Abstract: Entrepreneurship in South Africa extends beyond conventional sustainable development, incorporating several viewpoints that mirror the nation's distinct socio-economic and cultural context. This study examines several perspectives, emphasising the interaction of technical, artistic, socio-economic, historical, and institutional elements in influencing entrepreneurial activity. Technological advancements and management innovations create new opportunities for small and medium-sized enterprises (SMEs), while cultural diversity fosters creativity and innovation. Socio-economic issues, such as elevated unemployment rates and historical disparities, necessitate targeted entrepreneurial activities to promote job creation and community empowerment. This study elucidates how entrepreneurship in South Africa can extend beyond sustainable development, thereby fostering economic growth and social transformation by examining various perspectives. The qualitative study utilised original data collected from selected participants through interviews. The data was analysed through a thematic data analysis, and results show that entrepreneurship in South Africa is a dynamic and complex phenomenon that transcends the conventional emphasis on sustainable development. This study elucidates the critical influence of technology breakthroughs, cultural diversity, socio-economic issues, and historical disparities on entrepreneurial activity. These elements collectively enhance the resilience, inventiveness, and innovation of South African entrepreneurs.

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Entrepreneurship plays a critical role in addressing youth unemployment in emerging economies, yet the psychosocial mechanisms through which entrepreneurial attitudes are formed among first-year university students remain underexplored. This study examines whether creativity mediates the relationship between motivational factors and entrepreneurial attitude, and whether perceived family support moderates this mediation process. A cross-sectional survey was conducted with 600 first-year students from public and private universities in northern Peru (Trujillo, Piura, and Chiclayo). Results revealed that entrepreneurial creativity fully mediates the relationship between intrinsic motivation and entrepreneurial attitude, while the direct effect was nonsignificant. Family support significantly moderated the creativity-attitude relationship, with stronger effects at higher support levels. The integrated model explained 51% of variance in entrepreneurial attitude. These findings demonstrate that intrinsic motivation operates through creativity development to shape entrepreneurial attitudes, and that family support amplifies this transformation. Universities in collectivist cultures should prioritize creativity-enhancing pedagogies combined with structured family engagement programs to effectively cultivate sustainable entrepreneurial ecosystems among incoming students.

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In recent years, industry development has become closely connected with ICT and trade openness. This research explores how industry, ICT, and trade openness affect the environment, highlighting the importance of investing in low-carbon technologies and energy-efficient machinery. The goal of this research is to investigate the long-run and short-run impacts of industrialization, ICT, trade openness, and economic growth on per capita carbon emissions in Lithuania from 2000 to 2024. This study uses the ARDL econometric model along with several diagnostic tests. The Breusch-Godfrey Serial Correlation test confirmed no serial correlation, while the Breusch-Pagan-Godfrey test indicated that no heteroscedasticity exists. The Ramsey RESET test confirmed that the model is correctly specified and significant. Additionally, the VIF multicollinearity test shows that no multicollinearity exists between the research variables. The research outcomes show that industrialization, ICT, and economic growth have a positive relationship with per capita carbon emissions and are harmful to the environment, whereas trade openness has a negative effect on per capita carbon emissions in Lithuania and contribute environmental sustainability. The novelty of this research lies in its long-run and short-run analysis of the interaction among the selected variables. This research provides policy suggestions aimed at enhancing environmental quality.

Abstract: The oil and gas sector operates in a high-risk environment defined by capital intensity, regulatory uncertainty, and volatile commodity prices. While Artificial Intelligence (AI) technologies such as machine learning and predictive analytics promise to reduce risk and enhance profitability, the precise mechanisms converting AI adoption into tangible financial success remain under-researched. Grounded in the Resource-Based View and Technology Adoption Theory, this study employs a dual-methodological approach: a bibliometric analysis of 201 Scopus-indexed publications (2010–2025) and a focused financial analysis of industry supermajors (BP and Shell). The results demonstrate that AI adoption alone does not guarantee superior financial results. In-stead, the relationship is mediated by operational efficiency, which accounts for up to 45% of the variation in financial performance. Specifically, the application of AI in predictive maintenance and digital twins drives improvements in asset uptime and cost control, which directly correlate with stabilized Return on Average Capital Em-ployed (ROACE), even during periods of oil price volatility. By synthesizing five key research clusters, this study provides a strategic framework verifying that AI’s value is realized through a causal chain: AI enables operational efficiency, which in turn se-cures financial resilience and capital returns.

Abstract: Cognitive biases are evolutionarily adaptive mental shortcuts rooted in automatic processing, yet their expression varies widely across individuals due to differences in personality structure, cultural communication patterns, and generational socialization. Drawing on research in behavioral psychology, cognitive science, organizational behavior, and cross-cultural communication, this paper presents an integrated framework for predicting dominant cognitive biases by combining three complementary models: Kahler’s process communication model, Lewis’s cultural communication model, and Strauss and Howe’ generational cohort theory. The study outlines the design of an 11-item instrument grounded in these frameworks and evaluates its preliminary validity, reliability and perceived accuracy. By identifying how psychological, cultural, and temporal factors shape bias tendencies, the model offers insight into how individuals interpret organizational purpose, challenge assumptions, and adapt their decision-making in uncertain environments. This predictive approach also supports talent mapping, and the formation of cognitively diverse teams, which strengthen strategic adaptability, and contribute to more effective and inclusive organizational practices.

Abstract: The rapid integration of artificial intelligence (AI) and smart technologies is transforming hospitality operations, yet guest acceptance remains uneven, shaped by utilitarian, experiential, ethical, and cultural evaluations. This study develops and empirically tests a multicomponent framework to explain how these factors jointly influence two behavioral outcomes: whether AI-enabled features affect hotel choice and whether guests are willing to pay a premium. A cross-sectional survey of 689 hotel guests in Tirana, Albania, an emerging hospitality market and rapidly growing tourist destination in the Western Balkans, was analyzed using cumulative link models, partial proportional-odds models, nonlinear and interaction extensions, and binary robustness checks. Results show that prior experience with smart or AI-enabled hotels, higher awareness, and trust in AI, especially trust in responsible data handling, consistently increase both acceptance and willingness to pay. Perceived value, operationalized through the breadth of identified benefits and desired features, also exhibits robust positive effects. In contrast, privacy concerns selectively suppress strong acceptance, particularly financial willingness, while cultural–linguistic fit and support for human–AI collaboration contribute positively but modestly. Interaction analyses indicate that trust can mitigate concerns about reduced personal touch. Open-ended responses reinforce these patterns, highlighting the importance of privacy, human interaction, and staff–AI coexistence. Overall, findings underscore that successful AI adoption in hospitality requires aligning technological innovation with ethical transparency, experiential familiarity, and cultural adaptation.

Abstract: Capital mobilization is often portrayed as the main bottleneck to decarbonization in emerging economies, yet BRICS countries struggle to scale clean energy even when funding is formally available. This paper argues that capital is a necessary but insufficient condition for the energy transition in the BRICS because institutional fragility creates binding financial barriers. Drawing on a comparative, document based analysis of Brazil, Russia, India, China and South Africa, the study synthesizes academic literature, international reports and policy documents on clean energy investment, governance and financial systems. It develops a conceptual framework linking political and regulatory risk, fiscal constraints and the role of public development banks to the cost and allocation of capital for low carbon projects. The analysis highlights common patterns—such as high perceived country risk, policy instability and weak project pipelines—that raise the cost of capital and deter private investment, as well as country specific institutional configurations that condition outcomes. The paper concludes that improving governance, policy credibility and the design of public financial institutions is as important as mobilizing new capital, and discusses implications for climate finance strategies and future research on the political economy of the energy transition in emerging markets.

Abstract: The Fourth Industrial Revolution represents a fundamental challenge for organisational leaders attempting to navigate digital transformation successfully. Despite widespread technological advancement, many organisations struggle to convert their digital investments into meaningful business results global failure rates hover between 70-80%. This research explores the leadership approaches that drive successful Industry 4.0 adoption across various sectors and geographies, closing the gap between what organisations can do technologically and what they're actually ready to accomplish. We employed a mixed-methods approach combining quantitative analysis of secondary data from 847organisations across 15 countries with qualitative document analysis of 85 corporate reports and industry publications. Our findings reveal that transformational leadership particularly when leaders articulate clear digital visions and empower their employees correlates strongly with successful adoption (r=0.74, ρ <0.01).Organisations that invest in comprehensive leadership development see 43% higher digital transformation success rates. Three leadership dimensions stand out: digital literacy paired with strategic vision, hands-on change management capability, and the ability to build collaborative cultures. Perhaps most importantly, we discovered that the most successful leaders reverse conventional thinking they devote 70% of transformation resources to developing people and organisational culture, 20% to processes and systems, and only 10% to pure technology.

Abstract: Background— Youth unemployment persists across East Africa despite rapid tertiary-enrolment growth. Many graduates aspire to create ventures, yet student-loan obligations and absent safety nets truncate the time they can afford to experiment. This study tests whether post-graduation income-security instruments furnish an “entrepreneurial runway” that converts intention into business formation. Methods — We assemble a harmonised panel of 25,000 graduates from Kenya, Uganda, and Tanzania (2016-2022), linking higher-education loan-board ledgers, national labour-force surveys, enterprise registries, and UN-Habitat urban indicators. Logistic regressions with cubic-spline and interaction terms estimate the effect of income support—loan-repayment moratoria, stipends, or wage subsidies—on entrepreneurial entry within 24 months, with clustered errors at university level. Results — Receiving any income support raises the odds of launching a venture by 82 % (marginal effect +5.5 percentage points, p < 0.001). The relationship is non-linear: predicted entry peaks at 20 % when support lasts 9-12 months and tapers thereafter, indicating an optimal runway length. Direct stipends outperform loan holidays, while high student-debt burdens and multiple dependents attenuate, and urban opportunity density amplifies the treatment effect. Conclusions — Modest, time-bound income security demonstrably mitigates risk aversion and institutional voids, doubling graduate entrepreneurship across three national contexts. Policies pairing a one-year stipend or grace period with targeted debt relief and rural ecosystem investment could unlock inclusive, innovation-driven growth. Originality/Value — This is the first large-sample, cross-country evidence from Sub-Saharan Africa that quantifies a causal link between graduate income security and venture formation, integrating behavioural, institutional, and human-capital lenses to refine entrepreneurship theory and inform design of cost-effective support programmes.

Abstract: This article presents a decision support system developed as part of a Research and Development project undertaken by La Logistica srl, a third-party logistics company specializing in the storage and distribution of hydro-sanitary products. The approach is methodological and focuses on the comprehensive analysis of a case study related to freight exit processes with the aim of defining and implementing a software application to support the short-term management of picking and loading operations for product delivery. The developed decision support system integrates past data series analysis and projections, time series simulations, What-If analysis capabilities, and real-time monitoring within a single computational paradigm to anticipate peak points in the freight exit process. The developed decision support system is designed to accumulate and structure operational data from the warehouse management system software, to analyse the periodic rhythms of orders received to generate graphical projections of expected peak points and working hours based on the analysis of past data series and is able to dynamically review projections via real-time monitoring capabilities to adapt projections to actual progress made at any given time. Additionally, What-If analytics capabilities facilitate management's use of various workforce combinations to determine the feasibility of the process at any time, while identifying potential bottlenecks before they occur. Test results conducted with the corporate team indicate improvements in workload visibility and readiness for associated short-term programming strategies, while preventing operational disruptions through advance alerts on operational overload points.

Abstract: The study aimed to analyze the diversification and competitiveness of maize exports from the United States, Brazil, Argentina and Ukraine over the period 2020–2024. A quantitative, descriptive design was adopted, using secondary data from Trade Map. Export market concentration was measured with the Herfindahl–Hirschman Index, while international competitiveness was assessed through the normalized revealed comparative advantage, computed by country, year and main destination. The results indicated clearly differentiated patterns. The United States exhibited an increasingly concentrated structure centred on Mexico, with strong advantages only in Japan and Colombia, which heightened structural vulnerability. Brazil preserved low concentra-tion levels and displayed robust, widely distributed advantages in Middle Eastern and Asian markets. Argentina combined mostly favourable diversification with stable ad-vantages in Asia, Africa and South America, although a mild upward trend in concen-tration emerged toward 2024. Ukraine showed acceptable diversification but moderate and volatile competitiveness, constrained by logistical and geopolitical disruptions, particularly in trade with Türkiye. The study concluded that export sustainability de-pended on the joint dynamics of competitive specialisation and destination diversifica-tion, with Brazil and Argentina achieving the most balanced profiles. It was recom-mended that each country implement differentiated strategies in market risk manage-ment, logistical upgrading, macroeconomic and regulatory stabilisation, and penetration of emerging markets in order to reduce structural vulnerabilities in the global maize trade.

Abstract: Against the dual demands of green transformation and digital integration in the man-ufacturing industry, green value co-creation has become a core pathway for enterpris-es to integrate stakeholder resources and synergize ecological and economic values, with the widespread application of artificial intelligence (AI) providing critical ena-bling support. Drawing on panel data of Chinese A-share listed manufacturing firms on the Shanghai and Shenzhen Stock Exchanges from 2015 to 2024, this study investi-gates the impact of AI development on corporate green value co-creation and its intrinsic mechanism. The results demonstrate that: AI development exerts a signif-icantly positive effect on manufacturing enterprises’ green value co-creation, which is achieved by enhancing firms’ technological spillover capacity and total factor produc-tivity (TFP); financing constraints negatively moderate the aforementioned relation-ship, while corporate influence plays a positive moderating role; heterogeneity analysis reveals that this impact is more pronounced for enterprises under voluntary regula-tion, state-owned enterprises (SOEs), and high-pollution enterprises. This study eluci-dates AI’s role and mechanism in corporate green development at the micro level, provides empirical evidence for related research, and offers practical insights to pro-mote enterprise AI advancement and green value co-creation.

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Sustainable tourism is increasingly important for local sustainable livelihoods; however, its development is often hindered by financial limitations. Hence, a better understanding of the constraints on sustainable tourism and the strategies to tap into untapped funds to improve its financial situation is paramount. This study uses a novel CPBM approach to answer the question: Is there a strong relationship between customer satisfaction and their willingness to pay (WTP) for improving Community-Based Tourism (CBT) service? If so, how can satisfaction be improved? We employ a Culture Tower-based approach that integrates PLS-SEM and BMF method to analyze a dataset collected from 275 young people in December 2024. We found that alongside respondents’ financial status, environmental quality concerns, and environmental assessment, customer satisfaction is a strong predictor of WTP, showing a positive association with WTP for CBT service improvement. Furthermore, we also found that food, service quality, and infrastructure factors are strong determinants of customer satisfaction with CBT services. This finding suggests that improving customer satisfaction is the key to enhancing the financial sustainability of CBT, which in turn necessitates greater attention and investment from the local government and private sector. Our study offers significant policy implications regarding financial management, service quality, investment strategies, and the coordination of actor participation for facilitating sustainable tourism at the local level in Lac village and beyond.

Abstract: Micro, small and medium-sized enterprises (MSMEs) play a fundamental role in the socio-economic development of Ibero-America. However, they face structural and contextual challenges that limit their sustainable growth. This study analyses the determining factors for the growth of MSMEs in the region, using a quantitative approach based on the Random Forest model applied to a database of 1,796 observations obtained by a team of researchers from different universities belonging to the Foundation for Strategic Analysis and Development of Small and Medium-sized Enterprises (FAEDPYME). The results show that adequate corporate governance, human talent management with strong organisational communication, the development of skills that reduce the digital divide, innovation and perception of the environment are hierarchically decisive factors for their development and sustainability. Relevant patterns that enable business sustainability are discussed and a basis is provided for the formulation of public policies aimed at strengthening the productive fabric. The study provides scientific evidence that enriches the discussion on innovation and sustainability in Ibero-American MSMEs.

Abstract: Technological flexibility is widely recognised as a driver of product innovation, yet its role in enabling the transition toward sustainable products remains insufficiently understood. This study addresses this gap by examining how technological flexibility shapes product–market strategies through two pathways: (1) Technological Flexibility – Environmental Scanning – Product-Market Strategies (T–E–S) and (2) Technological Flexibility – Environmental Scanning – Circular Economy Principles – Product-Market Strategies (T–E–C–S). Using an abductive design and a survey of 300 medium and large manufacturing firms in Poland, we analyse eight dimensions of environmental scanning, seven circular-design principles, and four Ansoff-based strategic orientations. Non-parametric correlation tests and Kruskal-Wallis analyses and post hoc test show that technological flexibility significantly increases environmental-scanning intensity. Yet only five macro-environmental segments trigger circular-design behaviour, and only two principles – repairability and waste minimisation translate into sustainable product strategies. Results reveal two competing logics: a commercial logic focused on market opportunities and an ecological logic centred on sustainable design. Their integration occurs only under high technological flexibility and scanning engagement. The study contributes new empirical evidence on mechanisms linking flexibility, scanning, circularity, and sustainability-oriented product innovation.

Abstract: In the context of the growing prominence of socially responsible investment, the debate over whether sustainable corporate practices translate into sustained shareholder value has intensified. As a key tool for aligning their investment portfolios with responsible/sustainable corporate practices, investors rely on listed companies’ Environmental, Social, and Governance (ESG) ratings. This study aims to investigate the long-term impact of ESG practices on the stock performance of listed companies. We perform a Q1 2000 – Q1 2025 backtest to analyse the comparative performance of a Best-in-Class ESG portfolio, constructed by the top 30 listed companies with market capitalisations above USD 2 billion ranked by Morningstar Sustainalytics’ ESG Risk Ratings as of 31 March 2025 against the S&P 500 Total Return index. We found that ESG leaders exhibited superior risk-adjusted performance, outperforming the S&P 500 Total Return Index. The Best-in-Class portfolios achieved a substantially higher CAGR and Sharpe ratio, while maintaining maximum drawdowns that remained comparable to the benchmark S&P 500 Total Return index. We also found that ESG advantages were more pronounced in market downturns, with the Best-in-Class ESG portfolio showing better CAGR and Sortino ratios. The findings of this study demonstrate that responsible governance and management create benefits for all stakeholders, including investors, society and nature, in the broadest sense of these terms.

Abstract: The worldwide drive for decarbonization, alongside digitalization and decentraliza-tion, has caused a complete overhaul of electricity distribution networks. Distribution network operators need to develop smart platforms that integrate distributed energy resources (DERs) into sustainable power systems to achieve operational excellence and system reliability. The research evaluates existing studies to understand current DER integration techniques, the methodological constraints they entail in smart grid systems, and innovative integration methods. The Web of Science database search used an advanced query to retrieve 4049 articles spanning 1993 to 2025, yielding 94 relevant publications for thematic evaluation. The study identifies six essential strategic elements that form the basis of a four-stage conceptual framework for network distribution transformation across technological, sustainability, economic, social, resilience, and integration. The research studies demonstrate both methodological variety and thematic development, yet they reveal inadequate regulatory systems, poor interconnections, and limited prosumer participation in rural areas. The article provides an operational strategic framework which helps decision-makers and grid operators develop effective energy policies to tackle current challenges.

Abstract: This paper illustrates an original Data-Driven Analysis on logistics matters, developed within the scope of a research & development project conducted by La Logistica S.r.l. and financed by the Apulia Region. The paper uses real operative data for developing and validating a complete analytical tool to forecast volume saturation on pallets. Compared to other studies on similar matters, which frequently make use of simulated and ideal sets of data, this research provides a unique opportunity to test, on an empirical ground, how very advanced Data Science approaches could be successfully applied on industrial processes.The paper develops a complete analytical investigation combining diagnose analytics, forecasting and model-interpretability to compare the performances of many different Machine Learning algorithms, such us K-Nearest Neighbors, Decision Trees, Random Forest, Boosting, Support Vector Machines, and Linear Models. The results obtained after an overarching comparison show how KNN is significantly more accurate and trustful in relation to any other method, outperforming any other on any key error measure and interpretability factors. The addition of contribution and importance investigation on variables extends to this paper a unique degree of originality, showing which SKUs’ physical variables are most significantly influencing volume saturation on pallets, and offering immediate management implications related to their operative meaning. The complete methodological pipeline is grounded on real operative data regarding La Logistica S.r.l., making this paper capable to show how operative Data-Driven solutions could play an innovative role into improving operative continuity within logistics matters.

Abstract: This study examines the relationship between Wasta—a social network based on family, lineage, tribe, and extended family ties—practiced by senior HRM employees, and how it affects entrepreneurial creativity, innovation, and sustainable development in the MENA region. The study also explores why entrepreneurs and countries in the MENA region are not ranked among the top 100 innovators in the Global Innovation Index. Additionally, it addresses why Wasta, as practiced by HRM employees, can impede sustainable development. The author drew on Amabile's Componential Theory of Organizational Creativity and Model of Creativity and Innovation in Organizations. Evidence was gathered from articles on Wasta, secondary data from the Global Innovation Index (GII) for 2025, and the Global Entrepreneurship Monitor (GEM NECI) in 2024. Secondary datasets were analyzed using constant comparative analysis of documents. These datasets included accessible online indices, the Global Innovation Index in 2025, the World's Most Innovative Companies Index by Forbes, and the Top 100 Global Innovators 2024 Rankings by Clarivate. The study develops a theoretical framework for the link between Wasta and sustainable development. It concludes that Wasta, when practised by senior HRM employees, is likely a reason why MENA entrepreneurs fall short in achieving sustainable development and why the region's countries are not among the top 100 innovative countries globally. The study answers why Wasta hinders sustainable development among MENA entrepreneurs. It recommends that entrepreneurs recognise the importance of fair HRM practices in hiring, supervisor selection, candidate selection, and promotions to foster innovation and sustainable development. The conclusions may also encourage policymakers to create and enforce new rules to reduce Wasta if they aim to stimulate innovation, sustainable development, and economic advantage in the MENA region.

Abstract: To address the challenges of fragmented user behavior and delayed strategy response in digital sales of fast-moving consumer goods (FMCG), this study proposes a consumer behavior modeling and sales path optimization model that integrates a generative adversarial mechanism. Based on unified encoding of multi-source data, the model incorporates a multi-head attention mechanism and feature interaction strategy to extract user preference features, and employs a Generative Adversarial Network (GAN) to dynamically generate sales strategies. Experiments conducted on 18 months of behavioral data from an e-commerce platform in East China demonstrate that the model achieves AUC, NDCG@10, and HitRate@10 scores of 0.8642, 0.6798, and 0.451 respectively, showing significant improvements over GRU and Transformer-based structures. The results indicate superior strategy coverage and ranking accuracy.