Unlocking the dynamics of open innovation: A conceptual exploration

Abstract

This study explores the complex domain of open innovation, a conceptual framework in which the word "open" signifies sharing tacit organisational knowledge. Organisations increasingly expand their scope outside conventional bounds by using open innovation approaches and models to acquire necessary resources. The main aim of this academic undertaking is to examine informal networks that arise outside traditional contractual agreements and then analyse the tangible structure of these networks. The study's primary objective is to thoroughly examine, compare, and analyse nonlocal centrality measures, including global and local viewpoints, within a conceptual framework. This research investigates the utilisation of network analysis methodologies, such as Degree Centrality, Closeness Centrality, Eigenvector Centrality, Betweenness Centrality, Modularity, Community Detection, Vote Rank, and Digital Flaneurs, through the implementation of the Python NetworkX Programming Language. This research highlights its conceptual relevance as a significant and considerable addition to the current body of knowledge. This study provides a fundamental basis for further investigating and comprehending the complex interplay of nonlocal centrality measurements, dynamic capabilities, and open innovation tactics. Therefore, this study is a noteworthy contribution to the scholarly body of knowledge, presenting useful perspectives on the complex relationship between open innovation approaches, dynamic capabilities, and nonlocal centrality measurements. Furthermore, this research adds to the wider scholarly discourse on how organisations effectively manage the intricate challenges of the modern digital environment to achieve innovation and maintain a competitive edge.