Knowledge sharing, knowledge seeking, and emotions: A longitudinal study of hospital restructuring <scp>decision making</scp>
Bibliographic record
Abstract
Abstract Prior research examines factors that enable or hinder knowledge sharing and knowledge seeking in groups. However, individuals also share and seek knowledge outside group meetings, especially if the group is making strategic decisions over time. Therefore, this study examines how, during a longitudinal strategic decision‐making process, the emotions of group members and knowledge sharing within the group affect their knowledge‐sharing intentions and knowledge‐seeking behaviors beyond the group. We focus on a single organization, a hospital, whose board created a citizen advisory panel (CAP) of 28 individuals to gather community input on the restructuring of the hospital's activities to contain costs. The group met in five all‐day sessions to provide their input. We surveyed each member before the CAP process and after each CAP session. The resulting longitudinal data were analyzed using panel‐data techniques, with the findings being complemented by qualitative insights. The results indicate, somewhat surprisingly, that both positive and negative emotions (specifically enthusiasm and anxiety) positively affect both knowledge‐sharing intentions and knowledge sharing within groups in strategic decision‐making contexts. We also find that enthusiasm, anxiety, and perceived relative knowledge within groups positively affect subsequent knowledge‐seeking behaviors. Our findings contribute to the literature on knowledge management and organizational decision making. The study provides insights into how, in groups making strategic decisions over time, emotions as well as knowledge sharing within groups affect knowledge‐related intentions and behaviors beyond the groups. The study also adds to the theory of planned behavior to highlight the role of emotions in influencing intentions and 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".