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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 study aims to investigate whether and how digital transformation, in terms of digital collaboration, joint efforts with internal/external partners to achieve common goals and the adoption of digital tools supporting this practice, affect social innovation capital in the context of small innovative enterprises (SIEs).

The research hypotheses derived from the analysis of the literature, evaluating how sharing resources, sharing intensity and digital patterns affect the collective capacity of SIEs to innovate, were investigated by applying multiple regression analysis. Data were retrieved from a sample of Italian SIEs through an online survey.

The main findings suggest that the propensity to spread resources and the sharing intensity positively affect the collective capacity of SIEs to innovate. Also, the effect of resources sharing on collective innovation increases as more digital patterns are used as tools. The connection is weaker for the intensity of resources sharing.

The study is conducted on Italian SIEs, a particular cluster of small and medium enterprises (SMEs). It would be interesting to compare and contrast the results of an analysis of a large sample of international companies, of different sizes and belonging to digital and non-digital sectors.

The results enrich the existing literature on social innovation capital, by clarifying its competitive benefits on the characteristic context of the SIEs and underlining the mediating role of the digital patterns.

Building upon the insights of the resource-based view and internationalization theories, the purpose of this paper is to examine the role networks play in SMEs' readiness for internationalization. By investigating three different types of knowledge sharing, namely economic-setting, market-specific and customer-specific, the study analyzes their effect on SMEs' readiness for internationalization.

The four research hypotheses derived by from the analysis of the literature have been investigated by applying the multiple regression technique. By means of an online survey, 300 valid questionnaires were collected and information from a sample of Italian SMEs belonging to 11 agro-food consortia have been analyzed.

The results suggest that SMEs' readiness for internationalization could be supported by sharing customer-specific, market-specific and economic-setting knowledge with other firms operating within the same agro-food consortium. Additionally, data analysis highlights a negative relation between the risk perception in the process and readiness for internationalization, suggesting the importance of knowledge sharing in reducing the criticality issues of being a newcomer entering international markets.

From a theoretical perspective, this study aims to fill the gap in knowledge management and international relationship marketing literature. Since proposes a combination of different kinds of knowledge that contribute to reducing the criticalities SMEs must face by identifying useful information to be conveyed within the network. From a managerial perspective, the study provides useful insights for the agro-food sector, highlighting how experiential and network knowledge constitutes a pre-condition for managing internationalization complexity and discovering opportunities on foreign markets.

This paper analyzes the effect of circular economy practices on firm performance for a circular supply chain and explores the moderating role that big-data-driven supply chain plays within these relationships.

This study uses data collected through an online survey distributed to managers of 378 Italian firms that have adopted circular economy principles. The data are processed using multiple regression analysis.

The results indicate that the three categories of circular economy practices investigated – namely circular economy supply chain management design, circular economy supply chain relationship management and circular economy HR management – play a crucial role in enhancing firm performance from a circular economy perspective. A big-data-driven supply chain acts as a moderator of the relationship between circular economy HR management and firm performance for a circular economy supply chain.

This study makes a number of original contributions to research on circular economy practices in a big-data-driven supply chain and provides useful insights for practitioners. First, it answers the call to capture digital transformation trends and to extend research on sustainability in supply chain management. Second, it enhances the literature by investigating the relationships between three different kinds of circular economy supply chain practices and firm performance. Finally, it clarifies the moderating role of big data in making decisions and implementing circular supply chain solutions to achieve better environmental, social and economic benefits.