From free to paid: Effect of knowledge differentiation on market performance of paid knowledge products

Exploring the factors that affect the market performance of paid knowledge products is of great importance for knowledge payment platforms. Drawing on the sensations-familiarity framework and social capital theory, this study investigates how knowledge differentiation between paid and free knowledge impacts market performance, along with the moderating effect of knowledge providers’ social capital. Technically, a neural network-based text mining model is utilized to transform free and paid knowledge to semantic vectors, whose dissimilarity is calculated as knowledge differentiation. Empirical analysis on a real dataset reveals the positive (or negative) effect of knowledge differentiation on sales (or eWOM, electronic word of mouth), which will be more prominent with the increase of social capital. The results are reinforced with robustness checks regarding alternative knowledge-differentiation measures, more control variables and alternative regression methods. The present study extends our understanding of knowledge payment and free-to-paid consumption, and offers practical implications for content design and product management.