Understanding generative AI continuance in e-retailing: an extended motivation–opportunity–ability perspective

Purpose The emergence of generative artificial intelligence (GenAI) has substantially transformed the e-retailing landscape by enhancing business efficiency and long-term competitiveness. Nevertheless, the factors influencing retailers’ continued adoption of GenAI and the effects of this adoption on e-retailing performance are not yet fully understood. This study aims to elucidate the mechanisms underlying GenAI continuance intention and business performance by applying the extended Motivation–Opportunity–Ability (MOA) theory. Design/methodology/approach A quantitative research design was employed to collect data from 537 retailers through a structured online survey, and the research model was validated using structural equation modeling. Findings The findings indicate that the extended MOA theory offers a comprehensive explanation of retailers’ post-adoption intentions regarding GenAI and its role in enhancing e-retailing performance. Utilitarian and hedonic motivations, as well as the opportunity and ability to employ GenAI, significantly influence continuance intention. Moreover, continuance intention toward GenAI serves as a key driver of e-retailing performance. Originality/value This study provides a robust theoretical foundation and practical implications for retailers and AI providers operating in emerging economies. By applying the extended MOA framework from a retailer-centric perspective, this study advances research on GenAI and highlights the essential role of motivation, alongside opportunity and ability, in sustaining GenAI adoption. The findings offer comprehensive insights into the significance of GenAI for retail operations and present actionable strategies to support sustained performance within the context of digital transformation.