This study delves into strategies for enhancing user contributions in online communities, recognizing the challenge posed by the public goods nature of such contributions. It introduces a dynamic approach to understand and stimulate user participation by employing a hidden Markov model (HMM) to identify latent motivation states (low, medium, and high) among users. Focusing on three motivating mechanisms—reciprocity, peer recognition, and self-image—the research reveals that these mechanisms have varying impacts on users depending on their motivation state. Reciprocity is found to be effective in moving users from low to medium motivation states, while peer recognition has a universal positive effect across all states. Self-image, however, is only influential in elevating users to a high motivation state from low or medium ones but does not impact users who are already highly motivated. Through design simulations, the study offers actionable insights for platform designers to foster more engaged and sustainable online communities by tailoring IT artifacts to these dynamics.
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Chen, Wei, Xiahua Wei, and Kevin Xiaoguo Zhu. "Engaging Voluntary Contributions in Online Communities." Mis Quarterly 42.1 (2018): 83-A8.
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