Electronic Theses and Dissertations (PhDs)

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Browsing Electronic Theses and Dissertations (PhDs) by Keyword "Artificial intelligence (AI)"

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    The development of an artificial intelligence adoption framework for food retail marketing in South Africa.
    (University of the Witwatersrand, Johannesburg, 2023) Mpunzi, Sinenhlanhla; Saruchera, Fanny
    Industry 4.0 has taken the world by storm and impacted how we live, work, and behave. Focused on business transformation and revolution, industry 4.0 has given birth to one of the most celebrated inventions, Artificial Intelligence (AI). AI has provided endless opportunities for businesses respective of industry. However, AI adoption frameworks have been limited as AI is a new phenomenon in South Africa. Previous studies in food retail marketing have identified low interest in AI adoption due to a lack of guidance. Therefore, the study aimed to develop an AI adoption framework for the food retail marketing industry in South Africa. In achieving the main objective, the study examined the influence of AI on marketing strategy outcomes, the influential determinants of AI adoption in the food retail marketing industry, and the major AI technologies adopted by retail marketers and assessed the moderating effect of competitive intensity. Guided by the Innovation Diffusion Theory, Technology-Organization-Environment framework, Institutional and Productivity Paradox theories, the study established the influential factors that determine AI adoption. Using literature, theoretical constructs were drawn on AI technologies adopted, the marketing mix components (4Ps), the competition intensity elements, and strategy outcome measures. The study adopted the quantitative research method. Data were collected through self-administered questionnaires distributed to 380 respondents from food retail firms and marketing agencies with backgrounds in marketing, management, computer science, analytics, and sales. Data was analysed through SPSS version 27, where several analysis procedures were performed, such as CFA, EFA, model fitness and predictive power assessment. Partial Least Squares-Structural Equation Modelling was performed to examine the significance and ascertain relationships. The study found that systems complexity, finance, firm size, perceived AI risk, vendor participation, and external pressure influenced AI adoption in retail marketing. The research also discovered that AI technologies adopted (robots, chatbots, data analytics systems, CRM, and communication tools) improve marketing mix components by influencing the price, placement of products, R&D procedures, and sales techniques. The study found that competition intensity significantly moderates the relationship between AI adoption and marketing strategy outcome. This study further emphasizes the importance of integrating AI technology in the retail food industry, given that it enhances their marketing mix capabilities with direct positive implications on their marketing outcomes. It is evident that decision-makers need to re-strategize and pivot towards innovation integration. Therefore, the study recommends that food retail marketers adopt AI technologies as they positively influence sales, ROI, profit, and market share. Equally, food retailers must understand the adoption determinants, followed by the AI technologies that can effectively improve marketing tasks, examine how the 4Ps can be strategically tailored to suit AI integration and assess the impact through marketing strategy outcomes. The findings of this study contributed to the development of the first AI adoption framework contextualized for the food retail industry. Theoretically, the findings provide extended and new knowledge about AI adoption in food retail marketing. The empirical findings also settle debates surrounding inconclusive determinants of AI adoption. The study provides potential technologies that food retail marketers can use and ranks them according to their use and cost. The findings prove that AI integration can improve the marketing practices of food retail marketers. It gives clear solutions on how AI can be used for descriptive, diagnostic, prescriptive and predictive purposes. Future studies could focus on developing frameworks for other non- marketing functions and how AI can be regulated to avoid unforeseen consequences should they be successfully integrated