Abstract: The consumer advice network is a special type of social network in a social commerce environment. Users are able to establish online social relationship and sharing product evaluation, and search product related information from network members. As merchants start to spread product information through influential users in the consumer advice network, this activity lead to the emergence of social advertising, or user-generated advertising. In this background, merchants may rely on external incentives to stimulate uses to post and diffuse social advertising on the consumer advice network. By using social advertising tools, merchants make higher-than-true product quality evaluations for their products, or make lower-than-true product quality evaluations for competitors' products. Such deceptive evaluations are called untruthful evaluations. Based on the concept of continuous opinion and discrete behavior, the model of product adoption is constructed, and the user opinion learning rules are set up in the model. That is, the users who have not yet purchased the product learning the opinion from those who have higher honest index. The influence of social advertising, opinion learning rounds on opinion evolution and product adoption are studied by means of agent-based computational experiment. The results show that the model can reduce the product adoption proportion of users who are misleaded by the untruthful opinions. The untruthful opinions identification mechanism can effectively identify the "low quality with high evaluation" and "high quality with low evaluation" and reduce the misleading effect from untruthful opinions. The increase of opinion learning rounds can increase the recognition of untruthful opinion. This Research overcome the limitations in existing product diffusion model, such as no distinction between information diffusion and product adoption, regardless of the authenticity of product information, regardless of the different stages of product adoption.

Key words: social commerce, product adoption, untruthful opinions, opinion learning