Network Driven Social Responsibility of Small Enterprises Through Advanced Data Capture Technologies

Advanced data capture technologies (e.g., IoT devices, social media analytics) have become popular as they make business information accessible to large networks. These technologies provide small enterprises with a set of data-driven insights that trigger the formation of strategic networks as there are thousands of data points, interactions, with a multitude of inherently complex relationships between entities, which we model as an interconnected network. Managers may utilize network analysis understanding to reveal the true potential of CSR initiatives as this approach provides many valuable insights including identification of key stakeholders and their importance to community engagement and partnership formation on the network. The goal of this study is to identify and examine the structural and dynamic aspects of business networks, which are considered as critical factors in the largest component of the network. Two important pieces of information add to our understanding of network-driven CSR: the role (central nodes) and connectivity. We observe that the only consistent pattern common to business networks as they evolve into a sustainable CSR network can be best described as a scale-free function or a “power-law” function. We also find that one of the most significant features of business networks examined on this dataset appears to be clustering. That is, companies tend to form clusters among members of different groups. The findings further show that high centrality leads to communities of few key players and on the other hand high connectivity leads to communities of many members. We articulate managerial implications of network-driven CSR with respect to resource allocation and strategic partnerships as well.