The Impact Of Product Uniqueness On Consumers’ Acceptance Of AI Recommendations

Online shopping channels provide consumers with a huge number of products to choose from,and artificial intelligence(AI)recommendations that can help consumers choose products have been widely used in e-commerce platforms.However,only a small fraction of consumers believes that the recommendations they receive are closely related to them.As younger consumers gradually become the main purchasing power,consumers’ pursuit of uniqueness has become a trend,and it is worthwhile to investigate whether the recommended products are sufficiently unique.Therefore,this study focuses on the influence of product uniqueness on consumers’ willingness to accept AI recommendations.Currently,many AI recommendations are based on consumer similarities,without due attention to consumers’ individual uniqueness,and it is worth discussing whether consumers are willing to continue accepting recommendations based on similarities.By combing through relevant studies on consumers’ acceptance of AI recommendations,it can be found that there have been studies on the types of tasks for which AI recommendations are suitable,as well as analyses of consumers’perceptions towards AI recommendations.Nevertheless,there is limited research in the field of AI recommendations on how product uniqueness influences consumers’acceptance of AI recommendations.This paper focuses on the impact of product uniqueness on consumers’acceptance under the context of AI recommendations,and examines the mediating role of perceived expertise in the above effect and the moderating role of the need for uniqueness.This study verifies the four basic hypotheses using two preexperiments(Pre-Experiment 1 and Pre-Experiment 2)and two formal experiments(Experiment 1 and Experiment 2).Specifically,pre-experiment la produced the corresponding experimental materials for Experiment 1,excluding the possibility of the main effect from the interference of the pictures themselves.Pre-experiment 1b examined the manipulation of product uniqueness.Then,Experiment 1 indicated the level of product uniqueness by recommending products to consumers with a high or low proportion of similarity,using the acceptance of AI recommendations as the dependent variable and the need for uniqueness as the moderating variable.Moreover,Experiment 1 confirmed the effect of consumers’acceptance of AI recommendations as well as the moderating effect of the need for uniqueness on main effects.Pre-experiment 2 verified the validity of the manipulation methods of the need for uniqueness for their use in Experiment 2,which tested the mediation effects of the perceived expertise in a 2(product uniqueness:high vs.low)×2(need for uniqueness:high vs.low)between-subjects design.This paper has reached the following conclusion.Firstly,the higher the product uniqueness of AI recommendations,the more willing consumers are to accept them.Secondly,the need for uniqueness plays a moderating role between product uniqueness and consumers’ acceptance of AI recommendations,with the positive effect of product uniqueness on consumers’ acceptance of AI recommendations weakening as the consumers’ need for uniqueness decreases.Thirdly,perceived expertise plays a moderated mediating role between product uniqueness and consumers’ acceptance of AI recommendations,implying that product uniqueness influences consumers’ acceptance of AI recommendations through their perception of expertise in AI.And the mediation effect of perceived expertise strengthens as the consumers’ need for uniqueness increases.In conclusion,this study provides new perspectives for understanding consumers’ acceptance of AI recommendations,reveals the critical role of product uniqueness in AI recommendations,clarifies the influence mechanism and boundary conditions of the above effects,and enriches theoretical and empirical research on AI recommendations.Practically,this study can help marketers recognize the value of product uniqueness,as well as have an instructional influence on online merchants and e-commerce platforms on how to use product uniqueness to improve the success rate of AI recommendations.