Persuasion Strategies for Social Robot to Keep Humans Accepting Daily Different Recommendations

Abstract

Social robots are used in daily life. One of the applications of social robots is as recommendation systems. Previous research has mainly investigated how persuasive recommendations can be improved by focusing on the non-verbal/verbal behavior of robots. However, to use robots as recommendation systems every day, it is extremely important to examine the persistence of repeated persuasion over a long term, rather than the effect of one-time persuasion. Therefore, the objective of this study was to investigate the persistence of repeated persuasive of robots. For this purpose, robots with three types of behavior (Expert Behavior, Local Behavior, and Growth Behavior) recommended nutrition bars in a situation of daily consumption behavior for two weeks. We could confirm significant differences in the persistent persuasiveness in each behavior. The results suggested that the combination of value co-creation using local information and meta-trust expression had a significant impact on the persistence of the repeated persuasiveness of the robots in the longitudinal period. However, the acceptance of the recommendation robot system decreased due to the increase in the amount of information during recommendation; therefore, a new recommend system to solve this problem is desired.