This study addresses limitations in existing recognition systems within online knowledge-sharing communities by proposing a framework based on socio-technical design principles. The framework enables the design of a multi-criterion- and multi-segment-based recognition system that utilizes multiple user characteristics for differential recognition according to community goals. Using data from Yelp.com, the study demonstrates how this framework can be applied to recognize top members across different behavioral segments (amateurs, adepts, and enthusiasts) based on their performance on various relevant factors.
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Bhattacharyya, Samadrita, Shankhadeep Banerjee, and Indranil Bose. "One size does not fit all: Rethinking recognition system design for behaviorally heterogeneous online communities." Information & Management 57.7 (2020): 103245.
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