University of Stellenbosch Business School (USB)

Permanent URI for this community

https://scholar.sun.ac.za/handle/10019.1/75

Browse

Browsing University of Stellenbosch Business School (USB) by browse.metadata.advisor "Botha, Elsamari"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Artificial Intelligence (AI) in retail : the AI-enabled value chain
    (Stellenbosch : Stellenbosch University, 2022-04) Oosthuizen, Kim; Botha, Elsamari; Butler, Martin; Stellenbosch University. Faculty of Economic and Management Sciences. University of Stellenbosch Business School.
    ENGLISH SUMMARY: The competitive landscape is shifting for retailers, and many are scrambling to stay ahead of the competition by investing in new technologies like Artificial Intelligence (AI), automation, robotics and blockchain. Traditional retailers face disruption from competitors that can deliver value to their customers faster through these new technologies. AI, in particular, is earmarked to transform retailing, and its influence on retail is projected to be substantial. However, empirical research on AI in retail remains limited. This study investigates how AI is transforming the retail value chain through a qualitative two-stage research design, using four articles to answer the research question: How is AI transforming the retail value chain? The Leavitt Diamond model and the jobs-to-be-done theory are used to answer the research question. First, this study used all the Leavitt Diamond Model variables (i.e. structure, technology, tasks and people) to examine how AI transforms the retail value chain. The process offered a more comprehensive view of the organisational factors that need to be considered when adopting AI in the retail value chain. Previous research typically focuses on only one of these components. Articles one and three broadens our understanding of applying jobs theory and outcomes-based innovation in the context of AI in the retail value chain. In article one, the jobs-to-be-done approach was used as a lens to conceptually cluster the jobs AI technologies can perform in the retail value chain. The article conceptually proposed four AI technology dimensions that can fulfil most of the roles in the “traditional” retail value chain. Article one introduced a conceptual framework to understand AI's role in the retail value chain proposing an alternative AI-enabled value chain. Article two conducted a detailed review of AI's different tasks across the retail value chain. In article three, an outcomes-based approach was used to present a framework of four outcomes for applying AI in the retail value chain and tested the association between the AI outcome and the value chain stage. Therefore, this study proposes the appropriate theoretical lens to understand better the use of AI in the retail value chain. However, it also improves this framework in the final chapter, presenting an adapted conceptual lens. Finally, article four aimed to understand retailers' challenges better when implementing AI, using Leavitt’s Diamond Model. The overall findings suggest that AI transforms the retail value chain in three ways. First, the iterative nature of AI requires the shape of the retail value chain to change from linear to circular, with data and insight at the core of the successful value chain. Second, AI changes how retailers attain goals in the retail value chain through achieving specific outcomes. The outcomes are dependent on where AI is applied in the retail value chain. Third, there is a complex interplay between structure, technology, people and tasks when implementing AI into the retail value chain, transforming how retailers operate. This study broadens the understanding of how new technologies impact value chains in general and retail value chains in particular. For retailers to successfully implement AI into their business, they need a clear understanding of how it impacts people, organisational structure, other technology, and organisational tasks. This study created a framework of eight imperatives retailers need to consider when implementing AI, offering a holistic view of the consideration needed across people, structure, tasks and technology to ensure successful integration of AI into the business.
  • Thumbnail Image
    Item
    Drivers of entrepreneurial orientation and innovation capabilities in African Internet start-ups
    (Stellenbosch : Stellenbosch University, 2021-12) Onwu, Ekenedilichukwu Gilbert; Ungerer, Marius; Botha, Elsamari; Stellenbosch University. Faculty of Economic and Management Sciences. University of Stellenbosch Business School.
    ENGLISH SUMMARY : Tech startups worldwide begin their operations and strategic business initiatives with high expectations of success, but 90% of these startups collapse within their first year of operation. In most developing countries, that number is closer to 95%. This painful reality globally, which is more pronounced in developing markets, is reportedly said to be due to the lack of understanding of what capabilities to foster and the degrees to which each one should be focused on. In many developing countries of Africa, this knowledge gap has led to low levels of sustainable innovation and entrepreneurial activity, which pose a potential threat to employment creation and economic productivity. External capabilities, like infrastructural capabilities or macro-economic capabilities, are constantly changing and peculiar to different business environments making it very difficult for startups to control and develop in reality. However, startups who develop the appropriate internal capabilities, may better understand to what extent investments into certain capabilities may foster their internal business objectives. In addition, startups have often looked at capabilities in isolation as drivers of success. Consequently, such limited focus may not provide a comprehensive overview of the capabilities deemed necessary to drive success in an increasingly digitised business climate. Consequently, a comprehensive overview of the capabilities deemed necessary to drive success is lacking. This study therefore investigates which capabilities are necessary to foster innovation and entrepreneurship in tech startups, and how these capabilities directly influence startup performance. Using Teece’s SST (sense, seize, transform) dynamic capabilities-based approach, a framework of all the capabilities needed to drive tech startup performance is presented. This approach was premised on the use of capabilities employees and entrepreneurs of tech startups identified in the literature as relevant, meaningful and thought-provoking for tech startups looking to drive success. Hence, the study's focus was on those identified and include as follows: top-management capabilities, technological competence, organisational learning capabilities, innovation capabilities, entrepreneurial orientation and organisational performance. We investigate the proposed framework amongst tech startups in four African countries (Nigeria, Ghana, Kenya, and South Africa). A descriptive research design was used, where online surveys were used to target management staff using quota sampling. A sample of 254 individuals employed in tech startups across the four countries was surveyed. Structural equation modelling, in particular PLS-SEM, was used to test the proposed model. The results were confirmed with covariance-based SEM. This study therefore contributes to existing tech startup literature in four ways. First, it provides a comprehensive view of the dynamic capabilities that tech startups need in order to increase their likelihood of success, something isolated approaches have struggled with in the past. Second, this study directly links these capabilities to organisational performance and illustrate how the key mediators of innovation capabilities and entrepreneurial orientation contributes to organisational performance. Third, the findings also help better understand the combined mediating effects of innovation capabilities and entrepreneurial orientation - previously often studied separately – on fostering firm performance. Finally, we test this model in an emerging market context where there is a paucity of research and insight into the factors that contribute to startup success. The study's significance is that what initially began as a complex range of isolated drivers that were implicitly linked but in an unspecified way to a firm’s performance has been simplified into a concise, comprehensive capabilities-based SST model. The model's application suggests that it is likely to give startups and their founders better performance indicators that have the potential to influence their culture, mindset, behaviours, and ability to succeed, where so many seem to have failed before.