Title:From free to paid: Customer expertise and customer satisfaction on knowledge payment platforms
Presenter:Jin Zhang
Time: 2019.5.22 12:15-13:30
Location:A618
Abstract:
With the popularity of knowledge monetization, we still know little about what factors are influential to customer satisfaction of paid knowledge, especially among different customer segments. Thus, we take a combined view by integrating user activities from both free and paid platforms. Considering the complexity of knowledge acquisition, we first propose a novel measurement of "customer expertise" based on text mining, as a criteria for customer segmentation. Drawing upon the value-percept diversity theory, we then postulate a conceptual model proposing that customers with different expertise would react differently to the price of knowledge and historical knowledge-consuming transactions, in terms of customer satisfaction. We test the model empirically through the hierarchical OLS regression with data collected from Zhihu and Zhihu Live. Distinguishing expert and novice customers, we have findings that (1)expert customers are less sensitive to price; (2) historical price positively influences the satisfaction of novice customers, but negatively for expert customers; (3) expert customers are less influenced by historical satisfactions, which have important implication for market targeting and knowledge pricing strategy.
