Platform diffusion at temporary gatherings: Social coordination and ecosystem emergence
Bibliographic record
Abstract
Abstract Research Summary Software platforms create value by cultivating an ecosystem of complementary products and services. Existing explanations for how a prospective complementor chooses platforms to join assume the complementor has rich information about the range of available platforms. However, complementors lack this information in many ecosystems, raising the question of how complementors learn about platforms in the first place. We investigate whether attending a temporary gathering—a hackathon—impacts the platform choices of software developers. Through a large‐scale quantitative study of 1,302 developers and 167 hackathons, supported by qualitative research, we analyze the multiple channels—sponsorship, social learning, knowledge exchange, and social coordination—through which hackathons serve as a social forum for the diffusion of platform adoption among attendees. Managerial Summary A software platform such as Windows, iOS, or Amazon Web Services relies on third‐party developers to create applications that complement the platform and make it valuable for end users. However, developers face a wide range of possible platforms, and they may have limited information about which platforms would be worthwhile to develop for. A software platform business can educate and encourage developers to adopt their platform by supporting in‐person software development competitions, known as hackathons. Developers learn about prospective platforms that advertise at the hackathon. Developers also learn whether and how to use a platform by observing and teaching one other. Hackathons are particularly useful for spreading platform technologies: developers prefer to adopt widely used platforms, and hackathons permit developers to identify and join fashionable platforms.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".