This article draws on my recent longitudinal research examining how customers engage with artificial intelligence enabled services in international online fashion stores. The study investigates how key factors such as transaction utility, trust, product uniqueness and privacy shape customers’ intentions to use AI driven services across different stages of the online purchasing journey.
Artificial intelligence (AI) has become a transformative technology for improving the consumer experience and achieving company success in today’s highly competitive environment. Prominent global fashion stores, such as Jp, Shein, VIP, Walmart, Nike, and Amazon, have been actively exploring the use of AI technologies to enhance their customer experience and generate more revenues. AI-enabled services can assist online fashion retailers in understanding customer intentions, preferences, and needs, delivering personalized recommendations, and optimizing their marketing endeavors. AI-enabled technologies, such as voice assistants, chatbots, virtual reality (VR), and augmented reality (AR), provide various benefits to online fashion customers, such as product visualization, virtual trials, three-dimensional presentation of clothes and accessories, and streamlined interactions. The advancements mentioned have facilitated customization and enhanced customer experiences across all aspects of the fashion industry, such as clothing design and development, production, visual presentation, and the establishment of emotional connections with customers. Artificial intelligence (AI) services are vital in enhancing customer experience and purchase intentions in the international online fashion retail sector. This study explores customers’ intentions to use AI enabled services, focusing on transaction utility, trust and product uniqueness across the customer journey in the context of international online fashion stores. This study also assesses how privacy moderates customer intentions.
The study aims to understand whether (1) transaction utility, trust, and product uniqueness in international online fashion stores affect customers’ intentions to use AI-enabled services in all three stages of the purchase, (2) these effects are moderated by the privacy of the customers, and (3) the mediation effect of AI integration (pre-purchase, during purchase, post-purchase) among transaction utility, trust, product uniqueness, and intention to use AI-enabled services. A longitudinal design employing three waves of data collection was used to enhance the validity and depth of the findings. Consumer behavior studies using longitudinal design offer important insights into how consumer preferences and decision-making processes change over time. One of the main advantages of collecting data on intention over time is researchers can clearly grasp whether and how purchasing intentions created during one consumption occasion carry over to subsequent consumption occasions. In this study, longitudinal approach enables us to observe changes in customer intentions as they repeatedly engage with advancing AI enabled features in online fashion stores. In the first phase, the participants were surveyed in the pre-purchase stage.
During the pre-purchase stage, the study findings support previous research that highlights the significance of transaction utility, trust, and product uniqueness in AI-driven pre-purchase experiences. A previous study emphasized that transaction utility has a notable and favorable impact on customers’ inclination to engage in online group buying. Typically, customers tend to favor purchasing products at lower prices from overseas online apparel stores rather than buying higher-priced products in the domestic market. Customers now prioritize a high level of transaction utility while shopping in online fashion businesses. The findings of this study align with other research that highlights the importance of transaction utility as a strong predictor of customers’ propensity to utilize AI-enabled services in online fashion stores. Trust is regarded as a significant predictor in the online world, as highlighted by numerous previous studies. Establishing trust in online shopping is of utmost importance for an online fashion store, as it entails the exchange of sensitive consumer information throughout the entire purchasing process, hence reducing the perceived risk of making a purchase. The crucial significance of trust in the pre-purchase phase is consistent with previous research, which emphasizes that user trust is a widely acknowledged and essential factor in promoting customer acceptance of AI technology.
The study also discovered the significant influence of product uniqueness on the integration of AI. Multiple studies have confirmed a significant correlation between the uniqueness of a product and the intention to use AI. The uniqueness of the products attracts customers to purchase either from offline or online businesses. Similarly, the presence of distinctive clothing and accessories recommended by AI-powered international online fashion retailers enhances the customer’s desire to purchase.
Conversely, the incorporation of AI in the pre-purchase stage does not moderate the relationship between the antecedents’ variables (transaction utility, trust, and product uniqueness) and the intention to utilize AI-enabled services. Moreover, the presence of privacy does not have a moderating impact on the relationship between AI integration in the prepurchase stage and the intention to use AI-enabled services. This demonstrates that AI integration and privacy have no profound impact on the pre-purchase stage.
This study bridges important gaps in the literature by integrating AI-enabled services and customer behavior, contributing to a broader knowledge of customer interactions in global e-commerce fashion stores. The study examines multiple attributes that impact intention, such as transaction utility, trust, product uniqueness, AI integration in three stages of purchases (pre-purchase, during purchase and post-purchase) and privacy, using three major theories: mental accounting theory, trust commitment theory and commodity theory.
