Exploring the impact of human-computer interaction on service robot adoption intention in the service industry

Recent advancements in artificial intelligence have driven the widespread adoption of service robots across various service sectors, particularly in consumer-facing tasks to enhance customer experience. However, how service contact level (low vs. high) and human-computer interaction (HCI) role orientation (partner vs. master-servant) collectively influence consumer adoption remains underexplored. This paper addresses this void through three experiments in the hospitality and restaurant settings. Findings reveal that in low-contact settings, a master-servant HCI fosters cognitive trust and adoption, while in high-contact contexts, a partner-oriented HCI builds emotional trust and encourages adoption. Task complexity also moderates this relationship: high-complexity tasks benefit from a master-servant model in low-contact scenarios, while low-complexity tasks favor a partner model in both contact conditions. This paper contributes a novel “contact level–HCI relationship” alignment framework, offering theoretical insights and practical guidance for enhancing consumer acceptance and integration of service robots.