Business, Economics and Management

Abstract: This article examines the effects on internal auditing on consequence management within the KwaZulu-Natal Provincial Government (KZNPG), focusing on provincial departments and municipalities. This study is motivated by ongoing concerns about governance failures and accountability in the public sector. Despite a robust legislative framework and skilled personnel, high levels of irregular expenditure persist, particularly in key departments and local government institutions, prompting an exploration of the factors undermining effective consequence management. The study employs a qualitative methodology, analyzing internal audit processes and external audit findings from the Auditor General South Africa (AGSA), alongside existing literature on public sector auditing and accountability. Key findings reveal a significant gap between audit identification of governance failures and the subsequent enforcement of corrective actions. Systematic weaknesses, such as inadequate political will and ineffective enforcement mechanisms, consistently hinder the translation of audit recommendations into substantive changes in practice. In conclusion, the study underscores that while internal auditing has the potential to enhance accountability, its effectiveness is contingent upon political support and a conducive organizational culture. Recommendations include establishing stronger frameworks for accountability, reinforcing internal audit independence and fostering a culture of transparency within provincial departments and municipalities. The implication for the KZNPG are significant; implementing these recommendations could facilitate more effective consequence management, ultimately enhancing public trust and service delivery. By addressing the identified weaknesses, the government can mitigate irregular expenditures and improve governance outcomes in the region.

Abstract: Tourism seasonality remains one of the most persistent structural challenges of Mediterranean destinations, intensifying environmental, economic and social pressures during peak months while leaving tourism capacity underused in the remainder of the year. This paper examines the level, spatial distribution and structural determinants of tourism seasonality in 61 Mediterranean EU NUTS-2 regions from Croatia, Spain, Greece, Portugal and Italy over the period 2020–2024. Using harmonised Eurostat data, annual Gini coefficients are calculated from monthly overnight stays and analysed within a balanced panel of 305 region-year observations. The study tests whether seasonality is associated with international tourism dependency, hotel accommodation share and island geography, while controlling for regional GDP per capita, tourism intensity and the COVID-19 disruption period. Fixed and random effects models are estimated, with model choice guided by the Hausman test and cluster-robust standard errors applied at the regional level. The results show substantial regional heterogeneity: Greece and Italy record the highest average seasonal concentration, while Spain and Portugal display more balanced patterns, partly due to regions with year-round demand. Jadranska Hrvatska emerges as the most seasonally concentrated non-island region in the sample. The fixed effects results indicate that a higher hotel accommodation share is significantly associated with lower seasonality, while the COVID-19 years significantly increased seasonal concentration without producing a lasting structural shift after 2022. International tourism dependency is not significant in the within-region specification, and island geography is only weakly supported in the random effects model. The study contributes to comparative destination research by operationalising the Gini coefficient as a dependent variable in a regional panel framework and provides policy-relevant evidence for supply-side desezonalisation strategies in Mediterranean EU tourism regions.

Abstract: Manufacturing sectors and ecosystems are undergoing profound transformations as digital platforms converge with generative and agentic AI. This paper argues that manufacturing ecosystems can improve their innovation performance through digital platforms, connected intelligence, and collaborative settings that bring together experts and ecosystem members. This convergence of distributed capabilities across humans, communities, and machines generates intelligent environments that enable ecosystemic and transformative innovation. To examine this hypothesis, the paper follows a three-stage methodology. First, it proposes a modelling framework based on a vector autoregressive model, in which a weighted matrix representing binary couplings among human, collective, and machine intelligence drives the transition of a manufacturing ecosystem from a baseline innovation state to a more advanced one. Second, it presents the SmartGreenEcos experiment, which creates an intelligent environment adapted to a manufacturing ecosystem by combining digital platforms, e-services, and AI agents to support inter-company collaboration, experimentation and innovation. Third, it conducts a simulation-based analysis of the internal dynamics of intelligent environments, with particular attention to the eigenvalues and eigenvectors of the weighted matrix representing connected-intelligence couplings. The results provide insights into the design of intelligent environments and the interaction parameters of connected intelligence that drive innovation, with relevance not only for manufacturing ecosystems but also for other sectoral ecosystems seeking to enhance innovation through intelligent environments.

Abstract: Digital technologies have revolutionized the business environment and given Small and Medium-sized Enterprises (SMEs) in Africa new opportunities and challenges. The study explores approaches for African SMEs to harness digital ecosystems for revenue generation, cost savings, and business resilience. The study synthesises findings from a systematic narrative review of 54 peer-reviewed academic sources and 12 institutional reports, to examine how digital ecosystems evolve, their global manifestations and their unique characteristics in Africa. Key themes covered include the mobile-technology as the gateway to digital space in Africa, the evolution of Fintech and Agritech ecosystems, the impact of government policy and regulation, and persisting structural challenges to SMEs' digital participation, such as lack of access to finance, digital literacy gaps, and infrastructure gaps. The case studies of M-Pesa, Farmcrowdy, Twiga Foods, and Flutterwave provide evidence of the potential for a digital ecosystem to have tangible business impacts. Finally, the study outlines a policy framework and roadmap for digital transformation in the SME sector and recommends actions to be taken by governments, development partners, financial institutions, and the private sector.

Abstract: The unprecedented rate of technological advances, accelerated industry disruptions and social and environmental sustainability crises are requiring very different business organizations from the traditional paradigm. The main research question for this paper is: What change (paradigm shift) is needed for organizations to be future-fit? The aim is to contribute an integrated, transdisciplinary paradigmatic model of an emerging, progressive future business organization, and an understanding of the paradigm shift required in our socially constructed reality for organizations to be future-fit. A methodology based on complexity theory and a transdisciplinary approach was developed and applied. The researcher’s transdisciplinary conceptualization of a ‘paradigm’, focusing on language-based representations, serves as the foundation. Textual analyses, including corpus linguistics, of practitioner-focused literature were used to elicit concept maps (or domain models) of the shared, societal mental models of a business organization for two periods: (1) the Traditional Business Organization, and (2) a Progressive Future Business Organization. The outcomes were compared using a novel qualitative method, resulting in a set of societal level ontological shifts required for progressive future business organizations. The study shows a paradigm shift to complexity and social responsibility, and the need for transdisciplinarity to reflect complex, integrated organizational realities.

Abstract: The accelerating diffusion of artificial intelligence (AI) in Europe raises pressing distributional questions about employment, social cohesion, and sustainable development in disadvantaged regions. Research has concentrated on advanced urban economies, leaving the implications of AI for peripheral small and medium-sized enterprises (SMEs) operating under weak human capital, thin digital infrastructure, and constrained social capital — underexplored. We examine the interplay between AI adoption, social capital formation, workforce dynamics, and sustainable development in Eastern Macedonia and Thrace (EMT), one of the EU's least developed regions. Drawing on Bitsani's Biocultural City framework [11], which treats human, social, and cultural capital as interdependent dimensions of regional sustainability, we thematically analysed twelve semi-structured interviews with SME owners and managers conducted in early 2025 using Atlas.ti, yielding 19 codes grouped into six categories. Knowledge deficits and financial constraints emerge as primary barriers, while external technology partnerships, targeted education, and economic incentives operate as enablers, all mediated by social and human capital availability. AI adoption in peripheral economies is not a purely technological or financial challenge but a social and human capital challenge, embedded in a biocultural environment shaped by brain drain, institutional thinness, and weak civic intermediation. Without parallel investment in digital literacy, organizational culture, and inter-firm networks, AI will reproduce rather than reduce employment inequalities. The study draws policy implications for EU Cohesion programming and Sustainable Development Goals 4, 8, 9, 10, and 17.

Abstract: This study advances a modular microfoundational framework to examine how individual-level actions aggregate into macro-level technological innovation capabilities and operational performance in innovation intermediaries in emerging economies. Grounded in microfoundations theory (Coleman's bathtub model) and cybernetic principles (Viable System Model), we dissect three interdependent modules to diagnose systemic issues within institutional voids: (i) macro-level system viability and technological emergence; (ii) meso-level organizational practices mediating R&D collaboration; and (iii) micro-level behaviors of boundary-spanning agents driving knowledge integration. Empirical evidence from a Brazilian Research and Technology Organization (RTO) reveals how context-specific microfoundations determine operational efficiency and technological emergence. Theoretically, we contribute by operationalizing Coleman's micro-macro link to enable cross-context benchmarking of innovation intermediaries and decoding how meso-micro-level actions co-evolve with ecosystem-level innovation. By shifting the diagnostic focus to the fine-grained dynamics of individuals and their interactions, our study offers actionable levers for managers and policymakers to optimize operational viability in contexts of institutional uncertainty. Implications for innovation policy, ecosystem governance, and the design of intermediary organizations in late-development settings are discussed.

Abstract: The proposition that expanding education uniformly advances the 2030 Agenda has acquired the status of settled consensus — embedded in SDG 4, amplified by UNESCO, and routinely invoked in national development strategies. This paper shows that the consensus is empirically half-true. Using a balanced panel of 193 countries observed over 2000–2023, I estimate 96 two-way fixed-effects regressions connecting eight measures of education — spanning expenditure, enrolment, completion, attainment, and accumulated stock — to twelve Sustainable Development Goal outcomes. The estimates reveal a pronounced block asymmetry. On the social side, educational expansion is a first-order anti-poverty instrument: a one-standard-deviation increase in secondary enrolment is associated with a 0.16-log-point reduction in the $2.15/day extreme-poverty headcount and a 4.35-point reduction on the 0–100 SDG-1 composite, both significant at the 0.1% level. On the environmental side, the same treatment produces a β = +0.048 (p = 0.014) coefficient on production-based CO₂ per capita and a β = −0.260 (p = 0.031) coefficient on forest area — effects that are statistically significant but directionally perverse. Income inequality worsens rather than improves with schooling expansion (β = +0.71 on the Gini, p = 0.006). The asymmetry survives Driscoll–Kraay standard errors, Oster (2019) sensitivity bounds (δ > 1), and two-year lagged specifications. The findings qualify the optimistic narrative that frames education as "the key" to sustainable development: schooling is a central social block instrument, but cannot substitute for dedicated environmental policy. The 2030 architecture needs instrument–goal pairs, not universal keys.

Abstract: The transition to energy independence requires the application of flexible approaches and the diversification of distributed energy generation technologies. This article substantiates the feasibility of using a hybrid power supply system incorporating renewable generation sources and a cogeneration plant to enhance the reliability of power supply to centralised heat supply boiler houses. Criteria and approaches to structuring and balancing the hybrid system are proposed, based on an analysis of the nature of electricity consumption and the nature of generation by its structural elements, using the example of Ukraine’s district heating enterprises. The structure of the hybrid system has been determined, taking into account seasonal variations in operation and the stochastic nature of load and generation, whilst applying economic and environmental approaches in accordance with the institutional requirements of the European Union and Ukraine. A discrete mathematical model of the energy balance with a system of technical constraints is proposed to justify the parameters of the hybrid system’s components. It is proposed that the parameters of the structural elements of the photovoltaic power station, wind power station, storage battery and cogeneration plant be determined on the basis of actual electricity consumption data from a district heating boiler house. Operating modes of the hybrid power supply system have been established depending on technological requirements and conditions for integration with the centralised electricity grid. The results obtained can be used in the design of hybrid power supply systems for district heating enterprises.

Abstract: This study critically reviews 91 peer-reviewed publications assessing the sustainability of Mediterranean tourism destinations through indicator-based frameworks. Using the Scopus database, studies were selected based on defined keywords, geographical scope, and inclusion/exclusion criteria. Data were systematically coded across multiple dimensions including methodological approaches, indicator categories, sustainability dimensions, thematic focus, stakeholder involvement, and data sources. Findings reveal a dominance of quantitative methods, particularly composite indices, multi-criteria decision-making, and GIS-based analysis. Environmental indicators—addressing water management, waste, pollution, and carrying capacity—are most prevalent, followed by economic measures of employment and revenue. Social and governance dimensions remain underrepresented. Research is geographically concentrated in Spain, Greece, and Italy, with limited coverage of North African and Eastern Mediterranean contexts. The review highlights gaps in geographic representation, dimension balance, and indicator standardization. Addressing these will require integrated frameworks, stronger stakeholder engagement, and innovative data collection methods. The findings provide guidance for developing robust, comparable, and context-specific sustainability assessment tools for the Mediterranean region.

Abstract: Digitalization of European enterprises is a prerequisite for their long-term success and competitiveness. Within the framework of the European Green Deal and the United Nations Sustainable Development Goals (SDGs), digitalization is increasingly linked to key objectives such as Decent Work and Economic Growth, Industry, Innovation and Infrastructure, Responsible Consumption and Production, and Climate Action. This study examines the level of digitalization in European small and medium-sized enterprises (SMEs), with a particular focus on Slovenia. It evaluates the position of Slovenian companies relative to the EU average and explores the relationship between digital maturity and the achievement of sustainability goals. The research combines qualitative and quantitative approaches. In-depth interviews with SME managers were used to identify key digitalization parameters, which guided as the basis for selection of relevant indicators from the Eurostat survey on ICT usage (2020–2024). In total, 558 indicators were analysed at the country level, with a subset of 60 sustainability-relevant indicators examined in detail. Additionally, the study analyses the relationship between the Digital Economy and Society Index (DESI) and the SDG Index across EU Member States. These findings contribute to a better understanding of the role of digitalization in the context of sustainable development.

Abstract: Industry 4.0 technologies offer substantial opportunities for sustainable business transformation, yet organisations consistently struggle to translate technological in-vestments into successful project outcomes. This study investigates the inner workings the "black box” of Industry 4.0 project implementation by examining how project management practices, team competencies, and decision-making processes interact. Using a mixed-methods case study of a leading industrial automation company, in-cluding a survey of project team members (n=50) and interviews with project managers (n=5), The identification of recursive feedback loop: competency gaps directly cause decision failures, and poor decision processes subsequently widen those competency gaps. Conversely, structured decision reviews and transparent communication trans-form routine choices into competency-building opportunities. An Integrated Imple-mentation Model (IIM) was proposed that explains these dynamics and demonstrates that sustainability outcomes, like resource efficiency, waste reduction, and circular economy practices emerge naturally when organisations manage processes, people, and decisions together. For practitioners, the core message is that every Industry 4.0 project should be treated as an opportunity to build long-term organisational learning capacity, not merely as a technology installation. This study provides both a theoretical framework for understanding implementation dynamics and actionable guidance for sustainable digital transformation.

Abstract: The circular economy (CE) has emerged as a central paradigm for advancing sustainable development; however, most bibliometric studies rely on single-database approaches, potentially generating partial and structurally biased representations of the field. This study addresses this limitation by developing a multi-database bibliometric framework integrating Web of Science (WoS) and Scopus to support more robust, evidence-based circular economy solutions. A total of 17,836 records from Scopus and 13,552 from WoS (2015–2024) were analyzed using the Bibliometrix R-package, combining performance analysis, science mapping, and conceptual structure techniques. The results reveal the existence of dual and complementary knowledge structures: WoS predominantly captures theory-driven, policy-oriented, and strategic research, whereas Scopus expands techno-industrial, application-oriented, and innovation-driven domains. This study demonstrates that database selection functions as an epistemic mechanism shaping knowledge structures, introducing systematic biases when single-source approaches are used. By bridging conceptual and technological knowledge domains, the proposed framework enhances the design, evaluation, and scalability of circular strategies, particularly when aligned with life cycle-based approaches (LCA, S-LCA, and LCSA) and cleaner production systems. These findings contribute to sustainability science by reframing bibliometric structures as operational enablers of solution development, providing a robust decision-support perspective for sustainability transitions, climate change mitigation, and circular economy implementation across diverse sectors.

Abstract: The article is devoted to the developing methodological approaches to multi-criteria resource optimization of technological solutions in Nature Use Projects considering the growing shortage of water and energy resources, climate change, and post-war transformation of Ukraine’s agricultural sector. The need to transition from traditional technical and economic optimization models to integrated assessment approaches which consider ecological, resource, and economic aspects of the project implementation effectiveness is substantiated. The methodological basis of the study is a combination of Multi-Criteria Decision-Making and the Water-Energy-Food Nexus concept, enabling the necessary adaptive management and formalizing the process of project decision-making under multifactor uncertainty. A set of indicators of resource-ecological and economic efficiency is proposed, including indicators of productivity, weather and climate risk, resource use, environmental reliability, investment attractiveness, etc. A key feature of this approach is the transformation of resource-ecological indicators into a value form, ensuring their integration with economic indicators within a single optimization model. Based on a machine experiment for the conditions of the Kherson region, an assessment of the effectiveness of various irrigation regimes, which differ from the project irrigation regime in terms of watering and irrigation norms in terms of their level of provision with water and energy resources, was carried out. It was determined that, under the studied conditions, the permissible deficit threshold is approximately 30%, achieving a compromise between economic efficiency and environmental acceptability. Adaptive management of irrigation regimes has been shown to reduce the resource intensity of production without a significant loss of productivity. This creates a basis for revising outdated design standards, which focused on 100% satisfaction of water needs, in favor of adaptive models that account for the real resource potential of the territory. This approach transforms irrigation from a resource-intensive industry into a tool for sustainable territorial development, where the priority is the efficiency of each cubic meter of water and kilowatt-hour of energy used, rather than gross collection. It has been proven that the implementation of resource optimization as a basic principle of natural resource project management contributes to increasing the efficiency of natural capital use, minimizing environmental risks, and ensuring the sustainable development of the agricultural sector. The obtained results can be used to substantiate engineering solutions in projects for the restoration and modernization of water management and land reclamation systems in Ukraine.

Abstract: The routine availability of generative artificial intelligence (GenAI) has weakened the validity assumptions behind many university assessment formats, especially those that rely on a polished final product as the main evidence of learning. This paper develops a discipline-neutral, process-based framework for AI-aware assessment that treats GenAI as a persistent feature of students’ academic work rather than as a temporary anomaly. Drawing on recent scholarship on assessment validity, authenticity, transparency, and institutional governance, the paper synthesizes key risks of product-only assessment and translates them into a practical redesign logic. The framework rests on five principles: transparency of AI use, auditability through evidence-of-work, visibility of reasoning, contextual authenticity, and feasibility with inclusion. Building on these principles, the article proposes a toolkit that combines staged submissions, annotated decision logs, targeted checkpoints, short oral validations, and verification-by-design in quantitative and applied tasks. Three higher-education illustrations are used to show how the framework can be adapted to writing-intensive, quantitative, and team-based assignments. The paper argues that the central assessment question in the GenAI era should shift from authorship policing to competence verification, and it offers reusable design structures, rubric dimensions, and implementation guidance for institutions seeking to preserve academic standards while supporting responsible AI use.

Abstract: The transformation of the hospitality industry has increased the demand for managerial profiles capable of integrating technical, strategic, and socio-emotional competencies. However, a persistent gap remains between the competencies required by the labor market and those developed through formal education. This study aims to identify and validate the core competencies of hotel management and to translate them into a struc-tured training proposal. A two-round Delphi study was conducted with senior hotel management experts (n = 42 in round 1; n = 32 in round 2), using a competency matrix derived from prior research. Quantitative analysis included frequency distributions, weighted scores, and consensus indicators. The results show a high level of consensus stability (3.1% disagreement), leading to a final matrix of 43 competencies organized into four dimensions: operational, interpersonal, cultural-communicative, and strategic. In-terpersonal and leadership competencies emerged as the most prominent, highlighting their structural role in effective managerial performance. Based on these findings, a pro-gressive training framework is proposed, structured around three domains (operations, leadership, and strategy) and supported by a metacognitive pathway that integrates planning, monitoring, and evaluation processes. The study contributes to the profes-sionalization of hotel management by providing an empirically grounded competency model and a coherent framework for aligning educational programs with industry demands.

Abstract: The paper explores the barriers preventing engineering SMEs in Nigeria from adopting e-commerce. Using the TOE framework, the study employed a qualitative design and semi-structured interviews with ten SME owners and managers across major engineering sectors. Technological barriers comprise poor ICT infrastructure, unreliable internet connectivity, cybersecurity threats, and logistics inefficiencies. Organizational barriers include lack of technical skills, financial constraints, lack of leadership support, and change resistance. Environmental barriers include regulatory uncertainty, a preference for traditional transactions, economic instability, and low market readiness. The study extends the TOE framework by contextualizing the barriers to e-commerce in perspective within a project-based industry in a developing economy and recommends that Nigerian engineering SMEs, policymakers, and technology providers collaborate to enhance digital infrastructure, strengthen cybersecurity, promote digital literacy, offer financial and policy support, and develop affordable, innovative e-commerce and logistics solutions to accelerate e-commerce adoption

Abstract: Economic crises inflict severe damage on employment, output, and social welfare, making their early detection a priority for policymakers and financial institutions. Traditional early warning systems, built on logistic regression and signal-extraction methods, have shown limited out-of-sample accuracy and an inability to capture the nonlinear dynamics characteristic of modern financial systems. Over the past decade, machine learning (ML) and artificial intelligence (AI) methods have emerged as promising alternatives. This paper presents a systematic review of the literature on AI and ML applications in economic and financial crisis prediction, covering 47 studies published between 2008 and 2025. I categorize the methods into five families: tree-based ensembles, neural networks and deep learning, support vector machines, natural language processing approaches, and hybrid architectures. The review reveals that ensemble methods—particularly random forests and gradient boosting—consistently outperform logistic regression, with Bluwstein et al. (2023) reporting AUROC values of 0.870 for extremely randomized trees versus 0.822 for logistic regression across 17 countries from 1870 to 2016. Credit growth and yield curve slope are identified as the most robust predictors. Deep learning shows promise for temporal dependencies but faces data scarcity challenges. NLP-based approaches using central bank communications represent a rapidly growing frontier. We identify key challenges including event rarity, interpretability demands, and concept drift, and conclude with a research agenda for the field.

Abstract: Port State Control (PSC) inspections play a critical role in enforcing international maritime safety and environmental standards, yet little is known about how compliance behavior interacts with economic cycles. This study examines the relationship between vessel detentions and freight market conditions using monthly data from the Paris and Tokyo Memoranda of Understanding (MoUs) over the period 2010-2021. A system of simultaneous equations is estimated using the Generalized Method of Moments (GMM) to address the bidirectional relationship between detention activity and freight market conditions, proxied by the Baltic Dry Index (BDI). The results demonstrate a positive and statistically significant bidirectional relationship: vessel detentions increase during periods of strong freight market conditions, while past detentions contribute to higher freight rates by constraining effective fleet supply. Institutional factors, including flag state, classification society (IACS membership), and ISM-related deficiencies, are also found to significantly influence detention risk. These findings challenge the conventional expectation that stronger market conditions promote higher compliance and instead suggest the presence of opportunistic behavior during economic upswings. The study contributes to the literature by linking regulatory compliance with economic cycles and highlights the importance of adaptive, risk-based enforcement strategies.

Abstract: The transition toward sustainable food systems requires innovative approaches to man-aging perishable products, where inefficient inventory practices contribute significantly to global food loss and environmental degradation. This study develops a circular economy-oriented inventory optimisation framework for dairy supply chains that inte-grates environmental externalities and waste valorisation pathways into operational de-cision-making. Departing from traditional linear "produce-consume-dispose" models, we embed three core sustainability mechanisms into a stochastic dynamic program-ming framework: (1) progressive environmental cost internalisation aligned with EU Emissions Trading System carbon pricing, capturing both waste-related emissions and cold-chain energy footprints; (2) circular economy value-recovery channels that redirect near-expiry products to secondary applications (animal feed, biogas production, indus-trial processing) rather than disposal; and (3) deterioration-aware demand management that minimises resource throughput while maintaining service levels. Empirical cali-bration using Ukrainian dairy industry data demonstrates that sustainability-integrated inventory policies reduce waste generation by 4.8–10% relative to conventional ap-proaches, with high-deterioration products showing the greatest potential for improve-ment. We identify a critical threshold in the circular economy: when salvage recovery rates exceed 35%, waste transforms from an environmental liability into an economic and ecological asset, fundamentally altering the sustainability calculus of inventory de-cisions. Environmental costs account for 4.6% of total operating expenses at current car-bon prices, a share projected to increase substantially under tightening climate regula-tions. Our findings provide actionable guidance for dairy supply chain stakeholders pursuing the Sustainable Development Goals (SDGs 2, 12, 13): processors should estab-lish circular-economy partnerships that achieve salvage rates above 35%, implement product-specific policies for high-deterioration items, and proactively integrate carbon pricing into inventory optimisation. The framework bridges sustainable operations the-ory and circular economy practice, offering a replicable model for transitioning perish-able food supply chains toward closed-loop, low-waste configurations that simultaneous-ly reduce environmental impact and enhance economic performance.