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PATHWAYS TO ENTERPRISE RESILIENCE
Resilience is studied as a systemic property in several disciplines such as engineering, psychology, systems biology, and ecological sciences. Yet, the system view on resilience is not pervasive in management science. This dissertation is on Enterprise Resilience, which is an emerging topic within the fields of organization and management science. Corporate enterprises are viewed as type 1 complex adaptive systems (CAS) operating within an external business environment. Thus, perturbations occurring in the environment affect enterprises, whose resilience then depends on their adaptive response to them. Therefore, the focus is on system perturbances and on investigating drivers of the enterprises’ adaptive response. As a result, enterprise resilience is more granularly defined as an enterprise’s ability to continually remain valuable to stakeholders by simultaneously managing short-term shocks and long-term stressors. This re-definition brings forth an actionable pathway to enterprise resilience- the pursuit of improved management of the enterprise’s risk and growth management functions.
Two challenging issues plaguing the risk and growth functions are the lack of a comprehensive understanding of risks (especially of unknowns) and their inter-connections, and a weak link between risk management and the enterprise’s growth strategy intended to continually and increasingly generate value. This work addresses both issues via the development of an enterprise-agnostic comprehensive risk typology, and by building a conceptual link between risk and growth strategy through the business model construct and its use in the study of repeatable patterns of innovation. Therefore, this work develops one pathway toward enterprise resilience i.e., via improved risk management and systematic growth management. Furthermore, it advances knowledge by bridging the theoretical conceptualization of an enterprise as a CAS1 into actionable methods for practice in the form of risk management tools and systematic innovation frameworks that aid the enterprise’s adaptive response.
The interdisciplinary dissertation develops hypotheses and employs appropriate qualitative and quantitative methods to test them. Overall, a theory building process is undertaken using the constructionist school of thought and using methods based in inductive logic such as the scholarship of integration, thematic analysis, and case studies. Additionally, to achieve wide and comprehensive coverage, data-driven quantitative methods using advanced computing such as data mining, machine learning, and natural language processing are employed.
Doctoral Dissertation Research in DRMS: Building a comprehensive understanding of enterprise risks and their interdependencies for improved risk-intelligence
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