Business, Economics and Management

Abstract: Emerging-market equity exchanges require regime forecasting systems that are continuous in time, robust to heavy-tailed distributions, and optimised against false alarms. No existing method addresses all three simultaneously, and no prior study has reported a crisis false alarm rate on JSE equities. We propose S-NODE-ANF-RRC: a Stochastic Neural ODE embedded within an Adaptive Neuro-Fuzzy Risk-Regime Clustering architecture, motivated by the Heston stochastic volatility framework and integrated by a Milstein scheme with Lyapunov-regularised dual-loss training. The system is evaluated as a one-step-ahead probabilistic forecaster (h = 1 trading day) on 2696 daily observations across 17 JSE securities (March 2015–March 2026). Gaussian mixture clustering on raw features (kurtosis 54.8) inflates ARI by 1.3×; log-transformation corrects this systematic artefact. Two operational profiles emerge after correction: the N-ODE-ANF-RRC achieves the lowest cost (10,350 bp, 65.1% below GMM), longest lead time (0.71 days), and best MCC (0.596); the S-NODE-ANF-RRC achieves the lowest false alarm rate among probabilistic architectures (FAR = 0.051, log-loss = 1.07), with a 42.0% cost reduction versus GMM (bootstrap 95% CI [5, 250, 19, 600] bp; McNemar p = 0.027). Ablation confirms drift, diffusion, and dual-loss as the minimum viable daily-frequency configuration. The interdisciplinary fusion of physics-informed SDE dynamics, time series forecasting, and fuzzy interpretability yields two complementary JSE risk tools: an early-warning forecaster (N-ODE) and a low-false-alarm crisis classifier (S-NODE). Code and data: https://doi.org/10.5281/zenodo.19787658.

Abstract: Robo-advisors have expanded access to automated investment services, but many platforms continue to rely on relatively static onboarding procedures and limited forms of user interaction. This study examines how participants with investment experience respond to two next-generation robo-advisory design features: financial digital twins, understood as dynamic investor profiles that integrate goals, risk tolerance, cash-flow patterns, and anticipated life events, and conversational artificial intelligence (AI), understood as an interactive interface for explaining recommendations. Using a scenario-based randomized 2 × 2 online experiment, 336 adult respondents with self-reported investment experience, recruited through professional and academic networks, were assigned to one of four robo-advisor scenarios that varied the personalization architecture, standard versus digital twin, and the interface style, plain dashboard versus conversational AI, while holding the portfolio recommendation constant. The results show that digital-twin personalization increases perceived personalization and privacy concern, indicating that more adaptive advisory architectures may be viewed as both more relevant and more data-intensive. Conversational AI increases the perceived interactive quality of the advisory experience, while the clearest adoption-related patterns emerge when it is combined with digital-twin personalization, particularly for selected indicators of stated behavioral willingness. Given the limited internal consistency of several secondary composite measures, the findings are best interpreted as evidence of scenario-based investor responses rather than as validated evidence of actual adoption behavior or confirmed psychological mechanisms. The study contributes to behavioral FinTech research by clarifying the personalization-privacy tension in AI-enabled robo-advisory services and by offering design implications for more transparent, interactive, and responsibly personalized digital wealth management systems.

Abstract: Organizational resilience has become a strategic priority for firms facing environmental, economic, and institutional disruptions, particularly in emerging economies where access to financial and technological resources remains limited. This study examined the influence of organizational psychological resources on sustainable business resilience through the mediating role of green innovation in formalized firms in northern Peru. A quantitative, cross-sectional, and explanatory design was applied to data from 130 firms, each represented by a manager or coordinator. The model included three latent constructs: organizational psychological resources, green innovation, and sustainable business resilience, measured through 27 Likert-scale indicators. Data were analyzed using covariance-based structural equation modeling with the WLSMV estimator in lavaan. The results showed strong measurement quality, with standardized loadings between 0.898 and 0.988, Cronbach’s alpha values from 0.985 to 0.990, composite reliability above 0.992, and AVE above 0.929. The structural model showed satisfactory fit and confirmed that organizational psychological resources positively influenced green innovation, while green innovation positively influenced sustainable business resilience. The indirect effect was significant, indicating partial mediation. The findings suggest that psychological well-being, work engagement, and empowerment constitute key internal resources for transforming green innovation into sustainable resilience.

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: This paper investigates the causal relationship between artificial intelligence (AI) investment, smart city governance infrastructure, and urban total factor productivity (TFP) across ten leading digital economies over the period 2010--2026. Drawing on a novel panel dataset that integrates ICT capital expenditure, digital infrastructure indices, Global Innovation Index scores, and the United Nations E-Government Development Index, we estimate dynamic System Generalized Method of Moments (GMM) models combined with Spatial Durbin specifications and machine-learning-based regime clustering. Our results indicate a statistically and economically significant positive association between AI investment and urban TFP: a ten percent increase in AI investment (as a share of GDP) is associated with approximately 1.5 percent higher TFP, conditional on digital infrastructure endowment and innovation capacity. We further document an inverted-U (EKC-type) relationship between AI intensity and employment polarization, suggesting that economies surpassing a threshold AI investment level of approximately 5.2 percent of GDP begin to experience convergence in skill demand. Spatial spillover effects are quantitatively important, with indirect TFP effects accounting for roughly one-third of total impacts. These findings are robust across alternative specifications, sub-period analyses, and a jackknife leave-one-out procedure. Our study contributes to the emerging literature on AI-driven urban transformation by providing causal panel evidence and a tractable theoretical framework, and offers policy implications for economies at different stages of digital transition.

Abstract: Carbon-intensive firms face mounting pressure to develop substantive corporate climate risk management (CCRM), yet its firm-level and country-level antecedents remain unevenly understood. Drawing on stakeholder and institutional theory, we examine three drivers of CCRM: sustainability governance, voluntary climate-membership commitments, and regulatory quality. Our data cover 1,295 firm-year observations across 43 countries over 2018–2022. We estimate ordered logistic regressions with lagged regressors, with ordered probit, two-step system GMM, and sub-sample robustness checks. In the main specification, sustainability governance and regulatory quality are both positive antecedents (β = 2.441 and β = 1.676, p < 0.001); climate membership exerts a sector-conditional effect concentrated in energy and basic materials. Sub-sample analyses reveal that internal governance dominates among non-state-owned firms, while among state-owned firms (a sub-sample heavily concentrated in Chinese SOEs) regulatory quality dominates instead. We frame the latter as suggestive context-conditional substitution rather than a universal feature of state ownership. CCRM is highly persistent (system-GMM lagged coefficient = 0.693, p < 0.001), suggesting that climate risk management is best understood as a path-dependent organizational capability built incrementally over time. Firms strengthening CCRM should invest in integrated governance architecture; regulators should treat regulatory-quality reform as complementary to direct climate mandates.

Abstract: This study examines the relationships between innovation, green management implementation, and their effects on business sustainability and value creation in Indonesian healthcare companies. Innovation is measured using Value Added Intellectual Capital (VAIC) efficiency, while green management implementation is proxied by Environmental, Social, and Governance (ESG) scores. Business sustainability is operationalized through carbon disclosure practices, and value creation is measured via Market Value Added (MVA). Using panel data from 30 listed firms (2019–2023) and applying fixed and random effects regression models showed that green management implementation significantly and positively influences carbon disclosure, supporting the role of ESG practices in enhancing sustainability transparency. However, innovation capacity demonstrates no significant direct effects on either carbon disclosure or market value creation, challenging conventional assumptions about intellectual capital's impact on sustainability and value performance. Component-level analysis improves explanatory power, showing that structural capital efficiency has a marginally negative effect on carbon disclosure, while social ESG scores emerge as the strongest driver of transparency. However, neither carbon disclosure nor ESG performance translates into immediate value creation, with MVA explained more by firm-specific characteristics and profitability (ROA). These findings extend intellectual capital and transparency theories by showing how social performance channels shape disclosure practices.

Abstract: This paper provides the first systematic, cross-country empirical comparison of the Recovery and Resilience Facility (RRF) and Cohesion Policy funds (CPF) in the domain of renewable energy deployment. Covering 14 EU Member States, the analysis combines quantitative cross-country evidence on financing volumes, technology mixes, implementation speed, and reported capacity achievements. The findings show that the RRF represents a major amplification of EU renewable energy financing, with planned allocations exceeding Cohesion Policy expenditure by a factor of five to ten. At the same time, claims of superior performance-based delivery require qualification: green transition financial progress lags the general RRF disbursement rate, milestone fulfilment for renewable energy falls short of planned indicative rates in most countries, and reported operational capacity figures raise plausibility concerns. The analysis reveals no meaningful correlation between milestone and target fulfilment and progress with renewable energy Country-Specific Recommendations, suggesting that administrative compliance with milestones does not immediately translate into structural reform outcomes. These findings carry direct implications for the design of the post-2027 EU financial framework, particularly regarding the stabilisation of performance indicators, the introduction of attribution protocols for reform-linked achievements, and the preservation of complementarity between performance-based and non-performance-based approaches.

Abstract: This study examines the evolution of research on mergers and acquisitions (M&A) in the banking sector through a bibliometric analysis aimed at identifying the main con-tributors, dominant research themes, and emerging gaps in the literature. The study situates banking M&A research within broader discussions of efficiency, market structure, integration performance, and financial stability, while highlighting the need to better understand how scholarly influence and thematic development shape the field over time. Using bibliometric techniques, the analysis evaluates publication trends, journal con-centration, authorship patterns, international collaboration networks, citation struc-tures, keyword co-occurrence, and thematic mapping. The study synthesizes relation-ships among influential publications, institutions, and countries to assess how ideas circulate and which research themes remain central or underexplored within the liter-ature. The findings reveal a concentrated body of scholarship dominated by a limited number of journals, recurrent author networks, and strong transatlantic collaboration patterns, with relatively limited representation from the Global South. Research themes related to efficiency, firm performance, and market structure occupy the core of the literature, whereas technology integration, sustainability considerations, consumer outcomes, and conduct risk remain comparatively peripheral. Citation patterns are highly une-ven, reflecting a small group of highly influential studies alongside a broader set of context-specific contributions. The analysis also identifies a persistent gap between pre-merger strategic narratives and post-merger integration realities, particularly in relation to operational outcomes and systemic risk considerations. The study concludes that future research would benefit from integrating traditional finance approaches with transaction-level integration measures, governance and op-erational performance indicators, and more robust identification strategies around regulatory and policy shocks. The findings further suggest that banking practitioners should place greater emphasis on integration feasibility, risk-control capabilities, digi-tal transformation readiness, and sustainability considerations when evaluating and implementing merger strategies.

Abstract: Most audit fee studies treat the relationship between fees and client risk as symmetric. A unit increase and a unit decrease in client risk are assumed to produce equal but opposite fee responses. We examine whether that assumption holds in the U.S. audit market using 4,090 firm-year observations of U.S. listed companies from 2010 to 2022 and a first-difference specification with firm and year fixed effects. The data show that audit fees rise by about 1.06 percent for each one-unit increase in the Audit Analytics Risky Client Score (p < 0.001). The response of fees to risk decreases is not statistically different from zero (coefficient = 0.001, p = 0.708). The implied stickiness differential is 0.0093 (p = 0.058). The stickiness ratio is approximately 0.13. Fees adjust downward at about 13 percent of the rate at which they adjust upward following an equivalent risk movement in the opposite direction. The pattern is robust to a strict definition of risk decreases, holds in both early (2010–2016) and late (2017–2022) sub-samples, and is corroborated by an alternative risk proxy based on loss-status transitions, where fees rise 4.3 percent on entry to loss status and do not adjust on exit. The result has implications for audit pricing models, audit committee oversight, and the way fee dynamics are interpreted by users of audit fee data.

Abstract: Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Our findings reveal that AI-optimized biofuel systems on degraded lands recover 75-94% of prime-land bioenergy yields, sequester 8.3-10.5 t CO2e ha-1 over 30 years, reduce lifecycle GHG emissions by 55-88%, and generate internal rates of return of 9-22% when green finance instruments are systematically integrated. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively capable of reducing project risk scores by 30-45% and expanding the investable universe of degraded-land biofuel projects by an estimated 340%. We develop the AI-Biofuel-Land Restoration (ABLR) conceptual framework with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management.

Abstract: The study investigates overconfidence bias in the Bangladesh equity market through the relationship between the market returns, and the trading volume in a nonlinear, information-theoretic model. Building upon the traditional literature on returns and volume, the study differentiates between the total market returns and unexpected market returns, the latter being the unexpected information shocks represented under the Market Index Model. Transfer Entropy with bootstrap inference is used to determine directional and asymmetric causality across various market states, including bullish, bearish, crisis, extended crisis, and COVID-19. The findings indicate that the total market returns give weak and inconsistent evidence of overconfidence, which is bi-directional but limited information flow. Conversely, unexpected market returns have a statistically significant directional effect on trading volume, which represents strong evidence of overconfidence. The results also reveal that overconfidence is conditional as it is stronger in normal and bullish market contexts, and weaker during times of crisis. Asymmetric analysis reveals that the overreaction of investors is more pronounced when the market trends are negative, implying that unexpected losses stimulate an amplified trading effect due to the feeling of mispricing and recovery hopes. The results have significant implications on market efficiency, investor behavior and regulatory policies to improve market stability and facilitate informed financial decision-making.

Abstract: Generative artificial intelligence (GAI) is increasingly embedded in personal financial, yet little is known about how models make recommendations using financial information and demographic cues. This study audits three frontier GAI models, GPT 5.5, Gemini 3.1 Pro, and Claude Opus 4.7, using a full-profile conjoint experiment in which each model evaluated the same 1,000 hypothetical investor profiles and selected among standardized conservative, balanced, and aggressive portfolios. Investor profiles systematically varied attributes, including risk tolerance, time horizon, goal type, income, and age, gender, ethnicity, marital status, and employment type. Ordered logistic regressions and matched-profile comparisons show that all three models base recommendations primarily on legitimate financial inputs, especially risk tolerance and time horizon. Gender and ethnicity do not significantly influence recommendations, although age affects all models and marital status affects ChatGPT. However, the models are not interchangeable: they differ significantly in overall risk appetite and in how they translate risk tolerance, time horizon, goal type, and age into portfolio choices, with economically meaningful differences in predicted recommendations for identical clients. These findings suggest that contemporary GAI investment advice exhibits limited evidence of conventional demographic bias but introduces a distinct form of platform risk arising from model-specific advisory logic.

Abstract: This paper examines the transmission of monetary policy through bank credit by distinguishing between credit to non-financial corporations and household credit in an emerging economy. Using an ARDL–ECM framework with structural break analysis, it investigates the relationships between credit, the policy rate, and inflation over time. The results suggest that the conventional interest rate channel is limited, as the policy rate does not exhibit a statistically robust association with credit in either the short or long run. Inflation shows a differentiated and context-dependent association, remaining weak for corporate credit but more consistently related to household credit, which may reflect the role of nominal conditions in shaping borrowing behavior. Credit dynamics also differ across segments. Household credit appears more persistent and adjusts gradually, whereas corporate credit is less inertial but more sensitive to instability, pointing to heterogeneous adjustment processes. In addition, the estimated relationships are not stable over time, as their sensitivity to macroeconomic variables varies across periods. Overall, the findings indicate that monetary transmission is heterogeneous and time-varying rather than uniform. These results should be interpreted as conditional relationships within the empirical framework and provide a disaggregated perspective on how macroeconomic conditions are associated with credit dynamics in bank-based emerging economies.

Abstract: The study applies a multiphase, multimethod research approach based on the participatory methodology. It integrates perspectives of professionals in the travel industry and academic experts with the aim to develop an integrated conceptual model of the AI and IoT influence on work, skills development, and job attractiveness in the industry. The research provides a comprehensive understanding of the ways in which digital technologies indirectly shape employment through changes in work processes and development of transferable digital and socio-emotional skills. The paper aimed to donate to redefining the perception of work in tourism and hospitality, emphasizing the sector not only as a career choice but also as a platform for the acquisition of skills relevant in other industries as well. The outcomes revealed that the employees’ aspirations to enter or stay in the industry are not directly influenced by AI and IoT technologies; rather, their effects are mediated through changes in work processes and, more importantly, through the development of skills. The study contributes theoretically by evolving and analytically confirming an incorporated theoretical model that connects technology implementation, work transformation, skills development, and employment outcomes. Practically, the results underscore the importance of human-centered implementation strategies, emphasizing training, communication, and employee inclusion to maximize the benefits of digital technologies.

Abstract: Given the "dual-carbon" goals of China, research has been carried out on the impact of the digital economy on carbon emission intensity. Based on the panel data of the 288 cities in China from 2013 to 2022, a two-way fixed-effects model is employed in this paper to study how the digital economy affects carbon emission intensity at the level of cities and urban agglomerations. The results show that the development of the digital economy reduces the intensity of urban carbon emissions, and there is cross-regional spatial spillover at the level of urban agglomerations. According to the results of the mechanism test, there are two paths for "industry-technology" transmission: At the city level, the digital economy can reduce pollution by improving the structure and upgrades of the industrial system; At the level of urban agglomeration, it can strengthen green-technology innovation capabilities. According to the analysis of heterogeneity, polycentric agglomeration, optimisation-and-upgrading type agglomeration and coastal areas have relatively good carbon reduction effects. Based on the above, personalised regional policies will be formulated to promote the development of the digital economy in line with carbon reduction objectives.

Abstract: The paper seeks to find the direct and indirect association amongst capital structure and firm value among all the nonfinancial firms listed in PSX from (2014-2019). Secondary panel data was used to conduct analysis. Structural equation modelling technique in Stata was used to estimate the direct effects. MedSEM, a special package for Stata, was used to estimate the indirect effects. Results showed that capital structure had no direct effect on value of the firm, but financial distress mediated the association amongst capital structure and value of the firm. Substantial indirect effect clearly manifests the existence of indirect nature of association amongst capital structure and value of the firm.

Abstract: Against the backdrop of escalating global climate risks, this study systematically investigates the effects, boundary conditions, and interactive mechanisms of two core policy instruments—green finance and environmental regulation—on agricultural supply chain resilience. Using panel data of China’s Shanghai and Shenzhen A-share listed agribusiness firms from 2010 to 2025 and a two-way fixed effects model, we find: First, climate risk serves as a critical external pressure driving resilience building, yet its positive impact is conditional on a “capacity threshold”—only significant for firms with high resilience and large scale. Second, the two policy instruments exhibit heterogeneous structural thresholds: green finance demonstrates a “supporting-the-weak” effect, enhancing resilience primarily in small and medium-sized enterprises (SMEs) with low resilience, but is constrained by an “institutional–technological” double threshold. In contrast, environmental regulation displays a “scale bias”, with its statistically significant positive effect limited to large firms. Third, climate risk negatively moderates the effectiveness of green finance: under high-risk conditions, firms tend to divert green funds toward short-term relief, eroding long-term resilience investment, and this “policy failure” risk is particularly pronounced among SMEs. Fourth, mechanism tests rule out the traditional mediation channel of alleviating financing constraints; moreover, the two policy instruments have not yet formed significant synergistic effects under the current institutional framework. This study extends the application boundaries of the resource-based view and dynamic capabilities theory in high-risk contexts, provides micro-level empirical evidence on policy instrument implementation biases in heterogeneous market structures, and offers theoretical support and practical references for developing climate-smart agricultural supply chain policies.

Abstract: Technology plays a vital role in the way teacher’s work, communicate, and share their knowledge particularly after COVID pandemic. Thus, it is a matter of great importance both from theoretical and practical point of view to understand the factors that govern knowledge sharing through technologies. This study integrates Teo’s composite model of Technology Acceptance(TA) and Knowledge Sharing(KS) construct of Van den Hooff & Van Weenen, to empirically examine the relationship between technology acceptance and knowledge sharing among teachers. A study used a descriptive-correlational cross-sectional research approach. 225 participants responded to the survey. The study used Technology Acceptance Questionnaire, Teo( 2011) with 20 items(α=0.84) and Knowledge Sharing Questionnaire by Van den Hooff and Van Weenen ( 2004), with 12 items(α=0.96). One- sample t-test was used to find out the degree to which Technology Acceptance and Knowledge Sharing is practised by teachers. Pearson’s correlation was used to identify if there is any positive relationship between Technology Acceptance components and Knowledge Sharing elements. Structural equation modelling (SEM) was used to study whether any of the factors of Technology Acceptance can significantly predict the two types of Knowledge Sharing.Perceived Usefulness (PU) and Facilitating Conditions (FC) emerged as the most influential factors of Technology Acceptance in driving teachers’ Knowledge Sharing.

Abstract: This study examines how digital income diversification, measured by the non-interest income ratio (NII), affects bank performance and risk in emerging Asian markets. Drawing on panel data from 44 banks across China (36) and Thailand (8) over 2022-2025, the analysis employs fixed-effects regressions, mediation analysis, and subsample testing to unpack the performance implications of digital transformation. Results indicate that NII exerts a statistically significant positive effect on bank profitability (ROA and ROE), with no corresponding increase in risk exposure as measured by Z-score. The relationship is markedly stronger among large banks, consistent with scale advantages in technology infrastructure, network effects, and regulatory compliance cost amortization. Cost efficiency does not mediate the NII-performance nexus, suggesting that revenue-side mechanisms dominate in this context. Cross-country comparisons reveal stable but modest effects in China's mature digital ecosystem against larger but less precise coefficients in Thailand's early-stage transition. These findings challenge the Western-centric complexity-risk narrative and highlight institutional boundary conditions governing digital banking outcomes in emerging markets.