Bibliographic record
Abstract
Purpose The purpose of this paper is to determine the importance of research setting in conflict research design. Research studies conducted in a university setting were compared to other research conducted in workplaces. Design/methodology/approach A meta‐analysis of 28 related papers was conducted to compute effect sizes of the linkages between task conflict, relationship conflict, satisfaction and performance. The impact of the research setting (i.e. university vs workplace) as a moderator was also tested. Findings The research setting was found to be a significant moderator of the linkage between task conflict and satisfaction, task conflict and performance as well as relationship conflict and performance. In each case of moderation, the effect sizes were much greater when research was conducted in the workplace than in a university setting. Research limitations/implications The findings suggest that research conducted in a university setting likely underestimated the impact of conflicts on the level of satisfaction and the degree of performance as compared to research conducted in a workplace setting. Practical implications The author proposes that more conflict studies should be conducted in a field setting. In addition, it is proposed that such studies include more often satisfaction and related variables in research design. Originality/value The majority of conflict research is conducted in a university setting (e.g. students doing a project for credit), under the assumption that the setting is a fair approximation of the workplace. The present study shows that this assumption might not be true.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.150 | 0.347 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.043 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".