DERIVING TRUST-SUPPORTING DESIGN KNOWLEDGE FOR AI-BASED CHATBOTS IN CUSTOMER SERVICE: A USE CASE FROM THE AUTOMOTIVE INDUSTRY

Abstract

ABSTRACTIn the automotive industry, companies are increasingly implementing Artificial Intelligence (AI)-based chatbots to support various processes, especially in the context of customer service. However, there currently is a lack of knowledge, especially systematically derived design knowledge, regarding customer trust in interacting with AI-based chatbots. In this context, a lack of security and transparency, limited social features, and the communication style and quality-related issues of AI-based chatbots are just a few aspects that inhibit customer trust in interacting with this innovative technology, thereby hindering the adoption of chatbots. To address this knowledge gap, we adopted a design theory-based approach and developed a design concept for trust-supporting design knowledge regarding customer interaction with an AI-based chatbot. Design science provides a structured development and evaluation process to support, for example, the adoption of AI-based chatbots. Drawing on trust-based literature, a use case in customer service in the automotive industry, and seven semi-structured expert interviews, we propose 10 meta/user requirements and four design principles for trust-supporting design elements as (e.g. social) signals (stimuli) regarding the interaction with AI-based chatbots. We developed two click prototypes over two evaluation cycles. Each evaluation included an online survey with 180 participants. The findings that were obtained make a valuable contribution to solving the described lack of design knowledge by developing and evaluating different design approaches in the form of prototypical user interfaces. Moreover, the results show that visible design elements such as transparent and factual security signals (stimuli) and trust seals have a significant impact on customer trust.KEYWORDS: AI-based chatbotcustomer servicetrust signaldesign science research Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsMartin SonntagMartin Sonntag (LinkedIn profile) is an external doctoral student at Jade University of Applied Sciences and conducts research on Industry 4.0, Artificial Intelligence, and AI-based chatbots in customer service.Jens MehmannJens Mehmann studied industrial engineering with a focus on process optimization and project management at Osnabrück University of Applied Sciences. He completed his doctorate at the Chair of Business Accounting and Information Systems at the University of Osnabrück. Since 2018, he has been Professor of Supply Chain Management and Operations at Jade University. In 2021, he founded the Institute for Innovative Logistics and the Environment. He is also the founder of Leannova. Leannova is a management consultancy that accompanies companies in the context of digital transformation. In the business areas of process optimization, factory and warehouse planning and IT services, innovative concepts are developed and implemented together with the customers. The project work results in application-oriented research, which is also accompanied by final theses and student projects. An innovation driver can therefore be spoken of. He is author of scientific and practice-oriented publications. His research focuses on process optimization, automation and digitization, digital transformations and open innovation.Frank TeutebergDr. Frank Teuteberg is a full professor at the Osnabrück University in Germany. Since 2007 he has been Head of the Department of Accounting and Information Systems, which is part of the Institute of Information Management and Information Systems Engineering at the Osnabrück University. He is the spokesman of the research profile line Digital Society – Innovation – Regulation and the leader of several research projects with a funding volume of more than € 15 million in total. He is also the founder of synovacom. Synovacom is an IT consultancy that supports companies in the context of digital transformation, particularly in the areas of conception, implementation and evaluation of innovative digitization projects and digital solutions (e.g. AI solutions) as well as in the area of data analytics and digital business models. Furthermore, he is the author of more than 450 research papers with more than 7000 citations in numerous peer-reviewed journals (e.g. Technological Forecasting and Social Change, Information & Management) and conferences (e.g. International Conference on Information Systems) in the field of industrial Internet of things, e-health, artificial intelligence, blockchain, and human computer interaction.