Decision-Making in Data-Intensive Environments and Its Impact on Organisational Design: Dynamic Capabilities Approach

Karami, Hadi (2023) Decision-Making in Data-Intensive Environments and Its Impact on Organisational Design: Dynamic Capabilities Approach. Doctoral thesis (PhD), Manchester Metropolitan University.

Preview

Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview

Abstract

The main purpose of this research is to investigate how organisations that are dealing with large datasets can improve their decision-making processes by developing new and reconfiguring existing capabilities. Business environments are getting increasingly dynamic and data-intensive because of emerging technologies and advances in data science, information and communication technologies, which require enterprises to make regular and faster decisions. In this regard, one of the new phenomena that has revolutionised businesses is big data providing organisations with opportunities and also challenges. Based on big data’s capability to provide useful information for dynamic decision-making processes, this study aims to investigate how big data influences decision-making processes and, consequently organisational design. It seeks to identify how organisations change and design either of those processes and their organisational structure to make sense of big data. This study uses an integrated approach of dynamic capabilities as a lens to identify the sources of dynamism in organisations. This approach takes into account three levels of dynamism including individual, interpersonal and corporate levels, which are employed in the process of data analysis. In terms of methodology and methods, this study uses a multiple case-study approach in order to gain rich and illuminating data about each case and the phenomenon under investigation. The cases of this study are chosen from organisations that are using large datasets as a source of information for decision-making based in the UK. Nine cases were studied, from which twelve people were interviewed. Interviewees included business intelligence and analytics experts and managers who have a deep understanding of organisational and information-processing mechanisms. The reasoning technique is abductive, meaning that some of the concepts are taken from the literature and constant comparison are made between literature and data to identify emerging concepts. This study, contributes to decision-making theory by providing insights about dynamic decision-making in the context of big data and a better understanding of organisational strategies for working with and leveraging value from big data. In addition, for the practical aspect, it contributes to guiding practitioners in evaluating their organisations to inform improvement to become better enabled for big data-driven decision-making.