Service robot led by Mr. John: the mere disclosure human leader effect

Purpose Although robots have become increasingly intelligent, they face difficulties in gaining competence recognition when entering the service industry. The purpose of this paper is to introduce and test a novel, simple and low-cost strategy for mitigating consumers’ competence bias towards service robots. Design/methodology/approach Based on social categorization theory, this paper investigates whether the mere disclosure of the human leader of service robots (DHL) helps improve consumers’ evaluation of the robots’ competence by changing their categorization of service robots relative to human employees, as well as its boundary condition and downstream consequence. Four experimental studies were conducted to test the conceptual model across multiple service scenarios and robot types. Findings Study 1 demonstrates that DHL can effectively enhance consumers’ perceived competence of service robots. Study 2 further reveals the underlying mechanism of in-group identification. Study 3 identifies consumers’ construal level as a theoretically derived moderator. Moreover, Study 4 reveals the positive downstream effect of DHL on consumers’ intention to use service robots, thus highlighting the practical value of the DHL strategy. Research limitations/implications This research advances existing literature on strategies to mitigate consumers’ competence bias towards service robots. Practical implications This research offers concrete and actionable practical implications: companies can simply disclose the human leader of service robots (i.e. the DHL strategy) to mitigate consumers’ competence bias, thereby encouraging consumers to accept and use service robots in scenarios such as retail, catering and customer service. Originality/value This research introduces a novel DHL strategy to mitigate consumers’ competence bias towards service robots and offers a new social categorization perspective for understanding algorithm aversion.