Chatbots are website artificial intelligence-based and automated customer support tools to improve the customer experience, to reduce costs, and to improve service quality. This study aims to understand and analyze the user-technology interaction and technology-engagement success measures to assess online customer engagement with chatbots and the impact on repurchase intention, within e-commerce websites. The sample data consists of 227 online consumer responses collected through an electronic survey. Only 165 respondents, which have used a chatbot to assist the online purchase process, are included in the effective sample. This research contributes to the digital marketing literature by complementing existing research exploring human-technology interactions, assessing how consumers interact with chatbot technology and how it affects customer engagement and behavioral outcomes within e-retail contexts. The study findings provide several challenges for managers. Finally, it discusses emerging trends in the digital marketing field, offering insights for future research avenues.
TopIntroduction
Technology integration in e-commerce has been gaining attention in the retail environment. Several companies have turned toward advanced technologies such as, Artificial Intelligence (AI), including chatbots, voice assistants, and other more sophisticated software like, for instance, shopbots, physical robots, virtual reality (VR) and augmented reality (AR). These technologies are delivered on demand via the Internet and can provide consumers utility gains measured in convenience, efficiency, and, most importantly, engagement.
Chatbots are website Artificial Intelligence-based and automated customer support tools. With an increasing presence in e-commerce sites, chatbots are one of the ways to use technology to improve the customer experience, reduce costs and improve the consistency in the quality of the service.
The chatbot is a digital program that supports a dialogue between the consumer and the chatbot. The responses given by the chatbot are selected based on keywords identified in the user questions. In business and e-commerce, this dialogue system was used to help consumers compare and buy products online, to assist and make recommendations to consumers when they made their online purchasing decisions – in the background to improve the consumer experience (Tuzovic & Paluch, 2018). The introduction of bots in e-commerce platforms also has benefits in terms of speed of response, quality of service, and greater automation in the management of return processes, reducing resources spent on the team and increasing customer satisfaction and loyalty (Lester et al., 2004). Chatbots can also increase cross-selling and upselling (Tuzovic & Paluch, 2018) and make personalized recommendations (of products, services, and communication content) based on automated data collection tracking customer information, such as preferences and purchase history issues, increasing consumer satisfaction and buying behavior (Kamei et al., 2010). As chatbots can provide customized responses, their role is suitable for the biggest challenge of an online store: to make the customer feel unique (Lester et al., 2004).
However, although there are several challenges when using chatbots in e-commerce and online businesses, there is a lack of research analyzing customer behavior when interacting with chatbots during their customer journey. Even though e-commerce is named to be a promising field of AI technology application, only little research deals with the general usefulness of chatbots and their influence on society (Araújo, 2018; Ho, 2021; Kull et al., 2021; Mostafa & Kasamani, 2022; Rese et al., 2020). Furthermore, technology evolves quickly and thus, the results of the so far undertaken research in this area, in the past years, might have changed. This includes not only the technology utilized, but also the people´s perception and expectation of a shopping assistant chatbot. Little is known about consumers’ behavior, e.g., attitude and engagement with these emerging technologies, that incorporate robot technology in a retail ambient intelligence (Kamei et al., 2010; Moriuchi et al., 2021).
Therefore, to address these gaps, this chapter has three objectives. First, it reviews the ground theory on human-computer interaction, specifically the Theory of Conversation (ToC) (Pask, 1980), the Technology Acceptance Model (TAM) (Davis et al., 1989) and the User Engagement with Technology (O’Brien & Toms, 2008), proposing a framework-based for measuring and assessing customer experience with chatbots within the online shopping context. Secondly, it is intended to characterize the habits of using chatbots by Portuguese online consumers, surveying and comparing with other decision support tools and customer technical support available on the websites stores. Finally, it is intended to analyze the possible impact of chatbots interaction on the consumer's intention to repurchase outcome, considering the Online Customer Engagement, proposed by O'Brien et al. (2018), as the metric used in the digital context. In a digital context (e.g., video games, social networks, chatbots), Online Customer Engagement is characterized by the greater or lesser cognitive, affective and behavioral effort that the consumer applies when interacting with a digital system. The scarcity of studies that consider the link between online engagement and positive outcomes, when mediated by technology, such as, the repurchase intention, marketing actions response (Moriuchi et al., 2021; O’Brien et al., 2018; Solem & Pedersen, 2016) and automation marketing in particular (Mollen & Wilson, 2010), also suggests the need to study the relationship between the two constructs.