Communication style synergy in chatbot-human service teams: dividing warmth and competence to enhance customer satisfaction

Artificial intelligence (AI) chatbots are increasingly used in customer service, but customers still prefer human agents due to chatbots’ limited empathy. Hybrid service models that combine chatbots with human agents offer a solution, but it remains unclear how best to coordinate their communication styles. This research investigates whether mixing communication styles (one agent using a task-oriented, efficient approach while the other uses a warm, social approach) yields better outcomes than using a uniform style. Two scenario-based experiments with online participants were conducted to test these effects. Study 1 ( N = 800) employed a 2 (chatbot communication style: task-oriented vs. social-oriented) × 2 (human agent communication style: task-oriented vs. social-oriented) between-subjects design. Study 2 ( N = 1200) compared communication style sequences in which a social-oriented communication came first versus those in which a task-oriented communication came first and examined transfer initiation (user- vs. system-initiated) based on participants’ choice. Study 1 found that mixed-style chatbot-human teams generated higher customer satisfaction than same-style teams by fostering greater teamwork and empathy. In Study 2, the social-first sequence increased perceived collaboration more strongly in post-purchase scenarios. Transfer initiation further shaped the relative importance of each mediator for satisfaction: empathy was more influential under user-initiated transfers, whereas collaboration mattered more under system-initiated transfers. These findings provide novel evidence that dividing communication roles between a chatbot and a human agent can enhance customer satisfaction. They also offer practical guidance for designing more effective chatbot-human service teams.