariate data analysis techniques like exploratory factor analysis (EFA), cronbach's alpha and linear multiple regressions for moderators through Hayes process have been used to analyze the data. Data analysis postulates constructive set of findings. Results demonstrates that role of emotional labor is substantial in order to achieve customer satisfaction. Emotional labor is more effective due to deployment of three moderators which are self-monitoring, co-worker support and customer participation. Another noteworthy outcome of research is that, deep acting is more prone towards customer satisfaction in comparison with surface acting. Lastly, this study is dynamic for practitioners in hotel industry and provides valuable insight in this context. In the end limitations and future directions are made by researcher.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".