Dear Colleagues,

In recent decades, the evolution of the global competitive scenario has been increasingly pushing organizations to make the boundaries of their innovation processes permeable to inflows and outflows of knowledge, according to the principles of the open innovation paradigm [1]. Indeed, the recent literature has broadly investigated the strengths and weaknesses related to the adoption of the open innovation paradigm by organizations, from multiple perspectives e.g., [2,3].

However, in recent years, the rise of digitalization has been driving organizations in rethinking and, generally, enhancing their open innovation strategies [4]. In fact, thanks to the digital transformation of businesses, organizations have been implementing radical changes in their activities, processes, and capabilities [5]. Furthermore, the higher availability of data (so-called big data), as well as the opportunity to access continuous, reliable, and timely data streams [6], supports organizations in developing further knowledge that can be leveraged to stimulate their innovation processes under the open innovation paradigm.

In addition, data can be continuously exchanged with other organizations, hence favoring the interconnection between different players that may generate an open innovation ecosystem [7]; in turn, this will open a radically new perspective on the adoption of the open innovation paradigm.

In this scenario, it is worth highlighting that while, on one hand, big data could be interpreted as an enabler of open innovation strategies, on the other hand, organizations may be called to face issues related to the high availability and variety of data that may increase the difficulties in transforming data into useful information [8]. Furthermore, even though the recent evolution of big data analytics reveals itself as particularly relevant in these situations, still, a greater understanding about the use of big data to support the effectiveness of open innovation strategies is required both from a theoretical and a practical point of view [8].

Accordingly, the aim of this Special Issue is to advance our understanding about how big data may be employed to favor the adoption and the effectiveness of open innovation strategies, by stimulating and collecting state-of-the-art theoretical and empirical research. We welcome contributions adopting different and original theoretical perspectives and methodologies deemed useful to shed further light on this Special Issue’s topic. Furthermore, we also welcome studies discussing exemplar cases of organizations that successfully implemented big data to enhance their open innovation processes, with particular interest in the use of big data to sustain the transition from a closed innovation approach to an open innovation one.

References

[1] Chesbrough, H.W. Open Innovation: The new imperative for creating and profiting from technology. Harvard Business Press, 2003.

[2] Dahlander, L; Gann, D.M. How open is innovation? Research Policy, 2010, 39, 699–709.

[3] Bogers, M.; Chesbrough, H.; Moedas, C. Open innovation: Research, practices, and policies. Calif. Manag. Rev. 2018, 60, 5–16.

[4] Urbinati, A.; Chiaroni, D.; Chiesa, V.; Frattini, F. The role of digital technologies in open innovation processes: An exploratory multiple case study analysis. R&D Manag. 2020, 50, 136–160.

[5] Ardito, L.; Messeni Petruzzelli, A.; Panniello, U.; Garavelli, A.C. Towards Industry 4.0: Mapping digital technologies for supply chain management-marketing integration. Bus. Process. Manag. J. 2019, 25, 323–346.

[6] Pigni, F.; Piccoli, G.; Watson, R. Digital data streams: Creating value from the real-time flow of big data. Calif. Manag. Rev. 2016, 58, 5–25.

[7] Xie, X.; Wang, H. How can open innovation ecosystem modes push product innovation forward? An fsQCA analysis. J. Bus. Res. 2020, 108, 29–41.

[8] Del Vecchio, P.; Di Minin, A.; Messeni Petruzzelli, A.; Panniello, U.; Pirri, S. Big data for open innovation in SMEs and large corporations: Trends, opportunities, and challenges. Creat. Innov. Manag. 2018, 27, 6–22.

Dr. Umberto Panniello
Dr. Angelo Natalicchio
Guest Editors

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• open innovation
• big data
• big data analytics
• innovation processes
• external knowledge