Abstract

With the prosperity of the online knowledge payment market, obtaining knowledge through purchasing has become increasingly popular. The purpose of this paper is to determine the key factors that influence consumers’ knowledge payment intention. While previous research has separately investigated the impacts of knowledge platform-, product-, contributor-, or seeker-level factors, we draw upon the stimulus-organism-response framework and propose an integrated model that simultaneously considers all these levels. A multi-analytical approach that combines structural equation modeling (SEM) and neural network analysis is proposed to determine the significant predictors and their relative importance. Through an analysis of survey data from 450 Chinese respondents, our SEM results indicate that knowledge platform interactivity and information quality, knowledge rareness, knowledge contributor professionalism and charisma positively influence consumers’ knowledge payment intention through the mediation of consumer perceived value. Our neural network analysis further reveals that knowledge contributor professionalism is the strongest predictor.

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