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Data economy is a recent phenomenon, raised by digital transformation and platformisation, which has enabled the concentration of data that can be used in economic purposes. However, there is a lack of clear procedures and ethical rules on how data economy ecosystems are governed. As a response to the current situation, there has been criticism and demands for the governance of data use to prevent unethical consequences that have already manifested. Thus, ethical governance of the data economy ecosystems is needed. The purpose of this paper is to introduce a new ethical governance model for data economy ecosystems. The proposed model offers a more balanced solution for the current situation where a few global large-scale enterprises dominate the data market and may use oligopolistic power over other stakeholders.

This is a conceptual article that covers theory-based discourse ethical reflection of data economy ecosystems governance. The study is based on the premise of the discourse ethics where inclusion of all stakeholders is needed for creating a transparent and ethical data economy.

This article offers self-regulation tool for data economy ecosystems by discourse ethical approach which is designed in the governance model. The model aims to balance data “markets” by offering more transparent, democratic and equal system than currently.

By offering a new ethically justified governance model, we may create a trust structure where rules are visible and all stakeholders are treated fairly.

In this paper, the authors compare the open government data (OGD) ecosystems of Mexico, Russia and the USA in an effort to extract some of the major points of similarity and differentiation between these countries and to trace how variations in these ecosystems may be related to context-specific historical problems and politics, particularly with regard to the possibility of sustained and institutionalized practice.

The authors take a comparative approach, using a set of concepts commonly applied to the three countries to analyze similarities and differences within this group. The authors gathered textual data and information, the searches for relevant documents guided by a set of concepts or criteria that are frequently used in studies of government’s open data readiness assessment.

The authors conclude by focusing on the very different national exigencies that have given rise to open data ecosystems in the three countries, the variations in policy vehicles and implementation schemes that have instantiated open data practices within the three ecosystems and the common challenges that each country faces in institutionalizing OGD programs beyond the tenures of their current executives.

OGD is an information policy with near global relevance and increasing application. Practitioners and scholars alike have used the concept of an “ecosystem” to guide their approach to implementing this policy and to theorizing its scope and benefits. The international comparison is original and adds to the current understanding of an ecosystem approach to OGD.

The Open API (application programming interface) architecture will play an important role in promoting future FinTech service applications; however, it involves user data, and the current specialization and progression are less visible. Therefore, an evaluation framework for Open API development in the FinTech service ecosystem is constructed in this study.

This study preliminarily selects the four most important key objects and factors of this ecosystem and conducts expert interviews to revise the evaluation framework. Then, this study uses the fuzzy analytic hierarchy process (FAHP) to evaluate the objects and their factor weights and finally uses the FAHP analysis results to further apply the evaluation based on distance from average solution (EDAS) approach to explore the strategy optimization scenarios.

According to the analysis results, the co-creation object and productivity object are the two most significant objects, with weights of 0.275 and 0.272, respectively. The analysis shows that FinTech-related companies expect to increase productivity through co-creation. Finally, the results also indicate that mobile payment is the best Open API application scenario in the FinTech service ecosystem, followed by online banking. These results illustrate strategic and management implications.

This study screens key evaluation criteria with a literature review and expert questionnaire interviews to process quantitative research. It can determine the weights of objectives and criteria to clarify the strength of influence between the objectives and criteria. Next, this study measures the probable performance of Open API applied in various FinTech service ecosystem scenarios.

This chapter provides an integrated overview of digital implementation in smart city ecosystems, focusing on the data ecosystem, the data value chain, and the digital implementation in smart cities. It acknowledges the opportunities and challenges presented by the growth of big data and its impact on delivering public services and coordinating local ecosystems in smart cities. This chapter introduces the concept of a smart city data ecosystem and explores the implementation of digital solutions, highlighting the role of local management and internal and external providers. It examines the digitalization of processes and services, technological infrastructure, and social media in the context of the smart city, emphasizing the interaction between local and national data ecosystems. This chapter incorporates interviews with smart city managers, policymakers, and technicians involved in the digital development process, offering valuable insights into the practical implementation and usage of data management within smart cities and the potential impact on urban development.

Big Data (BD) is not only a quintessential part of many economic activities but also has evolved into a veritable business ecosystem. However, most Big Data ecosystem (BDE) models have a technical, bottom-up focus and mostly lack the capability for a broad socioeconomic analysis. This paper identifies the Millennium Ecosystem Assessment (MA) as a useful, operational framework and uses a metaphorical analogy to adapt it for the BDE. The top-down approach adopted here allows for seeing the big picture of the BD ecosystem. Meeting “end-user needs” is the main objective of the proposed BDE framework.

The methodology of this paper consists of two parts. First, the MA is adapted for the BDE through a metaphorical analogy. Then, to operationalize and validate the proposed framework, it is applied to an emerging BD ecosystem.

In total, four types of services are offered in the BD ecosystem: provisioning information and products; regulating; cultural and supporting services. Direct and indirect drivers of change impact ecosystem processes such as BD service provision. Based on the assessment results, interventions can be devised to remedy problems, sustain the ecosystem or accelerate growth. The proposed BDE assessment framework is applied to an emerging BDE as an example of operationalization and validation of the proposed BDE framework.

The strengths of the proposed BDE framework is that, in contrast to existing frameworks that are technical and bottom-up, it is constructed top-down by a metaphorical analogy from the proven MA framework. It is a generic framework with the ultimate objective of meeting the “end-user needs” and does not focus on a single sector or firm. Also, the proposed BDE framework is multi-faceted and considers broad socioeconomic issues such as regulating, cultural and supporting services and drivers of change.

Covid has increased the usage of multisided digital platforms. For companies, this has become a business opportunity. Data usage on platforms requires that platform companies co-create services for common customers. In this case, the target is not to make the same value proposition but rather to use the resources such as data, knowledge, technology, or institutions in a complementary manner. Platforms are characterized as a combination of hardware and software including standards, interfaces, and rules making it possible for different ecosystem players to complement and interact in the ecosystem. Current ecosystems include several platforms that do not work without resource integration. The purpose of this study is to increase understanding what do we mean by resource complementarity in service ecosystems.

This study was done via an in-depth qualitative case study in which a health service ecosystem co-creating technological surgery innovation was used as a unit of analysis.

The authors’ findings suggest that key resource capabilities, to enable complementarity in service ecosystems, are motivation, knowledge, skills, data and complementary designed technology components.

The authors’ study increases theoretical understanding of what does one mean by construct of resource complementarity.

From a managerial perspective, it is shown that organizations need to develop adaptive capabilities to utilize internal and external competencies and resources and enable co-creative processes within the service ecosystem.

Very few empirical studies in the marketing literature have focused on multi-sided digital platforms and their resource complementarity in the data-driven healthcare ecosystem settings.

This paper aims to spark a debate by presenting the need for developing data ecosystems in Europe that meet the social and public good while committing to democratic and ethical standards; suggesting a taxonomy of data infrastructures and institutions to support this need; using the case study of Barcelona as the flagship city trailblazing a critical policy agenda of smart cities to show the limitations and contradictions of the current state of affairs; and ultimately, proposing a preliminary roadmap for institutional and governance empowerment that could enable effective data ecosystems in Europe.

This paper draws on lessons learned in previous publications available in the sustainability (Calzada, 2018), regions (Calzada and Cowie, 2017; Calzada, 2019), Zenodo (Calzada and Almirall, 2019), RSA Journal (Calzada, 2019) and IJIS (Calzada, 2020) journals and ongoing and updated fieldwork about the Barcelona case study stemming from an intensive fieldwork action research that started in 2017. The methodology used in these publications was based on the mixed-method technique of triangulation via action research encompassing in-depth interviews, direct participation in policy events and desk research. The case study was identified as the most effective methodology.

This paper, drawing from lessons learned from the Barcelona case study, elucidates on the need to establish pan-European data infrastructures and institutions – collectively data ecosystems – to protect citizens’ digital rights in European cities and regions. The paper reveals three main priorities proposing a preliminary roadmap for local and regional governments, namely, advocacy, suggesting the need for city and regional networks; governance, requiring guidance and applied, neutral and non-partisan research in policy; and pan-European agencies, leading and mobilising data infrastructures and institutions at the European level.

From the very beginning, this paper acknowledges its ambition, and thus its limitations and clarifies its attempt to provide just an overview rather than a deep research analysis. This paper presents several research limitations and implications regarding the scope. The paper starts by presenting the need for data ecosystems, then structures this need through two taxonomies, all illustrated through the Barcelona case study and finally, concludes with a roadmap consisting of three priorities. The paper uses previous published and ongoing fieldwork findings in Barcelona as a way to lead, and thus encourage the proliferation of more cases through Cities Coalition for Digital Rights (CCDR).

This paper presents practical implications for local and regional authorities of the CCDR network. As such, the main three priorities of the preliminary roadmap could help those European cities and regions already part of the CCDR network to establish and build operational data ecosystems by establishing a comprehensive pan-European policy from the bottom-up that aligns with the timely policy developments advocated by the European Commission. This paper can inspire policymakers by providing guidelines to better coordinate among a diverse set of cities and regions in Europe.

The leading data governance models worldwide from China and the USA and the advent of Big Data are dramatically reshaping citizens’ relationship with data. Against this backdrop and directly influenced by the General Data Protection Regulation (GDPR), Europe has, perhaps, for the first time, spoken with its own voice by blending data and smart city research and policy formulations. Inquiries and emerging insights into the potential urban experiments on data ecosystems, consisting of data infrastructures and institutions operating in European cities and regions, become increasingly crucial. Thus, the main social implications are for those multi-stakeholder policy schemes already operating in European cities and regions.

In previous research, data ecosystems were not directly related to digital rights amidst the global digital geopolitical context and, more specifically, were not connected to the two taxonomies (on data infrastructures and institutions) that could be directly applied to a case study, like the one presented about Barcelona. Thus, this paper shows novelty and originality by also opening up (based on previous fieldwork action research) a way to take strategic action to establish a pan-European strategy among cities and regions through three specific priorities. This paper can ultimately support practice and lead to new research and policy avenues.

This study aimed to present a model for open-data management for developing innovative information flow in Iranian knowledge-based companies (businesses).

The method was mixed (qualitative-quantitative) and data collection tools were interview and questionnaire. The qualitative part was done to identify the influential components in open data management (ecosystem) using the grounded theory method. A questionnaire was developed based on the results of the qualitative section and the theoretical foundations, and the quantitative section was conducted by analytical survey method and the model was extracted using factor analysis and the integration of the qualitative section.

Seven categories of entrepreneurial incentives, sustainable value, innovative features, challenges and barriers, actors, business model and requirements are the main categories that should be considered in open data management (ecosystem) with all categories of research have a significant relationship with open data management.

The study focused on open data management from an innovation paradigm perspective and its role in developing innovative information flow. The study aimed to identify the key components of the open data ecosystem, open-data value creation, and the need to use the “open data” approach to develop data-driven and knowledge-based businesses in Iran–an emerging approach largely ignored.

The rise of the digital economy has led to a focus on how to manage big data and business intelligence for entrepreneurial purposes. The aim of this chapter is to discuss how to plan for open innovative ecosystems that harness the potential of new data analytics techniques in order to progress society. This means concentrating on the increasingly complex world of data analytics in order to derive information about potential entrepreneurial opportunities. The role of knowledge management in influencing an open innovation ecosystem predicated on big data and computing acumen is stated. This helps to understand how the future of the global economy relies on an open data policy that encourages the sharing and dissemination of information. Implications for managers are also suggested that emphasize the role of innovation ecosystems, data and government.

The purpose of this study is to identify and discuss the role of intelligent materials in the emergence of new business models based on the Internet of Things (IoT). The study suggests new areas for further research to better understand the influences of material intelligence on the business models in industry-wide service ecosystems.

The study uses data from an earlier study of intelligent materials in the steel industry networks. The insights are based on 34 qualitative interviews among 15 organizations in the industry. The data are reanalyzed for this study.

The observations from the steel industry show how material intelligence can be harnessed for value creation in IoT-based business ecosystems. The results suggest that not all “things” connected to the IoT need to be intelligent, if information related to the things are collected, stored and shared for collaborative value creation among the actors involved in the business ecosystem.

The study discusses how IoT deployments allow businesses to benefit from the velocity and variety of information associated with things and guides future research to study the ways in which value is created through IoT-enabled business models.

Rather than focusing on improving the efficiency of the supply network, the study presents new paths for competitive advantages in the new IoT ecosystems.

The study contributes to the mounting research on the IoT by identifying and discussing the critical aspects of how IoT can transform business models and supply networks within end-to-end ecosystems.