Bridging the gap: factors driving retail analytics adoption in traditional retail businesses

Moloi, Shaun

Bridging the gap: factors driving retail analytics adoption in traditional retail businesses

Thesis / Dissertation

2025

Publisher

University of Cape Town

Department

Department of Information Systems

Faculty

Faculty of Commerce

Abstract

This thesis investigates how South Africa's traditional retail sector adopts advanced analytics amid infrastructural, cultural, and economic constraints. Guided by the Technology Organisation-Environment (TOE) framework and Diffusion of Innovations (DOI) theory, it addresses a critical gap in understanding the interplay of legacy systems, limited IT resources, and stringent regulatory demands on data-driven decision-making. A multi-case qualitative study of three large and mid-tier traditional retail chains in South Africa was conducted, involving 15 participants including store managers, IT directors, data analysts, marketing executives, and senior decision-makers. Findings highlight how outdated point-of sale infrastructure, patchy internet connectivity, and frequent power outages impede real-time analytics. At the organisational level, siloed structures and staff concerns over job displacement slow adoption, despite growing leadership support for pilot projects and training programmes. External forces, particularly the Protection of Personal Information Act (POPIA), socio economic pressures such as high unemployment, and intense competition, further complicate large-scale analytics initiatives. Even promising solutions like loyalty cards and semi automated storefronts have yielded uneven returns when confronted by crime risks, transient consumer behaviour, or landlord restrictions. Nonetheless, incremental deployments (e.g., “mobile-first” analytics and phased cloud migrations) emerge as viable stopgaps to overcome resource and connectivity challenges. Findings underscore the importance of executive sponsorship, cross-functional collaboration, and targeted upskilling in fostering a data-centric culture. They also reveal that retailers can simultaneously advance sustainability goals, such as cutting waste and optimising energy usage, by harnessing predictive models that align with cost-saving strategies. Ultimately, this thesis argues that successful analytics adoption in emerging markets hinges on aligning technological ambitions with infrastructural realities and social imperatives. By integrating global best practices with localised approaches, retailers can enhance competitiveness, improve operational efficiencies, and contribute to inclusive, data-driven growth across South Africa's evolving retail landscape