Conflict‐handling style measurement: a best‐worst scaling application
Bibliographic record
Abstract
Purpose This study aims to develop and validate a best‐worst scaling (BWS) measure of preferred conflict‐handling styles, named the Conflict‐handling BWS (CHBWS). Design/methodology/approach The authors conducted three studies. Study 1 consisted of a sample of psychology students (n=136) from a Canadian university and was designed to assess the convergent validity of the CHBWS by comparing it with the ROCI‐II and DUTCH instruments. Study 2 consisted of a sample of psychology students (n=154) from a US university and was designed to assess the predictive validity of the CHBWS by relating conflict‐handling styles to consumer complaint behavior. Study 3 consisted of a random sample of adults registered with an online survey company in Australia (n=204) and Germany (n=214). This study was designed to assess the antecedent relationship of Schwartz's personal values to conflict‐handling styles. Findings The study shows that best‐worst scaling is a valid and advantageous way of measuring conflict‐handling styles. The CHBWS demonstrated both convergent and predictive validity, and was able to reproduce the structure of the dual‐concerns model. The study also showed that preferred conflict‐handling style influences the choice of complaint behavior in a retail service failure situation. Furthermore, the study demonstrated that Schwartz's personal values can influence the preferred conflict‐handling style in two individualistic cultures. Originality/value This is the first study to measure conflict‐handling style preferences using a BWS approach. Furthermore, it is the first study to relate consumer complaint behavior to preferred conflict‐handling style.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".