Relationship among Personality Traits and Conflict Handling Styles of Call Center Representatives and Appraisal of Existing Service Model
Bibliographic record
Abstract
In order to identify the relationship between conflict handling styles and personality traits of call centre representatives and to extract and appraise any existing model, i.e., style of public dealing/conflict resolving, we have carried out this research. A total of 128 call centre representatives from a Bank and a Telecommunication Company have participated in the study. We assumed that for Bank and for Telecommunication Company both, the most commonly used conflict handling styles would be integrating and obliging, regardless of the personality traits of the call centre representatives and without having control on gender and age. The participants were asked to complete two scales, the big five inventory and the conflict handling style scale. Results confirm our assumptions through correlations, factor analysis, and SEM. We have found significant relationships among personality traits and conflict handling styles. Three conflict-handling styles were persistent in all statistical analyses and i.e., integrating, obliging, and compromising. However, SEM extracts the same variables in models with low regression weights and high residual variance on Neuroticism that may be indicative of depiction of trait by representatives at certain places in certain situations.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".