Beyond satisfactio unpacking how ai chatbot service speed and process efficiency drive multidimensional customer engagement in E-Commerce

Abstract

The rapid proliferation of AI chatbots in e-commerce has transformed how consumers interact with online platforms, yet much of the academic literature continues to foreground customer satisfaction as the primary indicator of chatbot success (Gnewuch et al., 2022; Adam et al., 2021). This study seeks to move beyond this traditional lens by unpacking the mechanisms through which key technical attributes of AI chatbots - availability, accuracy, and ease of use - influence not only satisfaction but also a more nuanced, multi- dimensional conception of customer engagement (Marinkovic et al., 2023; So et al., 2020). Drawing on a robust sample of 800 Vietnamese online shoppers, this research employs partial least squares structural equation modeling (PLS-SEM) (Hair et al., 2019) to investigate a comprehensive model in which perceived service speed and process efficiency function as mediators. These variables quantify the extent to which chatbot interactions diminish waiting time and optimize intricate service operations, elements that are particularly esteemed in rapid, competitive digital markets (McLean and Osei-Frimpong, 2019). The approach incorporates perceived service difficulty as a moderator with direct and indirect paths, recognizing that customer experiences with chatbots may vary based on the complexity of their service demands (Kumar and Pansari, 2016). The results show that perceived service speed and process efficiency are critical in increasing customer happiness and, more significantly, encouraging deeper kinds of customer interaction (Hollebeek et al., 2019). Customers report higher levels of satisfaction and are more likely to engage in behaviors such as learning, sharing, socializing, advocating for the brand, and even co-developing new service features when chatbots are easily accessible, accurate, and simple to use (So et al., 2020; Hollebeek and Macky, 2019). Furthermore, these favorable benefits are amplified in cases where the service job is seen as more complicated, emphasizing the importance of chatbot integration for e-commerce platforms attempting to meet various client expectations (McLean and Osei-Frimpong, 2019). By delineating these underlying processes, this study advances the theoretical understanding of AI-driven customer engagement and provides empirical evidence for the argument that chatbots serve as more than just functional tools - they act as service accelerators, fundamentally shaping how customers interact, learn, and contribute within online ecosystems (Marinkovic et al., 2023; Kumar and Pansari, 2016). The implications for practitioners are significant: E-commerce managers are encouraged to invest in chatbot systems that excel not only in speed and efficiency but also in user-centered design, thereby maximizing both customer satisfaction and broader engagement outcomes (Adam et al., 2021). This study offers a novel viewpoint by delineating the connections between technical chatbot qualities and multi-faceted engagement, influenced by the intricacy of the service interaction. The results are particularly pertinent for developing economies like Vietnam, where digital transformation is advancing swiftly and customer expectations are rapidly evolving (Chuc and Anh, 2019).