Doctoral Degrees (Industrial Engineering)

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Browsing Doctoral Degrees (Industrial Engineering) by Author "Bester, Coenraad Petrus"

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    A function-based relevance model for making sense of technological change in the context of a firm
    (Stellenbosch : Stellenbosch University, 2022-04) Bester, Coenraad Petrus; Pistorius, Carl Wilhelm Irene; Grobbelaar, Sara; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH SUMMARY: The complexity of technological change in a hyper-connected world presents strategic business risks to businesses, manifested as opportunities as well as threats. Business leaders are confronted with the challenges posed by phenomena such as the Fourth Industrial Revolution (4IR) often without adequate decision-support tools. This is not helpful in a fast-changing technology landscape. There is a need for well-grounded management tools and methodologies that link technology changes to the strategy of a firm. The dissertation proposes a model and supportive methodology—an interpretive lens—that could facilitate sensemaking of technology by the leadership of a firm that can enable them to keep up with the ever-increasing rate of new emerging technologies within the context of their strategy. It describes the development of that model—referred to as the Function-based Relevance Mode (FRM) — from concept to working demonstrator. The research is positioned within the domains of technology, sensemaking, complex systems, “the job to be done”, and leadership challenges related to rapid technological change phenomena. It is underpinned by the notion that the value proposition of a firm captures “the job it does for customers” and argues that the business model (BM) of a firm may be viewed as its extended value proposition. It proposes a systems dynamics-based business model to articulate responsiveness to technological changes and support related decision-making. The FRM incorporates a specific grammatical structure of a function statement, namely, a “verb + adjective + noun” construct, which enables the extraction of function statements from appropriate text describing the functionalities of the technology and the firm. It applies corpus linguistics and analytics as supportive tools and demonstrates their use by the extraction of function statements of Artificial Intelligence, the Internet of Things, Virtual Reality, 3/3/4D-Printing, Edge Analytics, and Biotechnology as illustrative examples. A text network approach is applied to visualise and interpret the function statements. Network analytics are applied to compare sets of function statements to determine relevance between them. Group degree centrality and common group betweenness centrality are proposed as two components of a technology relevance index, which demonstrates how the dynamics of common node betweenness centrality scores may be interpreted to reveal various technology innovation opportunities to a firm, inclusive of marketpull and market-push opportunities. The FRM is applied to cases studies sourced from reputable business schools, inclusive of the introduction of robo-advisors in investment markets, the introduction of FinTech into the traditional banking market, mobile payment services opportunities, and online real estate marketing. The ability of the FRM to provide a consistent indication of technology relevance in support of strategic sense making in a firm is confirmed. Comparable approaches in the domain of technology opportunity analysis, technology forecasting, social media analysis, and patent analysis, point towards the validity of the FRM and illuminates its unique research contribution.