Upcoming Doctoral Dissertations

" Modern organizations recognize that competitive advantage hinges not just on what employees know, but on who they know. Despite efforts to manage talent networks, many leaders, particularly in large organizations that depend on strong internal labor markets, continue to rely on heuristic approaches, leaving them vulnerable to disruptive shocks. While social network techniques, organizational science, and interpersonal trust research have advanced significantly, no scalable, non-intrusive, and complete method has emerged to elicit and govern these interpersonal knowledge networks. This study addresses the need for a holistic, data-driven framework to understand, manage, and shape the interpersonal knowledge networks that underlie organizational performance. It integrates and extends research on trust formation, network weighting, cognitive capacity, resilience modeling, and agent-based policy simulation into a single coherent approach. " Read more...

" The matrix of today's world is fundamentally graph-based: from the interconnected web of supply chains to the intricacies of social networks, and from the sprawling expanse of online consumer behavior to the intertwined organizational collaborations. These graph structures encapsulate a wealth of patterns and insights waiting to be decoded. Although deep learning models have become crucial for managing the vast volumes of data that modern commerce produces, the potential of graph-based deep learning techniques remains largely untapped. A significant challenge is the opaqueness of these models that hampers informed decision-making, as stakeholders in the business realm grapple with the complexities of models they cannot elucidate. Consequently, there is a pressing need to architect explainable graph-based deep learning models to foster better, data-driven decisions." Read more...