The global economy is shifting labor from agriculture and manufacturing to services. Globe-spanning service-based business models enabled by information technology (IT) and increas-ingly specialized businesses and professions have transformed our economies. Service innova-tion is key in order to achieve growth for this more-service-focused-than-ever world economy to thrive.
Scholars recognize a need for new ways of value-creation that can propel economic growth and the development of more effective services (Vargo, Maglio, & Akaka, 2008). One answer to respond to that need is the re-organization of the production of services in so-called service systems.
This approach is particularly useful for knowledge-intensive industries and noticeable for example in the artificial intelligence (AI) industry, a rapidly evolving, hy-per-innovative ecosystem with new players coming up at frequent intervals. AI startups offer their services through smart service systems or they try to make their customer’s and their own service systems smarter by adding AI services to the process of value co-creation. The industry heavily relies on software as a service (SaaS) business models which represent the ideal-typical shift to a service-dominant (S-D) logic thinking.
When it comes to the acceptance of those new services, trust is a vital concern. While it has always been an important issue in services, trust in smart service systems becomes crucial. As AI startups’ service propositions are far from familiar to their potential clients, they have got to go the extra mile to build trust in their smart service systems.
This paper will provide answers to the research question How do AI startups build trust in their smart service systems? by applying the theory of trust to smart service systems and AI startups. As website quality is an important trust-building lever the research question will be answered by exploring trust building measures in a sample of 26 AI startups’ websites.
The major findings include that AI startups do not make their smart service systems as trans-parent as they could through their websites, that showcasing recognition by third parties oc-curs mostly through inexpensive tools that are easy to implement, and that all AI startups offer indirect channels to get in contact with them but less offer richer channels.
Inhaltsverzeichnis (Table of Contents)
- Introduction
- Theoretical Background
- Trust
- Definition and conceptualization
- Initial trust and continuous trust
- Service Systems
- Why AI startups have a hard time building trust in their smart service systems
- Smart Service Systems
- Strategic spheres of activity and hypotheses
- "Transparency"
- "Recognition by third parties"
- "Points of contact"
- Trust
- Method & Data
- Method: An abductive approach drawing on QCA
- Framework
- Coding
- Sample
- Discussion of results
- Description of results
- Discussion of hypotheses
- Additional findings
- Limitations
- Conclusion and outlook
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This paper aims to understand how AI startups build trust in their smart service systems. It investigates trust-building measures on the websites of 26 AI startups, focusing on the factors of transparency, recognition by third parties, and points of contact. The paper explores how these factors contribute to building trust in a rapidly evolving industry where smart service systems are becoming increasingly prevalent.
- Trust in Smart Service Systems
- Transparency and Trust Building
- Third-Party Recognition and its Impact on Trust
- The Role of Points of Contact in Building Trust
- Trust Building Strategies for AI Startups
Zusammenfassung der Kapitel (Chapter Summaries)
- Introduction: This chapter sets the stage for the research, highlighting the growing importance of service innovation in the global economy and the emergence of AI startups within this context. It emphasizes the crucial role of trust in the adoption of smart service systems, particularly as they are unfamiliar to many potential clients.
- Theoretical Background: This chapter delves into the theoretical foundations of trust, focusing on its definition, conceptualization, and different forms, including initial trust and continuous trust. It then introduces the concept of service systems and explores the challenges AI startups face in building trust in their smart service systems. The chapter concludes by outlining the strategic spheres of activity and hypotheses related to transparency, third-party recognition, and points of contact.
- Method & Data: This chapter presents the methodology employed in the research. It describes the abductive approach drawing on Qualitative Comparative Analysis (QCA) and outlines the framework, coding process, and sample selection. The chapter provides details on the data collection method, which involves analyzing websites of AI startups.
- Discussion of results: This chapter analyzes the findings from the study, presenting a detailed discussion of the data and drawing connections between the research results and the established theoretical framework. It explores the extent to which AI startups employ transparency, third-party recognition, and points of contact strategies to build trust in their smart service systems.
Schlüsselwörter (Keywords)
The research focuses on AI startups, smart service systems, trust building, transparency, third-party recognition, points of contact, and qualitative comparative analysis. The study explores the key concepts of trust, service innovation, and value co-creation within the context of a rapidly evolving digital economy.
- Citation du texte
- Lobosch Pannewitz (Auteur), 2017, How do AI startups build trust in their smart service systems?, Munich, GRIN Verlag, https://www.grin.com/document/450622
