Sharing knowledge, gaining business: value implications of social status in online knowledge communities
Bibliographic record
Abstract
Purpose Online knowledge communities in which business managers and professionals across organizations share professional knowledge for peer support are common on social media but sparsely examined in the business-to-business (B2B) context. Drawing on status theory, the purpose of this paper is to investigate the economic value of managers’ status in online knowledge communities, conferred from their professional knowledge sharing. The boundary conditions for this economic value are further analyzed under the guidance of transaction cost theory. Design/methodology/approach A panel data set of 12,007 e-businesses on 1688.com, a well-known B2B online marketplace in China, and their managers in the affiliated online knowledge community (club.1688.com) was collected to empirically validity of the hypotheses. Findings Managers’ status in the online knowledge community contributes to their e-store performance in the online B2B marketplace. In addition, e-businesses with high asset specificity, transaction uncertainty and transaction frequency benefit more from their managers’ status conferred from professional knowledge sharing. Originality/value This research contributes to online community and social commerce research by studying the conditional values of participating in online communities. This study also extends the applicability of status theory and transaction cost theory to the context of online B2B social commerce and provides new perspectives and insights in synthesizing these two theories to explain the value implication of online knowledge communities. Practically this research highlights the value and importance of professional knowledge sharing, in addition to the commonly studied customer-oriented content sharing, on social media and offers B2B managers guidance to optimize their e-business performance.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".