How do AI startups build trust in their smart service systems?

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"
• 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.