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The mandate of correctional services is to uphold and advance a just, peaceful, and secure society by ensuring that the corrections environment is secure, safe, and humane, while ideally rehabilitating criminals to minimise their probability of re-offending. Realising these objectives necessitates a shift from the traditional correctional system to a digital, intelligent, and data-driven approach, encompassing the gathering and analysis of information within correctional facilities to identify and mitigate hazards. Intelligence-driven corrections deploy correctional intelligence, data analysis, specialized software, and databases for mapping complicated relationships, assessing forensic evidence, and exchanging information across jurisdictions. They apply data analytics to foresee prospective dangers, such as the likelihood of violence, contraband introduction, or jail riots. The research examines enhanced corrections and intelligence-based advances in modern correctional services. A mixed-method approach was utilised, integrating qualitative and quantitative designs. Interviews, surveys, and secondary data were employed. The research indicated that more intelligent corrections utilise technology and data to oversee inmates, personnel, and visitors, minimise expenses, and enhance public safety and security. Intelligence-driven corrections utilise gathered and analysed intelligence to guide decision-making and resource distribution within correctional facilities, hence improving safety and security by recognising dangers and thwarting criminal activity. Digital technologies improve offender management, security, safety, surveillance, and efficiency through the use of biometrics, facial recognition, artificial intelligence, data analytics, robotics, and drone detection. Artificial intelligence in correctional services improves safety through real-time behaviour surveillance and incident identification, enhances efficiency by automating administrative functions, and offers personalised rehabilitation through customised educational and mental health initiatives, ultimately seeking to diminish recidivism. Blockchain technology in correctional systems provides improved security, transparency, and efficiency, especially in monitoring inmate movements and preserving the integrity of evidence. Evidence-based risk assessment facilitates tailored rehabilitation programs that aid in crime reduction. The study advocated for the adoption and implementation of advanced correction methods, intelligence-driven strategies, technological innovations, and digital devices to improve safety and security in South Africa, other African countries, and worldwide.

The nexus of Artificial Intelligence (AI) and indigenous knowledge systems (IKS) rarely features as a viable intervention in African food system policy and academic debates. This has created knowledge and practical gaps with policy and practice implications. This article argues that leveraging the AI-IKS nexus has significant potential to transform African food systems currently in crisis. This conceptual article explores the importance of IKS in Sub-Saharan Africa (SSA) and examines how AI solutions can transform regional food systems. The article shows that harnessing the AI-IKS nexus entails merging traditional, generational ecological wisdom with modern technology to enhance food security, sustainability, and resilience. Key applications include using AI to analyse indigenous drought-prediction techniques alongside meteorological data, identifying climate-resilient indigenous crops, and deploying Computer Vision and Natural Language Processing to digitise and disseminate traditional knowledge in local languages. The article argues that this synergy has the potential to enhance climate resilience, boost yields through precision agriculture, reduce post-harvest losses, and promote sustainable resource management, ultimately strengthening food security.

The global skills gap is the foremost obstacle to business transformation. Across sectors and regions, established forms of expertise are being reshaped or rendered insufficient, creating widespread uncertainty about the durability of existing skill sets. This condition, often described as skill instability, reflects a growing need for continual learning and adaptation as occupational demands evolve. This study employs a critical analysis of data from leading international organizations to map the distinct nature of this challenge across the world’s regions and industries. The study demonstrates that its nature differs fundamentally between regions and sectors. In advanced economies, the crisis is a shortage of specialized digital and green skills. In developing regions, the deficits are more foundational, rooted in basic digital literacy and technical-vocational training. Everywhere, however, a structural mismatch is worsening economies are creating complex new jobs faster than they are training workers for them, even as automation displaces lower-skilled roles. To navigate this dual challenge, we argue that technology, especially AI, must be seen as a tool to augment human potential. The solution is a decisive strategic pivot: from reactive hiring to proactive human capital development. This requires nuanced, region-specific policies and a foundational corporate commitment to lifelong learning to build a resilient and inclusive future workforce.

Climate change poses significant risks to rural livelihoods in semi-arid regions such as Bikita District, Zimbabwe, where recurrent droughts, erratic rainfall, rising temperatures, and water scarcity undermine food security and socio-economic stability. This study examines the climate-related risks faced by rural households, identifies the coping and adaptation strategies they employ, and assesses the effectiveness and sustainability of these responses. A qualitative case study design, guided by the Sustainable Livelihoods Framework, was adopted. Data were collected through in-depth interviews (n = 30), focus group discussions (two groups), and direct observations involving households, traditional leaders, and local stakeholders. Thematic analysis was used to identify patterns in risks, coping strategies, and institutional constraints. The findings show that households employ diverse strategies, including crop diversification, cultivation of drought-resistant small grains, water harvesting, livestock downsizing, livelihood diversification, horticulture, and reliance on social networks. However, many of these responses remain short-term coping mechanisms rather than sustainable adaptation strategies. Structural barriers, including limited access to credit, weak extension services, poor infrastructure, and inadequate market systems, constrain their effectiveness. In addition, some practices, including deforestation and over-reliance on food aid, are maladaptive, providing immediate relief while undermining long-term resilience. The study concludes that although rural households demonstrate significant adaptive capacity, their transition from short-term coping to sustainable adaptation is limited by systemic and institutional constraints. It highlights the need for integrated policy interventions that strengthen livelihood assets, enhance institutional support, improve access to finance and markets, and integrate indigenous knowledge with climate-smart practices. These findings contribute to climate adaptation scholarship and provide policy-relevant insights for building long-term resilience in rural Zimbabwe.

This study examined the effect of organisational culture on employees' readiness towards change at two Eswatini state-owned enterprises. Guided by a positive research philosophy, the study adopted a quantitative, cross-sectional research design. Daya was collected from a stratified sample of 543 permanent employees using the Organisational Culture Assessment Instrument (OCAI) and the Readiness Toward Change Scale (RTCS). Reliability was assessed by using Cronbach's alpha, while confirmatory factor analysis, correlation analysis, and multiple regression were employed to examine the relationship and predictive effects among the variables. The results revealed a positive and statistically significant relationship between organisational culture and readiness towards change. Market culture emerged as the strongest predictor of readiness towards change, followed by hierarchical culture, Adhocracy culture, and clan culture. These findings suggest that performance orientation, structural clarity, adaptability, and relational cohesion jointly contribute to enhancing employee readiness for organisational change. From the business ecosystem and strategic management perspective, the findings highlight the importance of cultivating an adaptive organisational culture as a key enabler of sustainable performance, ethical governance, and stakeholder trust. At a broader socio-economic level, strengthening organisational culture and employee readiness for change within state-owned enterprises can enhance productivity, promote social equity, and support inclusive growth, reinforcing Eswatini's business and socio-economic ecosystem.

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.