Open Access Dissertation
Date of Award
Industrial and Manufacturing Engineering
Large multinational organizations are struggling to adapt and innovate in the face of increasing turbulence, uncertainty, and complexity. The lack of adaptive capacity is one of the major risks facing such organizations as the rapid change in technology, urbanization, socio-economic trends, and regulations continues to accelerate and outpace their ability to adapt. This is a resilience problem that organizations are addressing by investing in Data and Analytics to improve their innovation and competitive capabilities. However, Data and Analytics projects are more likely to fail than to succeed. Competing on data and analytics is not only a technical challenge but also a challenge in promoting collaborative innovation networks that are based on two key characteristics of resilient systems. One characteristic is the ability to learn while the second is the ability to foster diversity.
In this study, we examine how a newly-established Data and Analytics function has evolved over a one-year period. First, we conduct a baseline survey with two sections. The first section captures the structure of Innovation, Expertise, and Projects networks using network science techniques. In the second section we extract four resilience-based workstyles that provide a behavioral representation of each phase of the Adaptive Cycle Theory. Following the survey, we conduct a controlled experiment where the Data and Analytics population is divided into four groups. One group acts as control mechanism while the remaining three groups are exposed to three different Virtual-Mirroring-Based Learning (VMBL) interventions. A virtual-mirror, which is a visualization of an employee’s own social network that provides a self-reflection as a learning process. The premise is that exposure to such self-insights leads to a change in collaborative behavior. After a period of nine months, the baseline survey is repeated and then the effects of the interventions are analyzed.
The findings provided original insights into the evolution of the Data and Analytics function, the characteristics of an effective VMBL design, and the relationship between resilience-based workstyles and brokerage roles in social networks. The applied and theoretical contributions of this research provide a template for practitioners while advancing the theory and measurement of resilience.
Raad, Nabil, "Understanding The Impact Of Virtual-Mirroring Based Learning On Collaboration In A Data And Analytics Function: A Resilience Perspective" (2019). Wayne State University Dissertations. 2180.