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With the intensity of the Fourth Industrial Revolution, Artificial Intelligence (AI) is being widely adopted to perform key tasks in economic activities. The audit profession has also embraced AI in the performance of its function in carrying out activities like audits, oversight, and advisory functions. The application of AI has been met with acceptance, given its advantages in some quarters and with resistance/scepticism in some. Proponents table benefits such as improved sampling procedures, reduced labour, and time in performing audits, increased efficiency, and effectiveness (due to increased audit coverage) including improved audit quality. Opponents raise pragmatic concerns such as the violation of ethical principles governing the audit profession, potential biases (loss of employment) as well as the challenges of coordinating machine and human activities. The study has two objectives. Firstly, to explore the role of AI in the external audit function. Secondly, to evaluate the ongoing debates on artificial intelligence and external auditing and assess the implications of using AI in the external audit function. The study adopts a qualitative research approach, employing a critical literature review. The study will accentuate the controversies and convergences among researchers on the role and implications of applying AI in external auditing to bring to light possible research gaps that can be explored by future researchers on the subject area. Highlighting the potential externalities of using AI has both theoretical and practical implications.

Service delivery protests in the recent past demonstrate the dire state of local government in South Africa. The absence of public sector innovation can be linked to service delivery backlogs hence embracing digital innovations (Internet of Things (IoT), Big data and analytics, cloud, Biometrics, computers, mobile applications, 3D machines) in the Fourth Industrial Revolution (4IR) is strategic for enhancing municipal service provision and uplifting the living standards of citizens in communities.  The study utilised an explorative qualitative case study design premised on an extensive literature and document review analysis to examine the barriers facing the City of Tshwane from adopting digital innovations in the 4IR. The analysis of documents revealed digital hesitancy, leadership void, lack of innovative research culture, weak municipal preparedness, and digital divide in adopting digital innovations are barriers to digital innovation adoption to enhance service delivery. The paper observes further the lack of systematic and evaluative studies to inform public sector innovation, or growth in digital innovations often led to implementation challenges in the City of Tshwane. Conclusions drawn for the article revealed that adopting digital innovations can be the panacea to accelerating urban service delivery in the City of Tshwane; hence institutional readiness, improved revenue streams, including a stable regulatory and policy environment, are imperatives in achieving a digitalised local government in the 4IR.

The utilization of powerful machine learning models in artificial intelligence offers novel prospects for the identification of fraudulent activities. Artificial Intelligence (AI) is a revolutionary technological tool that enhances the ability to detect and prevent fraud by improving efficiency and effectiveness. This research offers a thorough examination of the utilization of artificial intelligence technology in the realm of procurement fraud prevention and detection. Additionally, it highlights the obstacles that arise when employing machine learning techniques for the purpose of identifying and preventing fraudulent activities. A mixed methods approach was employed in this study, wherein data was collected through an unstructured interview and questionnaire. We conducted a comprehensive review of relevant scholarly articles and online resources. The findings indicate that fraudsters are progressively advancing in their skills, which poses a significant challenge in detecting fraudulent activities. The advent of AI in fraud detection has demonstrated its transformative impact. AI has achieved unparalleled precision and velocity in crime detection and prevention, surpassing the capabilities of any human. Artificial intelligence (AI) enhances the capacity for automation. Accessing unstructured data in the form of spreadsheets, digital documents, and email inboxes poses a significant issue for the procurement function. In order to achieve a successful procurement transformation, businesses should prioritize the creation of essential tools, guarantee acceptance through the establishment of a superior user experience, and integrate both novel and pre-existing technology. It is imperative to disseminate knowledge to the general public regarding the escalating sophistication of artificial intelligence (AI) in the realm of fraud detection and prevention. This includes elucidating the potential benefits of AI in identifying patterns of suspicious activities, assessing its efficacy in predicting potential threats or fraudulent activities prior to their manifestation, and exploring its utility in analyzing historical data pertaining to both familiar and unfamiliar forms of fraudulent behavior.

This article aims to assess the Integrated Development Planning (IDP) process for improved community participation. The IDP process was developed to improve strategic planning and community participation at the municipal level. Many South African municipalities have been struggling to use the IDP process to improve community participation. The methodology included 400 respondents who were chosen through a probability sampling technique and probed to complete the online survey using a closed-ended questionnaire guide, and ten respondents were also chosen through a non-probability sampling technique and were face-to-face interviewed using a semi-structured interview guide. The key findings revealed that poor community participation exists in the study area due to a lack of encouragement from responsible authorities. The Tzaneen municipality does not provide adequate feedback to the communities, and the inputs of the communities are not considered. There is a continuous lack of community consultation during the annual review of the IDP. The ineffective IDP process for improved community participation presents several challenges. Thus, this article concluded by providing a few recommendations to enhance the application of the IDP process and community participation. The Tzaneen municipal area should at all times uphold democratic principles by ensuring active public participation, transparency, and openness in municipal affairs. It also recommends making it easier for marginalised and previously disadvantaged people to participate in municipal policy and decision-making without prejudice. South African municipalities, including Tzaneen municipality, should develop and innovate institutional, and organisational capabilities to better utilise their resources, mitigate socio-economic challenges and enhance service delivery.

To improve the delivery of services, municipalities in South Africa are implementing information and communication technology (ICT) initiatives. The use of digitalization thus demands municipalities to introduce e-government services. With adequate resources and training, some municipalities also aim to advance their status as smart cities. However, However, not all municipalities are equipped to operate ICT-based services.  The article aims to explore this situation in the City of Ekurhuleni Metropolitan Municipality (herein referred to as CoE). The article is qualitative in nature and the data collected is analyzed descriptively. The article utilizes the triangulation of data (document review, literature review, interview) to strengthen validity and reliability in the research process. The findings explore the challenges related to leadership in the municipality that requires a willingness to adopt, adapt and implement ICT initiatives for smart governance; ICT infrastructure that requires a municipality to allocate adequate budget to smart governance platforms, training of employees to equip them to operate digitalized processes, to state a few. Overall, the study considers the status and implementation levels of ICT and smart governance in the CoE to be unsatisfactory and requires policy recommendations for effective realization to become a smart city. The article offers policy recommendations emphasizing incorporating ICT in municipal budgets to invest in digital infrastructure.

Effective leadership and organisational performance in technically demanding sectors increasingly depend on values-driven, human-centred approaches that extend beyond conventional managerial competencies. This qualitative study examines how workplace spirituality shapes leadership behaviour, organisational dynamics, employee development, and soft skill cultivation within a South African engineering organisation. Employing an interpretivist phenomenological design with fifteen purposively selected participants and inductive thematic analysis via Atlas.ti, six interrelated themes emerged: (1) understanding of spirituality; (2) challenges in employee development; (3) employee development strategies; (4) impact of spirituality on workplace dynamics; (5) spirituality and leadership decision-making; and (6) soft skills. Findings reveal that spirituality understood as connectedness, calling, integrity, and altruism enables transformational, servant, and spiritual leadership orientations that create psychologically safe, values-aligned environments. These environments demonstrably improve organisational commitment, ethical decision-making, human resource development outcomes, and soft skills formation, with direct implications for leadership strategy and organisational culture design. The study contributes to humanistic leadership and HRD scholarship in emerging economy engineering contexts, with practical implications for workforce transformation in post-apartheid South Africa.

This study investigates how a major South African private hospital group adopts and scales multiple renewable energy sources (MRES) within a mission-critical healthcare environment characterised by chronic grid instability. Using a qualitative single‑case study design, the research draws on semi-structured interviews with senior executives and technical managers, complemented by documentary analysis, to explore the barriers, enablers and governance mechanisms underpinning MRES adoption. The results reveal that financial and economic constraints, regulatory fragmentation, legacy infrastructure, and supply chain limitations constitute multidimensional barriers. In contrast, economic viability, executive leadership, risk-managed implementation, and energy-security imperatives serve as decisive enablers. The study demonstrates that effective adoption arises not from technology choices alone, but from an integrated organisational approach that combines diversified energy portfolios, phased implementation strategies, contractual procurement, and structured sustainability governance. The proposed Integrated MRES Adoption Framework synthesises these dimensions, offering a holistic model for energy transition in high-risk medical settings. The study concludes that hospitals can meaningfully advance renewable energy penetration when technical diversification is embedded within robust governance, financial feasibility assessments, and patient-safety-aligned risk management. Limitations include a single-case scope and senior-level perspectives; future research should compare public and private systems, evaluate long-term performance, and deepen inquiry into staff-level change processes.

Relational leadership is a key antecedent of employee attitudes and rewards perceptions. However, empirical evidence linking its specific dimensions to compensation outcomes within South Africa’s context remains limited. Drawing on social exchange and equity theories, this study examines how specific relational leadership dimensions influence compensation perceptions. Data was collected from (N = 122) permanently employed personnel at a South African organisation, using a quantitative, cross-sectional design and validated self-report measures. The data was analysed by using Pearson correlations and multiple regression to predict employee compensation through relational leadership. The ethical component emerged as the most consistent and dominant predictor of employee compensation across all models. The empowering component showed significance in one model, but the dimensions usually linked to RLP, including inclusive, caring, and visionary components, had no statistically significant impact on reward outcomes. These findings highlight a critical distinction between behaviours that foster a supportive climate and those that validate the structural legitimacy of reward systems. The study concluded that in socio-economically complex environments like South Africa, employees prioritise ethical governance and fairness over interpersonal warmth when evaluating compensation. Consequently, organisations should prioritise ethical decision-making and transparent accountability in LDPs to enhance satisfaction and perceived equity.

This systematic review examines how organisational culture (OC) influences organisational change in developing countries, addressing a literature gap concentrated in Western, high-income contexts. Following PRISMA 2020 guidelines, we analysed 35 studies published between 2000 and 2025, spanning healthcare, manufacturing, higher education, public administration, and finance across 15 developing nations. The synthesis employed thematic analysis guided by the Competing Values Framework and institutional theory. Results indicate that organisational culture operates as a contingent mediator rather than a uniform driver or barrier. Clan culture consistently facilitates change through empowerment, trust, and participative decision-making. Conversely, a hierarchy culture typically impedes implementation through centralised authority, information hoarding, and suppressed psychological safety. Adhocracy and market cultures produce context-dependent effects, moderated by sectoral constraints and managerial practices. National cultural dimensions, political interference, and resource scarcity significantly moderate these relationships, often neutralising formal change policies. The findings demonstrate that change management practices mediate the culture-outcome link but require multi-level alignment to succeed. This review contributes a contextual model for OC-change dynamics, extending Western frameworks by showing that hierarchy in developing contexts is frequently an institutionalised norm rather than a managerial preference. Practically, the study provides evidence-based guidance for diagnosing cultural fit, adapting change strategies to national realities, and addressing systemic resource constraints.

Financial institutions predict the probability of default when making lending decisions using conventional credit risk scoring models. However, in emerging economies, the underbanked, widows, small and medium enterprises owners and youth are not able to access traditional forms of collateral or identification required by financial institutions due to lack of data for them to get access to loans. Financial institutions cannot obtain much of the information required about an applicant and they use alternative data sources such as public data or social media to deal with the problems of information asymmetry, adverse selection and moral hazard. To eliminate such problems, credit risk assessment models must be built on the foundation of artificial intelligence and machine learning approaches in an effort to perform an accurate credit risk analysis, to properly assess the behaviour of the customers and subsequently perform a thorough verification of the potential of the applicant to repay the loan thereby allowing less privileged people to access credit. However, big data sourced from the internet and public data sources is characterized by huge volume, variety, veracity, the curse of dimensionality, class imbalance, concept drift and non-linearity among others. These challenges affect the generalization performance of credit risk assessment models used by most financial institutions. This paper proposes an efficient and effective credit risk assessment model capable of learning incrementally based on machine learning algorithm called Adaptive Heterogeneous Dynamic Ensemble Selection (AHDES) that uses big data from alternative sources for financial for the underbanked. The algorithm is agile and adaptive to unexpected world events, changes in customer behaviour and cognitively counter bias likely to be introduced by information asymmetry. Experimental results on four large credit datasets and five evaluation metrics show that our proposed algorithm performs better than other selected benchmark models allowing the underbanked and less privileged to access loans to stimulate economic growth.