PurposeThis study investigates whether and how the service quality of artificial intelligence (AI) chatbots affects customer loyalty to an organization.Design/methodology/approachBased on the sequential chain model of service quality loyalty, this study first classifies AI chatbot service quality into nine attributes and then develops a research model to explore the internal mechanism of how AI chatbot service quality affects customer loyalty. The analysis of survey data from 459 respondents provided insights into the interrelationships among AI chatbot service quality attributes, perceived value, cognitive and affective trust, satisfaction and customer loyalty.FindingsThe results show that AI chatbot service quality positively affects customer loyalty through perceived value, cognitive trust, affective trust and satisfaction.Originality/valueThis study captures the attributes of the service quality of AI chatbots and reveals the significant influence of service quality on customer loyalty. This study develops research on service quality in the information system (IS) field and extends the sequential chain model of quality loyalty to the context of AI services. The findings not only help an organization find a way to improve customers' perceived value, trust, satisfaction and loyalty but also provide guidance in the development, adoption, and post-adoption stages of AI chatbots.

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PurposeThis study investigates the impacts of return channel type on the relationships between return service quality (RSQ) and customer loyalty (CL) in an omnichannel retailing environment.Design/methodology/approachData comes from Chinese customers having a return experience in omnichannel retailing that uses the channel type of both buy-online-return-in-store (BORIS) and buy-in-store-return-to-online warehouses (BSROW). The authors use the structural equation modeling to test the hypotheses and the bootstrapping method to test the mediation and moderation effect.FindingsFor BORIS channel, satisfaction of customer returns (CRS) partially mediates the relationship between convenience and CL, and fully mediates that between CL and responsiveness, transparency and competence, respectively. For BSROW channel, CRS partially mediates the relationship between responsiveness and CL, and fully mediates that between CL and convenience, transparency and competence, respectively. The mediation effects indicate that omnichannel customers may feel more satisfied due to higher omnichannel fulfillment (responsiveness and convenience) and omnichannel trust (transparency and competence) provided by retailers. Return channel type moderates the relationship between RSQ-convenience and CL. The results show the different expectations between BORIS and BSROW customers in the return process.Research limitations/implicationsThis paper serves as a pioneering study to apply cognition-affect-behavior paradigm into the field of return management in omnichannel retailing.Practical implicationsThe findings suggest retailers develop their strategies on customer returns and post-sales service quality improvement in the omnichannel. Also, retailers should develop an integrated return system across channels to provide convenient service to BORIS customers and quick response to BSROW customers.Originality/valueStudying return service management in the omnichannel from customer's cognition appraisal, this study contributes to the literature of the reverse service management by bringing in the effect of omnichannel type to explore the relationship between RSQ and CL.

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