From Data to Value: Value Generation in Data Ecosystems

Abstract

Data has become a central economic resource and a pervasive part of contemporary digital life. It is generated continuously through wearables, software applications, connected devices, digital platforms, organizational systems, and, increasingly, synthetic and algorithmically produced traces. Yet the growing availability of data does not automatically translate into value. Instead, value is generated through sociotechnical arrangements that enable data to circulate, be interpreted, recombined, governed, and transformed into useful offerings. In this context, data ecosystems have gained prominence as a distinctive network of organizations through which multiple actors coordinate to mobilize data and collectively generate benefits that are difficult to achieve in isolation. Interest in data ecosystem research has grown significantly over the past several years. However, like many other emerging topics and areas of research, the data ecosystem literature is not yet mature. This immaturity is evident in the lack of a unified, universally accepted definition, an unclear explanation of the key components, fragmented views of value and value generation in the context of data ecosystems, and the activities through which data is converted into value in these ecosystems. Despite expanding scholarly interest in data ecosystems and data-driven value creation, existing research remains fragmented and insufficient, with many questions remaining unanswered. Specifically, while studies often examine isolated aspects such as ecosystem components, governance challenges, technical infrastructures, or business models, they rarely connect these into an integrated explanation of how value is generated across the ecosystem. As a result, the field lacks an explanation of the activities that trace how data’s latent potential becomes translated into value propositions and value capture and is eventually realized as observable outcomes for focal firms, users, and other stakeholders. To address this gap and make a significant contribution to the literature, there is a need for structured, focused studies that help reduce fragmentation and provide stronger foundations, enabling future studies to build on this work and move towards stronger theorization and maturity. Responding to this need, this thesis conducts a series of studies that investigate data ecosystems and their key subsystems, explore business model archetypes, and examine the ontological properties of data in enabling value propositions. Finally, the thesis proposes a synthesized framework of value generation in data ecosystems that integrates ecosystem structure and interrelations, business model archetypes, and the ontological properties of digital data. The thesis is structured as a paper-based dissertation comprising four studies (presented across five academic publications), which collectively address the main and sub-research questions. To frame the overall research contribution, the study follows a structured investigation to provide new knowledge that not only adds valuable insights to the existing literature but also facilitates future theorization and gives new insight to practitioners. Accordingly, four sub-research questions are posed, and by answering them, several contributions are made to the literature. Each of the four studies addresses a specific aspect of data ecosystems. First, the initial study conceptualizes data ecosystems as sociotechnical configurations comprising data, infrastructure, governance, and stakeholders, and identifies patterned interrelations among these subsystems that shape ecosystem functionality. Next, the second study explains why firms move toward data-ecosystem-oriented business models by tracing the progression of business models from simpler configurations toward ecosystem-based logics as data becomes strategically central and value creation increasingly depends on cross-actor coordination. Then, the third study specifies business model archetypes in data ecosystem contexts by identifying coherent configurations of value propositions and value capture mechanisms shaped by key design dimensions. Finally, the fourth study advances a nuanced view of data by identifying ontological properties that make digital data generative, while demonstrating empirically that value propositions built on data require sociotechnical activation rather than mere accumulation. Empirically, the thesis draws on an extensive and systematic qualitative evidence base across the four studies. This includes semi-structured interviews with experts, iterative follow-ups, and informal exchanges to clarify emergent insights. Moreover, triangulation strengthened the empirical foundation through the systematic use of secondary and archival materials, such as corporate materials, product and service catalogs, social media content, third-party databases, news websites, blogs, market reports, and industry documents. These sources complemented the company's internal information, such as slides, architecture diagrams, usage statistics, and partner briefings. Across studies, the analysis followed an interpretive and iterative logic, moving between empirical material and conceptual abstraction to develop robust categories and patterns. In this thesis, sociotechnical systems theory serves as a sensitizing device, aiming to interpret and make sense of the empirical findings. Building on this, the thesis synthesizes the findings of the four studies to explain the notion of the zone of control and to propose a framework for value generation. Specifically, the zone of control denotes resources, assets, and data ecosystem components that an actor can control or influence. This may include owned data, technology, partnerships, and customer relationships, among others. The value generation framework comprises four sets of activities: positioning, architecting, mobilizing, and reconfiguring. First, positioning refers to identifying the actor’s ecosystem role, dependencies, and control zone. Next, architecting is the design of the actor's business model to connect controllable resources with opportunities in the ecosystem. Mobilizing refers to leveraging data, infrastructure, capabilities, and relationships to create and capture value. Finally, reconfiguring explains how realized outcomes reshape the actor’s business model, ecosystem role, and control zone. The contribution of this thesis to Information Systems research is, first, strengthening the conceptual foundations of the data ecosystem construct, advancing a relational understanding of subsystem interdependence, refining the role of data as a constitutive subsystem with latent and relational value, and bridging the study of data ecosystems and business models through an integrated explanation of value generation. From a practical perspective, the thesis underscores that it is not enough to have advanced technologies and large data resources. Rather, generating value depends on aligning ecosystem components and business model configurations to support coordinated value generation.