Emergence and Evolution of Nascent Online Communities: What Inhibits Members to Contribute?
Bibliographic record
Abstract
Online communities like Wikipedia have the potential to significantly transform our global society and economy. Despite their growing importance, however, the extant literature has theorized only little about how collaboration through member contributions actually happens in these spaces. In particular, it is unclear what inhibits the emergence and evolution of member contributions and thus collaboration in the early stages of an online community’s existence. Understanding such inhibitors is critical as they can make or break the long-term success of an online community. This paper thus aims to answer this question by applying a multi-method approach to a longitudinal case study of AshokaHub, a nascent global online community of social entrepreneurs. Despite positive signs and expectations at its launch and considerable efforts by the management team to drive member engagement, member contribution remained limited in its first year of existence. We advance a theoretical model for understanding limited member contribution in nascent online communities. This model suggests that important inhibitors are, firstly, limited OC design work related to boundary spanning, boundary reinforcement and the embedding of existing communities and practices, secondly, constraining platform materiality, and, finally, the failure to obtain critical mass.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".