Ignitia Motjolopane

The use of Generative Artificial Intelligence (AI) presents both opportunities and challenges in the South African higher education sector, particularly when guidelines for its use are lacking. The absence of comprehensive policies and frameworks is problematic, as it enables the unethical deployment of these tools and fosters inappropriate institutional responses to their use. This chapter aims to explore the readiness and levels of Generative AI adoption in South African universities. The study used a systematic literature review to research the phenomenon. Data were sourced from databases such as ScienceDirect and Scopus, as well as institutional reports and policies, to ensure comprehensive coverage of the topic under investigation, and analysed using content analysis guided by the Generative AI maturity framework. The results highlight varying levels of adoption, from exploration to implementation. Therefore, this study presents a framework for institutions to assess their Generative AI readiness and to identify gaps, thereby informing the formulation of policies and guidelines for the use of these tools. The study contributes to the limited literature on universities’ readiness to foster a supportive environment for Generative AI tools in higher education. Additionally, it offers practical guidelines for policymakers to address potential readiness and adoption gaps.

Background and Aim of Study: Occupational health and risk management (OHRM) in the South African mining sector remains a critical national priority, where the life or death outcomes can be impacted by poor quality-data usage. Big data analytics (BDA) is increasingly used for hazards predictions and timely decision-making. The aim of the study: to explore critical data quality factors that influence the reliability and effectiveness of BDA for decision-making to guide occupational health practitioners and risk managers within South African mining sector. Material and Methods: The study employed a quantitative survey methodology, informed by the literature review, to identify key data quality factors of BDA impacting OHRM in the South African mining sector. Underpinned by Technological, Organizational and Environmental (TOE) theory and contextual factors within big data quality dimensions and big data sources. Data was collected from 103 OHRM experts determined by the population size of 140. Results: The results reveal the following factors to have influence on data quality for BDA within SA mining OHRM; Environmental factors with a predictive power of 25.0% (β=0.250) at p=0.014; followed by big data quality dimensions with 24.1% (β=0.241) at p=0.008; then, technological factors with 15.9% (β=0.159) at p=0.027; big data sources with 13.2% (β=0.132) at p=0.026; lastly organisational factors was less significant at p=0.228 with 10.0% (β=0.100). Conclusions: This study identifies the factors of data quality, highlighting its role in BDA for decision-making within OHRM. These factors can further be used to provide guidance for SA mining OHRM decision makers to target critical data quality improvement areas for enhanced decision making in the sector.

South Africa’s energy industry faces ongoing challenges including power shortages, ageing infrastructure, and supply chain inefficiencies, while, limited empirical evidence exists on how organisations in this industry prepare for Robotics Process Automation (RPA) adoption. This study examines the RPA readiness barriers and enablers within the supply chain of South Africa’s energy industry. The research adopts a qualitative design grounded in the Technology-Organisation-Environment (TOE) framework and the Awareness, Desire, Knowledge, Ability, Reinforcement (ADKAR) change management model to connect technological capability with individual and organisational readiness for change. Data were gathered through semi-structured interviews with 18 professionals representing eight stakeholder groups, including supply chain managers, IT specialists, process improvement leads, and employees affected by automation. Four key readiness barriers emerged: readiness gaps (61 mentions), organisational misalignment (158), infrastructure strain (83), and job security and resistance (60). Corresponding enablers included leadership accountability, RPA governance and alignment frameworks, readiness checklists, structured communication protocols, KPI frameworks, capability audits, investment planning, psychological safety, and regulatory alignment mechanisms. The integration of TOE and ADKAR offers a novel dual-lens perspective that extends existing knowledge. The findings provide practical guidance for managers and policymakers seeking to strengthen organisational systems and structures with human readiness factors in emerging economies.

Background and Aim of Study: Postgraduate studies in African countries face low completion rates due to capacity issues, hindering knowledge creation and innovation. The aim of the study: to map the steps involved in conducting a systematic literature review in Information Systems (IS) research to the identified review types, thereby providing a framework based on Generative Artificial Intelligence (AI)-based design science artefact for researchers and educators in the field IS for postgraduate teaching and learning. Material and Methods: A systematic literature review was conducted to identify the review types in IS research following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The Association of Information Systems (AIS) database was used to identify relevant articles. The initial filter produced 2775 results. When focusing only on journal articles, the record produced 221 results, resulting in five papers qualifying for inclusion in the study. These papers were augmented to eight articles using one journal article and two conference papers identified through snowballing. Results: The results indicate that there are few publications within the AIS database on the tools used to support systematic literature review processes. However, those that exist do not reflect the type of review used. Additionally, tools that were used to support systematic literature review were those assisting with data extraction. Thus, frameworks may be needed to conduct a methodical review on various review types to ensure rigour and transparency in the findings of the reviews. Conclusions: This paper proposes a framework to guide the design of tools that can holistically support systematic literature review processes, making these reviews more accurate and less tedious. Such artefacts, especially using Generative AI tools, could potentially support postgraduate students in conducting rigorous reviews, improving completion rates and promoting knowledge creation and innovation in African countries.

Assessing the readiness and maturity of small and medium enterprises (SMEs) is a foundation for implementing emerging technologies like big data analytics and artificial intelligence to drive their digital transformation endeavours. This study emphasises that readiness and maturity dimensions offer descriptive and prescriptive guidelines for gauging the current and desired levels of preparedness and maturity required to achieve desired digital transformation outcomes. However, prevailing readiness and maturity models overlook the diverse stages of advancement in big data analytics and artificial intelligence. This research explores the dimensions essential for assessing SMEs' readiness to adopt big data analytics and artificial intelligence. This paper identifies the key dimensions for evaluating SMEs' readiness and maturity across different categories of big data analytics and artificial intelligence by conducting a systematic literature review and employing cluster analysis. The study's principal findings underscore that SMEs' readiness for maturity is influenced prominently by strategic leadership and organisational culture, closely trailed by information technology, security, and business model transformation. Additionally, three pivotal dimensions encompass data analytics and governance, cost-benefit and risk management, and environmental factors. Consequently, proposing that evaluating digital readiness and maturity for SMEs should encompass these six dimensions, thoughtfully considering various prerequisites related to analytics and artificial intelligence.

Teaching students to conduct a rigorous systematic literature review (SLR) may be challenging, given the growing output of scientific literature and the increasing plethora of supporting software and artificial intelligence tools such as ChatGPT. A SLR supported by design science artefacts and emerging artificial intelligence tools have the potential to overcome the challenges posed by increasing scientific literature. However, guidelines and steps required to conduct a SLR still need to be clarified in the existing literature. Furthermore, existing design science artefacts and software tools do not support teaching students to conduct rigorous systematic reviews. This paper presents guidelines on the required steps for a rigorous SLR and proposes a Researchbuddie artefact to support teaching SLR. Using a systematic review and design science findings, identify SLRs four main phases of planning, conducting, evaluating and reporting reviews, each supported by itemised sub-steps. Furthermore, a design science artefact Researchbuddie is proposed to support teaching SLR. Therefore, the paper contributes to guidelines for teaching SLR for Information Systems students with phases, sub-steps and a proposed Researchbuddie artefact.

In an environment of changing customer needs, technological advancement and digitization, the importance of business model innovation in supporting companies to accrue benefits from increased digitization while counteracting threats has intensified. Factors driving business model innovation could be of value in facilitating business model innovation success and accrue benefits from increasing digitization. As the factors driving business model innovation are often triggers for the next cycle, however, there is limited empirical research on factors driving business model innovation. This paper presents empirical findings on factors driving business model innovation using unstructured interviews with nine executives from five sample companies in South Africa. Main finding from the research indicate that both external and internal factors such as existential crises, serve as valuable triggers for business model innovation. Furthermore, entrepreneurial inspirational leadership and quality of staff are important conditions for business model innovation success. The study findings imply that both companies and policymakers who seek to facilitate innovation need to take into account the impact of the business model innovation triggers on business model innovation success. The main contribution of the paper is the empirical finding on factors driving business model innovation using as a lens the sample case study companies.

Teaching research methodology is one of the core components of various degree programs. Scholarship around teaching research methodology is beginning to grow with most work concentrated in Social Work, Health, Social Science, and limited work in the area computing education. This paper presents a reflection on adopting a student centred approach towards teaching research methodology course to three different groups of fourth level university students. In this paper the strategies for facilitating deep learning in teaching research methods and research methods in the Information Systems domain will be discussed. In addition, reflections on the use of a student centred approach, student experiences and strategies used. The experiences and strategies relate to facilitating deep learning. The experiences are focused on topic identification, conducting, and writing up the literature reviews, developing an understanding of the research methodology inclusive of data analysis and presenting the research report. Doi: 10.28991/esj-2021-01255 Full Text: PDF