Designing Data Ecosystems: Value, Impacts, and Fundamentals

Permanent URI for this collectionhttps://hdl.handle.net/10125/107509

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EXPLORING DATA MONETIZATION IN ESTABLISHED ORGANIZATIONS: A DYNAMIC CAPABILITY APPROACH
(2024-01-03) Mirbagheri, Fatemeh; Ghanbari, Hadi; Rossi, Matti
The exponential growth of data, together with the increasing importance of analytics in a wide range of contexts, has given rise to data monetization, a phenomenon in which data and data-based offerings are traded for monetary value. Data monetization is relevant for established organizations since they often generate significant amounts of data which provides them with many opportunities for added revenue streams. Previous research shows that incumbents are falling short in harnessing this potential either because of the lack of knowledge of data-based business models or insufficient resources and capabilities. There is a paucity of research on how incumbents could develop such capabilities to successfully sell data and data-based offerings. To fill this gap, we conducted a multiple case study to explore what kind of capabilities are required by established organizations to successfully sell data. The paper contributes to the evolving discourse on data monetization by providing a new understanding of the required capabilities for selling data from the dynamic capability perspective.
Towards Efficient Information Sharing in Network Markets
(2024-01-03) Martens, Bertin; Parker, Geoffrey; Petropoulos, Georgios; Van Alstyne, Marshall
This paper develops a Salop differentiation model to study private and social incentives to share information within a platform ecosystem, between a platform intermediary and its business users. Information sharing can help business users to make more efficient decisions around their market and product innovation strategies improving social welfare. However, private and social incentives for information sharing do not coincide when the platform intermediary is vertically integrated and competes directly with its business users in the upstream market of the platform market. So, there is a scope for an \emph{ex-ante} regulation of mandatory data sharing. We argue that the location of data access matters and propose a regulatory framework that introduces a new data right for platform users, the \emph{in-situ} data right, which is associated with positive welfare gains. By construction, this right enables effective information sharing, together with its context, without reducing the value created by network effects. We discuss crucial elements of its implementation in order to achieve innovation-friendly and competitive digital markets.
Groping in the dark? Exploring customer perception of hidden actions in smart service ecosystems through the lens of agency theory
(2024-01-03) Schwinghammer, Ronja
Due to new technologies, providers of digital goods and services collect an ever-increasing amount of personal data. Although the GDPR mandates that providers must inform their customers about the handling of their data, past privacy scandals have shown that customers lack information. In this study, we adopt a qualitative-exploratory approach to develop a rich understanding of the practices about which customers are not fully informed. We rely on agency theory to understand hidden actions as an informational advantage of providers. By conducting focus groups, we identify perceptions of three key hidden actions of smart product customers in B2C service ecosystems. Building on the hidden actions, we understand the relationship between customer and provider in smart service ecosystems characterized by information asymmetries. With our research, we provide the first steps towards understanding the nature and role of hidden actions in the context of smart service ecosystems. For practitioners, we provide guidance on how to effectively reduce information asymmetries.
FinDEx: A Synthetic Data Sharing Platform for Financial Fraud Detection
(2024-01-03) Karst, Fabian; Li, Mahei; Leimeister, Jan
The rising number of financial frauds inflicted in the last year more than 800 billion USD in damages on the global economy. Although financial institutions possess advanced AI systems for fraud detection, the time required to accumulate a sufficient volume of fraudulent data for training models creates a costly vulnerability. Combined with the inability to share fraud detection training data among institutions due to data and privacy regulations, this poses a major challenge. To address this issue, we propose the concept of a synthetic data-sharing ecosystem platform (FinDEx). This platform ensures data anonymity by generating synthesized training data based on each institution's fraud detection datasets. Various synthetic data generation techniques are employed to rapidly construct a shared dataset for all ecosystem members. Using design science research, this paper leverages insights from financial fraud detection literature, data sharing practices, and modular systems theory to derive design knowledge for the platform architecture. Furthermore, the feasibility of using different data generation algorithms such as generative adversarial networks, variational auto encoder and Gaussian mixture model was evaluated and different methods for the integration of synthetic data into the training procedure were tested. Thus, contributing to the theory at the intersection between fraud detection and data sharing and providing practitioners with guidelines on how to design such systems.
Identification of Key Requirements for the Application of Data Sovereignty in the Context of Data Exchange
(2024-01-03) Biehs, Steffen; Stilling, Jonas
With the growing digitization in our modern world, data is becoming an increasingly valuable business asset. Therefore, it is crucial that individuals and organizations are aware of their data sovereignty when it comes to selling and sharing data assets. This research paper aims to identify the essential requirements for the application of data sovereignty in the context of data exchange. Currently, the literature falls short of providing a comprehensive overview of this subject. The study conducts a literature review and expert interviews to identify key requirements for applying data sovereignty in the context of data exchange. The result identifies eight key requirements including access control, usage control, location, technical aspects, legal considerations, organizational compliance, monetization, and data quality. Understanding and considering these requirements can enable organizations to achieve data sovereignty and facilitate secure and trusted data exchange.
What Does it Take to Connect? Unveiling Characteristics of Data Space Connectors
(2024-01-03) Gieß, Anna; Hupperz, Marius; Schoormann, Thorsten; Möller, Frederik
Data spaces are a novel data management approach to collect large-scale heterogeneous data distributed over various data sources in different formats. To access these data spaces, users require so-called connectors to ensure technical compliance (e.g., usage control policies) and ensure that users play by the ‘same rules’. While connectors are a critical component of data spaces and receive considerable attention in politics, practice, and research, there is still no shared understanding of what constitutes a connector. To address this gap, we analyzed 23 connector use cases, diverse types of practitioner literature (n = 14), 25 scientific papers, and a workshop with five experts to extract the characteristics of connectors. We synthesized our findings into a taxonomy of connectors that integrates insights from the conceptual and empirical analysis and finalized it by classifying two connectors within the taxonomy. Our paper contributes to understanding this novel artifact, which has implications for future businesses.
Introduction to the Minitrack on Designing Data Ecosystems: Value, Impacts, and Fundamentals
(2024-01-03) Strobel, Gero; Otto, Boris; Möller, Frederik; Schoormann, Thorsten

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