Help Converts Newcomers, Not Veterans: Generalized Reciprocity and Platform Engagement on Stack Overflow
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917383075004416 |
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| author | Strahringer, Lenard Prüß, Sven Eric Riemer, Kai |
| author_facet | Strahringer, Lenard Prüß, Sven Eric Riemer, Kai |
| contents | Generalized reciprocity -- the tendency to help others after receiving help oneself -- is widely theorized as a mechanism sustaining cooperation on online knowledge-sharing platforms. Yet robust empirical evidence from field settings remains surprisingly scarce. Prior studies relying on survey self-reports struggle to distinguish reciprocity from other prosocial motives, while observational designs confound reciprocity with baseline user activity, producing upward-biased estimates. We address these empirical challenges by developing a matched difference-in-differences survival analysis that leverages the temporal structure of help-seeking and help-giving on Stack Overflow. Using Cox proportional hazards models on over 21 million questions, we find that receiving an answer significantly increases a user's propensity to help others, but this effect is concentrated among newcomers and declines with platform experience. This pattern suggests that reciprocity functions primarily as a contributor-recruitment mechanism, operating before platform-specific incentives such as reputation and status displace the general moral impulse to reciprocate. Response time moderates the effect, but non-linearly: reciprocity peaks for answers arriving within a re-engagement window of roughly thirty to sixty minutes. These findings contribute to the theory of generalized reciprocity and have implications for platform design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03209 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Help Converts Newcomers, Not Veterans: Generalized Reciprocity and Platform Engagement on Stack Overflow Strahringer, Lenard Prüß, Sven Eric Riemer, Kai Social and Information Networks Human-Computer Interaction General Economics Economics Generalized reciprocity -- the tendency to help others after receiving help oneself -- is widely theorized as a mechanism sustaining cooperation on online knowledge-sharing platforms. Yet robust empirical evidence from field settings remains surprisingly scarce. Prior studies relying on survey self-reports struggle to distinguish reciprocity from other prosocial motives, while observational designs confound reciprocity with baseline user activity, producing upward-biased estimates. We address these empirical challenges by developing a matched difference-in-differences survival analysis that leverages the temporal structure of help-seeking and help-giving on Stack Overflow. Using Cox proportional hazards models on over 21 million questions, we find that receiving an answer significantly increases a user's propensity to help others, but this effect is concentrated among newcomers and declines with platform experience. This pattern suggests that reciprocity functions primarily as a contributor-recruitment mechanism, operating before platform-specific incentives such as reputation and status displace the general moral impulse to reciprocate. Response time moderates the effect, but non-linearly: reciprocity peaks for answers arriving within a re-engagement window of roughly thirty to sixty minutes. These findings contribute to the theory of generalized reciprocity and have implications for platform design. |
| title | Help Converts Newcomers, Not Veterans: Generalized Reciprocity and Platform Engagement on Stack Overflow |
| topic | Social and Information Networks Human-Computer Interaction General Economics Economics |
| url | https://arxiv.org/abs/2604.03209 |