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Digital startups’ use of AI technologies has significantly increased in recent years, bringing to the fore specific barriers to deployment, use, and extraction of business value from AI. Utilizing a quantitative framework regarding the themes of startup growth and scaling, we examine the scaling behavior of AI, platform, and service startups. We find evidence of a sublinear scaling ratio of revenue to age-discounted employment count. The results suggest that revenue-employee growth pattern of AI startups is close to that of service startups, and less so to that of platform startups. Furthermore, we find a superlinear growth pattern of acquired funding in relation to the employment size that is largest for AI startups, possibly suggesting hype tendencies around AI startups. We discuss implications in the light of new economies of scale and scope of AI startups related to decision-making and prediction.
Examples of scaling relationships for AI (red), platform (green), and service startups (blue): (a) the estimated amount of revenue vs. the number of employees scales sublinearly and (b) the total amount of funding vs. the number of employees scales superlinearly strengths are so vastly different for the startup groups, we believe it is an insightful variable in terms of group comparison. We included two further analyses by (i) splitting up the data geographically into Europe, Northern America and Asian-Pacific and (ii) examining different business categories of AI startups and included the results in Appendix 2. The regional analysis regarding revenue (i) reveals close to no differences for service startups but larger differences for platform and AI startups: The beta coefficient is smallest for Northern America (AI: 0.23; platform: 0.18) and highest for Asian-Pacific (AI: 0.42; platform: 0.44) with Europe in between (AI: 0.29; platform: 0.25). Since the average value of the target variable is close to similar for the three categories this hints to connection of employees and revenue that is much smaller in the US. For funding, the regional differences are much smaller. The business category analysis for AI startups (ii) reveals that in the FinTech and the Health Care sector employees and revenue are connected most closely. Since these sectors are heavily regulated, market entry and data sharing prove to be especially difficult [9, 57]. Regarding the funding, the more traditional sectors of Analytics and E-Commerce are less likely to gain more funding when having more employees. We ran several robustness checks to rule out other explanations. First, we cutoff revenue outliers at different top percentages to control for possible distortions by the most performant startups. For a 10% cutoff, the slope for every startup group dropped similarly by around 3% (AI startups) to 5% (service startups). Second, grouping the startups by age and running the same analyses shows that both the predictive quality and the slope β rise with a higher startup age, which is in line with the theory that bigger
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... The use of AI algorithms by start-ups in novel business models seems to be approaching hype [297]. However, this is not the case for healthcare start-ups. ...
Thesis
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This work has two main objectives: (1) to improve the understanding of semantic interoperability issues in healthcare and (2) to find possible solutions to these issues. Several research projects focusing on semantic interoperability support the work to achieve these goals. Semantic interoperability problems are caused by value conflicts between different stakeholders in the health care system over semantic resources that define the meaning of data. These conflicting values cause containment, which is the dominant business model in healthcare. For this purpose, data is not available where it is needed. In turn, this results in a significant power asymmetry with regard to semantic resources that are locked up in different systems. Furthermore, this power asymmetry causes problems in the management of health data. This thesis proposes a solution for this chain of effects of semantic interoperability in the form of a work practice for productive work on semantic resources. Ideas from participatory design and co-design support the work practice—specifically, technology-enhanced activity spaces as an approach to solving different value concepts of the participants. In addition, the work practice uses OpenEHR's detailed clinical modelling approach to create semantic resources. The theory from the Commons studies supports a governance model of the work practice that is independent of state and the market. The specification of such a work practice is considered a formalization innovation. This work leads to an interesting innovation that has the potential to help solve semantic interoperability problems or at least provide enough knowledge to improve their understanding. Thus, both goals of the work have been achieved.
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