The Impact of Emotional Distress and Empathy on Knowledge Sharing in Entrepreneurial Communities
Bibliographic record
Abstract
Are entrepreneurs in online communities more willing to respond to their peers who express emotional distress like sadness and anxiety? While emotional distress may arouse others’ empathy and promote prosocial behavior, it can signal a seeker’s high expectation of receiving helpful responses, which raises a provider’s emotional and cognitive costs. Building on the emotion as social information (EASI) model, we emphasize the role of a seeker’s expressed emotional distress in triggering a provider’s empathy as an affective reaction and a provider’s inference for cost-benefit analysis. We argue that online requests with more expressed emotional distress will likely receive fewer but above-average quality responses and that a provider’s empathy is the mediating mechanism that mitigates the cost concern. Furthermore, a provider’s tenure and a seeker’s reputation can moderate the impact of expressed emotional distress on the occurrence of knowledge sharing. We created a novel dataset comprising 8.3 million seeker–provider pairs from an online community of entrepreneurs and found full support for our hypotheses, using rare event logistic regressions and hierarchical OLS regressions. This study extends the EASI model to explain how and when negative emotions affect empathic help and enriches our understanding of online knowledge sharing among entrepreneurs.
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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 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".