Knowledge Sharing in Social Networking Sites: How Context Impacts Individuals’ Social and Intrinsic Motivation to Contribute in Online Communities
Why this work is in the frame
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Bibliographic record
Abstract
Knowledge-sharing research in online communities has primarily focused on communities of practice and the social factors of knowledge-sharing behavior in organizational contexts. Academic research has not rigorously examined non-business-oriented online communities as venues for facilitating knowledge sharing. Thus, in this paper, we address this research gap by examining the contextual roles of anonymity and community type on an individual’s social and individual drivers of knowledge-sharing attitude in social networking sites. Using social capital theory as a theoretical backbone, we propose and empirically validate a relational model through a survey of 329 users of Facebook, LinkedIn, and CNET. From analyzing the data with the partial least squares (PLS) method, we found strong explanatory power of the proposed research model. We discuss our study’s implications for both research and practice.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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