Exploring factors influencing the adoption of generative artificial intelligence tools by cross-border e-commerce operator: an elaboration likelihood model perspective

Purpose This study aims to investigate the key factors influencing the adoption of generative artificial intelligence (GenAI) tools by cross-border e-commerce (CBEC) operators. Using the elaboration likelihood model (ELM), this research explores how argument quality and source credibility affect CBEC operators’ subscription intention and willingness to pay more for GenAI services. Design/methodology/approach Data were collected through a questionnaire survey of 369 CBEC operators, primarily from Taiwan. Partial least squares structural equation modeling was used to analyze the data and test the hypothesized relationships based on the ELM framework. Findings The findings indicate that both argument quality and source credibility significantly and positively impact CBEC operators’ subscription intention and willingness to pay more for GenAI services. The study also reveals that outcome quality is a significant antecedent of argument quality, internet celebrity endorsement and user endorsement are key drivers of source credibility, add-on GenAI service satisfaction has no influence on argument quality and argument quality and source credibility positively influence both subscription intention and willingness to pay more for GenAI services. Originality/value This study extends and refines the ELM for the context of modern digital technology adoption. The authors challenge the conventional view of parallel persuasive routes by theorizing and demonstrating a recursive interplay, where argument quality (the central route) dynamically enhances source credibility (the peripheral route). While argument quality is the primary driver of subscription intention, source credibility becomes the dominant factor influencing users’ willingness to pay more. These refinements provide a more nuanced understanding of persuasion in staged, digitally mediated adoption decisions. Beyond theoretical contributions, the study offers actionable guidance for GenAI providers and CBEC operators.