Back to the future via human-centered artificial intelligence

Purpose Artificial intelligence service quality (AI-SQ) comprises both technical and functional quality. In this study, we investigate how AI-SQ affects trust and commitment in customer engagement, including customer purchases, referrals, social influence and knowledge sharing. Additionally, this study examines the role of AI marketing specialists (AIMSs) in moderating the relationship between AI-SQ and customer engagement. Design/methodology/approach Partial least squares structural equation modeling was applied to the data to evaluate composite reliability, convergent and discriminant validity, effect sizes (f2 and Q2) and the model’s predictive accuracy. Findings First, technical quality has a significant and direct impact on customer social influence and knowledge sharing, whereas functional quality significantly and directly affects customer purchases, referrals and knowledge sharing. Second, both trust and commitment significantly mediate the relationship between AI-SQ and customer engagement. Third, AIMSs significantly moderate the impact of technical quality on customer knowledge sharing. Originality/value This study contributes by developing and validating a human-centered AI-SQ framework that integrates AIMSs to enhance personalization while addressing the limitations of service quality dimensions in the context of AI-driven standardization and customization. These findings provide a foundation for developing human-centered AI marketing strategies, allowing firms to conduct comprehensive SWOT (strengths, weaknesses, opportunities and threats) analyses of AIMSs. Firms can leverage the positive moderating effects of AIMSs while mitigating potential challenges. Therefore, as the synergy between AI-SQ and AIMSs intensifies, the 5Ps of the marketing mix (i.e. product, price, place, promotion and professional) will increasingly serve as a practical framework for human-centered AI marketing strategies.