Purpose: – The purpose of this paper is to identify and describe the most prominent research areas connected with “Big Data” and propose a thorough definition of the term. Design/methodology/approach: – The authors have analysed a conspicuous corpus of industry and academia articles linked with Big Data to find commonalities among the topics they treated. The authors have also compiled a survey of existing definitions with a view of generating a more solid one that encompasses most of the work happening in the field. Findings: – The main themes of Big Data are: information, technology, methods and impact. The authors propose a new definition for the term that reads as follows: “Big Data is the Information asset characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value.” Practical implications: – The formal definition that is proposed can enable a more coherent development of the concept of Big Data, as it solely relies on the essential strands of current state-of-the-art and is coherent with the most popular definitions currently used. Originality/value: – This is among the first structured attempts of building a convincing definition of Big Data. It also contains an original exploration of the topic in connection with library management.

Purpose: Startup companies represent a powerful engine of open innovation (OI) processes. The purpose of this paper is to represent a first step in building a map of the state-of-the-art knowledge of the “startups in an OI context” phenomenon. Through the selection and analysis of relevant literature, this study aims at deepening our understanding of the theme and at providing directions for future research. Design/methodology/approach: By using an explicit method for the review (Pittaway et al., 2004) the authors selected a set of papers, which cover the knowledge domain object of this study. In total, 41 articles about “startups and OI” have been selected and the full papers have been analysed. Findings: The analysed literature has been synthesized in seven sub-topics, which have been evaluated as the most relevant in explaining the phenomenon of startups in relation to OI. Implications for research, for managers and for policy makers conclude the paper. Practical implications: The review produced valuable knowledge for both managers and policy decision-makers. The paper allows a better understanding of the role of startups in OI processes. This improved understanding can help managers of large firms as well as policy makers involved in OI in making their decisions. Besides, implications of OI strategies for startup managers have been singled-out. Originality/value: Startup companies are intrinsically open organizations, necessarily engaged in innovation processes. Research at the intersection between the themes of OI and startups is gaining momentum. This review of the literature represents the first attempt to organize the scientific knowledge related to the intersection between the startups and OI phenomena systematically.

Although Big Data is a trending buzzword in both academia and the industry, its meaning is still shrouded by much conceptual vagueness. The term is used to describe a wide range of concepts: from the technological ability to store, aggregate, and process data, to the cultural shift that is pervasively invading business and society, both drowning in information overload. The lack of a formal definition has led research to evolve into multiple and inconsistent paths. Furthermore, the existing ambiguity among researchers and practitioners undermines an efficient development of the subject. In this paper we have reviewed the existing literature on Big Data and analyzed its previous definitions in order to pursue two results: first, to provide a summary of the key research areas related to the phenomenon, identifying emerging trends and suggesting opportunities for future development; second, to provide a consensual definition for Big Data, by synthesizing common themes of existing works and patterns in previous definitions.

Purpose: – The purpose of this paper is to identify the recurrences in the empirical evidences that link open innovation (OI) actions and innovation performance in European countries. It provides managers with useful strategic suggestions, emphasizes the limitations of the state of the art, and recommends future directions of research.Design/methodology/approach: – The authors systematically reviewed empirical articles linking OI actions and innovation performance in European countries, published on peer reviewed journals from January 2003 until May 2013. The authors organized the evidences according to a novel taxonomy grounded in the literature.Findings: – The paper shows an increasing interest in the research of empirical evidence regarding OI and innovation performance. Nonetheless, evidence of the role played by outbound OI activities are extremely rare. The authors found that process innovations are more likely to benefit from coupled OI activities rather than inbound activities. Moreover, the effect of coupled depth actions on both product and process innovation performance was always positive in the reviewed articles. The authors also discuss how scholars measure innovation performance, pointing out the criticalities.Research limitations/implications: – The paper allows analysing the empirical evidences found in the literature, emphasizing the limitations of the state of the art and recommending future directions of research.Practical implications: – The systematization of the empirical evidences found in the European literature provides managers with useful strategic suggestions to improve their organizations’ innovation performances.Originality/value: – The paper contains a complete and extensive analysis of empirical OI literature with respect to European countries. The articles and their findings are organized according to a novel taxonomy useful to identify evidences and recurrences in a synoptic manner.