Event Forecasting in Organizational Networks: A Discrete Dynamical System Approach

Abstract

Both inter- and intraorganizational networks draw the attention of researchers and practitioners from various disciplines who view them as the fabric of the socioeconomic world. The network perspective is believed to successfully model most of the socioeconomic phenomena, which, in combination with the prospects of continuously advancing tools for automated data mining and machine learning, gives a tempting promise to effectively forecast socioeconomic events occurring in our societies and businesses. Despite their significance, the topic of event forecasting in the context of organizational networks appears unexplored. Therefore, the objective of this study was (1) to fill the theoretical gap by proposing a mathematical model for organizational network event forecasting, rooted in the social science to remain consistent with the theory, and (2) to experimentally evaluate how the model performs on real data and validate if the results support its use in practical applications. An implementation of the proposed model, based on a decision tree classifier, achieved a prediction accuracy of 87% on a longitudinal data sample and thus demonstrated the practical usability of the model.