‘I’m busy (and competitive)!’ Antecedents of knowledge sharing under pressure
Bibliographic record
Abstract
This study considers the dilemma faced by employees every time a colleague requests knowledge: should they share their knowledge? We use adaptive cost theory and self-efficacy theory to examine how individual characteristics (i.e., self-efficacy and trait competitiveness) and situational perceptions (i.e., ‘busyness’ and perceived competition) affect knowledge sharing behaviours. A study was conducted with 403 students who completed a problem-solving exercise and who were permitted (but not required) to respond to requests for knowledge from people who were doing the same activity. Our results suggest that people who perceive significant time pressure are less likely to share knowledge. Trait competitiveness predicted perceived competition. This and low task self-efficacy created a sense of time pressure, which in turn led to people feeling ‘too busy’ to share their knowledge when it was requested. Perceived competition was not directly related to knowledge sharing. Implications for research and practitioners 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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".