The PRVAs, product recommendation virtual agent, are the agents that take part in the clerks on the onlineshopping. For their aims, it is very important for the PRVAs to be trusted by users. However, trustworthy the PRVA design was not be studied yet. In this paper, we suggest the user’s trust transition model that is consisted by two parameters. One parameter is user’s emotion, and the other is agent’s knowledge. We suggested the transition operators that transited these two parameters by executing when the PRVAs recommend. Emotion transition operators are agent’s smile and gestures. Knowledge transition operators is long product recommendation text. We carried on three experiments to estimate these model and transition operators. In experiment 1, we executed no transition operators. In experiment 2, we executed emotion transition operators and added knowledge transition operators in the latter half. In experiment 3, we executed knowledge transition operators in the first half and added emotion transition operators in the latter half. As a results, it is discovered that transition operators and a transition model are effective. In experiment 1, there are no transition in the participants’ trust state. In experiment 2, the participants’knowledge perceived and trust for agent transited after executing knowledge transition operators. In experiment 3, the participants’ emotion transited after executing positive emotion operators, however, trust didn’t transited. From these result, we concluded that trust is based on each of the user’s emotion and the agent’s knowledge.
Although the number of applications of product recommendation virtual agents (PRVAs) for online shopping is rapidly increasing, few studies on effective PRVAs have been done. Hence, we investigated which types of PRVAs are effective through experiments with participants. First, we focused on variation in the appearance of PRVAs and prepared six PRVAs having various appearances from human-like to text. Then, we prepared six products that customers might want in any situation, and conduct withinparticipant experiments in which the participants reported their impressions on the agents and evaluated their buyer motivation for all combinations of the PRVAs and the products. By seeing the averages and performing statistic tests on the results, we found that the human-like PRVA was the most effective among the various PRVAs including video of a real human, and a robot-like PRVA. Furthermore, by performing a factor analysis on the experimental results, we also found the two important factors of the PRVAs, familiarity and intelligence, to influence the recommendation effects.