Artificial intelligence service agents (AISA), such as chatbots and virtual assistants, are becoming increasingly pervasive in service. Research to date has not adequately addressed how the unique nature of AISA shape consumers’ service quality expectations. A deeper understanding of AISA service quality is important for their successful deployment in the service sector. To address this gap, we reviewed marketing and information systems literatures and conducted qualitative in-depth interviews with 37 informants, inclusive of 28 AISA users and nine AISA experts. We developed a conceptual framework for how consumers use and evaluate AISA. Twelve service quality dimensions emerged from the qualitative evidence representing AISA service quality, two of which align with AISA’s unique characteristics. The study extends the service quality theory to a new context and offers fresh insights for theory and practice. It culminates with a research agenda to advance research on AISA service quality.
Increased use of artificial intelligence service agents (AISA) has been associated with improvements in AISA service performance. Whilst there is consensus that unique forms of attachment develop between users and AISA that manifest as parasocial relationships (PSRs), the literature is less clear about the AISA service attributes and how they influence PSR and the users' subjective well-being. Based on a dataset collected from 408 virtual assistant users from the US, this research develops and tests a model that can explain how AISA-enabled service influences subjective well-being through the mediating effect of PSR. Findings also indicate significant gender and AISA experience differences in the PSR effect on subjective well-being. This study advances current understanding of AISA in service encounters by investigating the mediating role of PSR in AISA's effect on users' subjective well-being. We also discuss managerial implications for practitioners who are increasingly using AISA for delivering customer service.
Purpose Service providers and consumers alike are increasingly adopting artificial intelligence service agents (AISA) for service. Yet, no service quality scale exists that can fully capture the key factors influencing AISA service quality. This study aims to address this shortcoming by developing a scale for measuring AISA service quality (AISAQUAL). Design/methodology/approach Based on extant service quality research and established scale development techniques, the study constructs, refines and validates a multidimensional AISAQUAL scale through a series of pilot and validation studies. Findings AISAQUAL contains 26 items across six dimensions: efficiency, security, availability, enjoyment, contact and anthropomorphism. The new scale demonstrates good psychometric properties and can be used to evaluate service quality across AISA, providing a means of examining the relationships between AISA service quality and satisfaction, perceived value as well as loyalty. Research limitations/implications Future research should validate AISAQUAL with other AISA types, as they diffuse throughout the service sector. Moderating factors related to services, the customer and the AISA can be investigated to uncover the boundary conditions under which AISAQUAL is likely to influence service outcomes. Longitudinal studies can be carried out to assess how ongoing use of AISA can change service outcomes. Practical implications Service managers can use AISAQUAL to effectively monitor, diagnose and improve services provided by AISA while enhancing their understanding of how AISA can deliver better service quality and customer loyalty outcomes. Originality/value Anthropomorphism is identified as a new service quality dimension. AISAQUAL facilitates theory development by providing a reliable scale to improve the current understanding of consumers’ perspectives concerning AISA services.
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