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

Table of Contents

1 Introduction

2 Theoretical Background

2.1 Trust

2.1.1 Definition and conceptualization

2.1.2 Initial trust and continuous trust

2.2 Why AI startups have a hard time building trust in their smart service systems

2.2.1 Service Systems

2.2.2 Smart Service Systems

2.3 Strategic spheres of activity and hypotheses

2.3.1 “Transparency”

2.3.2 “Recognition by third parties”

2.3.3 “Points of contact”

3 Method & Data

3.1 Method: An abductive approach drawing on QCA

3.2 Framework

3.3 Coding

3.4 Sample

4 Discussion of results

4.1 Description of results

4.2 Discussion of hypotheses

4.3 Additional findings

4.4 Limitations

5 Conclusion and outlook

Research Objectives and Key Themes

This thesis investigates how AI startups build initial trust in their smart service systems. Given that AI services are often perceived as complex and unfamiliar by potential clients, the study explores specific trust-building mechanisms employed on company websites to overcome these challenges and facilitate market acceptance.

• Theoretical conceptualization of initial trust in smart service systems.
• Identification of trust-building strategies: Transparency, Recognition, and Points of contact.
• Empirical analysis of 26 AI startup websites using a qualitative comparative approach.
• Evaluation of the prominence and usage frequency of specific trust-building tools.
• Assessment of the correlation between startup characteristics and trust-building efforts.

Excerpt from the Book

Summary of Chapters

1 Introduction: Introduces the growing importance of service-based business models and the specific challenges AI startups face in building trust for their smart service systems.

2 Theoretical Background: Conceptualizes initial trust and explores three strategic spheres—transparency, third-party recognition, and contact points—to develop hypotheses for trust-building.

3 Method & Data: Describes the abductive research approach, the qualitative comparative analysis method, and the selection criteria for the 26 AI startups analyzed.

4 Discussion of results: Presents the findings of the website analysis, evaluates the established hypotheses, and discusses additional findings and methodological limitations.

5 Conclusion and outlook: Summarizes key insights and suggests future research directions regarding trust building in the evolving landscape of AI-driven services.

Key Keywords

AI startups, Smart service systems, Initial trust, Trust-building, Website analysis, Transparency, Third-party recognition, Points of contact, B2B, SaaS, Value co-creation, Qualitative comparative analysis, Service innovation, Digital trust, Customer acceptance

Frequently Asked Questions

What is the core focus of this bachelor thesis?

The thesis focuses on how AI startups build initial trust in their smart service systems, specifically examining the trust-building measures implemented on their websites.

What are the central thematic fields covered in the work?

The core fields include the conceptualization of initial trust, the challenges of smart service systems in B2B environments, and the strategic website-based tools utilized to foster customer trust.

What is the primary research question?

The research question is: "How do AI startups build trust in their smart service systems?"

Which scientific methodology is applied?

The author uses an abductive, two-step approach drawing on Qualitative Comparative Analysis (QCA) to evaluate trust-building tools on a sample of 26 AI startup websites.

What is analyzed in the main part of the study?

The main part examines 22 specific trust-building tools across three categories—transparency, recognition by third parties, and points of contact—analyzing their prominence and usage frequency.

Which keywords characterize this paper?

Key terms include AI startups, smart service systems, initial trust, website analysis, transparency, and B2B SaaS models.

Why is "initial trust" specifically emphasized?

Initial trust is critical because AI startups are often new players in the market without established brand reputations, necessitating extra efforts to reduce customer risk perception during the first website visit.

How does the study evaluate the prominence of trust tools?

The research uses an ordinal scale from 0 to 3, where 0 indicates the tool is not found and 3 indicates maximum prominence on the landing page.

What were the main findings regarding the transparency of AI startups?

The study found that transparency tools were only used at a 50% rate, suggesting significant room for improvement, and noted that "feature text/lists" were the most commonly used transparency tools.

Does the origin of the startup impact trust-building?

The study observed that while startups from different countries show varied tool usage, there is a universally high necessity for trust-building across different cultures.