Entrepreneurship and open data are key elements in the sustainable development field, improving economic, social, and environmental dimensions. However, entrepreneurship and open data are barely studied together in the literature from a theoretical perspective. Therefore, this study identifies the main themes in the previous studies and proposes a conceptual model for analyzing entrepreneurship through open data. For this purpose, a descriptive analysis and a co-word analysis were performed. Results show that the subject is multidisciplinary, and the main theme of study is how different agents reuse information released by public administrations to generate new entrepreneurial initiatives, especially novel business models associated with new mobile applications. Open data sources, innovation, and business models are studied as critical factors for analyzing entrepreneurship through open data. Likewise, a conceptual model is presented and emerging themes for future research are proposed. Among them, the importance of encouraging collaboration between different agents in the open data ecosystem for service development and improvement is emphasized. Our study identifies an emerging theme that is still in an early phase: The study of sustainable entrepreneurship through open data as a value creation initiative to address global sustainable development.

Purpose: This paper aims to analyse the open data business models (ODBMs) as a source of knowledge and innovation to generate economic and social value. A framework for understanding ODBMs is presented. First, the knowledge structure of the ODBMs literature is identified. Second, a conceptual model for analysing the ODBMs is proposed. And finally, the future trends in ODBMs research are discussed. Design/methodology/approach: In this paper, co-word analysis is performed to identify the topics related with ODBMs. Findings: The ODBMs structure of knowledge is articulated in five themes: business model, smart city, business ecosystem, decision-making and innovation. Based on these results, a five-step model for analysing ODBMs is proposed. Finally, a discussion of the future trends of ODBMs focussed on a knowledge management perspective, open data ecosystems and business intelligence is presented. Originality/value: The paper presents a picture of what, where, for whom and for what ODBMs have been studied and develops a new model to explain the value creation process of ODBMs. Taking a step further, applying the principles and models of knowledge management and business intelligence to ODBMs is also recommended in order to transfer and transform open data into valuable knowledge that can be used for developing apps. In that context, the importance of encouraging collaboration between different agents in the so-called open data ecosystem is presented.

In this paper, we review some characteristics of the literature that studies the uses and applications of open data for open innovation. Three research questions are proposed about both topics: (1) What journals, conferences and authors have published papers about the use of open data for open innovation? (2) What knowledge areas have been analysed in research on open data for open innovation? and (3) What are the methodological characteristics of the papers on open data for open innovation? To answer the first question, we use a descriptive analysis to identify the relevant journals and authors. To address the second question, we identify the knowledge areas of the studies about open data for open innovation. Finally, we analyse the methodological characteristics of the literature (type of study, analytical techniques, sources of information and geographical area). Our results show that the applications of open data for open innovation are interesting but their multidisciplinary nature makes the context complex and diverse, opening up many future avenues for research. To develop a future research agenda, we propose a theoretical model and some research questions to analyse the open data impact process for open innovation.

Open data and open innovation are two topics currently attracting the attention of academics. But no previous studies consider these fields in combination while using a bibliometric approach. Thus, the aim of this paper is to understand the relationship between open innovation and open data. Two research questions have been formulated: 1) What are the main topics studied in the literature that combine both lines of research? and 2) How can the open innovation paradigm be integrated in the open data impact process? To address the first question, a co-word analysis is used to identify the main topics investigated in the open innovation and open data literature. Based on our results, to answer the second research question, the topics are grouped and analyzed considering a model of the open data impact process. Finally, some future research lines to analyze the open data impact process for open innovation are presented. For example, future research could focus on questions such as (1) What kind of applications can be created through the reuse of open data?; and (2) How do open innovation processes influence the reuse of open data?.

Open innovation, which blurs the boundaries of organizations using inflows and outflows of knowledge to boost their innovation processes, has transformed the innovation paradigm, evolving from higher degrees of protectionism to cooperative relationships. Nevertheless, frequently the management of the huge amount of information and data generated in an open innovation ecosystem requires the use of information and communication technologies. In this context, artificial intelligence can be a major help to profit from all the opportunities derived from open innovation. Considering the growing body of academic literature dealing with the use of artificial intelligence tools in the context of open innovation environments, the objective of this article is revealing the main references, the academic trends and the hottest topics dealing with this subject, disentangling the knowledge structure of the research through a bibliometric analysis carried out over 63 papers selected from Web of Science database, using both co-word analysis and bibliographic coupling. The recent burst in the academic production anticipates a potentially massive interest in this topic, which is studied by the literature at three different levels (operational, managerial, and social). This study reveals the existence of relevant research opportunities, specially related with the management of the potential conflicts that may stem from the fuzzy ownership of the data generated by an artificial intelligence, and the roles of the different agents in such context.