What's to Automate? A Task Analysis of AI-enabled Start-ups

Automation of tasks as a result of advances in Artificial Intelligence (AI) is currently one of the major economical drivers. However, the varying effectiveness of AI usage across occupations and industries suggests that the impact of AI diffusion is uneven. Thus, it is imperative to understand which types of tasks are more or less prevalent in AI-enabled businesses. Using a cross-sectional dataset of 27,700 start-ups and occupation data, we utilize word embedding to link start-ups to their respective underlying tasks. We compare the task types of AI-enabled with non-AI start-ups in the services and platforms domain using a suitability for machine learning metric. The results show that analytical, logistical, and statistical tasks predominate among AI-enabled start-ups while services with customer proximity have a smaller share and the overall task diversity is lower. The implications of our findings are discussed in the light of labor theory and the economies of scale of AI start-ups

A Scaling Perspective on AI Startups

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

On the Heterogeneity of Digital Infrastructure in Entrepreneurial Ecosystems

Digital infrastructure represents for startups in entrepreneurial ecosystems an important asset but also a major risk. Drawing on studies about digital entrepreneurship and ecosystems, we examine the determinants of the heterogeneity of startups’ tech stacks in ecosystems. Using publicly available data from the data aggregators Stackshare and Crunchbase, we identify popular endogenous categories in startups’ tech stacks. Then we conduct a visual network analysis and a multivariate regression analysis, utilizing the identified technology categories to measure the heterogeneity of the startups’ tech stacks. The analysis supports the propositions that firm age and increased funding are positively associated with tech stack heterogeneity, whereas funding rounds are negatively associated with tech stack heterogeneity. Implications of our findings on digital entrepreneurship and ecosystems are discussed

The Digital Platform Otto.de: A Case Study of Growth, Complexity, and Generativity

We analyze the growth, complexity, and generativity of the digital platform Otto.de, a revelatory case of a large German company that has opened up its internal IT platform to outside developers. We find indication for a superlinear growth pattern fueled by external developers and the introduction of microservices as well as the emergence of a structural separation within the platform. Furthermore, our research shows ways to explain the generativity of a digital platform based on the attention and activity received

A Social Citizen Dashboard for Participatory Urban Planning in Berlin: Prototype and Evaluation

Participatory urban planning enables citizens to make their voices heard in the urban planning process. The resulting measures are more likely to be accepted by the community. However, the parti-cipation process becomes more effortful and time-consuming. New approaches have been developed using digital technologies to facilitate citizen participation, such as topic modeling based on social media. Using Twitter data for the city of Berlin, we explore how social media and topic modeling can be used to classify and analyze citizen opinions. We develop a Social Citizen Dashboard allowing for a better understanding of changes in citizens’ priorities and incorporating constant cycles of feedback throughout planning phases. Evaluation interviews indicate the dashboard’s potential usefulness and implications as well as point to limitation in data quality and spur further research potentials

DATA GOVERNANCE STRATEGIES FOR DATA PLATFORMS – A MULTIPLE CASE STUDY IN NURSING CARE

It is long established that data from platforms can be useful for deriving patterned insights into people’s behavior and conduct. Data platforms are important in fields with limited data availability and strict regulatory and hierarchical structures, such as healthcare and nursing analytics. Hence, we carefully examine three forerunner initiatives in establishing data platforms in the context of nursing care along normative, organizational, and technical dimensions of governance. The cases were selected due to their high level of comparability and to demonstrate three different types of data governance strategies understood as actions to reconcile conflicting interests regarding data and dealing with prevalent data protection law – ranging from strictly processual approaches to the creation of synthetic data. These findings highlight the importance of considering data governance strategies concisely when building data platforms and suggest considerable variety in the configuration of data governance arrangements

Das Reifegradmodell für den Öffentlichen Gesundheitsdienst:Ein Instrument zur Erfassung und Verbesserung des digitalen Reifegrades von deutschen Gesundheitsämtern

The COVID 19 crisis has highlighted the key role of the public health service (PHS), with its approximately 375 municipal health offices involved in the pandemic response. Here, in addition to a lack of human resources, the insufficient digital maturity of many public health departments posed a hurdle to effective and scalable infection reporting and contact tracing. In this article, we present the maturity model (MM) for the digitization of health offices, the development of which took place between January 2021 and February 2022 and was funded by the German Federal Ministry of Health. It has been applied since the beginning of 2022 with the aim of strengthening the digitization of the PHS. The MM aims to guide public health departments step by step to increase their digital maturity to be prepared for future challenges. The MM was developed and evaluated based on qualitative interviews with employees of public health departments and other experts in the public health sector as well as in workshops and with a quantitative survey. The MM allows the measurement of digital maturity in eight dimensions, each of which is subdivided into two to five subdimensions. Within the subdimensions a classification is made on five different maturity levels. Currently, in addition to recording the digital maturity of individual health departments, the MM also serves as a management tool for planning digitization projects. The aim is to use the MM as a basis for promoting targeted communication between the health departments to exchange best practices for the different dimensions