Virtual Assistance in Any Context: A Taxonomy of Design Elements for Domain-Specific Chatbots

Several domain-specific assistants in the form of chatbots have conquered many commercial and private areas. However, there is still a limited level of systematic knowledge of the distinctive characteristics of design elements for chatbots to facilitate development, adoption, implementation, and further research. To close this gap, the paper outlines a taxonomy of design elements for chatbots with 17 dimensions organized into the perspectives intelligence, interaction and context. The conceptually grounded design elements of the taxonomy are used to analyze 103 chatbots from 23 different application domains. Through a clustering-based approach, five chatbot archetypes that currently exist for domain-specific chatbots are identified. The developed taxonomy provides a structure to differentiate and categorize domain-specific chatbots according to archetypal qualities that guide practitioners when taking design decisions. Moreover, the taxonomy serves academics as a foundation for conducting further research on chatbot design while integrating scientific and practical knowledge

How to Make chatbots productive – A user-oriented implementation framework

Many organizations are pursuing the implementation of chatbots to enable automation of service processes. However, previous research has highlighted the existence of practical setbacks in the implementation of chatbots in corporate environments. To gain practical insights on the issues related to the implementation processes from several perspectives and stages of deployment, we conducted semi-structured interviews with developers and experts of chatbot development. Using qualitative content analysis and based on a review of literature on human computer interaction (HCI), information systems (IS), and chatbots, we present an implementation framework that supports the successful deployment of chatbots and discuss the implementation of chatbots through a user-oriented lens. The proposed framework contains 101 guiding questions to support chatbot implementation in an eight-step process. The questions are structured according to the people, activity, context, and technology (PACT) framework. The adapted PACT framework is evaluated through expert interviews and a focus group discussion (FGD) and is further applied in a case study. The framework can be seen as a bridge between science and practice that serves as a notional structure for practitioners to introduce a chatbot in a structured and user-oriented manner

What determines FinTech success? — A taxonomy-based analysis of FinTech success factors

Value creation in the financial services sector has been fundamentally transformed by digitally born financial technology (FinTech) companies. FinTech companies synthesize information systems with financial services. Given its disruptive power, the FinTech phenomenon has received great attention in academic research, practice, and media. Still, limited systematic research provides a structure and holistic view of FinTechs’ success. Aiming to enhance understanding of the factors enabling FinTech success, we classify success factors across extant scientific literature on distinct FinTech business model archetypes. Our analysis reveals that the “cost–benefit dynamic of the innovation,” “technology adoption,” “security, privacy, and transparency,” “user trust,” “user-perceived quality,” and “industry rivalry” are crucial factors for FinTech success and can be seen as “grand challenges” for the FinTech ecosystem. In addition, we validate and discuss our findings with real-world examples from the FinTech industry and two interviews with stakeholders from the FinTech ecosystem. Our study contributes to the knowledge of FinTechs by providing a classification system of success factors for practitioners and researchers

Influencing factors for the digital transformation in the financial services sector

Digital transformation affects almost every area in societies and has consequences for incumbent companies. With qualitative research, we explore the influencing factors for digital transformation in the financial services sector. We use a PEST-model and Porter’s Five Forces as the underlying structure for our analysis. Our interviews and findings show that the financial services sector face the same current challenges, but their impact is perceived higher in the banking than in the insurance sector concerning social factors and bargaining power of buyers. The character of the current development is evolutionary rather than disruptive. Almost all incumbents currently focus on modernizing and consolidating their backend-systems. The aim is to enable them for new customer-oriented services. A primary driver for the digital transformation is the threat of a broader market entry by BigTechs. Our research provides a comprehensive overlook about the influencing factors of digital transformation using statements from experts in the field. © 2020, The Author(s)

See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons

Users interact with chatbots for various purposes and motivations – and for different periods of time. However, since chatbots are considered social actors and given that time is an essential component of social interactions, the question arises as to how chatbots need to be designed depending on whether they aim to help individuals achieve short-, medium- or long-term goals. Following a taxonomy development approach, we compile 22 empirically and conceptually grounded design dimensions contingent on chatbots’ temporal profiles. Based upon the classification and analysis of 120 chatbots therein, we abstract three time-dependent chatbot design archetypes: Ad-hoc Supporters, Temporary Assistants, and Persistent Companions. While the taxonomy serves as a blueprint for chatbot researchers and designers developing and evaluating chatbots in general, our archetypes also offer practitioners and academics alike a shared understanding and naming convention to study and design chatbots with different temporal profiles

See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons

Users interact with chatbots for various purposes and motivations – and for different periods of time. However, since chatbots are considered social actors and given that time is an essential component of social interactions, the question arises as to how chatbots need to be designed depending on whether they aim to help individuals achieve short-, medium- or long-term goals. Following a taxonomy development approach, we compile 22 empirically and conceptually grounded design dimensions contingent on chatbots’ temporal profiles. Based upon the classification and analysis of 120 chatbots therein, we abstract three time-dependent chatbot design archetypes: Ad-hoc Supporters, Temporary Assistants, and Persistent Companions. While the taxonomy serves as a blueprint for chatbot researchers and designers developing and evaluating chatbots in general, our archetypes also offer practitioners and academics alike a shared understanding and naming convention to study and design chatbots with different temporal profiles. © 2021 The Author

A Matter of Trust? Examination of Chatbot Usage in Insurance Business

Critical success factors such as trust and privacy concerns have been recognized as grand challenges for research of intelligent interactive technologies. Not only their ethical, legal, and social implications, but also their role in the intention to use these technologies within high risk and uncertainty contexts must be investigated. Nonetheless, there is a lack of empirical evidence about the factors influencing user’s intention to use insurance chatbots (ICB). To close this gap, we analyze (i) the effect of trust and privacy concerns on the intention to use ICB and (ii) the importance of these factors in comparison with the widely studied technology acceptance variables of perceived usefulness and perceived ease of use. Based on the results of our online survey with 215 respondents and partial least squares structural equation modelling (PLS-SEM), our findings indicate that although trust is important, other factors, such as the perceived usefulness, are most critical for ICB usage

A Mixed Methods Analysis of the Adoption and Diffusion of Chatbot Technology in the German Insurance Sector

In recent years, gradual improvements in information, computing, communication and connectivity technologies have enabled new technical possibilities for the adoption of Chatbots across diverse sectors. In the case of the insurance sector, the implementation of service innovations based on Chatbot technology can contribute, among other benefits, to improve the efficiency across the insurance value chain, reduce costs and generate customer loyalty and trust (Barrett et al., 2015; Ross et al., 2016). However, despite the advantages, the adoption success of Chatbot Technology depends on the understanding of the ambivalent perceptions, attitudes, and beliefs of the main social actors (i.e. practitioners and potential users) towards the customer interface. Using a mixed methods design based on an interpretive paradigm and within the frameworks of acceptance and diffusion research, we identified the “relative advantages” and “IS infrastructure” as the most critical ambivalent socio-technical factors for the adoption and diffusion of Chatbot technology in Germany

Challenges of the Financial Industry - An Analysis of Critical Success Factors for FinTechs

FinTechs are attracting ongoing interest in both academia and practice. With the use of techniques borrowed from grounded theory, we analyze material from 10 interviews with managers and Chief Executive Officers at FinTechs and 8 interviews with venture capitalists (VCs). We examined 15 critical success factors (CSFs) for FinTech ventures. These are divided into 9 factors that generally apply to general ventures: (1) team, (2) entrepreneur, (3) capital, (4) product/market fit, (5) idea and execution, (6) pivoting and continuous learning, (7) customer acquisition, (8) internationalization, and (9) networking. In addition, we examine 6 factors that have specific relevance to FinTech venture success, namely, (10) technological advantage, (11) regulatory knowledge, (12) B2B focus, (13) incumbent partnerships, (14) growth potential, and (15) exit options for VCs. Our study expands the literature on CSFs for FinTechs and provides recommendations for entrepreneurs to be more successful