Twenty-third International Conference on Knowledge, Culture, and Change in Organizations

Abstract

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. 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 global 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. The findings provided original insights into the evolution of the data and analytics function, the characteristics of an effective Virtual Mirroring-Based Learning design, and the relationship between resilience-based workstyles and brokerage roles in social networks. The applied and theoretical contributions of this applied research provide a template for practitioners while advancing the theory and measurement of resilience in organzations.