Organizational social activities and knowledge management behaviors: An affective events perspective
Bibliographic record
Abstract
Abstract Research indicates that relationship‐oriented HR practices can increase organizational knowledge, yet we know little about the effects of relationship‐oriented HR practices on employee knowledge management behaviors. Drawing from affective events theory, we examine the indirect effect of participation in one type of relationship‐oriented HR practice (i.e., organizational social activities) on three knowledge management behaviors (i.e., knowledge sharing, knowledge hiding, and knowledge manipulating) via positive affect, as well as the conditional indirect effect of intrinsic motivation for organizational social activities on these relationships. Utilizing a time‐separated field study ( n = 163), our analysis reveals positive affect fully mediates the relationship between participation in organizational social activities and (a) knowledge sharing and (b) knowledge hiding, and partially mediates the relationship between participation in organizational social activities, and (c) knowledge manipulating. Most interestingly, we unexpectedly found a positive direct effect of participation in organizational social activities on knowledge manipulation, even though the indirect effect via positive affect was negative. The results also indicate that, for individuals with high intrinsic motivation for social activities, there is a significant indirect effect of participation in organizational social activities on all three knowledge management behaviors.
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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".