Sustainable resource acquisition path: A dynamic model of embedded entrepreneurial network governance under uncertainty

Abstract

When dealing with complex entrepreneurial network problems, such as sustainable resource flows, the highly uncertainty in environment that brings cognitive bias in entrepreneurs' decision-making means which entrepreneurs who are expert in using the entrepreneurial network can acquire sustainable resources by reducing external interference. To answer a link decision problem of the role played by network features in the entrepreneurial process of resource acquisition, we introduce an exploratory model design by the Naïve Bayesian classification with EM (Expectation Maximization) algorithm based on SNA (Social Network Analysis) theory that is focused on filling the missing data of uncertainty, in order to describe the path of entrepreneurial network resources acquisition. An inter-dynamic model has established between network structure and the value of resources to predict linking probabilities. By expectation-maximization method for Naïve Bayesian, the paper concludes with an empirical evaluation to verify the accuracy of resource acquisition prediction, in 201 entrepreneurial companies, and application in uncertain environmental network governance decision-making problem regarding the selection of optimal resource paths for creating a new company. We hope which this work can stimulate a broader research agenda focused on the impact of network structure on entrepreneurs' decision-making under uncertainty, especially for developing countries where has a new round of entrepreneurial enthusiasm with high uncertainty.