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Digital technologies nowadays play a major role in innovation within the agri-food domain. The evolution of IT systems has currently arrived at a level that involves complex systems integration and business ecosystems in which many stakeholders in different roles are involved. A new paradigm for digital innovation is needed that copes with this increased complexity. This paper presents an empirically informed framework for analysing and designing viable, sustainable digital innovation ecosystems in the agri-food domain. The research is based on a series of European large-scale public-private innovation projects from 2011 to 2021 with a total budget of 73 M€. They involved hundreds of stakeholders that were developing a large number of digital solutions through which a digital innovation ecosystem for agri-food was formed. In a longitudinal study, a conceptual framework was used to analyse these projects and describe how the digital innovation ecosystem has developed. Lessons learnt are translated into a number of design principles and an organizational approach to foster digital innovation ecosystems in agri-food. The conceptual framework consists of 6 key concepts: (i) innovation strategy, (ii) innovation organization, (iii) innovation network that contains (iv) the innovation process and (v) the innovation object and finally (vi) an innovation infrastructure. Along these 6 concepts, lessons learnt and in total 21 design principles are derived from analysing the projects forming a basis for the organizational framework. At the core of this framework is a lean multi-actor approach to trials and use case development interacting with a set of multidisciplinary activities: (i) developing a common technical collaboration infrastructure, (ii) identifying value streams with user engagement, (iii) engaging the right partners and stakeholders at the right time supported by strategic project planning and dynamic management. The most important conclusion is that effective, successful and quick use of appropriate IT in agri-food requires that actors should not be analysed in isolation from both their technological and business environment. Another consequence is that a 'minimal viable ecosystem' only emerges after considerable time, resources and ingenuity is invested and may require outside (government) intervention. Results from this paper can be used both by public and private stakeholders to diagnose and improve digital innovation projects and develop viable, sustainable digital innovation ecosystems in agri-food. [Display omitted] • A data economy for food systems based on data spaces requires understanding and optimizing digital innovation ecosystems. • A conceptual framework is used to analyse the development of large digital innovation ecosystems in agri-food in Europe. • Lessons learnt are translated into design principles and approach to organize digital innovation ecosystems in agri-food. • The core is a lean multi-actor approach in use case development interacting with multidisciplinary activities. • The framework, design principles and approach can be used by stakeholders to foster viable digital innovation ecosystems. [ABSTRACT FROM AUTHOR]

In recent years, the rapid growth of user-generated content has led to much research evaluating the patterns of online information exchange. These studies demonstrate that online communities are valuable data sources which provide rich, longitudinal data that would otherwise be difficult, if not impossible to access. Given the increased research interest, mining and analysing online social networks has become an important research domain, encompassing a variety of approaches. To analyse the large number of observations commonly found in online communities, we propose to first mine the data using a so-called Webscraper and then combine Social Network Analysis (SNA) with Sentiment Analysis to explore both content and relationships. The hands-on approach described in this article is targeted at researchers without a background in technical disciplines. Instead of focusing on some of the specific algorithms that facilitate the mining and analysis of online data, we describe how to use and combine out-of-the-box solutions to collect and analyse the online network data. Moreover, we document the steps taken and present important lessons learnt throughout the process of collecting and analysing data from an online health community with 108,569 registered users who contributed to 197,980 discussions with a total of 484,250 replies. In sum, our method proposes to:•Extract all relevant data from an openly accessible online community using a Webscraper.•Determine and visualise the relationships between users and the properties of the social network as a whole using Social Network Analysis.•Conduct Sentiment Analysis to detect the emotional tone of the online contributions, and to possibly infer further variables from the text such as the personality characteristics of users., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)