Paper Type
ERF
Abstract
Crowdsourcing contests have become an important mechanism for solving innovative data-driven problems across domains. On platforms like Kaggle, solvers compete either individually or in teams, and teams can merge repeatedly during an ongoing contest. This feature enables dynamic coalition formation and reshapes the competitive landscape. However, prior research has largely modeled solvers as static entities and has not systematically examined how merge timing and repeated merging shape contest outcomes. Drawing on the knowledge-based view and absorptive capacity, we argue that repeated merging leads to diminishing performance returns as growing knowledge diversity increases integration demands that strain teams’ absorptive capacity. We further argue that merge timing influences team performance due to coordination and integration challenges, and task uncertainty moderates the effects of both repeated merging and merge timing. This paper extends crowdsourcing contest research from a static to a dynamic conception of solvers and offers implications for platforms, sponsors, and solvers.
Paper Number
1403
Recommended Citation
Xiong, Tianhao and Dissanayake, Indika, "Merging to Win: How Merge Timing and Repeated Merging Shape Team Performance in Crowdsourcing Contests" (2026). AMCIS 2026 Proceedings. 3.
https://aisel.aisnet.org/amcis2026/sig_dspe/sig_dspe/3
Merging to Win: How Merge Timing and Repeated Merging Shape Team Performance in Crowdsourcing Contests
Crowdsourcing contests have become an important mechanism for solving innovative data-driven problems across domains. On platforms like Kaggle, solvers compete either individually or in teams, and teams can merge repeatedly during an ongoing contest. This feature enables dynamic coalition formation and reshapes the competitive landscape. However, prior research has largely modeled solvers as static entities and has not systematically examined how merge timing and repeated merging shape contest outcomes. Drawing on the knowledge-based view and absorptive capacity, we argue that repeated merging leads to diminishing performance returns as growing knowledge diversity increases integration demands that strain teams’ absorptive capacity. We further argue that merge timing influences team performance due to coordination and integration challenges, and task uncertainty moderates the effects of both repeated merging and merge timing. This paper extends crowdsourcing contest research from a static to a dynamic conception of solvers and offers implications for platforms, sponsors, and solvers.
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