Motivation to share knowledge using wiki technology and the moderating effect of role perceptions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the key challenges for innovation and technology‐mediated knowledge collaboration within organizational settings is motivating contributors to share their knowledge. Drawing upon self‐determination theory, we investigate 2 forms of motivation: internally driven (autonomous motivation) and externally driven (controlled motivation). Knowledge sharing could be viewed as a required in‐role activity or as discretionary extra‐role behavior. In this study, we examine the moderating effect of role perceptions on the relations between each of the two motivational constructs and knowledge sharing, paying particular attention to the affordances of the enabling information technology. An analysis of survey data from a wiki‐based organizational encyclopedia in a large, multinational firm reveals that when contributors' motivation is externally driven, they are more likely to share knowledge if this activity is viewed as in‐role behavior. However, when contributors' motivation is internally driven, they are more likely to participate in knowledge sharing when this activity is viewed as extra‐role behavior. Theoretical and practical implications are discussed.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it