Data Ecosystem Business Models: Value and control in Data Ecosystems

Purpose: Organizations evolve from using and governing data internally towards the exchange of data in multi-organizational data ecosystems. The purpose of this research is to determine a business model framework for actors operating in and/or entering a data ecosystem. Methodology: To determine a business model framework in data ecosystems. an analysis was made based on how the research fields of “business models”, “data governance”, “data ecosystems”, “data sharing”, “business ecosystem” complement each other. A business model framework was created, which was applied to three use case studies in the field of Smart Cities and Urban Digital Twins: The Helsinki Digital Twin, the Rotterdam Digital Twin, and the Smart Retail Dashboard in Flanders. Findings: The business model of actors in a data ecosystem is determined by value and control factors. Value is determined by the capability to create value through the exchange of data in the ecosystem, and to capture value through revenue (sharing) models and cost (sharing) models. Control is determined by ecosystem control. Governance models on the ecosystem level are required to enable the collaboration and to ensure trust to allow for the willingness to share data. Additionally, data governance on an ecosystem level is required, enabling the data exchange between the actors. Research Limitations: The model was applied to three use cases in Smart Cities and Urban Digital Twins. Consequently, the data ecosystems concern a high presence of public actors, yet also includes private companies. The applicability needs to be identified in other sectors in further research. Additionally, as the scope of the study was on business models, data governance, data-sharing and data ecosystems, abstraction was made of fields of study beyond these topics. Value and practical implications: The Data Ecosystem Business Model framework can serve as a guideline for organizations entering a data ecosystem, as well as for actors aiming to establish novel data ecosystems. Additionally, the framework can serve as a high-level overview for further research into the field of business models in data ecosystems.

Overcoming barriers to experimentation in business-to-business living labs

Business-to-business (B2B) living lab projects have been mentioned in different areas of academic research, but the innovation management literature requires deeper analysis of their potential opportunities and challenges. Real-life experimentation is a key requirement for living labs as it enables deeper insights in the potential success of innovations. However, the literature has not provided insights on how living lab projects can implement real-life experimentation in B2B innovation projects and does not describe appropriate conditions for experimentation in these settings. In this study, we identified three main barriers preventing real-life experimentation in B2B living lab projects: the technological complexity, the need for integration, and the difficulty in identifying testers. The barriers are discussed in detailed and potential solutions are provided to help overcome these barriers and stimulate the adoption of real-life experimentation in B2B innovation projects

The potential of experimentation in business-to-business living labs

The demand for business-to-business (b-to-b) Living Lab projects is growing significantly within iMinds Living Labs. Real-life experimentation is a key requirement for Living Labs as it enables deeper insights in the potential success of the innovation. However, literature has not provided insights on whether the Living Lab methodology is an appropriate approach for real-life experimentation with b-to-b innovations and does not provide conditions where experimenting in b-to-b Living Lab projects is applicable. Within this paper we performed a cross-case analysis of eight b-to-b Living Lab cases. We conclude that real-life experimentation is possible in Living Lab projects but the possibilities vary on a case level. Three barriers have been identified that help to determine the possibility of real-life experimentation in a b-to-b Living Lab project: the technological complexity, the need for integration and the difficulty to identify testers. Finally, we also described how these blocking factors can be overcome. This can be interesting for the reader to identify whether real-life experimentation will be possible or not in a b-to-b context

Belgium: Adoption of the Sharing Economy

The debate on the sharing economy in Belgium has been mainly focused on its economic, quantitative, and digital aspects. Given the fact that the adoption of the sharing economy has accelerated lately, this report wanted to contribute to further open up the debate on the adoption of this economy in relation to an aspect that is too little discussed, namely sustainability. Based on some smaller studies, this report identifies different drivers for concrete sustainable sharing economy initiatives to develop that situate themselves on the level of people’s daily life practices, social and cultural developments, and policy developments. Next to these drivers, there were issues detected that interact closely with the further development of this economy. The report ends with a suggestion for more systematic research of the drivers behind the initiation, adoption, and sustaining of sharing economy initiatives and their contributions to a more sustainable Belgian society

Personal Data Ecosystems: Clarifying data providers’ decision to grant data control_ AHP analysis dataset

The aim of this research is to investigate the pivotal business dimensions influencing data providers’ decision to grant data control to data subjects, contributing to the resource-based view and theory of value co-creation. It first discerns business dimensions based on the networked level business model in personal data ecosystems:  value (value capturing, user value and ecosystem value) and control (actor relationship, data competitiveness and privacy risk). Next, employing Analytical Hierarchy Process (AHP) analysis, the study identifies the preferences of data providers regarding granting data control to data subjects, and afterwards scrutinizes three use cases with distinct ecosystem setups within the Solid Mobility Profile. The research identified the preferences of business dimensions data providers consider in the decision of data providers to grant data control are, in descending order of significance, value capturing, actor relationship and data competitiveness. The influence of these dimensions exhibits market variations across sectors and associated business models. The study accentuates the intricate decision made by data providers between value and control dimensions, demonstrated through the analysis of three Solid Mobility Profile use cases within the realms of Mobility as a Service (MAAS) and Cooperative Intelligent Transport Systems (C-ITS). The findings illuminate the nuanced considerations and sector preferences influences that shape data providers’ decisions regarding granting data control to data subjects in distinct ecosystem setups. </p