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.
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
2.2 Why AI startups have a hard time building trust in their smart service systems
AI startups’ unique selling proposition (USP) consists of built-in AI entities - machine subsystems that think or even act like humans or at least rationally - that enhance their products and services since the very beginning of artificial intelligence (Russell & Norvig, 1995). Many AI startups offer integration of smart service systems for their customers where machines take care of the value creation to some extent. Most companies are principally willing to integrate smart service systems into their production system, some even have AI strategies (cf. Ivens, 2015). Nevertheless, to become generally accepted as a player in this very cognitive (Chai, Malhotra, & Alpert, 2015) and entirely new field from a customer’s perspective, trust building is key in the AI industry.
There are some obvious challenges regarding initial trust related to the introduction of AI-enabled services and service systems in the market: AI services heavily rely on the use of big data (Demirkan et al., 2015). Sending sensitive data to a provider typically requires initial trust, the certainty that the data is going to be used only in contractual ways and the security of data transfer and transmission on the customer side. Aside from that, even if the term of artificial intelligence is by now widely known in the economy and society, there are still perceptions of AI as not much more than a buzzword (cf. Shannon, 1984) which leads to people being skeptical about anything that is labelled as AI. AI startups thus have to prove to their potential customers that AI actually adds value to their services.
However, the most important aspect is to understand that AI startups provide their services by striking new paths of value creation and building very complex systems around these. The resulting, complex services and the integration of smart service systems by AI startups in a B2B context are hard to fully understand for potential customers. That causes a higher level of unfamiliarity and a lower level of initial trust at the end of a potential customer (Gefen, 2000). AI startups therefore have an especially hard time to build trust in their smart service systems.
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.
- Arbeit zitieren
- Lobosch Pannewitz (Autor:in), 2017, How do AI startups build trust in their smart service systems?, München, GRIN Verlag, https://www.grin.com/document/450622
