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THE ASYMMETRIC EFFECTS OF ATTITUDE TOWARD THE BRAND (SYMBOLIC vs. FUNCTIONAL) UPON RECOMMENDATION SYSTEM (ARTIFICIAL INTELLIGENCE vs. HUMAN)
New product entails risk, causing resistance to adoption. The recommendation system may decrease the psychological risk by guiding decision making process to be more efficient (Häubl and Trifts, 2000). AI (Artificial Intelligence) has been getting smarter and smarter and widely applied to the recommendation system. Even while you are browsing on your Facebook, AI recommends you the products that you may like based on the customized analysis of your interest. However, do people always love to adopt the smart recommends from AI? Definitely no! Then when and why people reluctantly accept AI recommendation? We assume that the product or service where the sense and feeling is important, people might be reluctant to accept the recommendation from artificial intelligence. This is because people might feel threatened when the AI challenges against human uniqueness (Gray and Wegner, 2012). Thus, in this study we investigated how the recommendation system types (AI vs. Human) affect brand attitude depending on the brand image (Symbolic vs. Functional). We found consumers are reluctant to accept a recommendation from AI in symbolic brand where human sense and feel are considered to be critical factors (Study1). This effect was further explained by uncanny-feeling toward the AI recommendation system (Study2). This research is meaningful in that it is the first attempt to apply the artificial intelligence recommendation concept to the marketing strategy by incorporating the concept of brand image. We predicted and found AI based recommendation system is reluctantly accepted for symbolic brand. Furthermore, we discovered the underlying process for this phenomenon as uncanny feeling. People seemed to have uncomfortable feelings against AI recommendation when the brand image is related to sense and feel considered as nature of human uniqueness. Thus, marketers should be very cautious when utilizing the AI recommendation system not to threaten human uniqueness area.
