Unraveling the customer orientation paradox
Bibliographic record
Abstract
Purpose This study aims to examine the roles of customer orientation (CO) and two distinct stress coping strategies – problem-focused (PC) and emotion-focused (EC) – in the positive relationship between customer incivility (CI) and job stress (JS). Design/methodology/approach Data were collected via a survey of casino dealers in South Korea. Common method variance was assessed using an unmeasured latent method construct, confirming both convergent and discriminant validity. Collinearity diagnostics were conducted to evaluate potential multicollinearity among independent variables. Hypotheses were tested using PROCESS Macro Models 1 and 3 to examine moderating effects and three-way interactions. Findings CI is positively related to JS. Employees with high CO experience greater JS when faced with CI compared to those with low CO. Highly customer-oriented employees with low coping strategies encounter significant JS when dealing with uncivil casino patrons. Practical implications Casino practitioners should balance CO strategies with effective stress management and support systems. This finding calls for a reevaluation of training programs and policies to maintain high service quality while ensuring employee well-being. Originality/value This study challenges the traditional view of CO as merely a stress-buffering factor by revealing its paradoxical role. It identifies individuals more susceptible to JS and demonstrates how the interaction between CI, CO and coping strategies (i.e. PC or EC) can escalate JS.
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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 | 0.002 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".