The development and validation of the Incivility from Customers Scale.
Bibliographic record
Abstract
Scant research has examined customers as sources of workplace incivility, despite evidence suggesting that mistreatment is more common from organizational outsiders, including customers, than from organizational members (Grandey, Kern, & Frone, 2007; Schat & Kelloway, 2005). As an important step in extending the literature on customer incivility, we conducted two studies to develop and validate a measure of this construct. Study 1 used focus groups of retail and restaurant employees (n = 30) to elicit a list of uncivil customer behaviors, based on which we wrote initial scale items. Study 2 used a correlational survey design (n = 439) to pare down the number of scale items to 10 and to garner reliability and validity evidence for the scale. Exploratory and confirmatory factor analyses show that the scale is unidimensional and distinguishable from measures of the related, but distinct, constructs of interpersonal justice and psychological aggression from customers. Reliability analyses show that the scale is internally consistent. Significant correlations between the scale and individuals' job satisfaction, turnover intentions, and general and job-specific psychological strain provide evidence of criterion-related validity. Hierarchical regression analyses show that the scale significantly predicts three of four organizational and personal strain outcomes over and above a workplace incivility measure adapted for customer incivility, providing some evidence of incremental validity. Limitations and future research directions are discussed.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".