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This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation.

This study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014–2019 and were investigated using an ordinary least squares regression analysis.

Because of its twofold lens based on both knowledge management and OI, this study sheds light on OI collaboration modes and highlights the crucial role they could play in innovation. In particular, the results suggest that OI collaboration modes have a strong effect on innovation performance, stimulating the search for RKE.

This study furthers a deeper understanding of RKE, which is shown to be an important mechanism that incentivizes firms to increase their efforts in the innovation process. Further, RKE supports firms in taking full advantage of the innovative knowledge they generate within their inter-organizational network.

The purpose of this paper is to propose a new statistical approach to evaluate complex open innovation projects on a quantitative basis. In certain circumstances, open innovation entails a radical change of policy that involves various different functions of a company such as R&D, production, and management over a period of years and gives rise to mechanisms of mutual interaction with several business partners, such as collaboration with other companies, universities and R&D institutions, and new suppliers. Then, the question arises of how to measure the impact of such complex open innovation processes on the overall performances of companies.

A holistic case study is applied to analyze the effect of open innovation projects on a corporate company’s stock price dynamics. The scope is to identify two different scenarios pre- and post-adoption of an open innovation model by a multinational company, Fujifilm. In particular, a stochastic model, namely the log-normal model, is applied along with three statistical tests: Kolmogorov-Smirnov, Cramer von Mises, and F-test for equal variances, in order to verify if the adoption of an open innovation model causes any significant change in the stock price dynamics of the corporate company.

From the findings emerges evidence that open innovation projects have a moderate effect on Fujifilm’s stock price dynamics, but a greater improvement of the perception of Fujifilm’s stock value. This enhances the management and financial literature review by offering a novel, empirical perspective on the effect of the adoption of an open innovation model on a corporate company’s stock price dynamics.

This research is limited to a single case study, but it can be extended to other stock market companies and therefore improve on the present study.

An original application of Kolmogorov-Smirnov tests to detect and measure the differences between the two regimes of pre-open innovation and post-innovation regimes.

The study aims to explore the interplay between open innovation and intellectual property. Differently from previous studies, we argue that open innovation fosters firm's patenting activity.

We use linear regression analysis to test model's hypotheses. Data are drawn from the Eurostat statistics and refer to a large sample of European firms (NACE Rev.2).

The findings confirm that open innovation fosters patenting activity in health care, also thanks to huge governments' expenditures in this market.

The study focuses solely on European firms and it adopts a traditional linear approach. So, we cannot exclude that different dynamics may occur across European borders. Future research should address this concern by focusing on multi-country comparative studies.

Open innovation is the most suitable model for health industry, because it improves both innovation performance and intellectual capital of firms.

The study tackles an existing gap of the literature by considering how the presence of large customers impacts the strength of intellectual property protection.