AGORA INTERNATIONAL JOURNAL OF ECONOMICAL SCIENCES
ISSN 2067-3310. Online-ISSN 2067-7669 . Acronym: AIJES. Agora International Journal of Economical Sciences is published yearly, beginning with 2007, and includes studies that are structured on the following scientific fields: General Economics, Management, Marketing, Tourism, Accountancy, Finance. AIJES is currently indexed in the following international scientific databases: ERIH PLUS, CEEOL, DOAJ, EZB, Econstor, Index Copernicus, Worldcat, EconBiz, COBIB.SI, Scipio, MIAR. AIJES is ranked Category A in ANVUR, Category B+ in CNCSIS2011 In 2023, Ljubljana School of Business, Slovenia, joined as a co-publisher and, based on this bilateral cooperation, AIJES is published twice per year.
ARTIFICIAL INTELLIGENCE ADOPTION IN STARTUPS VS. CORPORATIONS
This paper examines differences in artificial intelligence (AI) adoption between startups and established corporations from a management and business strategy perspective. The purpose of the study is to explore how organizational structure, managerial flexibility, and strategic priorities influence AI adoption decisions across different organizational forms. Using a comparative qualitative approach, the study draws on secondary data analysis and insights from prior empirical research to identify dominant adoption patterns. The findings indicate that startups tend to adopt AI more rapidly and experimentally, driven by agility and fast decision-making processes, whereas corporations approach AI adoption more cautiously due to hierarchical structures, risk management practices, and governance considerations. The paper contributes to the management literature by clarifying how organizational characteristics shape AI-related strategic behavior and offers practical implications for managers seeking to align AI initiatives with business strategy.
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FINANCE-GROWTH NEXUS AND ADOPTION OF ARTIFICIAL INTELLIGENCE IN AZERBAIJAN
This article investigates the relationship between financial development and economic growth in Azerbaijan within 2000–2024 period. Findings from the Autoregressive Distributed Lag (ARDL) Bounds Test reveal a significant long-term cointegration among financial development, physical capital investments, and economic growth, suggesting that access to finance for the private sector is a strong accelerator for economic growth. While domestic credit to the private sector shows a weak positive short-run impact, its long-run impact is negative. The divergence between the positive impact of physical capital and the negative impact of credit expansion in the long run implies that even though physical investments enhance productivity, the financial markets might be inefficient in the allocation of resources by moving funds toward non-productive sectors and creating a misallocation constraint on economic growth. To improve the efficiency in financial markets, digitalisation of financial services and integration of AI-based models were evaluated as key strategic priorities. This study also employs a SWOT analysis to explore opportunities and challenges for successful adoption of AI.
FUTURE OF LOGISTICS: AI-DRIVEN TRANSFORMATION IN MULTINATIONAL CORPORATIONS
The current work explores how multinational companies (MNCs) utilize artificial intelligence (AI) in logistics processes while ensuring the value-added contribution of human expertise cannot be substituted. A systematic literature review combined with a comparative case study approach that analyzes six MNCs provides an assessment of how different AI capabilities and human contributions are employed in four key logistics sectors: demand forecasting, transport optimization, warehouse operation, and risk management within the supply chain. It is shown that there are tangible benefits from using artificial intelligence, such as cost savings estimated at 100 million miles a year achieved by UPS ORION as well as over 750 thousand autonomous robots used by Amazon without reducing the number of employees. Still, artificial intelligence performs poorly when dealing with non-stationary situations and new risks, thus requiring human expertise for efficient performance. The major conclusion made in this work is that the most successful logistics operations treat artificial intelligence and human contribution as complements rather than substitutes.
THE EU CARBON BORDER ADJUSTMENT MECHANISM (CBAM) AND ALGERIA’S HYDROCARBON EXPORTS: LEGAL FRAMEWORK AND ECONOMIC ADAPTATION STRATEGIES
The European Union's carbon border adjustment system was implemented in October 2023. This mechanism influenced international commerce by linking climate objectives to trade restrictions and levying taxes primarily on carbon-intensive and high-emission imports. The objective of this approach was to avert carbon leakage and to guarantee equitable competition. This mechanism poses significant challenges for developing nations dependent on fossil fuel exports, like Algeria, where exports to the EU primarily consist of fossil fuels and carbon-intensive goods, potentially reducing its revenue and market access opportunities. This study employs a descriptive and analytical methodology to delineate the legal framework of the carbon border adjustment mechanism, assess its compliance with World Trade Organization regulations, and evaluate its economic implications for Algeria. The data indicate that the mechanism has a negligible impact on Algeria's economy, accounting for approximately 0.2% of the nation's GDP. The mechanism's impact is concentrated in the fertilizer, steel, and iron sectors, jeopardizing the nation's competitiveness and employment opportunities. Furthermore, the carbon tax exacerbates the existing structural vulnerabilities within the Algerian economy. The vulnerabilities include overdependence on hydrocarbons, high carbon intensity, insufficient use of renewable energy, and the absence of a regional carbon pricing mechanism.
FESTIVAL CRUISES AS A HYBRID TOURISM FORMAT: AN EXPLORATORY TARGET GROUP ANALYSIS IN GERMANY
This study addresses a notable research gap concerning festival cruises, a hybrid format largely unexplored scientifically, aiming to identify potential target groups and analyze their needs and expectations for successful long-term establishment. A quantitative-empirical research design with an explorative approach was employed, utilizing a standardized online questionnaire completed by 833 participants, with 579 interested individuals forming the basis for further analysis. Data analysis involved a Two-Step Cluster analysis to segment target groups based on socio-demographic and travel behavioral characteristics, followed by a success factor analysis using descriptive statistics and ABC analysis, differentiated by cluster. The results identified two distinct target segments: a younger, festival-affine, lower-income group without prior cruise experience (Cluster 1) and an older, comfort-oriented, higher-income group with extensive cruise and music festival experience (Cluster 2). Both clusters prioritize a fitting music genre, retreat areas, long restaurant opening hours, and an uncomplicated online booking process, while also identifying unsuitable music and financial aspects as key booking barriers. However, differences exist, with Cluster 1 emphasizing festival atmosphere and sustainability more, and Cluster 2 showing a slightly higher willingness for premium pricing. These findings indicate that festival cruises appeal to diverse segments with shared core requirements but also distinct preferences, necessitating differentiated offerings and communication strategies for successful market positioning.
DIGITAL BUSINESS TRANSFORMATION IS PRIMARILY A HUMAN CHALLENGE, NOT A DIGITAL ONE
Digital transformation (DX) failures are more often rooted in organizational and human factors than in technological inadequacy. This paper shows that the frequently cited 70% failure rate of DX initiatives is closely related to insufficient attention to shared vision, stakeholder alignment, change leadership, continuous learning, and organizational culture. The paper integrates David Rogers’ digital transformation roadmap, John Kotter’s change leadership framework, Peter Senge’s learning organization principles, and Nigel Vaz’s emphasis on learning, unlearning, and relearning. To complement this conceptual synthesis, the paper includes an exploratory descriptive analysis of four course-based analytical tasks completed by master’s-level students with substantial professional experience. The descriptive findings are consistent with the theoretical emphasis on strategic coherence, psychological safety, problem validation, learning capability, and structural alignment. The paper therefore frames digital business transformation primarily as an organizational challenge in which technology functions as an enabler rather than the primary determinant of success.
THE ROLE OF ARTIFICIAL INTELLIGENCE IN ASSESSING THE REVENUE MANAGEMENT EFFECTIVENESS
This paper develops and evaluates an organizational mechanism for AI-enhanced revenue management (RM) in hospitality that combines monitoring and analytics, machine-learning-driven dynamic pricing, advanced AI segmentation, strategic partnerships, and staff motivation programs. The main objective is to propose and validate a dual-metric assessment framework that enables holistic optimization of RM subsystems by linking quantitative financial, HR, and resource indicators with complementary qualitative marketing, environmental, and digital metrics. Methodologically, the study uses mixed methods: quantitative modelling and metric definition (CostPar—average cost per occupied room, Occ—average occupancy rate, staff labor productivity, and Market Penetration Index) are paired with qualitative socio-economic evaluation (ecological activity cost coefficient and digitalization cost coefficient); these metrics are tested through scenario analyses and comparative assessment to demonstrate their integrative capability. Results show that (1) combining AI-driven pricing and segmentation with real-time monitoring improves revenue capture and occupancy efficiency; (2) the dual-metric framework more accurately reflects subsystem performance than single-dimension measures, revealing trade-offs between short-term financial gains and longer-term marketing, ecological, and digital investments; and (3) specific metrics (CostPar, Occ, staff productivity, MPI, ecological and digital cost coefficients) provide actionable insights for targeted interventions. The discussion highlights practical implications: hospitality managers should adopt the proposed framework to balance revenue maximization with sustainability and digital transformation goals; AI tools must be embedded within organizational processes and staff incentive systems; and multi-indicator evaluation is necessary for justified, generalized RM assessments. The framework supports data-driven decision-making and offers a roadmap for implementing AI-enabled, sustainable RM in hospitality.
THE IMPACT OF INNOVATION STRATEGIES ON BUSINESS PERFORMANCE: EVIDENCE FROM SUPERMARKET CHAINS IN ALBANIA
Innovation is a key driver of competitiveness and business performance, particularly in the retail sector. This study examines the impact of innovation strategies on supermarket chains in Albania, focusing on product, organizational, technological, and digital business model innovations. A quantitative methodology is employed through structured questionnaires administered to supermarket managers and consumers, enabling a comparative analysis of perceptions regarding innovation effectiveness and its influence on performance and customer experience. The findings reveal a strong positive relationship between innovation and business performance. Specifically, 95% of consumers reported improved convenience from online ordering and delivery, 90% were attracted by regularly updated product assortments, and 70% of managers confirmed a positive impact of innovation on performance. Technological and customer-oriented innovations have the greatest effect on customer satisfaction and competitive differentiation, while organizational innovations enhance operational efficiency and long-term sustainability. The results provide practical insights for managers and policymakers in transitional economies.
WHY STARTUPS LEAVE: DELAWARE INCORPORATION AS AN INSTITUTIONAL SIGNAL IN EMERGING STARTUP ECOSYSTEMS
Small developing startup ecosystems frequently lack the legal infrastructure required to attract international venture investment, placing domestically incorporated companies at a structural disadvantage relative to counterparts registered in established legal jurisdictions. The study examines the Delaware Flip phenomenon - the practice whereby startups incorporate in Delaware, USA, rather than in their home country - as a measurable indicator of legal framework inadequacy within small, developing startup ecosystems, with an empirical focus on the case of Georgia. This study employs documentary profiling of 28 Georgian tech startups alongside semi-structured interviews with founders, legal practitioners, and ecosystem actors, applying North's institutional theory and the Triple Helix framework as analytical lenses to interpret structural patterns of offshore incorporation. The results reveal that more than a third of sampled startups with international investor engagement have undergone full or partial Delaware incorporation, driven by four structural legal gaps: the absence of SAFE note legislation, inadequate employee stock ownership plan frameworks, the lack of specialized commercial dispute resolution mechanisms, and exclusion from global payment infrastructure incompatible with domestic incorporation. The results further show that these deficiencies collectively render foreign incorporation a functional necessity rather than a voluntary strategic preference, affecting the regulatory anchoring of the startup cohort. The findings of this study suggest that the Delaware Flip represents a loss not only of tax revenue but of legal identity, institutional capital, and ecosystem anchoring, and that a state simultaneously funding startup development while maintaining structural conditions that necessitate foreign incorporation produces outcomes contrary to its own developmental objectives.
ARTIFICIAL INTELLIGENCE AND LABOR DEMAND IN ROMANIA: EARLY EVIDENCE FROM ONLINE JOB POSTINGS
The rapid diffusion of artificial intelligence (AI) technologies is expected to reshape labour demand, yet empirical evidence for emerging European economies remains limited. This paper examines short-run trends in AI-related labour demand in Romania using a high-frequency dataset of 1,874 online job postings from October 2025 to March 2026. Adopting Web Labour Market Information (LMI) approach, the study aims to elucidate the structural characteristics and professional archetypes of AI diffusion in Romanian economy. Results highlight increasing concentration of AI-related labour demand in service sector (35,8%) and a transition towards standardized production phase characterized by a robust technical middle class. Evidence reveals a shift toward skills-based hiring behaviour, where 54% of vacancies do not require a formal degree. Furthermore, the market exhibits signs of opportunistic upskilling, where employers are raising entry barriers. Hard skills were found to be the strongest predictor of the number of years of professional experience required for a position, whereas soft skills showed no statistically significant impact on the seniority level. The objective of the paper is to deliver a quantitative assessment of the impact of AI transformation on the Romanian labour market. By providing early empirical evidence from a high-frequency vacancy dataset, the paper contributes to the literature on AI, labour markets, and technological diffusion in emerging EU economies.
THE INFLUENCE OF LEADER-MEMBER EXCHANGE (LMX) ON EMPLOYEE JOB SATISFACTION IN THE HOSPITALITY INDUSTRY: EVIDENCE FROM GEORGIA
In the Georgian hospitality sector, which is frequently marked by labor shortages and poor levels of work satisfaction, this study investigates the link between leader-follower exchange (LMX) and employee job satisfaction. A substantial positive connection between LMX and job satisfaction was found in data gathered from a survey of 155 employees (r = 0.715, p = 0.001). 52% of the observed variation in satisfaction was explained by the proposed model (R2 = 0.520). Experience sharing and a collaborative work environment served as mediators in this connection, whereas communication (= 0.513) and support (= 0.233) were shown to be significant predictors. These results have significant ramifications for management approaches to leadership in the hotel industry as well as theoretical advancement.
THE USE OF LARGE LANGUAGE MODELS IN THE EDUCATIONAL PROCESS: AN EMPIRICAL ANALYSIS AMONG UNIVERSITY AND SECONDARY SCHOOL STUDENTS
The increasing use of large language models (LLMs) in education has raised important questions regarding their impact on learning, trust, ethics, and data protection. This study examines the use of LLMs among secondary school and university students in Slovenia based on an online survey conducted in 2024 (N = 120). The main objectives were to analyze patterns of use, levels of trust and verification, perceptions of ethical issues, and the perceived impact of LLMs on learning efficiency. The study also investigates whether differences exist between educational levels and gender. The results indicate that LLMs are widely used for educational purposes, particularly for preparing assignments and presentations. Students report moderate trust in AI-generated information and frequently verify its accuracy, suggesting a cautious approach. Perceived time savings are significant, while privacy concerns remain high. No statistically significant differences were found between university and secondary school students or between genders. The findings highlight the growing integration of LLMs into education and emphasize the need for clear guidelines on their ethical and responsible use.
THE IESBA CODE AND THE EU AI ACT: NORMATIVE GAPS AND GOVERNANCE IMPLICATIONS FOR ROMANIAN ACCOUNTANTS
The IESBA Code of Ethics (2023) does not mention artificial intelligence, automation bias, or large language models. The EU AI Act (Regulation EU 2024/1689) does so, imposing binding obligations on accounting organisations deploying AI in high-risk contexts, with full implementation required by August 2026. This study maps the normative gap between these two instruments and assesses its practical consequences for Romanian accounting practitioners. The analysis combines normative comparison of the IESBA Code against the AI Act, scenario-based risk assessment calibrated to the Romanian professional market, and deductive synthesis into a governance framework, drawing on secondary empirical evidence from the Special Eurobarometer 554 (European Commission, 2024) and the AICPA & CIMA Future-Ready Finance Survey (2025). Two critical gaps have been confirmed: the objectivity principle contains no provision for automation bias, and the confidentiality principle does not address the processing of client data by third-party AI systems. Both gaps are structurally worsened in Romania, where sole practitioners are the majority, enterprise AI with contractual data protection is financially out of reach for most practices, and the fiscal legislative framework changes frequently, making LLM outputs unreliable for tax interpretation. The only point of genuine normative convergence-the human oversight requirement shared by the IESBA competence principle and AI Act Article 14-remains aspirational rather than practical, given the absence of any AI guidance from CECCAR or CAFR since August 2024. The governance framework developed in response shows that these risks can be addressed through a coordinated reading of obligations already present across the IESBA Code, the AI Act, the GDPR, and the ISA standards, without requiring new legislation.
EMPLOYEE WELLBEING, CUSTOMER SATISFACTION AND ORGANIZATIONAL PERFORMANCE: AN INTEGRATED MIXED-METHODS ANALYSIS
Employee wellbeing has emerged as a critical factor linking internal organizational dynamics with external market outcomes, particularly in shaping customer satisfaction and loyalty in contemporary management contexts. This article examines the extent to which employee wellbeing functions as a strategic driver of customer-related outcomes, addressing the research problem of whether it can be considered a key determinant of customer trust and long-term loyalty. The main objective of the study is to analyse how employee wellbeing—as a multidimensional construct encompassing engagement, motivation, and work experience—translates into measurable customer satisfaction and loyalty within an integrated empirical framework. The study adopts a mixed-methods approach, combining qualitative analysis of international secondary datasets (Gallup, Deloitte, Eurofound, PwC) with quantitative primary data collected through a survey of 162 employees across different sectors. The findings indicate a strong positive association between employee wellbeing and customer satisfaction, suggesting that higher levels of engagement, organizational support, and work–life balance are associated with improved customer trust and loyalty. The results highlight the strategic importance of investing in employee wellbeing as a driver of both internal performance and external competitiveness, offering valuable implications for managers seeking to enhance organizational effectiveness and sustainable business success.
CAN EDUCATION SPARK A REVOLUTION? EXPLORING HOW SUSTAINABLE ENTREPRENEURSHIP TRANSFORMS WOMEN’S ROLE IN AZERBAIJAN’S LABOR MARKET
This study explores how sustainable entrepreneurship education can support Azerbaijani women in overcoming the systemic barriers they face in accessing and participating in the labour market. While official narratives often highlight progress in gender equality, the reality is more complex. Women in Azerbaijan continue to encounter deeply rooted obstacles ranging from limited access to financing and mentorship to prevailing gender norms that discourage risk-taking and entrepreneurial activity. To address these challenges, this study asks: How and to what extent does entrepreneurship education influence women's empowerment and labour market integration in Azerbaijan? A mixed-methods approach was adopted. Findings show that tailored entrepreneurship education can increase confidence, improve access to opportunities, and encourage sustained labour force participation, especially outside urban centers.
ARTIFICIAL INTELLIGENCE AS A DRIVER OF DIGITAL ECONOMY DEVELOPMENT: INSIGHTS FROM HUNGARY AND AZERBAIJAN
This study investigates the role of artificial intelligence in shaping digital economy development through a comparative analysis of Hungary and Azerbaijan. It examines how different governance models, institutional capacities, and innovation ecosystems influence AI adoption and its economic outcomes. The research applies a comparative analytical approach using secondary data from international databases, national statistics, and policy documents covering the period 2018–2025. The findings show that Hungary, as an EU-integrated economy, demonstrates more advanced digital infrastructure, stronger human capital, and a more developed innovation ecosystem, resulting in higher levels of AI readiness and digital economy performance. Azerbaijan, in contrast, has achieved notable progress in public sector digitalization and e-government services, driven by state-led reforms, but remains limited in SME digital adoption and private-sector innovation. The study also highlights that AI contributes to productivity growth and new business models, while simultaneously generating challenges related to skills gaps and institutional readiness. Overall, the comparison confirms that balanced development across infrastructure, skills, and innovation systems is essential for effective AI-driven digital transformation.
DIGITAL-DRIVEN RECONSTRUCTION OF UKRAINE: CONSTRUCTION INNOVATION, MIGRATION AND ECONOMIC RECOVERY
The study is devoted to digitally controlled reconstruction of Ukraine as a strategic mechanism for accelerating reconstruction and economic recovery in the context of migration-induced labour shortages in construction. The aim of the article is to determine the impact of digital innovations (BIM, digital twins, AI planning, e-permits, modular construction and digital procurement) on increasing the productivity of the industry, compensating for staff losses and strengthening the investment capacity of reconstruction. The methodology includes systematisation of scientific approaches to the reconstruction economy, analysis of modern digital solutions in the construction sector, and a comparative review of international practices in post-crisis reconstruction. Key findings show that the digitalisation of construction processes increases productivity, partially compensates for labour shortages, reduces time and cost losses in projects, technological productivity gains partially offset labour shortages, reduce corruption risks and strengthen donor confidence, which contributes to increased investment and accelerated economic recovery in Ukraine.
ARTIFICIAL INTELLIGENCE AND TOURISM-DRIVEN ECONOMIC DEVELOPMENT IN ALBANIA
Albania recorded approximately 11.7 million foreign arrivals in 2024, a figure that places growing strain on infrastructure, management systems, and the natural environment. This paper examines how that tourism expansion is shaping the adoption of artificial intelligence across the sector, and what the resulting economic, environmental, and ethical consequences look like in practice. Drawing on a systematic review of academic literature published between 2019 and 2025, supplemented by secondary statistical data from INSTAT, Eurostat, and UN Tourism, we find that AI adoption is uneven: larger, urban-based businesses are realising tangible gains in efficiency and revenue, while rural SMEs continue to face significant barriers related to cost, connectivity, and skills. We argue that the question is no longer whether Albania should integrate AI into tourism, but how to do so in a way that preserves the human character of the sector.
ECONOMIC SECURITY IN THE CONTEXT OF THE SUSTAINABLE DEVELOPMENT PARADIGM: CONCEPTUAL FOUNDATIONS AND STRATEGIC TRAJECTORIES
This study examines the conceptual and functional relationship between economic security and sustainable development in the context of climate change, resource scarcity, biodiversity loss, and increasing geo-economic tensions in the 21st century. It explores the contradictions between economic growth and the regenerative capacity of the biosphere, emphasizing the strategic importance of the green economy as a development priority. The research applies systemic analysis, comparative assessment, and an integrative approach, interpreting economic security as a protective mechanism against external shocks and sustainable development as the internal adaptive capacity of socio-economic systems. A conceptual framework titled the Sustainable Development Matrix of Socio-Economic Systems is proposed, integrating resource efficiency, social stability, ecological balance, and innovation-driven security indicators. The findings indicate that ecological factors should be considered an essential component of economic security. Sustainable development is framed as non-destructive progress based on a synthesis of anthropocentric and biosphere-centric perspectives. The study concludes that noospherogenesis-based development models provide a robust strategic framework for enhancing long-term competitiveness and resilience of national economies.
MACROECONOMIC DETERMINANTS OF NPLS IN EU: A PANEL DATA ANALYSIS (2010–2023)
Non-performing loans (NPLs) present a significant risk to the banking systems, and become worse during periods of economic turmoil. This study aims to identify and quantify the macroeconomic determinants influencing non-performing loans in EU member states, using annual panel data from 2010 to 2023. A Pooled Ordinary Least Squares regression approach along with robust and cluster-robust standard errors are utilized to assess the effects of GDP growth, unemployment rate, and inflation on NPL ratios. The findings indicate that unemployment is the strongest factor affecting NPLs. GDP growth is positively connected with NPLs, contrary to prevailing expectations, suggesting that economic expansion might encourage over-leveraging. Inflation has no statistically significant effect, while NPL ratios tend to rise during recessions. The study highlights the importance of macroeconomic stability for protecting financial systems.
FINANCIAL INCLUSION AND HOUSEHOLD ECONOMIC WELL-BEING: A LITERATURE REVIEW
Financial inclusion has become an important topic in economic and financial research due to its potential to improve household economic well-being and promote inclusive development. This paper aims to review and synthesize the existing literature on the relationship between financial inclusion and household economic well-being. The study examines key concepts, determinants, and empirical evidence related to access to and use of formal financial services, including savings, credit, insurance, and digital financial products. The reviewed literature indicates that financial inclusion contributes positively to household well-being by improving income management, encouraging savings, expanding investment opportunities, and strengthening resilience to economic shocks. The findings also highlight the importance of financial literacy, income levels, and financial system development in enhancing the effectiveness of financial inclusion initiatives. Overall, the literature suggests that greater financial inclusion can significantly improve household economic well-being, particularly among low-income and financially vulnerable groups.
AI CHATBOTS DRASTICALLY OUTPERFORM SECONDARY STUDENTS ON THE TEST OF ECONOMIC LITERACY
This paper examines the performance of generative artificial intelligence models (OpenAI ChatGPT, Google Gemini, Microsoft Copilot) on the Test of Economic Literacy and compares their results with those of U.S. and Slovenian secondary school students. The study aims to assess whether AI systems demonstrate comparable or even superior levels of economic knowledge and to explore potential language-related differences in test performance. Descriptive statistics and item-level analysis are used to identify patterns and inconsistencies across languages when comparing AI test-takers and secondary students. Results show that AI models achieved near-perfect accuracy, while students performed significantly lower on average. The analysis also highlights differences in the length and explanatory depth of responses among the AI models. Findings contribute to current debates on assessment validity, AI use in economic education, and the future of multiple-choice testing in digitally augmented learning environments. The paper underscores the need for responsible integration of AI into educational frameworks, safeguarding the credibility of learning and the support for the sustainable development of economic knowledge.
FINTECH AND MACROECONOMIC DYNAMICS: THEIR IMPACT ON GROWTH DURING ECONOMIC UNCERTAINTY
This study examines the role of FinTech integration, financial leverage, investment and inflation in growth during economic disruption. The empirical framework employs a multiple regression model estimated using Ordinary Least Squares on a balanced panel dataset of 29 countries from 2016 to 2024, incorporating a dummy variable to capture crisis consequences. Fintech-driven solutions play an essential role in managing excessive debt and investment allocation under uncertainty. The findings show that financial leverage has a significant detrimental effect on economic growth distinctly from the favorable influence of investment alongside moderate inflation supporting macroeconomic stability. Digital finance enhances economic growth through financial inclusion and investment efficiency, fostering development. The severe contractionary impact of pandemic crisis is reflected by the dummy variable. Findings emphasize the importance of FinTech adoption in finance, investment and growth policy insights.
ENERGY INFRASTRUCTURE AND AI-DRIVEN GROWTH IN THE EUROPEAN ECONOMY
The widespread adoption of Artificial Intelligence (AI) is reshaping economic growth trajectories across the European Union. However, AI is not only a digital phenomenon, but it is also an energy-intensive one, which relies on physical infrastructure that is subject to policy, investment, and capacity constraints. This paper examines whether energy infrastructure is becoming a strategic bottleneck for AI-driven growth in the EU. It extends the endogenous growth model to incorporate energy as a separable input to AI production, developing three analytical scenarios of increasing specificity. The third and most realistic scenario demonstrates that when energy capital is insufficiently expanded relative to AI adoption, growth slows and a crowding-out effect emerges, compressing energy availability for households, industry, and other sectors. The theoretical findings are supported by an illustrative calibration, which confirms that the bottleneck and crowding-out dynamics identified in the model are consistent with observable trends in EU energy investment and data centre demand. The paper contributes a conceptual framework linking AI expansion, energy supply, and energy constraints. It also suggests directions for future empirical research on infrastructure constraints and AI-led growth in Europe.
SMART AGRICULTURE AND ICT ADOPTION: EVIDENCE FROM AZERBAIJAN
It is impossible to imagine our modern era without electronic services and digital technologies. Therefore, among the strategic tasks of our time, as well as many complex issues, as well as increasing the economic efficiency of ICT in the agricultural sector, intensifying the application of digital technologies, increasing productivity in all areas of activity due to these technologies, and making additional contributions to solving the efficiency problems of the agricultural sector as a whole is more of a priority. In the world, the processes of active use of "smart" technologies have risen to the maximum attractive level for most countries, and as a result, ICT technologies are being applied in leading fields of activity. Among the selected approaches and measures related to digitization of the agricultural sector and increasing the economic efficiency of the use of information technologies, providing necessary technological assistance to entrepreneurial and economic structures, providing advice and organizing training for the organization of effective use of ICT, modeling the use of natural resources, including land and water resources, preparation of targeted action programs, forecasting of climate changes, etc. directions are more requested. These mentioned problems and tasks are a sufficient priority for Azerbaijan. It is for this reason that the article paid special attention to the issues of increasing the economic efficiency of information and communication technologies in the agricultural field in accordance with the goals of the "Strategic Roadmap for the development of telecommunications and information technologies in the Republic of Azerbaijan" as well as "Azerbaijan 2030: National Priorities for socio-economic development". Because the agrarian sphere is an important economic sphere of strategic importance for Azerbaijan.
ARTIFICIAL INTELLIGENCE IN MARKETING: AN ANALYSIS OF CONSUMER DEMAND MANAGEMENT IN AZERBAIJAN’S LIGHT INDUSTRY
This study analyzes the role of artificial intelligence in marketing strategies aimed at managing consumer demand in Azerbaijan’s light industry. The research focuses on innovation-oriented marketing practices and their contribution to improving enterprise performance and enhancing market competitiveness. Particular attention is given to the processes of introducing, commercializing, and diffusing innovative products, where data-driven decision-making and demand forecasting play a crucial role. Using the cases of “Azarxalcha” JSC and “Gilan Textile Park” LLC, the study examines the application of digital and AI-based marketing tools, including consumer behavior analysis, market segmentation, and predictive analytics. The empirical findings demonstrate that the integration of artificial intelligence into marketing strategies supports more accurate demand management and strengthens strategic planning capabilities. The results also reveal persistent structural challenges within the sector, such as a high degree of import dependence, limitations in domestic production capacity, and the need for technological modernization. In response to these challenges, the study highlights the importance of state support mechanisms, cluster-based industrial development, and the wider adoption of artificial intelligence and analytical technologies to improve managerial effectiveness and market adaptability.
PUBLIC POLICIES TO SUPPORT INNOVATION IN EUROPE: A COMPARATIVE APPROACH OF E-GOVERNMENT
This article examines the legal and policy frameworks that support innovation across European jurisdictions, employing a comparative approach to identify common strategies, divergences, and best practices. Through analysis of intellectual property regimes, research and development incentives, and regulatory frameworks, we demonstrate how different European states balance innovation promotion with broader policy objectives. Our findings suggest that while harmonization efforts exist within the EU framework, significant variations persist in implementation and effectiveness. This comparative analysis provides insights for policymakers seeking to optimize innovation support mechanisms.
THE ROLE OF ARTIFICIAL INTELLIGENCE IN FINANCIAL ANALYSIS: INTEGRATION AND NECESSITY
The increasing availability of large and complex financial datasets has intensified the need for advanced analytical tools, positioning artificial intelligence (AI) as a critical component of contemporary financial analysis. This paper investigates the role of artificial intelligence in financial analysis, with a particular focus on its integration into traditional analytical frameworks and its growing necessity in modern financial decision-making. The study is based on a qualitative methodological approach, combining a systematic review of relevant academic literature with an analytical examination of current AI applications in financial forecasting, risk assessment, and anomaly detection. The research results indicate that while artificial intelligence-based techniques—primarily machine learning algorithms and natural language processing—possess the potential to enhance efficiency, they exhibit significant vulnerability regarding accuracy when processing unstructured tables, leading to exponential error propagation and the systemic convergence of inaccurate results within complex financial indicators. The paper argues that artificial intelligence does not replace human expertise, but rather enhances and accelerates the analytical role of financial professionals by supporting more informed, evidence-based decision-making.
ARTIFICIAL INTELLIGENCE IN FINANCIAL DECISION-MAKING AND COMPETITIVE ADVANTAGE: EVIDENCE FROM SELECTED EUROPEAN INDUSTRIAL FIRMS
This paper analyzes the role of artificial intelligence (AI) in improving financial decision-making and creating competitive advantage in selected European industrial companies. The aim of the study is to assess how integrating AI into financial planning, risk management, performance analysis, operational processes, and strategic decision-making affects firms’ efficiency, resilience, and competitive positioning. The study employs a qualitative multiple-case study approach using secondary data, including annual reports, sustainability reports, investor materials, and official corporate documents from three European industrial companies: Siemens AG, Schneider Electric SE, and Krka. d., Novo mesto. The findings show that AI contributes to more accurate financial forecasting, cost optimization, data-driven risk management, operational efficiency, and improved strategic decision support. Siemens AG and Schneider Electric SE demonstrate a stronger AI-driven competitiveness model in which AI is closely linked to digital platforms, automation, industrial innovation, and sustainability-oriented value creation. In contrast, Krka reflects a more gradual digital transformation path, in which competitiveness is primarily supported by financial discipline, operational efficiency, risk control, and ESG governance. The study contributes to the literature by demonstrating that AI enhances competitive advantage when combined with financial management, organizational capabilities, digital maturity, and responsible governance among European industrial firms.
BEYOND CLASSROOM INCLUSION: DEVELOPING ECONOMIC CITIZENSHIP COMPETENCES FOR ALL
This paper addresses in the Italian system the relationship between school inclusion, individualised educational planning, and economic citizenship, arguing for the integration of financial literacy within the Individualised Education Plan as a structural competence oriented towards adult life. Within this perspective, special pedagogy is understood as a foundational body of knowledge underpinning the entire teaching profession, whilst the PEI is conceived as a pedagogical instrument for designing personal autonomy and social participation, in accordance with the United Nations Convention on the Rights of Persons with Disabilities (2006) and the EU Strategy for the Rights of Persons with Disabilities 2021–2030. The theoretical framework draws upon the capability approach (Sen, 1999; Nussbaum, 2011) and self-determination theory (Wehmeyer, 2005; Shogren et al., 2015), which identify autonomous decision-making and the management of personal economic resources as constitutive dimensions of adult quality of life. The paper demonstrates how the principal international financial literacy strategies — including OECD/INFE frameworks and the European guidelines on “Pathways to School Success” (Council of the EU, 2022) — remain insufficiently oriented towards learners with disabilities and social vulnerabilities, notwithstanding that these groups are among those most exposed to the risk of economic exclusion and dependency. A pilot pedagogical proposal is advanced through an interdisciplinary Learning Unit integrable within the PEI, articulating essential economic competences across five domains: money and transactions, budget management, conscious consumption, economic security, and autonomous decision-making. Economic citizenship is thus reframed as a universal educational entitlement and an integral component of the life project, within the perspective of an inclusive school committed to fostering learners capable of participating freely, critically, and self-determinedly in adult society.
THE RELEVANCE OF FINANCIAL EDUCATION: STRUCTURAL AND CULTURAL FACTORS IN CONTEMPORARY SOCIETY
Financial education has become an increasingly important policy priority worldwide, yet its growing relevance extends beyond persistent deficits in financial literacy. This paper argues that the need to invest in effective financial education is driven by a combination of structural and cultural transformations that have reshaped the contexts in which individuals make financial decisions. Drawing on international literature review, with particular attention to Italy and Romania, the paper identifies and discusses the main factors underlying this trend. The paper argues that these developments require a broader conception of financial education that goes beyond the transmission of technical knowledge to integrate critical thinking, digital literacy, risk awareness, and civic competence. It concludes by outlining policy implications for education systems and public institutions, advocating cross-curricular approaches, learner-centred pedagogies, and stronger links between financial education, consumer protection, financial inclusion, and responsible digital innovation to foster social inclusion, economic resilience, and informed participation in contemporary society.
DARK PATTERNS IN THE AGE OF ARTIFICIAL INTELLIGENCE: HOW AI MISLEADS CONSUMERS IN GLOBAL E-COMMERCE
Dark patterns are user interfaces that trick people and limit their choices. As online shopping relies more on artificial intelligence (AI), new manipulative tactics have appeared, such as creating fake urgency, using surveillance pricing, posting fake reviews, and building highly adaptive interfaces. This paper examines how AI makes these deceptive practices more powerful by drawing on ideas from behavioural economics, human-computer interaction, and law to sort and analyse them. Existing laws target clear cases of deception, but they do not address the subtler problems posed by AI-based manipulative designs. To address these issues, this paper proposes several policy steps to mitigate the harm. These include recommendations that lawmakers broaden the definition of manipulative design, require more transparency about algorithms, set up checks for high-risk AI, create a “Fair Patterns” principle, clarify corporate liability, strengthen consumer-detection tools, and improve international cooperation. These steps are meant to protect people's autonomy and help rebuild trust in digital marketplaces.
SUSTAINABLE TOURISM IN SRI LANKA: GREEN HOTEL PRACTICES AND IMPLEMENTATION BARRIERS OF LUXURY HOTELS IN COLOMBO
In the framework of sustainable tourism development, the study focused on green hotel practices and barriers faced in implementing those practices in luxury hotels in Colombo, Sri Lanka. Also, a comparison of practices across the hotel categories. Semi-structured interviews were used to gather information from managers and sustainability officers from three-, four-, and five-star hotels using a qualitative research approach. According to the findings, the majority of luxury hotels have included waste management programs, water-saving devices, energy efficiency measures, and guest and employee engagement tactics into their daily operations. The degree of implementation, however, differs greatly by hotel category, with mid-scale hotels adopting limited practices due to budgetary limitations and a lack of technical competence, while five-star hotels lead the way due to their higher resources and global competitiveness. These findings contribute to understanding sustainable tourism practices in Sri Lanka and provide recommendations for improving implementation across the hotel sector.
THE EFFECTIVENESS OF AI-POWERED MARKETING STRATEGIES IN SUSTAINABLE TOURISM
The scientific paper examines the current impact of artificial intelligence (AI) driven marketing strategies, within the area of sustainable tourism, and with a special focus on the usage of generative AI. Since the modern approach to tourism marketing relies heavily on hyper-personalization within the concept of smart travel, this paper examines how much automated communication and predictive analytics generally aid in creating sustainable tourism practices. The question is followed by an examination of the critical potentials and limitations of AI-related technologies that affect tourists' behavior, perceptions of sustainability, and the overall competitiveness of travel destinations. The main analysis indicates that AI-based marketing strategies can improve promotion effectiveness and tourist experience, but only if paired with ethical, environmental and social concerns. In conclusion, the use of AI is highly desirable for the development of sustainable tourism, but only when appropriately managed.
THE PRACTICAL MODEL MOTIVATION 4.0 AND ARTIFICIAL INTELLIGENCE AS FACTORS FOR BUILDING A DIGITAL ORGANIZATIONAL CULTURE
The article explores the role of the Motivation 4.0 Practical Model and artificial intelligence as key factors in building a digital organizational culture in modern organizations. The aim of the study is to analyze how the integration of motivation-based management models with artificial intelligence–driven tools supports organizational adaptability, employee engagement, and value-oriented digital transformation. Using a conceptual and analytical approach, the article examines the interaction between Motivation 4.0 and artificial intelligence in the context of learning, communication, and decision-making processes. The findings suggest that artificial intelligence may enhance the practical applicability of Motivation 4.0, particularly in digitally mature business organizations. At the same time, the model provides a human-centered framework that balances emotional intelligence, green human resource management, corporate social responsibility, and benchmarking. The study concludes that the synergy between Motivation 4.0 and artificial intelligence contributes to the sustainable development of a digital organizational culture based on trust, innovation, and continuous learning.
TRANSFORMING JOB ROLES WITH GENERATIVE AI: EVIDENCE FROM JOB POSTINGS IN THE GEORGIAN LABOR MARKET
This paper examines how generative artificial intelligence (GenAI) is reshaping business roles in the Georgian labor market. Based on a dataset of job postings collected from major hiring platforms, the study applies a five-type transformation typology: role expansion, enrichment, redesign, merging, and new role creation to identify patterns of change. The findings show that GenAI is primarily integrated into existing roles while also contributing to the emergence of new AI-focused positions. More disruptive transformations, such as role redesign and merging, remain less widespread, indicating that deeper structural changes are still developing. Overall, the results suggest that the Georgian labor market is in an early but active phase of AI adoption, where augmentation dominates but institutionalization is increasing within technical fields. The study provides structured empirical evidence on GenAI-driven role transformation in Georgia and offers implications for workforce policy, education systems, and employer strategies.
THE EFFECTS OF THE ECONOMIC CRISIS ON THE ROMANIAN ECONOMY IN THE PERIOD 2024–2026
During the period 2024–2026, the Romanian economy went through a difficult phase, characterized primarily by a strong fiscal impulse in 2024, persistent inflationary pressures, and policy adjustments in 2025. To understand why these problems accumulated, it is useful to look at preceding years as well: in 2022–2023, Romania had relatively loose fiscal policies and rapid wage growth, and these trends created vulnerabilities that were amplified in 2024–2025. The earlier years thus explain why the 2024 impulse had such a powerful impact, and the analysis must focus in particular on what happened in 2024 and 2025, when the imbalances became evident.
THE EVOLUTION OF THE IT SECTOR IN ROMANIA AND ITS MACROECONOMIC IMPACT
This paper examines the evolution of the Information Technology (IT) sector in Romania between 2021 and 2026, highlighting its profound macroeconomic impact. Over the last five years, the Romanian IT industry has transitioned from a primarily outsourcing-oriented market to a maturing ecosystem increasingly focused on innovation, research, and proprietary product development. By analyzing data regarding Gross Domestic Product (GDP) contributions, export dynamics, and labor market trends, this study demonstrates that the software and IT services sector has been one of the most resilient pillars of the national economy. Furthermore, the paper discusses the role of government incentives, specifically fiscal exemptions, and the challenges posed by global economic uncertainties and regional competition. The findings indicate that while the sector's growth rate has normalized following the post-pandemic digital boom, its strategic importance for Romania's macroeconomic stability and external balance of trade remains paramount.