Effect of Humanlikeness on Satisfaction with the Recommender System: Expectancy-Disconfirmation Model Perspective
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Recommender system is a service agent that provides personalized recommendations based on customers' past behavioral data and general product information. This agent takes the role of salesperson in online shopping by providing necessary information to assist customers to fulfill their shopping goal. As salesperson's advice and assistance is important for customers' satisfaction, how much recommender system is perceived as a human (like a salesperson) was expected to be an important predictor of satisfaction. Using expectancy-disconfirmation theory, this study was designed to investigate the relationships between consumers' personality traits, their perception of humanlikeness of RS, and cognitive and affective satisfaction responses to RS. A structural equation model (N=419) was used to test the hypotheses. The results confirmed the importance of humanlikeness of RS in online shopping satisfaction. Additionally, personality traits (i.e., propensity to trust, openness, and conscientiousness) were found to be related to one's perception of RS's humanlikness.