Water scarcity has become one of the most serious structural constraints on development in the Middle East and North Africa, or MENA. Despite substantial investment in supply-side technologies, including groundwater development, irrigation modernization, desalination, and wastewater reuse, regional water stress remains persistently high. This study examines whether these technological responses reduce water stress or whether their effects are offset by agricultural dependence and rebound dynamics. Using a balanced macro panel of ten MENA countries from 1990 to 2024, we estimate several econometric specifications and conduct robustness checks. Agricultural water dependence is consistently associated with greater water stress, whereas the relationship between technology and stress varies across specifications. It is negative in pooled models but positive after controlling for country heterogeneity. Combined with evidence of bidirectional Granger causality, these results suggest that infrastructure expansion often responds to existing scarcity rather than resolving it. Sustainable water security therefore requires not only technological innovation but also institutional reform, demand management, and agricultural transition.
In the context of climate change and the growing need for sustainable development, green finance and technological innovation have become important instruments for achieving economic growth while ensuring environmental protection. This study investigates the effects of green finance and technological innovation on sustainable growth in 86 countries, including 40 developed and 46 developing countries, over the period 2010 to 2024. The study applies pooled ordinary least squares regression, fixed effects models, random effects models and generalized method of moments estimation to address endogeneity and the dynamic characteristics of panel data. The empirical findings indicate that green finance may have a negative short-term effect on sustainable growth, whereas technological innovation contributes positively to sustainable growth, particularly in developing countries. Institutional quality is also identified as a key condition that enhances the effectiveness of green finance and technological innovation in promoting sustainable growth. The results suggest that countries should strengthen institutional quality, improve the efficiency of resource allocation and support technological innovation to maximize the benefits of green finance in the transition toward sustainable growth.
Venture capital decision-making is increasingly influenced by uncertainty, information asymmetry, and the growing volume of unstructured data. Although previous research has advanced analytical methods for portfolio selection, existing approaches rarely integrate dynamic system behaviour with qualitative market intelligence within a unified framework. This study proposes an AI-augmented decision framework that combines digital twins with large language model-based market intelligence to support venture capital investment under uncertainty. The framework integrates structured financial data with real-time qualitative insights from market narratives, while digital twins simulate startup development and scenario-based performance. Embedded within a multi-criteria decision structure, these inputs enable more adaptive, transparent, and interpretable portfolio selection. Empirical findings indicate that the proposed framework improves decision quality by increasing expected returns while reducing risk and prediction error. It also strengthens decision stability and supports more effective evaluation of trade-offs in complex investment environments. The study contributes to the literature on AI-enabled decision-making and innovation finance by demonstrating how emerging technologies can improve investment evaluation and reduce uncertainty in entrepreneurial ecosystems.
This study investigates how the internal audit function, particularly its sourcing arrangements and the level of investment allocated to it, influences the likelihood of a firm receiving a modified audit opinion. As a key component of an organization’s internal corporate governance and monitoring framework, the internal audit function plays an important role in supporting the quality and reliability of external financial reporting. The study analyses 132 firm-year observations from companies across seven sectors listed on the Main Market and ACE Market of Bursa Malaysia between 2009 and 2011. A logistic regression model with a matched-pairs sample design is employed to examine the relationship between internal audit characteristics and the probability of receiving a modified audit opinion. The findings suggest that greater investment in the internal audit function strengthens its monitoring effectiveness and reduces the likelihood of a modified audit opinion. These results are consistent with the Bursa Malaysia Corporate Governance Guide (2009) and the Malaysian Code on Corporate Governance (2007), both of which recognize internal auditing as a fundamental pillar of effective corporate governance and reliable financial reporting. By exploring the influence of internal audit sourcing arrangements and investment on modified audit opinions in the context of Malaysia, this study extends the existing literature on audit quality and corporate governance while providing empirical evidence from an emerging Asian economy.
Achieving long-term and sustainable economic development in developing countries requires overcoming the middle-income trap. This challenge is particularly important for transition economies seeking to promote balanced development across regions. This study applies the System Generalized Method of Moments (SYS-GMM) estimator to examine income convergence across Vietnam’s 63 provinces and centrally governed cities. The empirical results provide no statistically significant evidence of absolute convergence among the examined localities. However, strong evidence of conditional convergence is identified. The estimated annual rate of conditional convergence is 3.08%, indicating that approximately 22.53 years would be required for provinces and cities to reduce by half the gap between their current income levels and their respective steady-state equilibria. The findings further suggest that accelerating economic growth requires substantial progress in industrial development. Nevertheless, such development should be aligned with Vietnam’s long-term Net Zero commitments and accompanied by effective population policies, as higher population growth is found to have a negative effect on per capita economic growth.
This study investigates the effects of the digital economy on GDP growth in nine ASEAN countries from 2004 to 2023. The digital economy is measured using three commonly applied indicators: mobile technology penetration, fixed broadband subscriptions, and the share of the population using the Internet. Data were obtained from the World Bank’s World Development Indicators database and analysed using a dynamic panel model estimated through the System Generalized Method of Moments (System GMM). The findings demonstrate that the digital economy has a positive and statistically significant influence on GDP growth across ASEAN countries, although the magnitude of this effect differs among the selected indicators. The proportion of Internet users represents the most influential channel through which digitalisation contributes to economic growth, followed by mobile technology penetration. By contrast, the impact of fixed broadband subscriptions is comparatively limited. The results also show that government expenditure and trade openness make important contributions to economic growth, while the effects of capital investment and foreign direct investment depend on specific economic conditions. This study contributes to the literature by identifying the relative importance of different dimensions of the digital economy in promoting GDP growth in ASEAN. It also highlights that the productive and effective use of digital technologies is more important for economic performance than the mere availability of digital infrastructure.
This study examines whether the effect of financial inclusion on income inequality depends on institutional quality. Specifically, it tests whether control of corruption moderates the relationship between financial inclusion and income inequality. Using panel data for 15 emerging Asian economies from 2008 to 2022 and applying the System Generalized Method of Moments estimator, the results show that financial inclusion has no significant direct effect on inequality. Its distributional effect, however, depends strongly on control of corruption. Financial inclusion reduces inequality only in countries with sufficiently strong institutions, with the effect emerging once the control of corruption index exceeds approximately 0.23. The analysis also identifies an inverted U-shaped relationship, whereby inequality begins to decline only after the financial inclusion index surpasses approximately 0.72. The findings indicate that institutional reform and effective control of corruption are essential for financial inclusion to reduce income inequality.
This study examines the determinants of regional economic performance in Kazakhstan in the context of its transition toward a circular economy. It focuses on the combined effects of environmental protection expenditure, small and medium-sized enterprise activity, and fixed capital investment. Using panel data for 16 regions from 2005 to 2024, the study applies two-way fixed effects models and a dynamic panel approach based on the Anderson-Hsiao estimator to account for growth persistence and potential endogeneity. The results show that the growth effects of environmental expenditure, SME activity, and fixed capital investment vary across model specifications. Environmental protection expenditure is positively associated with regional growth in static models, but this effect becomes insignificant once endogeneity is addressed. SME activity shows a negative association in static specifications, but a positive effect in dynamic IV estimates. Fixed capital investment is positively significant in static models, although its effect weakens in dynamic specifications. Overall, the findings suggest that institutional quality, regional heterogeneity, and policy coordination shape the economic outcomes of sustainability-oriented policies. By integrating environmental, entrepreneurial, and investment-related factors within a single empirical framework, the study offers new evidence on the context-dependent nature of regional growth in a transition economy and provides policy insights for designing regionally differentiated development strategies.
This study investigates the effects of the circular economy and sustainable tourism on sustainable entrepreneurship in Saudi Arabia, with digitalisation examined as a moderating factor. A quantitative research design was employed, and data were collected from directors, managers, and employees of small and medium-sized enterprises (SMEs) operating in the tourism and waste recycling sectors. A structured questionnaire, developed through Google Forms, was distributed via email and LinkedIn professional networks. In total, 407 valid responses were obtained and analysed using SmartPLS 4.0, following a two-stage procedure comprising measurement model assessment and structural model evaluation. The findings reveal that both the circular economy and sustainable tourism exert significant positive effects on sustainable entrepreneurship. Moreover, digitalisation strengthens these relationships, confirming its important moderating role. The study contributes to the literature by developing and empirically testing an integrated model of sustainable entrepreneurship in the Saudi Arabian context. Its findings are also aligned with Saudi Vision 2030 and the Sustainable Development Goals, particularly SDG 8, which focuses on decent work and economic growth, and SDG 12, which promotes responsible consumption and production. In addition, the study offers practical and policy implications for fostering sustainable entrepreneurship, digital transformation, and environmentally responsible business practices in emerging economies.
This article examines the complexity of university dropout in the context of digital transformation and growing labor market uncertainty associated with the rapid development of artificial intelligence. Designed as a conceptual and methodological contribution rather than an empirical study, it evaluates the diagnostic potential of institutional information systems, using the CloudA platform as an illustrative case, and proposes methodological improvements intended to support student retention. The first part reviews established theoretical models of student dropout and considers their relevance to the contemporary use of learning analytics. The central contribution of the article is an original classification of direct indicators available within institutional information systems, organized into academic, behavioral, and institutional dimensions. The paper also proposes composite indicators, including the Moment of Trouble and Financial Risk scales, which are intended to transform transactional data into measurable early warning signals. These indicators remain conceptual and require future empirical validation. The discussion identifies important limitations in the capacity of institutional information systems to capture psychosocial factors. It therefore emphasizes the need to combine data-based analysis with human-centered institutional support. The effectiveness of dropout prevention is likely to depend not only on the accuracy of analytical indicators, but also on the speed, appropriateness, and quality of the institutional response to the risks identified.
This study aims to develop accounting models for the initial recognition and measurement of tangible cultural heritage in the financial statements of public sector entities. The proposed models are based on a comprehensive theoretical and methodological review of the existing literature, supported by an analysis of international accounting practices. Their implications are considered in the context of sustainable public sector development and adaptation to the evolving global economic environment.
This study examines the causal effects of fiscal policy on macroeconomic stability and non-oil GDP growth in hydrocarbon-dependent GCC countries and diversified high-income economies. Using a balanced panel of 17 countries from 2000 to 2024, comprising 425 country-year observations, it applies Double Machine Learning to address endogeneity, nonlinearities, and high-dimensional confounding. The analysis focuses on fiscal balance, government expenditure, and tax revenue as shares of GDP, while controlling for institutional and structural factors such as fiscal transparency, sovereign wealth fund governance, public investment efficiency, and economic complexity. The model combines Lasso, Random Forest, and Gradient Boosting with cross-fitting, instrumental-variable checks, fixed-effects OLS, mediation analysis, and heterogeneity tests. The results show significant positive effects of fiscal balance (0.052), government expenditure (0.038), and tax revenue (0.033) on GDP growth (p < 0.01). These effects are partly mediated by public investment efficiency and financial development and are stronger in diversified economies with better institutions. The findings suggest that GCC countries can improve fiscal policy effectiveness through institutional reform and economic diversification.
This paper examines the relationship between research and development (R&D) expenditure and innovation outputs across European Union (EU) member states, with particular emphasis on identifying differences between observed and expected innovation performance after controlling for the structural characteristics of national economies. Special attention is given to the cross-country heterogeneity and temporal evolution of these deviations. The empirical analysis is based on panel data for 27 EU countries covering the period 2011-2024. The relationship between R&D investment and innovation outcomes is estimated using a Correlated Random Effects panel model. Innovation performance is assessed through two complementary indicators: patent activity and high-tech exports. Using the residual component of the model, the paper develops a transformation gap indicator, which measures the extent to which actual innovation output deviates from its expected level, given R&D investment and relevant structural conditions. The findings reveal substantial heterogeneity among EU countries, as well as distinct dynamic patterns in the evolution of the transformation gap. While some countries demonstrate signs of convergence toward expected innovation performance, others experience widening deviations. The paper contributes to the literature by combining static and dynamic perspectives and by providing new empirical evidence on the performance of national innovation systems within the EU.
The growing use of digital communication technologies, including instant messaging, videoconferencing, and real-time collaboration platforms, has transformed organisational communication and reshaped working conditions across Europe. The effects of these technologies on employees’ work experiences are multifaceted. On the one hand, digital communication can increase autonomy, flexibility, coordination, and efficiency. However, it may create expectations of constant connectivity and continuous digital availability. The widespread adoption of digital tools can therefore improve collaboration while simultaneously increasing the risks of information overload, weakened interpersonal relationships, and technostress. This paper presents selected findings on employees’ attitudes towards digital communication in the workplace, drawing on recent cross-national data from Round 10 of the European Social Survey and covering 13 former transition economies. The survey includes a set of questionnaire items that enable the construction of variables for examining respondents’ perceptions of work-related digital communication practices. The analysis focuses on respondents’ assessments of the extent to which digital communication facilitates work coordination and improves job effectiveness. Particular attention is given to communication with colleagues and line managers in written form, including text messages, emails, and messaging applications, and through screen-based communication such as videoconferencing. The study also considers differences associated with respondents’ sociodemographic characteristics, including gender, age, and educational attainment. The findings contribute to a better understanding of how employees experience digitally mediated communication in the workplace. They also reveal considerable potential for further research into the relationships between digital communication, work-life interference, job satisfaction, labour-market inequalities, technostress, and other relevant dimensions of contemporary employment.
This study examines the relationship between diversification, structural transformation, and economic growth in peripheral rural economies. Although diversification is widely regarded as an important component of rural development, the mechanisms through which it contributes to growth remain unclear. Existing studies often rely on static measures that capture the level of transformation rather than the pace at which structural change occurs. To address this limitation, the study introduces Economic Transformation Velocity, defined as the rate of structural upgrading between two transformation states. The analysis uses longitudinal data collected from 80 household and business units in the mountain regions of Northern Albania between 2023 and 2025, representing 74.8 per cent of the study population. The empirical framework links diversification, investment and finance, economic transformation, transformation velocity, and growth. Composite indices are constructed using Principal Component Analysis, while the hypotheses are tested through robust OLS estimation, nonlinear models, threshold analysis, bootstrap resampling, and additional robustness checks. The results show that diversification is positively associated with economic transformation but has no significant direct relationship with economic growth. Economic Transformation Velocity is more strongly associated with growth, whereas the level of transformation remains statistically insignificant. Nonlinear and threshold analyses further indicate that the growth effect strengthens as transformation velocity increases, suggesting cumulative dynamics of structural upgrading. The study contributes to development economics by distinguishing between transformation levels and transformation dynamics and by introducing Economic Transformation Velocity as an indicator of structural change. The findings suggest that rural economic development depends not only on the degree of transformation achieved, but also on the speed at which structural upgrading takes place.
Using microdata from the 2015 and 2022 Household Labor Force Survey, this study assesses public-private sector wage differentials in Türkiye. By presenting descriptive statistics, kernel density estimates, cumulative distribution functions, OLS regressions, Oaxaca-Blinder decompositions, and unconditional quantile approach through a sequence of methods, the analysis describes the average and distributional patterns in the wage disparities. Findings show a persistent wage advantage for those in the public sector, but the gap narrows over time. A large portion of the differential has been explained by observable features like education, occupational type, and geographic location, and the factor linked to sector-specific returns decline by 2022. There is some distributional evidence that suggests broad-spectrum heterogeneity along the wage scale: in 2015, the public sector premiums are largest around mid and the upper quantiles, but by 2022 they become more moderate and more evenly distributed. These changes indicate that there has been some convergence between the public and private sectors on rewarding worker characteristics and are indicative of changing wage-setting dynamics during this period. The results provide new insights in Türkiye about the development of wage structures, as well as the need to pay attention to the relationship between worker characteristics and their resulting returns in the distribution leading to wage gap analysis for labour-market policy decisions and public-sector compensation structuring.
This exploratory research provides a study to examine the relationships between perceived sustainability (PS) and perceived cost (PC) on consumer attitudes (AT), intention to use (IU), and actual choice of delivery methods (DM) in e-commerce in Vietnam under Stimulus-Organism-Response (S-O-R) framework. PS has a positive effect on AT, indicating that there is increasing ecological awareness, while PC has a negative effect on AT, indicating cost sensitivity. AT is a strong predictor of IU, but IU has little influence on DM. Instead, AT causes behaviour, and so the latter one suggests intrinsic values may drive sustainable behaviour more than intentions. Age and gender have no significant effect, but income negatively affects AT, consumers with higher income may see the existence of eco-friendly options as inconvenient. An Importance-Performance Map Analysis (IPMA) results reveal PS can be considered a high-performance strategic driver of AT and PC a barrier. The findings advise businesses to focus on sustainability communication and provide financial incentives to ease cost issues. Policymakers need to promote sustainable logistics adoption via subsidies and public-private partnerships to alleviate cost barriers and encourage green last-mile solutions through policies and incentives promoting affordability.
Peer-to-peer (P2P) energy trading is an industry where energy producers and consumers buy and sell energy directly by using a platform and in most cases, the technology behind P2P energy trading is blockchain. Tracking performance indicators of blockchain-based P2P energy trading is important to evaluate the effectiveness of a project and to help investors manage resources effectively. However, the literature is still scarce, and the risks of strategic decision making are higher. To fill this gap, we present a novel method to prioritize strategies in P2P energy trading. The model combines molecular fuzzy-based cognitive maps with molecular fuzzy ranking. It contributes to the literature by creating a novel method for ranking alternatives across different geometric shapes. So, the testing of reliability of ranking can be done and the accuracy and robustness of the model can be improved. The Q-learning approach also allows expert weights to be calculated objectively which mitigates the subjectivity of the results and helps investors make informed decisions. By providing a reliable framework for strategy development, this study contributes significantly to the literature and thus investors can make well-informed decisions. The results show that blockchain scalability and grid integration are the most critical performance indicators to enhance these projects. Community empowerment through local partnerships for microgrid development is also the most important investment option.
Across Asia, this study looks at how public spending on education impacts the quality of schools and considers the possibility that there may be a point at which extra expenditure will no longer improve standards. Using a dataset from 36 Asian countries for the year 2023, we applied a spline regression model to explore how education spending by governments (expressed as a percentage of GDP), the budget allocation to education, school duration and total pupil enrolment affected educational standards. This variation can be attributed to 86.8% by the model specifications. Across all segments of the cubic function, we found that the budgetary priorities and duration of schooling have a positive and statistically significant effect on educational standards. Total school intake was not found to be significant. Below a certain GDP level, fiscal policy’s marginal effect on the economy is very weak but beyond a certain threshold, such effects become quite significant. It appears that the fiscal situation in a country may mean that the way government funds for education is allocated could be more important than the amount of money available for education, at least in certain cases. From the viewpoint of public finance, the data obtained here highlights the need to consider expenditure efficiency, as well as the proper alignment of institutions, along with budgeting that is informed by results, in any moves to increase funding allocations.
The rapid integration of generative artificial intelligence (AI) in education has created a growing need to understand the factors that shape students’ adoption of these technologies. This study examines the behavioural, cognitive, and ethical determinants of AI chatbot use among university students. Drawing on the Technology Acceptance Model and the Technology Readiness Index, the study incorporates additional constructs, including transparency and ethics. Data were collected through an online questionnaire administered to 285 students and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that optimism significantly influences perceived ease of use, which, together with perceived usefulness and transparency/ethics, positively affects students’ intention to use AI chatbots. Transparency/ethics also exerts a direct effect on actual usage behaviour, underscoring the increasing importance of trust and responsible AI in educational contexts. The study provides both theoretical and practical implications for developers, educators, and policymakers seeking to encourage meaningful and responsible AI adoption in higher education.
