The influence mechanism of rewards on knowledge sharing behaviors in virtual communities
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the effects of organizational rewards on two forms of knowledge sharing – explicit knowledge sharing and tacit knowledge sharing in virtual communities, and further to explore the mediating effect of intrinsic motivation on the effect of virtual community rewards on implicit knowledge sharing. Design/methodology/approach Based on relevant knowledge sharing theories, this study develops an integrated framework to explore virtual community rewards and tacit and explicit knowledge sharing in a virtual context. This study then collected data from 429 virtual community users in four virtual communities via an online survey. Hierarchical regression analyzes were used to test the proposed research model. Findings The results of this study show that virtual rewards have a significantly positive linear relationship with explicit knowledge sharing but have an inverse U-shape relationship with tacit knowledge sharing in virtual communities. In addition, intrinsic motivations including enjoyment and self-efficacy mediate the relationship between rewards and tacit knowledge sharing. Practical implications This study suggests more virtual community rewards may not always lead to more tacit knowledge sharing. Instead, too many rewards may weaken the motivation for tacit knowledge sharing. Knowledge management practitioners should make full use of the positive impact of self-efficacy and enjoyment to set up appropriate reward incentives to encourage knowledge-sharing, in particular, tacit knowledge sharing and to better manage virtual communities. Originality/value This study explores knowledge-sharing behavior in virtual communities, an important step toward more integrated knowledge-sharing theories. While online communities have become increasingly important for today’s knowledge economy, few studies have explored knowledge and knowledge sharing in a virtual context and this study helps to bridge the gap. In addition, this study develops an integrated framework to explore the mechanism through which virtual community rewards affect knowledge sharing with intrinsic motivation mediating this relationship in online communities, which further enriches the understanding on how to use virtual rewards to motivate knowledge sharing behaviors in the virtual context.
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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 | 0.003 | 0.033 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".