HARNESSING ARTIFICIAL INTELLIGENCE FOR REPURCHASE INTENTION: THE MEDIATING ROLE OF SOCIAL PLATFORMS AND CONSUMER EXPERIENCE
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Abstract
Rapid advancements in artificial intelligence (AI) technologies have substantially transformed online service processes in the digital travel and flight booking industry, where customer interaction, conversion efficiency, and experience are critical drivers of repurchase intention. This study examines the direct and indirect effects of AI technology on repurchase intention by incorporating customer interaction in service processes (CISP), conversion rate optimization (CRO), and customer experience satisfaction (SCE) as mediating variables. Adopting descriptive and causal-comparative research designs, data were collected from 480 respondents in the Kathmandu Valley who had booked flights online within the preceding six months, using a convenience sampling technique. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed for data analysis. The results indicate that AI technology has a significant positive effect on customer interaction (β = 0.349, t = 2.784, p < 0.001) and conversion rate optimization (β = 0.516, t = 1.995, p < 0.001). Customer interaction significantly influences customer experience (β = 0.567, t = 3.706, p = 0.004), while conversion rate optimization also has a strong positive effect on customer experience (β = 0.458, t = 5.856, p < 0.001). Satisfying customer experience significantly predicts repurchase intention (β = 0.419, t = 1.991, p < 0.001). However, the direct effect of AI technology on repurchase intention is not statistically significant (β = −0.063, t = 0.801, p = 0.451). Mediation analysis confirms full mediation, as AI technology indirectly influences repurchase intention through CRO and SCE (β = 0.402, t = 2.743, p = 0.048) and through CISP and SCE (β = 0.302, t = 3.006, p = 0.005). Overall, the model explains 47.3% of the variance in repurchase intention, demonstrating that AI-driven service features enhance repeat purchase behaviour primarily through improved interactions, optimized conversion processes, and satisfying customer experiences, rather than through a direct effect.
JEL Classification Codes: M31, M15, O33.
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