Investigating the Challenges in the Implementation of Big Data Analytics Solutions to Influence the Retail Business Models

  • Osita Chukwuma

Student thesis: Doctoral Thesis


Big Data (BD) is characterised by three major attributes: Volume, Variety and Veracity. It holds immense potential and capabilities for businesses and individuals but is complex and cannot be managed by the conventional Information Technology (IT) systems. To harness its potentials, practitioners and researchers have evolved an uncommon set of IT systems called Big Data Analytics Solution (BDAS). This study explores how BDAS can be successfully
implemented in retail companies to influence a retail business model (RBM).

This study is a qualitative one that touches on four knowledge domains: Big Data, Business Operations, Project Management and Retailing, and begins with a structured review of the extant literature. Following the review of qualitative research methods, the constructivist variant of grounded theory methodology with a multiple case study method was carefully adopted for the study. This design is mainly suited to the research context in which the social processes and actions of actants feature significantly, and in which the objectives include theory development. Three companies in the United Kingdom’s retail sector were selected for the case study, including one of the largest retail companies in Europe. Twenty-five respondents were selected across the three case companies and were interviewed in 2019 and 2020, with follow-up sessions on the study’s findings review taking place in 2022. The study’s primary data were collected during the combination of face to face and teleconference interviews, and Nvivo 12 was the tool of choice in the collation and analysis of the data.

Six major themes emerged from the data analysis and underpin the central category of the findings, entitled Responsive Delivery Distillation (RDD). The six themes (organisational competency, delivery perspective, delivery approach, constraining factors (challenges), technology outcomes and business outcomes) with the evolved central category led to the development of a framework or model through which any BDAS may be successfully implemented in a retail company to impact the business model and deliver value.

This study established a comprehensive set of behaviours, processes and practices through which retail companies implement BDAS to impact the business model and deliver business value. The study makes six theoretical contributions to existing knowledge: extending existing theory on BDAS implementation challenges and success enablers through the new factors uncovered, evolving a BDAS implementation model, demonstrating the outcomes of BDAS implementation in a retail company and linking them to the RBM, and closing some identified contextual literature gaps. Additionally, the study makes a number of industrial contributions, of which the most significant is the evolved BDAS implementation model and accompanying guidelines that can be used as a toolkit/template for successful BDAS implementation in any organisation. It is hoped that the findings from this study will bolster the change of mindset of the decision-makers in the retail industry towards BDAS implementation and will help practitioners to understand the peculiarities of BDAS implementation in companies, as well as the challenges and success enablers.

Therefore, this thesis presents the details of the study on the Big Data Analytics Solutions (BDAS) implementation challenges and success enablers (CSEs), showing how a retail business model (RBM) is impacted. The study explores the pain points and triggers that act as precursors to BDAS selection and implementation, issues encountered during implementation and adoption, components of the RBM impacted, value created and benefits delivered.
Date of Award3 May 2023
Original languageEnglish
SupervisorAbhijit Sharma (Main Supervisor)

Cite this