Business, Economics and Management

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: While generative AI enhances efficiency, it simultaneously undermines the professional authority of high performers, thereby posing an expert identity threat to these high performers. Contrary to traditional perspectives that view identity threats as triggers of passive defensiveness, this paper constructs a moderated mediation model based on the dual-dimensional structure of identity (centrality-solidarity). A two-stage questionnaire analysis of 323 employees reveals that: (1) high performers perceive stronger expert identity threats; (2) these threats conversely prompt them to guide colleagues in using AI; (3) task-AI fit positively moderates this mediation effect. This study breaks through the traditional defense paradigm, confirming that high performers achieve identity compensation and power regeneration through helping others and empowering them, providing insights for organizations to promote the transformation of excellent employees into "human-AI collaboration mentor ".

Abstract: This study expatiates on the entrepreneurial cognitive process, schema and interjection with an Adapted Strong Structuration Theory (MMSST) i.e., Mixed Methods Strong Structuration Theory based on established Social Cognitive theory using schemas, scripts, metacognition, frames and evocation model. Schemas and Entrepreneurial Meta-Cognitive composite processes based on units of analysis: individuals, teams, organizations and networks in UBIs and CBIs are expanded upon. The study proposes an integration of (Meta)Cognition in UBIs and CBIs with MMSST and also a framework that observes composite of different structures (knowledge, beliefs, learning, collaborations, co-creation) in performing entrepreneurial tasks, adapting to the environment or external conditions and during value co-creation. The core synopsis within this study is that socio-human structures (internal: habitus and conjectures) in MMSST emanates from schemas(cognitive) and are developed overtime based on higher-order cognition(meta-cognition) evoked via frames using evocation models. This study integrates and identifies schemas and meta cognition taking cognizance of environmental and behavioral patterns as it impacts UBIs and CBIs studies while being grounded in MMSST. The study contributes to the understanding of the roles of entrepreneurial cognition and meta-cognition in UBIs and CBI on value co-creation.

Abstract: Contemporary stakeholders persist in pressuring firms toward environmental stewardship, dynamic organizations demonstrate high-level capability and strategically respond to market evolutions and aligned operations toward sustainability. The study aimed at investigating the connection between environmental knowledge and awareness and products/service innovation, the mediating roles of knowledge process capability-application dimension and sustainable innovation and the moderating effect of innovation orientation. Through questionnaire, empirical data was collected from 354 bank employees operating in Western Nigeria whose bank made the list of the most sustainable bank in Nigeria in 2024 sustainability ranking. SPSS and SmartPLS was used for data analysis and testing of hypotheses. The findings showed a significant positive association between environmental knowledge and awareness and products/service innovation, additionally, knowledge process capability-application and sustainable innovation significantly mediate the relationship between environmental knowledge and awareness and products/service innovation, while innovation orientation negatively moderate between environmental knowledge and awareness and knowledge process capability-application. However, innovation orientation did not moderate the relationship between sustainable innovation and product/service innovation. The study underscore the importance of knowledge management strategy, firm innovation orientation toward sustainability stewardship in service organizations and provide valuable theoretical insights for management scholars. An integrated dynamic capability and absorptive capacity theories underpinned the study.

Abstract: Nigeria operated multiple exchange rate regimes which have created significant distortions in the economy resulting to low standard of living, inflation, and loss of investors' confidence. To address these challenges, the Nigerian government officially implemented exchange rate unification to close the gap between official and parallel market rates to reduce poverty level in the economy. The purpose of this study is to examine the effect of exchange rate unification on poverty in Nigeria which is yet to be investigated in the literature. The Autoregressive Distributed Lag model (ARDL) was used as a method of analysis whilst poverty is measured using multidimensional poverty. Data on Multidimensional Poverty, Exchange Rate Unification (ERU) (ratio between official and parallel rate), inflation, economic growth, unemployment, and government social expenditure were extracted from World Bank’s data base and Central Bank of Nigeria statistical bulletin. The study found that exchange rate unification is very significant in explaining long run poverty dynamics than conventional macro economic variables in Nigeria. The study further reveals that exchange rate unification exerts significant reduction in multidimensional poverty, contradicting the perception that unification harms the poor through short-run inflationary pressures, thereby establishing exchange rate unification as an effective policy instrument for inclusive development.

Abstract: Cryptocurrency markets are characterized by elevated volatility, structural instability, and rapidly changing investor sentiment, which significantly challenge traditional portfolio optimization methodologies. Under such conditions, static portfolio allocation models frequently fail to ade-quately incorporate uncertainty, behavioral adaptation, and dynamic market responsiveness into the investment decision-making process. In parallel, recent advances in artificial intelligence and data-driven financial systems have transformed modern portfolio management by enabling adaptive investment strategies capable of processing large volumes of financial information in real time. Nevertheless, AI-assisted portfolio systems often remain highly sensitive to noisy market signals, parameter instability, and excessive portfolio rebalancing, particularly in volatile crypto-currency environments. This study proposes a behaviorally adaptive portfolio optimization framework under interval uncertainty that integrates robust optimization principles, behavioral finance mechanisms, non-linear transaction costs, and AI-assisted allocation adjustment within a unified cryptocurrency in-vestment structure. The proposed methodology introduces a behavioral preference parameter capable of dynamically adjusting portfolio composition according to varying investor attitudes toward uncertainty and market expectations. Simultaneously, interval uncertainty modeling is employed to represent ambiguous financial parameters through bounded intervals, while artificial intelligence mechanisms act as adaptive decision-support tools that improve portfolio respon-siveness under changing market conditions. The empirical analysis is conducted using major cryptocurrency assets, including Bitcoin, Ethereum, Solana, and Binance Coin, over the period January–June 2025. Multiple behavioral portfolio configurations are evaluated in order to analyze the interaction between profitability, downside risk, portfolio stability, and transaction cost efficiency. The results indicate that the proposed framework improves risk-adjusted portfolio performance and generates more stable allocation strategies compared to traditional static portfolio optimization approaches. In particular, the integration of behavioral adaptation and robust optimization contributes to reduced sensitivity to estimation uncertainty and unstable market fluctuations. The findings further demonstrate that AI-assisted adaptive allocation mechanisms improve portfolio flexibility and support smoother portfolio transitions under volatile market conditions. Moreover, the incorporation of nonlinear transaction costs leads to more realistic and practically implementable cryptocurrency investment strategies. Overall, the proposed framework provides a robust and behaviorally interpretable approach for modern cryptocurrency portfolio management and contributes to the growing literature at the intersection of artificial intelligence, behavioral fi-nance, and uncertainty-aware portfolio optimization.

Abstract: The introduction of green cosmetics has undoubtedly captured researchers’ interest following the global surge in environmental awareness and sustainable consumption. However, consumers do not consistently opt for green products, causing businesses to approach green marketing from an opportunistic standpoint. This study explores consumer awareness of green cosmetics in South Africa and Zimbabwe, two emerging markets with contrasting economies. Using an exploratory qualitative design, data were collected through purposive sampling using in-depth interviews with six participants from South Africa and six from Zimbabwe. The findings reveal that participants in both countries prefer to receive information from reference groups and social media rather than from business advertisements. In Zimbabwe, lack of awareness and product availability hinder adoption, while South African consumers are guided by packaging cues and sustainability claims. In addition, limited production and product unavailability affect the purchasing of green cosmetics, as indicated by the participants in both countries. This study contributes to the limited body of knowledge on green consumerism in African markets by offering comparative qualitative insights from two contrasting economies and offers implications for marketers and policymakers promoting sustainable consumption.

Abstract: We employ an empirical framework for real-estate securities that incorporates portfolio optimization, return distribution tail diagnostics, risk metrics, modeling of long-range dependence in return volatility, regression against benchmark indices, and option pricing; treating these as necessary layers of a risk management structure that concentrates on downside risk. Optimization compared mean-variance against downside sensitive conditional value at risk. Tail behavior was assessed via skewness, kurtosis and extreme value theory; volatility persistence was examined using ARMA--FIGARCH models. Benchmark dependence was examined via the capital asset pricing model (CAPM) employing endogenous and exogenous market proxies. Insurance instruments via European options were priced using a doubly subordinated normal inverse Gaussian pricing model capable of modeling skewed, heavy-tailed return distributions. Significant findings for the optimized portfolios include: return distributions with losses that are heavier-tailed than gains; a transition in time from moderate to high long-range dependence in conditional volatility; smaller values of CAPM ``alpha'' and ``beta'' for minimum-risk portfolios compared to tangent portfolios; and significant implied volatility values.

Abstract: Although small and medium enterprises (SMEs) anchor employment and output across Sub-Saharan Africa, their uptake of artificial intelligence (AI) lags global benchmarks, and prevailing explanations dwell on capital, infrastructure, and institutional voids while overlooking the leadership competencies that determine whether available resources are mobilised at all. Addressing this gap, the present study asks how the digital leadership capabilities of SME owner-managers shape their intention to adopt AI in Nigeria, and through what organizational mechanism and under what boundary condition this influence operates. Anchored in the Diffusion of Innovation Theory and the Tigre–Henriques–Curado model of digital leadership, a cross-sectional survey was administered to owner-managers of registered SMEs drawn from six states; a sample of 390 was derived from a population of 23,290 firms using the Taro Yamane formula with proportionate allocation, and 306 valid responses were retained. Partial Least Squares Structural Equation Modeling (WarpPLS 8.0) was applied after confirming reliability (Cronbach's \( \alpha \): 0.69–0.84; composite reliability: 0.83–0.88), convergent validity (AVE: 0.56–0.67), and common method bias control. Strategic (\( \beta \) = 0.298), interpersonal (\( \beta \) = 0.245), and personal-attribute (\( \beta \) = 0.129) capabilities each significantly raised AI adoption intention, whereas delivery-related capabilities (\( \beta \)= 0.090, p= 0.057) did not, indicating that pre-adoption intention is governed by cognitive-strategic and relational competencies rather than execution skills. Organizational innovation climate partially transmitted the effects of strategic and interpersonal capabilities, and firm size amplified the interpersonal pathway in medium-sized firms. The study contributes a leadership-centred account of AI adoption in an under-researched African setting and, by estimating mediation and moderation within a single framework, clarifies both why and when digital leadership translates into AI readiness, yielding capability-specific guidance for owner-managers and SME-support policy.

Abstract: Small and medium-sized enterprises (SMEs) in the food and beverage industry play an important role in advancing economic, social and environmental sustainability in emerging economies. However, many resource-endowed SMEs remain unable to translate their potential into sustainable business outcomes. This study examines the factors influencing the business sustainability of food and beverage SMEs in Northeastern Thailand and explains how organizational capabilities are hierarchically structured to create sustainable competitive advantage. A mixed-methods design was employed. Qualitative in-depth interviews with entrepreneurship-incubation experts informed the development of the research instrument, while quantitative data were collected from 401 entrepreneurs in the three highest-GPP provinces: Nakhon Ratchasima, Khon Kaen and Ubon Ratchathani. Structural equation modelling was used to test causal relationships among strategic planning, finance and accounting, leadership, business knowledge, product development and external factors. The model demonstrated acceptable fit and explained 92% of the variance in business sustainability. Strategic planning emerged as the strongest direct driver, supported by business knowledge and product development as complementary capabilities. In contrast, finance, leadership and external factors showed no significant direct effects, suggesting indirect or contextual roles. The study proposes a Planning-Centric Sustainability Model and contributes to Resource-Based View and Dynamic Capabilities perspectives in emerging economies.

Abstract: This paper examines how behavioral intention, combined with risk tolerance, financial confidence, and self-control, relates to consumer credit usage. Inspired by the Theory of Planned Behavior, which suggests that behavioral intention is the direct precursor to actual behavior, our study investigates how these financial personality traits moderate the relationship between intention and the uptake of consumer credit. Using a combination of survey and bank register data, we focus on the amount of outstanding balance on consumer credit as the objective measure of consumer credit behavior. The results show that higher risk tolerance and greater financial confidence both are associated with increased credit use among those with the intention to borrow, while self-control mitigates this relationship. We observe that gender differences in financial behavior are notable: men who report high confidence and an intention to use consumer credit tend to carry higher outstanding balance, whereas higher self-control in men is linked to lower credit use. Additionally, although string behavioral intention and higher income both predict greater consumer credit use, self-control mitigates this association among high-income individuals. Our study adds to consumer credit research by revealing the complex interplay between behavioral intention, risk tolerance, financial confidence, and self-control in relation to actual consumer credit usage.

Abstract: Operational labor shortages have emerged as a critical challenge for hospitality organizations, particularly in highly seasonal tourism destinations such as Greece, where service experiences are closely associated with cultural identity and authentic hospitality. While existing hospitality research has primarily examined understaffing through operational and human resource management perspectives, limited attention has been given to its implications for the organizational capacity to sustain authentic hospitality experiences. Drawing on Service-Dominant Logic (SDL), this study conceptualizes authentic hospitality as a co-created organizational outcome shaped through employee interactions, cultural transmission, and service delivery processes. Using survey data collected from 201 hotel employees in Greece, the study examines the relationship between operational labor shortages, organizational pressures, and perceived threats to authentic hospitality delivery within hotel operations. The findings indicate significant positive associations between work stress and service quality decline, as well as between cultural knowledge and perceived threats to authentic hospitality delivery. Multiple regression analysis further demonstrates that reactive hiring, serious understaffing, and payroll cost pressure significantly predict perceived risks to authentic hospitality co-creation, while service quality decline showed a positive but statistically non-significant effect in the final regression model. The study extends hospitality authenticity literature by conceptualizing authenticity not solely as a tourist perception construct, but also as an internally produced organizational outcome dependent on workforce stability, cultural integration, and service co-creation capacity. The findings further highlight the strategic importance of workforce planning, recruitment quality, and cultural onboarding for the long-term sustainability of authentic hospitality experiences in Greek hotels.

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Against the backdrop of rapid digital acceleration and a tightening UK labor market, Small and Medium-sized Enterprises (SMEs) are increasingly pressured to move beyond manual administrative processes to bridge the national "productivity gap." While digital transformation is often framed within a large corporate context, this research investigates the specific role of Human Resource Information Systems (HRIS) in driving operational efficiency and strategic performance within resource-constrained SME environments. This study aims to evaluate how HRIS integration transforms HR functions from an administrative burden into a strategic asset, while identifying the unique implementation hurdles and performance outcomes experienced by resource-constrained UK firms.This study adopts an interpretivist, qualitative approach to examine how HRIS integration transforms HR functions from administrative burdens into strategic assets. Data were collected through semi-structured interviews with 12 HR managers across the hospitality, retail, and recruitment sectors, and analyzed using thematic analysis.The findings reveal a three-stage, non-linear process of value creation: (1) administrative liberation through automation, (2) strategic visibility via data-driven insights, and (3) digital friction arising from cultural and technical barriers. While HRIS enhances operational efficiency and decision-making capability, its strategic impact is contingent upon organizational readiness, particularly digital literacy and change management practices. This study contributes to the HRIS and SME digital transformation literature by conceptualizing "digital friction" as a critical mediating construct, demonstrating that value creation in SMEs is an iterative and context-dependent process rather than a linear implementation. For practitioners, the study provides a roadmap for navigating digital transitions, emphasizing that the "human element" of change management is as vital as the technological infrastructure. While limited by its qualitative scope, the research sets a foundation for future longitudinal studies to measure the long-term ROI of integrated HR platforms in diversifying SME sectors.

Abstract: The purpose of this study is to investigate the extent to which tourists appreciate sus-tainable tourism and what effect eco-friendly practices have on the decision-making process of selecting a hotel. Through the use of large-scale analysis of online reviews of hotels and the application of sentiment analysis techniques, the research investigates the impact of environmental factors (e.g., energy usage reduction, minimizing waste, and promoting nature experiences) on customer perspectives and decision-making processes for lodging. This research adopts an approach that utilizes machine-learning based sentiment analysis as its source of understanding. The results of this research demonstrate that as more individuals become aware of sustainable tourism; however, sustainability often plays a secondary role in determining whether or not to stay at a specific hotel when compared to such lodging attributes, as comfort, price and quality service. Based upon these findings, this research indicates that while many tourists’ value sustainable tourism and make an effort to choose eco-friendly lodging establishments, the influence of sus-tainability on tourists' lodging decisions is not as strong as other attributes. These results indicate important implications for hotel managers that will help them to balance envi-ronmental stewardship with a competitive stance.

Abstract: The article aims to determine the dynamics of international trade in cultural goods, using the European Union market in the years 2016–2024 as a case study. The analysis takes into account changes in the total value of transactions as well as the shares of individual Member States in imports and exports. The research is based on a desk research method (secondary data analysis), drawing primarily on data from the Statistical Office of the European Union (Eurostat) and selected secondary sources. The main thesis assumes that trade in cultural goods within the EU was characterised by significant dynamics in the period 2016–2024, revealing substantial differences between national markets in terms of transaction value and, consequently, their shares in the structure of EU imports and exports. The findings confirm considerable quantitative transformations in international trade in cultural goods within the EU during the analysed period. Until 2019, the total value of trade increased markedly, while the temporary decline resulted from the United Kingdom’s withdrawal from the EU in January 2020. Growth dynamics became visible again in the years 2020–2023, with exports exceeding imports. Germany, France and Italy maintained their leading positions in trade in cultural goods. At the same time, particular attention should be paid to the dynamic rise in the role of countries such as Ireland and Romania, both in the structure of EU imports and exports.

Abstract: Family enterprises are a significant component of developed and emerging economies. However, the factors influencing their performance remain inadequately explored, particularly regarding social-emotional wealth (SEW). This study aimed to investigate whether the dimensions of SEW, as delineated by the FIBER model, serve as predictors of family firms' self-assessment concerning their economic status. Utilizing data from 365 Polish family-owned enterprises, gathered through Computer-Assisted Telephone Interviewing (CATI) and Computer-Assisted Web Interviewing (CAWI) methods, six algorithms of over-patterned machine learning were employed: the naive Bayes classifier, linear and nonlinear support vector machines, decision trees, the k-nearest neighbours algorithm, and logistic regression. Model efficacy was assessed through cross-validation, with relevance (accuracy) identified as the principal evaluative criterion. The findings revealed that SEW variables exhibited a robust predictive capability for softer performance indicators, such as customer satisfaction, product/service quality, and employee retention. Conversely, their explanatory power for hard financial metrics was constrained. The results underscore the value of integrating behavioural constructs with mathematical models to enhance the understanding of organisational performance, offering fresh theoretical and practical insights into machine learning applications within family business research.

Abstract: This research examines the determinants of Bitcoin (BTC) valuation from January 2011 to December 2025 using Autoregressive Distributed Lag (ARDL) models. The empirical evidence supports the hypothesis that the monetary policy of the United States Federal Reserve—specifically liquidity expansion and interest rate adjustments—drives price dynamics, confirming a pro-cyclical nexus. At the microeconomic level, the density of active institutional addresses and the marginal cost of production significantly influence price trajectories. Furthermore, heightened market volatility, represented by the VIX, exerts a statistically significant negative impact on BTC returns. The findings suggest that Bitcoin has transitioned into a sophisticated value asset, underpinned by production efficiencies and an expanding institutional base. Consequently, Bitcoin represents a viable alternative to centralised financial systems, offering a potential hedge against inflation and the erosion of purchasing power. The study concludes that digital assets warrant inclusion within conservative institutional portfolios, notwithstanding the inherent speculative nature of the market.

Abstract: ESG-themed funds rely heavily on agency ratings to guide portfolio decisions, yet substantial disagreement persists across rating providers. This study examines whether and how ESG rating disagreement affects holdings by ESG-themed funds. Using a sample of Chinese A-share listed firms from 2015 to 2022, we employ a two-way fixed-effects model to identify its impact. We find that greater rating disagreement significantly reduces both the likelihood of ESG fund ownership and the scale of fund holdings. This finding is robust to alternative dependent variable specifications, extended governance controls, propensity score matching, and lagged model designs. Mechanism analysis reveals that ESG rating disagreement suppresses ESG fund holdings by degrading the corporate information environment, as evidenced by two complementary proxies: the firm-level information ecosystem quality and auditor-assessed information risk. Heterogeneity tests show that the inhibitory effect is concentrated among non-state-owned enterprises and firms in eastern China, and is particularly pronounced during the mature stage of the firm life cycle. Our findings contribute to the literatures on information frictions in capital markets and the determinants of ESG institutional investment, with practical implications for rating standardization and portfolio risk management.

Abstract: Credit risk prediction is a significant challenge in modern financial systems due to the dynamic and nonlinear nature of borrower behavior. This study introduces a Bayesian-Optimised Hybrid DeepSurv–LSTM framework for dynamic credit risk forecasting, integrating survival analysis with temporal deep learning methodologies. The framework combines DeepSurv for hazard modeling and Long Short-Term Memory (LSTM) networks for analyzing borrower repayment behavior, using Bayesian optimization to identify optimal hyperparameters and enhance model generalization. It was assessed using borrower-level financial data, including demographic, behavioral, and transactional variables. Results showed that the Bayesian-optimised DeepSurv–LSTM model outperformed XGBoost, standalone DeepSurv, and standalone LSTM models across classification and survival-analysis metrics. The hybrid model achieved a C-index of 0.8617, ROC-AUC of 0.9726, accuracy of 94.83%, F1-score of 0.9197, and the lowest Integrated Brier Score of 0.1293. Statistical validation confirmed the significance of these improvements. The findings suggest that integrating survival-aware hazard modeling with temporal deep learning enhances credit default prediction and provides a robust framework for financial risk management and early credit risk monitoring in dynamic banking environments.

Abstract: This study examines customer retention intention in neobanking environments using a theory-informed explainable machine learning framework. Existing digital banking research typically relies on linear modelling approaches to explain retention behaviour, potentially overlooking nonlinear, value-range-dependent, and interaction-based predictive patterns. Using a publicly available survey of 305 neobank users, this study compares regularized linear models, a partial least squares structural equation modelling (PLS-SEM)-inspired benchmark, and XGBoost under repeated nested cross-validation. SHapley Additive exPlanations (SHAP)-based explainability, SHAP interaction analysis, generalized additive model (GAM) diagnostics, construct-level aggregation, and construct-sensitivity checks are used to interpret model behaviour and assess robustness. The results show that XGBoost substantially outperforms the linear benchmarks, achieving the lowest average RMSE and highest average R² across 100 out-of-sample test-fold estimates. Trust-related indicators provide the largest share of model-based predictive importance, followed by perceived security and switching costs. SHAP and GAM diagnostics suggest that trust and switching costs may contribute to retention intention in heterogeneous and nonlinear ways, while perceived security displays a more stable positive predictive pattern. Age-related nonlinearities appear weak and should be interpreted cautiously given the young sample profile. The analysis also suggests possible non-additive relationships between trust and perceived security. The study contributes to digital banking and FinTech research by showing how explainable machine learning can complement theory-driven retention models, identify potentially nonlinear predictive patterns, and preserve interpretability. The findings offer practical insight for trust-building, visible security assurance, and retention diagnostics in neobanking contexts.