A Framework and Content Analysis of Social Cues in the Introductions of Customer Service Chatbots

Organizations are increasingly implementing chatbots to address customers’ inquiries, but customers still have unsatisfactory encounters with them. In order to successfully deploy customer service chatbots, it is important for organizations and designers to understand howto introduce them to customers.Arguably, how a chatbot introduces itself as well as its services might influence customers’ perceptions about the chatbot. Therefore, a framework was developed to annotate the social cues in chatbot introductions. In order to validate our framework, we conducted a content analysis of introductions of customer service chatbots (n = 88). The results showed that the framework turned out to be a reliable identification instrument. Moreover, the most prevalent social cue in chatbot introductions was a humanlike avatar, whereas communication cues, indicating the chatbot’s functionalities, hardly occurred. The paper ends with implications for the design of chatbot introductions and possibilities for future research

User Perceptions, Experiences and Interactions with Municipalities’ Chatbots Differing in Human Likeness and Interaction Design

Municipalities are increasingly implementing chatbots as a part of their digital service provision. The extent to which users embrace the chatbot plays a role in determining the success of this implemen- tation. Multiple factors play a role in users’ perceptions, experiences, and interactions with chatbots, such as the human likeness (e.g., avatar, name, and communication style) and interaction design (e.g., free text versus buttons). This project examines how users perceive and interact with Dutch municipality chatbots. A unique feature of the project is that users interact with multiple Dutch municipal chatbots that differ in terms of humanlikeness and interaction designs. A mixed-methods ap- proach is adopted encompassing both a qualitative interview study and a content analysis. The project is expected to have key implications for theory and practice on municipality chatbots

Conversational Repair Strategies to Cope with Errors and Breakdowns in Customer Service Chatbot Conversations

This study aimed to investigate (1) what errors and conversational repair strategies appear during conversations with a real-life customer service chatbot and (2) how people perceive these errors and repair strategies in terms of user satisfaction, brand attitude, and trust. This study involved a corpus study of real-life conversations (N=100) with a customer service chatbot to investigate which errors and repairs occurred to inform a follow-up online experiment (N=150) on the perception of these errors and repairs. The experiment employed a 3 (error; excess of information, unsolvable question, lack of information) by 3 (repair strategy; repeat, options, defer) mixed subject design with the type of error as between-subjects factor and repair strategy as within-subjects factor. The results revealed that the repair strategy defer most positively impacted perceptions of trust and brand attitude, followed by the strategy options, and lastly repeat. In contrast, no significant main effects of error type nor interaction effects were found on user satisfaction, trust, and brand attitude. However, the open-ended questions revealed that there might be a connection between the nature of the customer request and the repair strategy