Who cares if it is rainy or sunny? Optimizing repurchase intention through weather and service quality of AI tools

Purpose This study aims to build a computational framework that delves into the interplay between service quality of artificial intelligence (AI) tools and repurchase intention as well as to identify the predictors of service quality of AI tools and their individual impact on repurchase intention. In addition, this study seeks to confirm that weather does have an effect on repurchase intention, specifically as it pertains to promoting inclinations to make repeat purchases. It has also been investigated whether reverse logistics act as a moderator between weather and the intention to repurchase. Design/methodology/approach This study uses information processing theory and affect-as-information theory to classify the service quality of AI tools into six attributes. It then proceeds to create a research model that investigates the inner processes by which the service quality of AI tools and weather conditions influence repurchase intention. The data were collected through an internet-based survey using Google form. This study analyzed an aggregate of 207 responses collected through online methods. Findings The findings indicate that weather is a more dependable determinant of the intention to repurchase compared to the service quality of AI tools. In addition, the analysis confirms the moderating influence of reverse logistics. Originality/value In the online shopping setting, this study confirms the empirical association among service quality of AI tools, weather and repurchase intention with reverse logistics acting as a moderator. These forces play a vital role in shaping online purchasing behavior and the probability of future purchases.