Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems

Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems Martin Lnenicka, Anastasija Nikiforova, Mariusz Luterek, Petar Milic, Daniel Rudmark, Sebastian Neumaier, Karlo Kevic, Anneke Zuiderwijk, Manuel Pedro Rodríguez Bolívar Abstract: There is a lack of understanding of the elements that constitute different types of valueadding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems. The aim of the study is twofold: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems. Moreover, this study develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations that differ in terms of (a) components and relationships, (b) stakeholders, (c) actors and their roles, (d) data types, (e) processes and activities, and (f) data lifecycle phases. Finally, three avenues for a future research agenda are proposed. This study is relevant for practitioners suggesting what elements of public data ecosystems have the most potential to generate value and should thus be part of public data ecosystems. As a scientific contribution, this study integrates conceptual knowledge about the elements of public data ecosystems, the evolution of these ecosystems, defines a future research agenda, and thereby moves towards defining public data ecosystems of the new generation. Keywords: public data ecosystem; evolution; generation; conceptual model; typology; systematic literature review 1 Introduction The practice of governments becoming more digital evolves into a new governance paradigm in which a large range of stakeholders engages in public-private collaborations (Clement et al., 2022; Janowski, 2015; Lis and Otto, 2021). In this new paradigm, digital transformation creates an ecosystem where data about all aspects of our world are distributed across multiple information systems (Curry and Ojo, 2020). In data ecosystems, data is the central resource, where access to data can be fully open or more restricted between stakeholders. The ecosystem environment includes the deployment and management of infrastructure resources, as well as activities and tools for interactions between stakeholders and other internal and external elements (Geisler et al., 2022; Heimstädt et al., 2014a; Oliveira and Lóscio, 2018; Van Schalkwyk et al., 2016). When functioning, Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. i.e., reaching a state of equilibrium, data ecosystems enable the creation of innovative products and services, often using standard formats and protocols (Gama and Loscio, 2014; Oliveira et al., 2019). Public data ecosystems differ from each other in terms of the composition and importance of elements, the relationships between those elements, and their contribution towards value creation. Exploring these co-evolutionary elements and formative characteristics of data ecosystems is critical to their development and growth in terms of value creation (Azkan et al., 2020). Moreover, existing research on data ecosystems has been noted to be fragmented, both thematically and methodologically (Martin et al., 2017), and is mainly focused on data ecosystems that are open (Susha et al., 2023). A systematic review of the literature on public data ecosystems could provide new insights into their types, formative characteristics, and their resilience over time, including ecosystems with different forms of access to data. Existing efforts to understand how data ecosystems emerge and evolve include Heimstädt et al. (2014a) and Heimstädt et al. (2014b) focused on the evolution of UK open data ecosystems, while Styrin et al. (2017) explored the conditions for ecosystem creation and development. A more recent study was conducted by Gelhaar and Otto (2020), who focused on challenges at the emergence stage, postulating that it is crucial to build trust among ecosystem participants. Oliveira et al. (2019), in turn, conducted a systematic review of data ecosystems, recognizing the importance of theory, models, and engineering, the consideration of which can contribute to a better understanding of how to develop and advance the field of data ecosystem, and concluding that data ecosystem theory is not well developed. While some studies have advanced an understanding of data ecosystems, there is a lack of synthesis of existing data ecosystem research, emphasising the temporal aspect of ecosystem management, not to say about open and public data ecosystems and their evolution, or its contribution towards value for the data economy (Zillner et al., 2021). As a result, synthesising finding, reconciling conflicting evidence, and drawing a comprehensive understanding of phenomena are crucial for both academic and practitioner communities, providing a strong platform for future research in this area (Palmatier et al., 2018). However, a disconnect in research often leads to redundant investigations, hindering knowledge advancement and leaving certain areas underrepresented, particularly pronounced in the diverse landscape of public data ecosystems. Scattered studies make it challenging to consolidate insights for a particular type of data ecosystem across the body of research, exacerbating this challenge due to various perspectives within the domain. Given the dynamic nature of knowledge generation, systematic reviews become imperative to prevent replicative research that do not substantially advance knowledge, highlighting the research gaps and clarifying directions for future research to substantially advance knowledge (Moher et al., 2009; Paul and Criado, 2020; Paul et al., 2021; Paul et al., 2023). To fill this gap, this study, first, synthesises the literature, classifying it by the type of public data ecosystem, which also includes definitions of these often similar, but at the same time very different types of public data ecosystems. In this way, the reviewed papers are grouped in a meaningful way to guide the reader toward a better understanding of the phenomenon and provide a foundation for insights about future research directions (Palmatier et al., 2018; Snyder, 2019). Conceptualising a public data ecosystem through the synthesis of multiple perspectives offers clarity and a comprehensive checklist of constituent elements, facilitating deeper understanding and ensuring the Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. inclusion of all pertinent factors. Lastly, the dynamic nature of data ecosystems requires attention yet to be received in the literature, impacting the elements that form these ecosystems and their interrelationships, requires understanding of their development over time. This study defines and answers three Research Questions (RQ): RQ1: How are public data ecosystems conceptualised in the literature? RQ2: What are public data ecosystems' value-adding elements and their mutual relationships? RQ3: What are the types and evolutionary generations of public data ecosystems over the past 30 years? To answer the research questions, we use a Systematic Literature Review (SLR) and a deductive approach examining studies published between 1994 and 2023 (inclusively), including an exploratory retrospective analysis of the results. Hence, the study synthesises the literature on public data ecosystems, their formative characteristics and evolution over almost 30 years. Framework-based systematic review (Palmatier et al., 2018; Paul et al., 2023) is conducted having the potential to help researchers, policymakers, and professionals keep track of research findings (Paul et al., 2021) and significantly advance the field being based on a rigorous, transparent, and robust synthesis of past studies and enabling for the development of research agendas that add value. The findings of this study are relevant for practitioners in developing, designing, and managing a public data ecosystem that has the potential to create value associated with public data ecosystems. Firstly, this study provides practitioners with a better understanding of what the value-adding elements of public data ecosystems are, how these elements are interconnected, and to determine to which evolutionary generation a specific public data ecosystem belongs. Once practitioners have gained these insights, this allows them to identify what value-adding elements are missing or need to be redesigned, as well as what relationships exist between the above elements. These findings are relevant for building and developing the ecosystem as well as redesigning an already existing public data ecosystem. Ultimately, the findings of this study contribute to the current theoretical body of the knowledge by summarising, structuring, and conceptualising current research in the area of public data ecosystems. As a scientific contribution, this study defines the evolution of public data ecosystems, their elements, the relationships between them, and the milestones that trigger the transition from one generation of the ecosystem – more static in the past – to another, including the recent and forward-looking generation that is more dynamic and that focuses on modern technologies, stakeholders' engagement, and value (co-)creation. Finally, this study identifies a future research agenda to improve the practical understanding and application of public data ecosystems by addressing real-world challenges and contributing to the practical application of scientific findings, especially within the forward-looking generation of public data ecosystems. The remainder of the paper is structured as follows: Section 2 defines the key concepts to establish their common understanding, Section 3 outlines the research approach, describing the concrete steps. Section 4 presents the results of the SLR and their analysis. Section 5 presents discussion, future Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. research agenda, limitations, and theoretical and practical implications of the study, while Section 6 concludes the paper. 2 Research background This section provides the background for this study. Before we discuss how the literature defines public data ecosystems, we define the general concepts of data ecosystems and data infrastructures. 2.1 Data ecosystem The data ecosystem, of which public data ecosystems are subsets, has been a constant topic of interest in many areas, with various definitions emerging over the years. In the context of the information network of relationships, an ecosystem can be defined as "a system of people practices, values and technologies in a particular local environment" (Nardi and O'Day; 1999, p. 49), wherein the present case, the environment of the ecosystem is government, at all levels, including its organisational practices, policies, and technical platforms developed to facilitate the development of this ecosystem (Dawes et al., 2016). From a more technical point of view, data ecosystems are virtual data spaces utilising current technology and standards, along with recognized governance frameworks for the data economy, to enable safe, standardised data interchange and simple data linking (Petersen et al., 2019). Furthermore, data ecosystems are distributed, open, and adaptive information systems that possess self-organising, scalable, and sustainable characteristics (Geisler et al., 2022). In comparison to "business ecosystems" and "software ecosystems", data ecosystems depend on a large and diverse group of actors, each with unique traits, skills, and expectations. Data ecosystems deal with heterogeneous resources, where actors may use a variety of methods and environments to create and consume resources, as well as in different settings, where most elements are dynamic and evolve over time (Oliveira and Lóscio, 2018). Data ecosystems encompass distributed, heterogeneous, dynamic, and evolving actors and resources constituting "a set of networks composed by autonomous actors that directly or indirectly consume, produce, or provide data and other related resources (e.g., software, services, and infrastructure). Each actor performs one or more roles and is connected to other actors through relationships, in such a way that actors' collaboration and competition promotes data ecosystem self-regulation" (Oliveira and Lóscio, 2018, p.4). With such an approach, the main elements of the data ecosystem are actors, roles, relationships, and resources, so their definitions are nested within the actor–network theory and consider the social and technical relationships between elements, how they interact and collaborate with data resources throughout their lifecycle to create value (Oliveira et al., 2018; Oliveira et al., 2019). Kontokosta (2013) claims that the way we use the data ecosystem can alter market behaviour, and individual decision models and contribute to public gain by supporting the decision-making processes of each end user and influencing both the producers and consumers of these data. From the institutional point of view, a data ecosystem can be used as a means for supporting decision-making and planning (Haak et al., 2018). Similarly, Gelhaar and Otto (2020) point out that data ecosystems are derived from business ecosystems that aim to develop new value propositions that serve as the foundation for future innovation by collaborating with both supply and demand parties. Moreover, they argue that it is crucial to build trust among ecosystem participants. Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. The essential components of the data ecosystem are the relationships between data users, data providers, tools, and the data infrastructure (see the next section) (Charalabidis et al., 2018; Diran et al., 2020). Curry and Sheth (2018) believe that massive collaboration within a data ecosystem should be accompanied by a proper data governance model that fully considers ethical, legal, and privacy concerns. McLoughlin et al. (2019) go even further, stating that data ecosystems can be understood as interpretive communities that give meaning to data and how they should be used, linked, shared, and – according to Wilson (2019) – how to produce meaningful insights from data. Relationships facilitate the creation, processing, and use of data between actors for analytical purposes that create added value for all stakeholders (Azkan et al., 2020; D'Hauwers et al. 2022). Those complex interconnections between the actors within the ecosystem lead to situations in which actors are working cooperatively and competitively at the same time – also known as coopetition (Gelhaar et al., 2021). 2.2 Data infrastructure As several definitions presented in the previous sections demonstrated, some studies tend to define the data ecosystem through or using the term "infrastructure", which is considered to be a foundation necessary for the operation of society or enterprise, and which, together with data ecosystems, is claimed to be viewed as a metaphor deployed in a twin manner (Davies, 2011). Infrastructures can be of several types, where in the context of data ecosystems, with two of the most important in the context of data ecosystems being data infrastructure and digital infrastructure, where data infrastructure is a public good, which is part of the digital infrastructure. The concept of national data infrastructure is more open in terms of data, implementation alternatives, application sectors, and aims (Klievink et al., 2017). It is a distributed technical infrastructure (comprising portals, platforms, and services) that enables data access and exchange depending on predefined rules (Estermann et al., 2018). According to Gulson and Sellar (2019), data infrastructures establish novel relationships between various stakeholders and generate forces that both produce and operate across these network spaces. Gelhaar et al. (2021) claim that the data infrastructure is provided by a platform that supports the sharing and use of data within an ecosystem. Platform, in turn, is defined as a coherent / holistic part of the ecosystem in terms of its features and capabilities for working with and reuse open datasets by stakeholders, which is usually technological in nature (Bonina and Eaton, 2020; Danneels et al., 2017). Nevertheless, infrastructure is an important prerequisite for building and enabling data ecosystems and represents a step forward in the design of intelligent systems, i.e., smart systems. 2.3 Public data ecosystem A public data ecosystem represents a distinct subset of a data ecosystem, primarily composed of data sourced from and/or funded by governmental entities (central and local government or any other public body), which are also responsible for setting the legal framework governing its operation. Since public data ecosystems rely heavily on currently available technology, there are gaps in the research body in this field arising from a lack of common understanding of the public data ecosystem, particularly one that should be compliant with current trends, and its elements. For example, there is Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. very limited research on how conceptual approaches to Open Government Data (OGD) governance should incorporate Artificial Intelligence (AI)-related concepts: especially in the way they change the data acquisition and data processing (Tan, 2022). Thus, the public data ecosystem is understood by us as a dynamic and adaptable network of elements and interrelations between them, driven by occurring internal and external data flows and requirements arising within it. Element, in this definition stands for each item / component that can be used to describe the context of an ecosystem and / or to affect (influence) the dynamics of data ecosystem management and development. Interrelation between these elements or relationships is a connection between elements, the nature of which depends on the nature of the elements with which it is connected. Most of the relationships are represented by data activities through which data are disclosed and reused (McLeod and McNaughton, 2016). Dynamism and adaptability of the network of these elements and interrelations between them, in turn, refers to changes in the state of a data ecosystem that occur through a series of successive / sequential steps over time, including its key milestones, which is called evolution 1 . 3 Research approach: systematic literature review SLR approach was used to answer or build the foundation for answering our RQs. More precisely, objectives of our literature review were to (1) understand how are public data ecosystems conceptualised in the literature studying all relevant literature covering this topic (RQ1), (2) identify value-adding elements that constitute public data ecosystems and mutual relationships between them (RQ2), (3) identify how public ecosystem evolution is defined in the literature, what are the types of public data ecosystems, and what are the formative characteristics affecting or triggering the transition from one evolutionary generation to another (as part of RQ3). Following Kitchenham (2004), the SLR is conducted in five steps: (1) study identification, (2) study selection, (3) study relevance and quality assessment, (4) data extraction and (5) data synthesis, which are documented in detail in Supplementary Materials. To identify relevant literature, the SLR was carried out to form the knowledge base by querying digital libraries covered by Scopus and Web of Science (WoS). Given the specificity of the topic, we searched the Digital Government Research Library (DGRL). Finally, we searched Google Scholar. The search query (see Table 1) was defined as a combination of terms "public" and "data ecosystem", where the latter has been provided with various alternative namings found in the literature, e.g., Oliveira et al. (2019), and based on our own experience, including "data infrastructure" 2 , "data space", "data collection ecosystem", "dataset ecosystem", "data on the web ecosystem". In the case 1 definition adapted from the Cambridge Dictionary definition of “evolution” defined as (1) “a gradual process of change and development”, 2) “evolution is the process by which the physical characteristics of types of creatures change over time, new types of creatures develop, and others disappear”, https://dictionary.cambridge.org/dictionary/english/evolution 2 Although "data ecosystem" and "data infrastructure" cannot be considered synonymous given their semantic meaning, for the sake of completeness of the results, we included these terms since researchers sometimes use them interchangeably. Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. one of the above terms referred to a topic other than the public data ecosystem, this study was excluded from further analysis after the first round of results filtering. Table 1. Search terms used for the literature review. Databases Query Scopus, WoS, DGRL (“data ecosystem” OR “data infrastructure” OR “data space” OR “data system” OR “data collection ecosystem” OR “dataset* ecosystem” OR “data set* ecosystem” OR “data on the web ecosystem”) AND (“public”) After selecting studies, assessing their relevance and quality, resulting in 148 studies, and extracting data using a protocol we designed (see Table A.2, Supplementary Materials, Appendix A), we systematically analysed the obtained raw data (see Section 4). Then, by synthesising the literature review, we designed conceptual models of public data ecosystems and a conceptual model of the evolutionary generations of public data ecosystems. These models are designed using the deductive approach, which entails “identify[ing]dimensions and characteristics [...] by a logical process derived from a sound conceptual or theoretical foundation” (Nickerson et al., 2013, p. 340). The procedure used to design it consisted of several activities. First, each of the authors of the study individually reviewed the information collected about each of the selected papers. By comparing the data derived for each metadata dimension (see Table A.2, Supplementary Materials, Appendix A) for 148 studies, each author derived patterns. These were discussed, and prioritised among the first two authors, and then one author took the lead in developing the draft model. Then, another author was asked to review the model. Then, eight authors were asked to review the model, its elements, the relationships between them, and the milestones that triggered transition from one generation of the ecosystem to another, and provide the feedback on it, suggesting changes – adding new elements, relationships, or milestones, suggesting removing one of the above, or modifying. Then, all suggestions were incorporated in the revised version of the model, the two authors reviewed the model, and the model has been reviewed by the authors again, leading to the final version of the model. As such, the design of the model is based on the informed assessment of 148 studies and further analysis with the authors of this study. 4 Literature review – results and their analysis This section outlines the results we obtained and conclusions we reached after analysing the selected studies. As part of the study, we conducted bibliographic analysis, descriptive analysis, approachand research designrelated information analysis, and public data ecosystem-related information analysis. The results of the former parts are available in the Supplementary Materials (Appendix B), while in this paper we focus on the RQs-related aspects of our SLR, namely, the analysis of public data ecosystem-related information, reflecting only briefly on the former parts. The data underlying our study are publicly available through Zenodo 3 . 3 https://zenodo.org/records/13842100 Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. The popularity of the topic of public data ecosystems has grown since the early 2010s, with further significant increase in recent years (starting in 2019), with the number doubling and tripling since the 2010s. Examination of the objectives and contributions made by 148 selected studies with heir further classification by topic they focused on, coding these studies in several iterations (Supplementary Materials, Appendix B, Table B.1), made it apparent that existing studies cover a wide range of categories (we classified them into 15 categories) with varying degrees of popularity (from 3 to 19 studies in each category). Among them, some topics seem particularly important and popular, while others promise future research and application. At the forefront of scholarly attention in terms of the number of studies addressing relevant topics are studies focusing on the development and evaluation of frameworks tailored to understanding, analysing, and managing data ecosystems in use (19 studies). The popularity of this topic underscores the importance of establishing robust frameworks for navigating the complexities inherent in data ecosystems. This emphasis on theoretical foundations is complemented by empirical examinations and case studies that provide real-world insight into the functioning, challenges, and opportunities in data ecosystems in often very specific contexts or domains. These studies offer practical insights that are critical for effective decision-making and policy formulation, although they often remain relevant to the context being studied. In addition, analyses focusing on policy formulation and implementation have received attention because understanding the impact and effectiveness of policies governing data ecosystems is imperative to ensure responsible and effective management of the entire data ecosystem, data sources, or other specific components of these ecosystems. In addition, healthcare, social services, and environmental sustainability may gain greater popularity as one of the key types of public data ecosystems, as well as the ongoing evolution of technology necessitates deeper exploration into the technical components and system development within data ecosystems. Primarily, the studies conducted qualitative research (n=125, which is 84.5%), which might be due to the explorative nature of public data ecosystem research. Other studies leveraged mixed research methods or quantitative research. Literature reviews, content analysis, surveys, and interviews emerged as the predominant methods. Upon examination of whether the selected studies referred to any theory and/or theoretical concepts and/or approaches, we observed that almost half of the studies (n=63, which is 42.6%) omitted reference to these. Of the remainder, the ecosystem approach, ecosystem thinking approach, systems theory or platform theory were most frequently employed. Most studies predominantly employ a country-level focus, typically describing and/or comparing at least two countries (use cases). The second most common case envelops various enterprises within a particular industry or individual organisations within specified public sector domains. 4.1 Research question 1: Conceptualization of public data ecosystems This section answers our first research question: How are public data ecosystems conceptualised in the literature? During our analysis of selected studies, we identified various conceptualizations of public data ecosystems in the scientific literature that we classified into categories depending on their focus, unique characteristics, stakeholders, governance mechanisms, and challenges by which they are characterised in the respective studies (the full version of the table with all relevant studies by category is available in Supplementary Materials, Appendix C, Table C.1). Our analysis revealed a diverse landscape of data ecosystem types. Among them, some types have garnered significant Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. attention in the form of studies, while others, even if not as popular, offer promising opportunities for future research and application. At the forefront of research interest are SDIs Ecosystems and OGD Ecosystems. The oldest type that we identified is the geodata / spatial / geospatial data ecosystem, which since the early 1990s has been implemented as the SDI connecting geodata / spatial / geospatial data, Geographic Information Systems (GIS), users and tools. SDI can be defined as a set of networks and infrastructures that link and integrate vertical (local, regional, national) and horizontal (data categories) (McLaughlin and Nichols, 1994) components. This definition was later extended to encompass the ecosystem approaches and corresponding processes such the facilitation and coordination of data exchange and sharing between stakeholders (Mulder et al., 2020; Rajabifard et al., 2002; Shakeri et al., 2013; Vancauwenberghe et al., 2014), concepts such as open government, open data, and open-source software (Sveen, 2017), and respect the needs and demands of users to collect, manage, distribute/disseminate, and more effectively utilise geodata and associated services (Crompvoets et al., 2011; Grus et al., 2010; Kok and Van Loenen, 2005). Moreover, it expanded to include various policies, frameworks, technologies, systems and infrastructures, the financial and human resources required to guarantee that stakeholders working with these data, whether at a local, regional, national, or global level, are not limited in achieving their goals (Kalliola et al., 2019; Parida and Tripathi, 2018). Overall, these ecosystems play a pivotal role in supporting applications ranging from urban planning to environmental management. As open government and transparency initiatives have evolved around the world, a new type of public data ecosystem has emerged – OGD ecosystem. However, we identified that over time, three distinct ecosystems have materialised in the literature. The first was the open government ecosystem, in which data was not the most important element, but the openness of this type of ecosystem was ensured by supporting the flows of information and data. Harrison et al. (2012) argued that the interactions between governments and other public officials with citizens, businesses, and civil sector organisations drive the dynamics of these flows. After establishing open data principles, two other types have come to the forefront, i.e., open data ecosystems and OGD ecosystems. The main difference between the two is that while the OGD ecosystem is usually strictly focused on data provided by governments, either national, regional, or local, open data ecosystems include more data meeting open data principles that are produced and disclosed by other stakeholders such as research institutions, and businesses. With a significant number of studies dedicated to this type, OGD ecosystems are recognized as crucial for fostering transparency, innovation, and improved public services. Additionally, Big Data ecosystems have received attention, reflecting the growing importance of big data in contemporary discourse. These ecosystems encompass the collection, analysis, and use of large volumes of data to extract insights and support decision-making across various domains. In other words, they can be described as complex multi-layered, data-intensive digital-physical systems (Orenga-Roglá and Chalmeta, 2019; Shah et al., 2021b) that comprises a set of interdependent components used throughout the data lifecycle to cope with the evolution of data from multiple data sources, models, applications, services, and associated infrastructures (Demchenko et al., 2014; Orenga-Roglá and Chalmeta, 2019; Shah et al., 2021a), which desired characteristics are (1) Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. explanations of the nature of the objects being studied or future objects to help us understand these objects. Thus, we decided to limit the number of levels for identified dimensions – data, stakeholders, process – to a maximum of four levels. This does not mean, however, that this fourth level is the last one that can be defined, since the model can be expanded (in breadth and depth). For example, the technical standards component (part of the data infrastructure subcomponent) can be expanded by adding the above-mentioned subcomponents, namely data formats, protocols, metadata schemas, and semantic standards that, in turn, could lead to the definition of the sixth level that would include common vocabularies, ontologies and semantic models, while the data retrieval component can be broken down into download and API retrieval, etc. 4.3 Research question 3: Types and evolutionary generations of public data ecosystems This section answers our third research question: What are the types and evolutionary generations of public data ecosystems over the past 30 years? First, we present the results of the SLR by reflecting on the selected studies, which then serve as an input for the conceptual model that captures evolutionary generations of public data ecosystems designed using the deductive approach. 4.3.1 Evolution of public data ecosystems in the literature The evolution of public data ecosystems, their elements and formative characteristics are primarily shaped by the technological dimension and the level of its penetration among stakeholders, specifically, what technologies, tools, standards etc. are available to operate these ecosystems and the degree to which stakeholders can use them effectively and efficiently (Beverungen et al., 2022; Elwood, 2008; Heimstädt et al., 2014b; Klievink et al., 2017; Linåker and Runeson, 2020; Mulder et al., 2020; Rajabifard et al., 2002; Vancauwenberghe and van Loenen, 2018). As information and database systems have progressed over time, they have reshaped how public data are processed, stored, and shared. The emergence of networked data infrastructures, particularly with the advent of the Internet, has played a pivotal role in facilitating data transmission and sharing among stakeholders. I.e., since the 1970s these systems began to be connected through networks, and in the early 1990s, with the rise of the Internet, public data began to be transmitted and shared among stakeholders through these networks. These infrastructures are essential for launching, operating, and interacting with elements reliant on them. Additionally, the 21st-century data revolution, characterized by advancements like AI, IoT, and cloud computing, continues to shape the evolution of public data ecosystems, influencing how data is acquired, processed, stored, managed, and shared. Furthermore, beyond technological dimensions, various drivers contribute to the creation and development of public data ecosystems, including digital technology emergence and political and institutional efforts (Kalaitzakis et al., 2019). Government policies, data management practices, and stakeholder involvement are key determinants in shaping these ecosystems. The evolution from spatial data infrastructures (SDIs) to open data ecosystems reflects a transition towards more open and transparent data-sharing practices, aligned with principles of accountability and openness (Welle Donker, 2016). As these ecosystems continue to evolve, concepts like sustainability and resilience come into focus, emphasizing the importance of government intervention and collaborative efforts Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. among stakeholders to ensure the continued development and functionality of these ecosystems (see more detailed description of this evolution in Supplementary Materials, Appendix E). As such, we can define the evolution of public data ecosystems as the developments of their elements over time, enabled by arising and emerging technologies and interactions that occur within ecosystems to achieve their purpose. Considering this aspect, it is easier to understand evolutionary stages and development milestones (Heimstädt et al., 2014a; Heimstädt et al., 2014b; Jean-Quartier et al., 2022). Evolution is based on the involvement of other stakeholders outside the public sector in the ecosystem and its development (Liva et al., 2023; Vancauwenberghe and van Loenen, 2018). The evolution is also known to be externally affected by comparisons of performance and impact, and benchmarking with other countries, as well as best practices (McBride et al., 2020; Styrin et al., 2017). 4.3.2 Conceptual model of the evolutionary generations As the result of the SLR presented in the previous sections, and considering the ecosystem perspective, the conceptual model was developed to capture evolutionary generations of public data ecosystems that we call Evolutionary Model of Public Data Ecosystems (EMPDE) (Figure 3). These generations are described based on their value-adding elements and formative characteristics that represent the public data ecosystem in each generation. As with the previous conceptual model, the deductive approach was used to design it. The SLR results show that the evolution of public data ecosystems concerns several value-adding elements and formative characteristics, which can be divided into five groups or meta-characteristics that represent the public data ecosystem in each generation. These meta-characteristics are: (1) formative components and relationships, (2) stakeholders, (3) actors and their roles, (4) data types, (5) processes and activities and/or data lifecycle phases. We chose an additive approach, i.e., each successive generation includes characteristics of the previous generation and at the same time adds new characteristics that best describe it. The boundaries between generations are not clearly defined (therefore only a dashed line separates them) and at the same time not every subsequent generation necessarily contains the previous characteristics. Then, eight authors of this paper were asked to review the model, its elements, the relationships between them, and the milestones that triggered transition from one generation of the ecosystem to another, and provide the feedback on it, suggesting changes – adding new elements, relationships, or milestones, suggesting removing one of the above, or modifying. In other words, both substruction and reduction (Bailey, 1994, p. 24) were conducted until a satisfactorily complete model was reached. Then, all suggestions were incorporated in the revised version of the model, the two authors reviewed the model, and the model has been reviewed by the authors again, leading to the final version of the model. Finally, each author provided an opinion on the relevance of the model, each generation, components, and relationships between them for actual data ecosystems they are familiar with. Based on the generalisation of the concepts identified during the SLR, we identified six evolutionary generations (see also Figure 3): 1. the first generation - "raw data-centred generation" (typically before the 1990s to 1991/1993) was represented by stand-alone and usually isolated data infrastructures, in which Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. only governments and some public organisations focused on data generation and transfer (in terms of digitization of analogue data), data were used only within internal networks; 2. the second generation - "(geo)spatial data-centred generation" (typically from 1991/1993 to 2000s) is characterised by the increasing popularity of SDIs. In this period, data policy and other related policies were established to govern the (geo)spatial data. Citizens and academia were involved in this generation of public data ecosystems to support innovation, improve data quality, or visualise the data to make them more accessible to users; 3. the third generation - "public data sharing generation" (typically from the 2000s to 2007/2009) is partly affected by the development of e-government and the spread of ICT and access to the Internet among citizens and businesses. To standardise public data and information, various initiatives, guidelines, and recommendations were developed, and some public organisations were obliged to publish public sector data online, including metadata. Data sharing, participation, and collaboration (and co-creation) are supported by governments; 4. the fourth generation - "open (government) data generation" (typically from 2007/2009 to 2013/2015) – this is the generation of the first open (government) data ecosystems, in which dynamic processes and activities oriented towards an increased transparency, openness, engagement, and (re)use were performed to develop new applications, services, and gain value from public data. Users and developers were key for this generation of ecosystems, and they were encouraged to improve their data-related competencies and skills; 5. the fifth generation - "public data-driven generation" (typically from 2013/2015 to 2020s) is the second generation of open (government) data ecosystems, which are characterised by new components, such as big and LOD and cloud computing. They supported reuse and linking of big data to improve decision-making, sustainability, and overall impact of public data in the society. This generation is also more open than the previous ones and involves new stakeholders such as international organisations, which results in external pressures on the development of the ecosystem; 6. the sixth generation - "intelligent public data generation" (typically from the 2020s to today) – this generation is represented by intelligent algorithms, machine learning, natural language processing tools and AI in a broader sense that change the work with public data, as well as how users consume and interact with them. Intelligent (smart) public data form this ecosystem to support its resilience and interoperability. Storytelling is the key way to bring large volumes and a variety of data closer to citizens and other stakeholders. New actors and roles, such as ecosystem managers, data curators, data consultants, also emerge to manage public data ecosystems more efficiently. However, this generation can be seen as a forwardlooking generation, and while its characteristics are defined based on the SLR results, some Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. of those studies were of rather conceptual nature setting an agenda for the public data ecosystems of a new generation, i.e. characteristics they are expected to meet. 7. Figure 3. Conceptual model of the evolutionary generations of public data ecosystems resulting from the SLR. During internal revision of the model and discussions among the eight authors, which included analysis of the model in terms of its compliance with the real-world, i.e., practical experience of the authors with public data ecosystems, e.g., of their countries, it has been found that while these generations are generally valid, some deviations from practical experience can be observed. First, some generations are valid, except for the periods mentioned above, which were identified from the literature and are not necessarily generalizable. Secondly, in some generations the characteristics mentioned for subsequent generations may also be present, but they are not included due to their lower level of impact on the overall public data ecosystem, i.e., they do not yet shape an ecosystem, but are rather characteristics that exist but without a significant effect. This was the case with a stakeholder "data user", which did not exist in the initial version of the model for the first generation of ecosystems, since the very first generation of public data ecosystems was focused on publishing data with limited focus on the data user and reuse by external parties. However, in this case, considering the discussion among the authors, it was decided to include this stakeholder as an "internal data user", since this stakeholder was part of the ecosystem, while the "external data user" - the user in a more traditional understanding, became a formative characteristic only for the second generation of public data ecosystems. Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. Thirdly, in some countries it may be that a particular generation of the public data ecosystem had most characteristics it should be characterised by, but one or several were missing, becoming of greater interest in subsequent years, when other characteristics would allow the public data ecosystem to be classified as the ecosystem of the next generation. For some countries, one or more characteristics are still missing, and consideration of their exclusion from the model was rejected by other authors because it was found to be relevant for all other cases with which the authors are familiar with, and in the end, the model conceptualise three decades of the research, i.e., the literature on this topic. Fourthly, some generations – the first and the last – turned out to be the most difficult to assess their compliance with the real situation in practice. For the first generation of public data ecosystems, this was mainly due to limited or no transparency and awareness of the existence of public data at that point, as well as a very low level of its maturity, high decentralisation, and level of fragmentation of various systems that hardly constituted an ecosystem. For the latest, namely the sixth generation, this is due to the fact that it is based on the results of the SLR, where some of those resources can be characterised as calls for actions, and forward-looking ideas about what public data ecosystems are expected to look like in the future with a dominant focus on their resilience, sustainability and compliance with both user-centric design and emerging technologies. This is typically the case for theoretical and conceptual literature on the sustainable open data ecosystems, data spaces, and fair data ecosystems built around the concept of data republic. Therefore, we rather see this generation of public data ecosystems as a more provisionary and forward-looking generation of public data ecosystems. 5 Discussion and limitations In this section, we first discuss how public data ecosystems have evolved in the literature, their valueadding elements, and generational shifts and identify future research directions as implied from our study. We then address the limitations of this study, some of which may also inform future research directions. Finally, we present practical and theoretical implications of this research. 5.1 Discussion Data in all volumes, formats, from various stakeholders are forming public data ecosystems. However, not all data within these ecosystems get the attention they need in the context of public data lifecycle, which in turn affects the value creation from these data and their contributions to decisionmaking support. The proliferation of the Internet, cloud computing, and data analytics have significantly influenced the evolution of public data ecosystems, especially how the collection, storage, analysis, and sharing of larger and more complex datasets unfolds. Further, the emergence of the open data movement has changed how governments and organisations have embraced open data policies to make public information more accessible and transparent. The development of data governance frameworks and policies that guide the responsible and ethical use of public data shaped data governance practices. Citizens and communities have become active contributors to data collection and analysis, influencing decision-making processes. Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. An important requirement to achieve value-adding public data ecosystems is to know what data public sector organisations and institutions have and how they should work with them to get value from them. Considering the ecosystems perspective, which provides an approach to describe relationships between formative components and relationships, stakeholders, actors and their roles, data types, processes and activities and/or data lifecycle phases – it becomes imperative to discuss how such a perspective influences the evolution over time and how public data ecosystems should be prepared for these changes. According to Aaen et al. (2022), there will always be bright spots and dark spots in the value of the data ecosystem as it grows. In this regard, policymakers and managers need to carefully focus on aligning stakeholders, capabilities, and data, not only noting the benefits but also considering the risks of an evolving data ecosystem. Social, psychological, and ethical aspects resulting from the dynamics of data ecosystems can also affect the development of the ecosystem. Public data ecosystems are also influenced by local culture, political institutions, and historical influences that form the institutional conditions in which ecosystems are located or in which they must be embedded. Such conditions shape the functioning of actors within the ecosystem and the arrangement of various ecosystem elements (Haak et al., 2018). This understanding is now more critical than ever, considering the current transformation of Society 4.0 into Society 5.0 (Fukuyama, 2018), which is expected to increase transparency and active participation in solving social challenges. Ensuring equal opportunities for all and integrating innovative technologies and society through public data ecosystems can be seen as prerequisites for this transition (Dewi et al., 2021; Nikiforova et al., 2023a). International comparisons of public data ecosystems aid in discerning similarities and differences in countries' experiences, thereby contributing to a deeper understanding of the impact of data policies (Bates, 2014). Comparing and contrasting these ecosystems should facilitate identification of the key processes and resources, essential for their development. Furthermore, it is also valuable to explore the implications of different types of implementation experiences (Styrin et al., 2017). An established and mature e-government infrastructure further enhances the quality of this ecosystem (Ahn and Chu, 2021). Another aspect that is important for the development of the public data ecosystems is the extent of interaction between involved stakeholders, the clarity of processes occurring in the ecosystem, and their contribution to value creation. According to Jetzek (2017), all actors, especially data providers and data users, must have a reasonable understanding of prospective future values and what is required to maximise those benefits. Equally crucial is to consider the incentives for various stakeholders to share (or not share) the data to promote the creation and evolution of a data ecosystem (Van den Homberg and Susha, 2018). Although data ecosystems are considered as socio-technical systems (Gelhaar and Otto, 2020; Oliveira et al., 2019), the technical perspective and its associated requirements are often overlooked in the evolution (lifecycle) of these ecosystems. Specifically, attributes such as scalability, extensibility and other quality indicators are not sufficiently represented. Additionally, decisions and constraints regarding technologies, protocols, standards, and contractors, and how they are implemented and evolved, can lead to infrastructure fragmentation (Kitchin and Moore-Cherry, 2021). In addition, there are many challenges regarding infrastructure, governance, systems Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. engineering, and human-centricity, i.e., trusted data platforms, ecosystem data governance, and incrementally evolving systems engineering (Curry and Sheth, 2018). We also found that public data ecosystems are built at different levels – country or national, city or local or municipal, international, or supranational. Each of the levels possesses its own distinct elements, boundaries, and environments, fostering the growth of the ecosystem, where more collaboration leads to higher operational efficiency, suggesting a positive scale effect (Clement et al., 2022; Janssen and Estevez, 2013). However, the current research suggests that as the local governments have limited resources to manage the ecosystem, increased collaboration is likely to be observed only up to a certain point (Botequilha-Leitão and Díaz-Varela, 2020). Many governments and public agencies still refuse or fail to share their data and thus are unable to utilise the offerings of data ecosystems. However, the actual sharing of data can also generate costs in terms of effort and time. While previous research has made contributions to public data ecosystems, it often overlooks to theoretically integrate the various elements of these ecosystems and describe their evolution over different generations. Hence, this study extends the findings of earlier comprehensive review approaches towards different types of public data ecosystems such as geodata / spatial / geospatial data ecosystems (e.g., Coetzee and Wolff-Piggott, 2015; Oliveira and Lisboa Filho, 2015; Rajabifard et al., 2002), open (government) data ecosystems (e.g., Harrison et al., 2012; Heimstädt et al., 2014a), big data ecosystems (e.g., Munshi, 2018; Parida and Tripathi, 2018, Shah et al., 2020), Smart City and IoT data ecosystems (e.g., Curry and Ojo, 2020; Gupta et al. 2020; Kalaitzakis et al., 2019; Tron, 2020), domain-specific data ecosystems (e.g., Aaen et al., 2022; Gulson and Sellar, 2019; Haak et al., 2018; Hardy and Liu, 2022; Hoeyer, 2020; Park and Gil-Garcia, 2017; Ramalli and Pernici, 2022), stakeholder-centred data ecosystems / collaborative data partnerships (e.g., Otto and Jarke, 2019; Ruijer et al., 2023; Susha et al., 2023), policy and governance data ecosystems (Calzada and Almirall, 2020; Curry and Ojo, 2020; Hardy and Liu, 2022; Hoeyer, 2020; Hokka, 2022; Linåker and Runeson, 2021; Schneider and Comandé, 2022), and public data ecosystems in general (Gelhaar et al., 2021; Heinz et al., 2022; Lis and Otto, 2021; Oliveira et al., 2019). In addition, two rather emerging types of public data ecosystems were identified, namely fair data ecosystem (Calzati and van Loenen, 2023) and public data space (Beverungen et al., 2022). Since these studies lack a coherent linkage and the display of the relationships between different value-adding elements and how they contribute to forming of evolutionary generations, we offer a more holistic view on this topic. Given the increased momentum, the expanding breadth and velocity in the accumulation of public data ecosystems knowledge, this paper conducts a domain-based systematic review (Paul et al., 2021; Paul et al., 2023) synthesises existing, often disparate findings, explore trends and multivariate relationships between constructs by providing a comprehensive and unbiased summary of existing state-of-the-art findings, thereby drawing the big picture for a comprehensive and deeper understanding of the domain by both the academic, policy, and practitioner communities (Palmatier et al., 2018). It conceptually intersects with relevant information systems, systems theory, and public sector research, highlighting research gaps and explaining directions for future research (Moher et al., 2009; Paul and Criado, 2020; Paul et al., 2023; Snyder, 2019). Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. In this study, studies found within SLR are classified by topics they focused on and types of public data ecosystems they consider. These findings, obtained by summarising, organising, and conceptualising current research on public data ecosystems, have contributed to the development of understanding of the evolution of these ecosystems. As a result, we proposed the conceptual model that outlines the evolution of these ecosystems over time, from early stages focused on basic data infrastructure to more advanced stages leveraging emerging technologies, including key milestones that facilitate the transition from one generation of the ecosystem to the next. Each generation builds upon the previous one, incorporating new technologies, policies, and stakeholders, where the sixth generation is described as forward-looking and somewhat speculative, as it anticipates the future development of public data ecosystems based on current trends and conceptual studies. Consequently, we have identified future research directions. Although we briefly mentioned several potential directions earlier, the next section presents a structured and more detailed research agenda as implied from our research. 5.2 Future research agenda Our findings from the SLR revealed limited research on (1) the diversity of different types of public data ecosystems and their impact on different groups of stakeholders; (2) governance models and frameworks, and their impact on the various processes and data requirements occurring in public data ecosystems; and (3) emerging technologies and how they shape the structure and processes of public data ecosystems. Thus, we consider these topics to be particularly important for future research. We synthesised insights from the current literature on public data ecosystems and results obtained from answering the RQs, i.e., the typology of public data ecosystems (Figure 1), the conceptual model of the public data ecosystem and its meta-characteristics (Figure 2), and, in particular, the conceptual model of the evolutionary generations of public data ecosystems and its sixth forward-looking generation (Figure 3) to identify the key future research avenues and corresponding subtopics. These future avenues can be classified into three main research streams: (a) the impact of public ecosystems on public value models; (b) technical and governance aspects in public data ecosystems; and (c) the impact of technological advances on the structure and processes of public data ecosystems. 5.2.1 Impact of public data ecosystems on public value models Recent research shows the growing interest of cities, especially smart cities, in implementing effective and efficient local government data ecosystems or smart city data ecosystems (Bonina and Eaton, 2020; Liva et al., 2023; Lnenicka et al., 2024a, Ruijer et al., 2024; Wilson and Cong, 2021) to better understand the needs of citizens at lower levels of public administration. Better use of the vast amounts of data generated by cities, which often remain underutilised (Liva et al., 2023), can help local governments implement more open and collaborative governance models, as well as to provide citizen-centric services, improving both the urban planning and the efficient resource allocation (Lnenicka et al., 2022). Thus, future research could explore whether public data ecosystems have an impact on stakeholder participation in government and public decision-making and whether smart cities that implement smart city data ecosystems reach higher levels of citizen-centric services and higher levels of public value creation. Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. Nonetheless, the implementation of smart city data ecosystems at an urban scale can be difficult both due to the existing resources (technical, financial, etc.) and due to the complex, holistic, and large volume of data and information that needs to be structured, processed, and distributed into these ecosystems. In this way, public administrations could find it more feasible to focus their efforts on implementing domain-specific data ecosystems in accordance with their strategic focus at the national, regional or city level, or even at sectoral or industry level (e.g., transportation or healthcare). The implementation of domain-specific public data ecosystems can be an objective of public administrations to tackle environmental, social, economic challenges to achieve the Sustainable Development Goals (SDGs) (Hein et al., 2023) or even as a complement to other global smart city data ecosystems to focus cities' attention on relevant urban challenges that need to be addressed. These domain-specific data ecosystems may include data as one of their core elements, but be complemented by other equally important elements such as processes (sustainability, innovation, and interoperability) and stakeholders (e.g., networks and partnerships), with the ultimate goal of creating public value. As such, future research directions could analyse empirical evidence of domain-specific data ecosystems regarding their potential ability to both meet the SDGs and solve urban challenges. Future research can explore the different elements of these domain-specific data ecosystems and their impact on the process of creating public value. Effective implementation of public data ecosystems, in turn, requires citizens' educational initiatives and training programs aimed at improving data literacy and skills to develop a workforce capable of harnessing the potential of data ecosystems (Ansari et al., 2022; GascóHernández et al., 2018; Lnenicka et al., 2024b). As noted by Hokka (2022), in a data society, higher level of citizens' digital and data infrastructure literacy can help them clearly understand data and information from various digital sources, better exercise their rights, and to better support their active participation in society (Reggi and Dawes, 2016; Visser, 2013), which may influence the implementation of participatory and collaborative governance models. In addition, data literacy plays an important role, not only in the use of data by either users or producers (Santos-Hermosa et al., 2023), but also in data governance (Clark, 2020; Sharp et al., 2022), opening the possibilities of embedding the potential of both OGD and open research data (Santos-Hermosa et al., 2023). Therefore, future research could examine to what extent data literacy is present in citizens and other stakeholders in different contexts (smart cities, administrative cultures, specific domains) and at different administrative levels (national, regional, or local) as well as the influence of data literacy on data usage, on citizen engagement in public decisions and on data governance models. In addition, it is necessary to provide training and support to frontline civil servants to achieve sufficient levels of data literacy or even open data literacy (Lnenicka et al., 2024b; Loría-Solano and Raffaghelli, 2021) and knowledge in advanced analytics, including data science (Lnenicka and Komarkova, 2019; Umbach, 2022). These competencies are essential for data-driven decisionmaking in governments of all levels, including cities, which means understanding what data explains reality, knowing how to use data to inform practice, and understanding the broader implications of the datafication of society (Dingelstad et al., 2022). However, government efforts to train public administration staff are uneven. Thus, while some city governments are investing heavily in organisation-wide knowledge base and literacy, others barely put data/algorithms on the agenda (Fest et al., 2022). Moreover, such data literacy training becomes even more relevant in the new age of Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. emerging technologies such as AI (European Union, 2022; Stankovich et al., 2023). In this regard, future research directions could examine the relationship between investments in the digital literacy of public servants and the wider adoption of emerging technologies in both public services and decision-making processes. In addition, new research could be conducted to analyse this relationship with both the higher quality of public services provided and the impact of public decision-making processes on a higher level of citizens' quality of life. Last, but not least important, another unsolved issue is how to provide data literacy to citizens and other stakeholders (Salomão Filho et al., 2023). Although public administrations should be the responsible bodies for implementing data literacy programs, the way such programs are implemented can take many different forms. Some research streams point to educational institutions as being responsible for providing data literacy training to students in public education (Hilger et al., 2023) or even national government programs using various tools for providing data literacy training, for example, government projects using workshops to solve quizzes, riddles, and questions about privacy and personal data protection (Seymoens et al., 2020), digital innovation competitions, hackathons or datathons for civil society or specific target audience, such as enterprises and start-ups (Kitsios and Kamariotou, 2023) or schools (Nikiforova, 2022; Wolff et al., 2019). Others point to establishing connections with other anchor institutions, such as NGOs (Salomão Filho et al., 2023) or public libraries (Gasco-Hernandez et al., 2022), as complementary ways to teach data literacy. Even others point to the need for joint educational programs using public-private funding but with continued political support (Calzati and van Loenen, 2023). Finally, Solano et al. (2023) point out that open data activities also create opportunities to develop citizens' technical data literacy, allowing them to understand and interact with data-driven decision-making processes. However, little attention has so far been paid to the effectiveness of various methods for acquiring data literacy. Thus, future research could analyse the methods used to acquire data literacy and their impact on both data use and data governance models. 5.2.2 Technical and governance aspects in public data ecosystems Another research stream focuses on analysing the technical and governance aspects of public data ecosystems. First, global challenges, such as those related to climate change or pandemic (and crises as a whole), have highlighted the need for international cross-border cooperation and collaboration in data sharing (Barthelemy et al., 2022). The increasing range of data coming from different sources increases the complexity of data ecosystems and requires a focus on data interoperability as a key element for effectively harnessing the value of these data (European Commission, 2020), especially regarding their reuse (European Parliament, 2019). However, although the standardisation for the data economy and data interoperability has gained momentum in recent years and raises many issues to be addressed, until now public data interoperability has received relatively little attention, especially in the field of open data ecosystems research (Ali et al., 2022). Among other reasons, the convergence between technological development and interoperability has not occurred completely in parallel, mainly due to the many complex aspects, social, economic, and political dimensions involved (Serrano et al., 2022), which made interoperability aspects fall behind. In this regard, one of the key areas to be analysed is how organisations can ensure that their internal IT landscape (data requirements and processes) is Citation: Lnenicka, M., Nikiforova, A., Luterek, M., Milic, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A. and Bolívar, M.P.R., 2024. Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems. Telematics and Informatics, p.102190. 1. References Aaen, J., Nielsen, J. A., & Carugati, A. (2022). The dark side of data ecosystems: a longitudinal study of the DAMD project. European Journal of Information Systems, 31(3), 288-312. 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