Promoting Sales of Knowledge Products on Knowledge Payment Platforms: A LargeScale Study with a Machine Learning Approach

Abstract

With the digital transformation of the global economy, a new mode of knowledge service has emerged on open innovation platforms, such as those for the sharing economy. This mode is the paid knowledge-sharing service, where knowledge providers share knowledge only with those who have paid for it. Since an individual customer’s purchases are influenced by others around them, we adopted social influence theory to explain sales of such services on paid knowledge-sharing platforms. A machine learning approach was applied to analyze 27,223 text reviews from the Zhihu Live platform, a well-known and large-scale open knowledge community in China. Hierarchical regression models were built to verify twelve proposed hypotheses about the knowledge providers, knowledge quality, interaction quality, and ratings. The results confirm the positive effect on sales of responsiveness, a dimension of interaction quality, and the negative effect on sales of free provider-driven knowledge contributions. In summary, this study provides a comprehensive framework for antecedent factors of sales of knowledge-sharing services. By introducing knowledge management notions from the field of e-commerce (e.g., price, quality), this study broadens the understanding of the free-to-paid phenomenon on knowledge-sharing platforms.