To address this challenge, we employ an experimental design based on an AI-based chatbot (hereafter simply “chatbot”), which is a particular type of CAs that is designed for turn-by-turn conversations with human users based on textual input. However, if customers choose not to conform with or adapt to the recommendations and requests given by the CAs this calls into question the raison d’être of this self-service technology (Cialdini and Goldstein 2004). Interactions with these systems might thus trigger unwanted behaviors in customers such as a noncompliance that can negatively affect both the service providers as well as users (Bowman et al. With AI-based CAs displacing human chat service agents, the question arises whether live chat services will continue to be effective, as skepticism and resistance against the technology might obstruct task completion and inhibit successful service encounters. CAs may, for instance, provide unsuitable responses to the user requests, leading to a gap between the user’s expectation and the system’s performance (Luger and Sellen 2016 Orlowski 2017). However, despite the technical advances, customers continue to have unsatisfactory encounters with CAs that are based on AI.
![online chatbots ai online chatbots ai](https://s3.amazonaws.com/clarityfm-production/attachments/13858/original/AI-Enabled-chatbot-startup-idea.png)
![online chatbots ai online chatbots ai](https://www.callcentrehelper.com/images/stories/2018/03/chatbot-at-desk-messaging-760.png)
![online chatbots ai online chatbots ai](https://blog-assets.freshworks.com/freshdesk/wp-content/uploads/2020/12/24165645/Types-of-chatbots-infographic-976x1536.png)
Though rudimentary CAs emerged as early as the 1960s (Weizenbaum 1966), the “second wave of artificial intelligence” (Launchbury 2018) has renewed the interest and strengthened the commitment to this technology, because it has paved the way for systems that are capable of more human-like interactions (e.g., Gnewuch et al. More recently, and fueled by technological advances in artificial intelligence (AI), human chat service agents are frequently replaced by conversational software agents (CAs) such as chatbots, which are systems such as chatbots designed to communicate with human users by means of natural language (e.g., Gnewuch et al. Over the last decade, chat services have become the preferred option to obtain customer support (Charlton 2013). The real-time nature of chat services has transformed customer service into a two-way communication with significant effects on trust, satisfaction, and repurchase as well as WOM intentions (Mero 2018). Customers use these chat services to obtain information (e.g., product details) or assistance (e.g., solving technical problems). Moreover, the results show that social presence mediates the effect of anthropomorphic design cues on user compliance.Ĭommunicating with customers through live chat interfaces has become an increasingly popular means to provide real-time customer service in e-commerce settings. Our results demonstrate that both anthropomorphism as well as the need to stay consistent significantly increase the likelihood that users comply with a chatbot’s request for service feedback. Drawing on social response and commitment-consistency theory, we empirically examine through a randomized online experiment how verbal anthropomorphic design cues and the foot-in-the-door technique affect user request compliance. Though cost- and time-saving opportunities triggered a widespread implementation of AI-based chatbots, they still frequently fail to meet customer expectations, potentially resulting in users being less inclined to comply with requests made by the chatbot. Today, human chat service agents are frequently replaced by conversational software agents or chatbots, which are systems designed to communicate with human users by means of natural language often based on artificial intelligence (AI). Communicating with customers through live chat interfaces has become an increasingly popular means to provide real-time customer service in many e-commerce settings.