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Top1. Introduction
‘Artificial Intelligence’ (AI) denotes conception of intelligent machines that can imitate humans (Stoeffler et al., 2019; Casillo et al., 2020). Simply put, AI extends innovation of machine(s) that can perform like humans (Zheng et al., 2019). Intelligent machines are categorised as weak and strong. The former can address specific situations as weak AI machines cannot think and act independently (Tran and Luong, 2020). In contrast, the latter are look-alike version of humans. The strong AI machines can actually replace humans as they think and act as good as a human brain does (Cuayáhuitl et al., 2019). Majorly, AI machines are designed to minimise fatalities like wars, accidents and natural calamities (Peng et al., 2019). Some real-life examples of AI include self-driven vehicles, google maps and Chatbots (Cameron et al., 2017; Lee et al., 2017; Huang et al., 2018). Undoubtedly, the rise of AI based applications opened the gateway of opportunities to the business firms to offer enriched customer experience (Cheung et al., 2003; Tran and Luong, 2020). Amongst all, ‘Chatbots’ are drawing human attention immensely (Tran and Luong, 2020). The reason is that Chatbots allow interactions between human and services like a real time human to human experience (Peng et al., 2019). This virtual assistant is used by top brands for virtual interaction with the customers for a better service (Ren et al., 2019).
Shawar and Atwell (2007) define Chatbots as ‘computer programs that interact with humans through natural language’. The available literature confirms different types of Chatbots depending upon their usage. The foremost is dialogic Chatbots, which are expected to understand user and their expectations. In this, the Chatbots are provided with oral inputs, further analysed with desired language processing tools that produces suitable responses (Peng et al., 2019). The second type is rational Chatbots (Yang and Evans 2019). These Chatbots use existing external knowledge base and common sense to answer and solve human queries (Cuayáhuitl et al., 2019). Generally, they provide user specific content information (Tran and Luong, 2020). The third category is embodied Chatbots. These are the earliest Chatbots and are generally preferred by ordinary users who are not machine savvy (Cummings and Kunzelman, 2015). The present stage of Chatbots is reasonably advance (Ni et al., 2017; Oh et al., 2017). Contemporarily, the companies are adopting Chatbots due to three main reasons. Firstly, Chatbots are capable of answering customer service requests (Gu et al., 2019). Moreover, Ren et al. (2019) argues that after few rounds of training/coaching, Chatbots genuinely ensure improved results (Yang and Evans 2019). Secondly, Chatbots provide a convenient experience to exchange information through text messages. It is as good as natural way of interaction (Cummings and Kunzelman, 2015). Thirdly, Chatbots enable companies to understand the aspects of digital customer service experiences (Blythe and Buie, 2014; Przegalinska et al., 2019). The anticipation of required customer services by Chatbots is really providing benefits to companies (Cuayáhuitl et al., 2019). Companies implement Chatbots services for varied reasons like cost savings, product recommendation, 24X7 access, brand awareness and promotion (Patel, 2019). Major firms using Chatbots in India include HDFC, Fify, Meru cabs, Yatra, Gaana, Niki, Funds tiger and Yes tag (Maharshi, 2017). Despite the potential Chatbots offer, very limited research has been conducted to examine the motives that influence users to choose Chatbots (Blythe and Buie, 2014). This research attempts to study the factors influencing individuals’ intention to adopt Chatbots. The current study is founded on the technology acceptance model and studies users’ behavioural intention to use Chatbots for customer service support during online purchase.