Driving AI Adoption in the EU: A Quantitative Analysis of Macroeconomic Influences

Drago, Carlo and Costantiello, Alberto and Savorgnan, Marco and Leogrande, Angelo (2025): Driving AI Adoption in the EU: A Quantitative Analysis of Macroeconomic Influences. Published in: (8 June 2025)

Abstract

This article investigates macroeconomic factors that support the adoption of Artificial Intelligence (AI) technologies by large European Union (EU) enterprises. In this analysis, panel data regression is combined with machine learning to investigate how macroeconomic variables like health spending, domestic credit, exports, gross capital formation, and inflation, along with health spending and trade openness, influence the share of enterprises that adopt at least one type of AI technology (ALOAI). The results of the estimations—based on fixed and random effects models with 151 observations—show that health spending, inflation, and trade and GDP per capita have positively significant associations with adoption, with significant negative correlations visible with and among domestic credit, exports, and gross capital formation. In adjunct to this, the regression of machine learning models (KNN, Boosting, Random Forest) is benchmarked with MSE, RMSE, MAE, MAPE, and R² measures with KNN performing perfectly on all measures, although with some concerns regarding data overfitting. Furthermore, cluster analysis (Hierarchical, Density-Based, Neighborhood-Based) identifies hidden EU country groups with comparable macroeconomic variables and comparable ALOAI. Notably, those with characteristics of high integration in international trade, access to credit, and strong GDP per capita indicate large ALOAI levels, whereas those with macroeconomic volatility and under-investment in innovation trail behind. These findings suggest that securing the adoption of AI is not merely about finance and infrastructure but also about policy alignment and institutional preparedness. This work provides evidence-driven policy advice by presenting an integrated data-driven analytical framework to comprehend and manage AI diffusion within EU industry sectors.

• Item Type: MPRA Paper
• Language: English
• Keywords: Artificial Intelligence Adoption, Macroeconomic Indicators, Panel Data Regression, Machine Learning Models, EU Policy and Innovation.
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E22 - Investment ; Capital ; Intangible Capital ; Capacity; L - Industrial Organization > L8 - Industry Studies: Services > L86 - Information and Internet Services ; Computer Software; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes

References

Abdelaal, M. (2024). AI in Manufacturing: Market Analysis and Opportunities. arXiv preprint arXiv:2407.05426.

Abrardi, L., Cambini, C., & Rondi, L. (2019). The economics of artificial intelligence: A survey.

Acemoglu, D. (2025). The simple macroeconomics of AI. Economic Policy, 40(121), 13-58.

Acemoglu, D., Autor, D., Hazell, J., and Restrepo, P. (2022). Artificial intelligence and jobs: Evidence from online vacancies. Journal of Labor Economics, 40(S1):S293–S340.

Adhikari, P., Hamal, P., & Jnr, F. B. (2024). The impact of AI on personal finance and wealth management in the US.

Aghion, P., Jones, B. F., & Jones, C. I. (2017). Artificial intelligence and economic growth (Vol. 23928). Cambridge, MA: National Bureau of Economic Research.

Agrawal, A. K., Gans, J. S., & Goldfarb, A. (2021). Ai adoption and system-wide change (No. w28811). National Bureau of Economic Research.

Agrawal, A., Gans, J., & Goldfarb, A. (Eds.). (2019). The economics of artificial intelligence: An agenda. University of Chicago Press.

Alayo, M., Iturralde, T., & Maseda, A. (2022). Innovation and internationalization in family SMEs: Analyzing the role of family involvement. European Journal of Innovation Management, 25(2), 454-478.

Albanesi, S., Silva, A. D., Jimeno, J. F., Lamo, A., and Wabitsch, A. (2023). New Technologies and Jobs in Europe. IZA Discussion Papers 16227, Institute of Labor Economics (IZA).

Aldasoro, I., Doerr, S., Gambacorta, L., & Rees, D. M. (2024). The impact of artificial intelligence on output and inflation. Bank for International Settlements, Monetary and Economic Department.

Ambarsari, E. W., Dwitiyanti, N., Selvia, N., Cholifah, W. N., & Mardika, P. D. (2023). Comparison Approaches of the Fuzzy C-Means and Gaussian Mixture Model in Clustering the Welfare of the Indonesian People. KnE Social Sciences, 16-22.

Andrejovská, A., & Andrejkovičova, I. (2024). TAX REVENUES AND TAX RATES IN THE CONTEXT OF MACROECONOMIC DETERMINANTS. Journal of Entrepreneurship & Sustainability Issues, 12(1).

Aromolaran, O., Ngepah, N., & Saba, C. S. (2024). Macroeconomic determinants of poverty in South Africa: the role of investments in artificial intelligence. Access Journal, 5(2), 288-305.

Arora, G., Tiwari, S., Wu, Y., & Mei, X. (2024). An Exploration to the Correlation Structure and Clustering of Macroeconomic Variables. arXiv preprint arXiv:2401.10162.

Auliani, S. N., Novita, R., & Afdal, M. (2024). Implementation of Density-Based Spatial Clustering of Applications with Noise and Fuzzy C–Means for Clustering Car Sales. The Indonesian Journal of Computer Science, 13(4).

Azkeskin, S. A., & Aladağ, Z. (2025). Evaluating regional sustainable energy potential through hierarchical clustering and machine learning. Environmental Research Communications, 7(1), 015002.

Babina, T., Fedyk, A., He, A., and Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151:103745.

Bańkowski, K., Checherita-Westphal, C. D., Jesionek, J., Muggenthaler, P., Briodeau, C., Schoonackers, R., ... & Prammer, D. (2023). The effects of high inflation on public finances in the euro area.

Baumgartner, M., Horvat, D., Kinkel, S., & Kick, E. (2024). Key competences for the adoption of AI-based innovations in organisations. International Journal of Innovation Management, 28(09n10), 2440002.

Benigno, P., Canofari, P., Bartolomeo, G., & Messori, M. (2023). Inflation dynamics and monetary policy in the euro area. Economic Governance and EMU Scrutiny Unit

Bickley, S. J., Chan, H. F., & Torgler, B. (2022). Artificial intelligence in the field of economics. Scientometrics, 127(4), 2055-2084.

Bonab, A. B., Rudko, I., & Bellini, F. (2021). A review and a proposal about socio-economic impacts of artificial intelligence. In Business Revolution in a Digital Era: 14th International Conference on Business Excellence, ICBE 2020, Bucharest, Romania (pp. 251-270). Springer International Publishing.

Bonfiglioli, A., Gancia, G., Papadakis, I., and Crin`o, R. (2023). Artificial intelligence and jobs: Evidence from us commuting zones. Technical report, CESifo Working Paper.

Bosna, J., Valčić, S. B., & Peša, A. (2024). Measuring the effect of entry into the eurozone on economic growth–data storytelling using clustering and ANFIS. Technological and economic development of economy, 30(3), 646-666.

Bresnahan, T. F. and Trajtenberg, M. (1995). General purpose technologies ‘engines of growth’? Journal of Econometrics, 65(1):83–108.

Brey, B., & van der Marel, E. (2024). The role of human-capital in artificial intelligence adoption. Economics Letters, 244, 111949.

Brynjolfsson, E., & Unger, G. (2023). The macroeconomics of artificial intelligence.

Brynjolfsson, E., Li, D., and Raymond, L. R. (2023). Generative ai at work. Technical report, National Bureau of Economic Research.

Brynjolfsson, E., Rock, D., and Syverson, C. (2018). Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics, pages 23–57. University of Chicago Press.

Buglea, A., Cișmașu, I. D., Gligor, D. A. G., & Jurcuț, C. N. (2025). Exploring the Impact of Digital Transformation on Non-Financial Performance in Central and Eastern European Countries. Electronics, 14(6), 1226.

Camerer, C. F. (2018). Artificial intelligence and behavioral economics. In The economics of artificial intelligence: An agenda (pp. 587-608). University of Chicago Press.

Challoumis, C. (2024). AI AND THE ECONOMY-A DEEP DIVE INTO THE NEW FINANCIAL PARADIGM. In XVI International Scientific Conference (pp. 176-200).

Chandra, M., Vimal, K. E. K., & Rajak, S. (2024). A comparative study of machine learning algorithms in the prediction of bead geometry in wire-arc additive manufacturing. International Journal on Interactive Design and Manufacturing (IJIDeM), 18(9), 6625-6638.

Chaurasia, P., McClean, S., Nugent, C. D., Cleland, I., Zhang, S., Donnelly, M. P., ... & Tschanz, J. (2022). Modelling mobile-based technology adoption among people with dementia. Personal and Ubiquitous Computing, 26(2), 365-384.

Chen, N., Christensen, L., Gallagher, K., Mate, R., & Rafert, G. (2016). Global economic impacts associated with artificial intelligence. Analysis Group, 1.

Chen, W., Xinyuan, Terrence Tianshuo, S., and Srinivasan, S. (2024). The Value of AI Innovations. Harvard Business School Working Paper No. 24-069

Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (Vol. 24449). Cambridge, MA, USA: National bureau of economic research.

Comunale, M. and Manera, A. (2024). The economic impacts and the regulation of ai: A review of the academic literature and policy actions.

Costantini, V., Delgado, F. J., & Presno, M. J. (2023). Environmental innovations in the EU: A club convergence analysis of the eco-innovation index and driving factors of the clusters. Environmental Innovation and Societal Transitions, 46, 100698.

Criste, A., Lupu, I., & Lupu, R. (2021). Coherence and entropy of credit cycles across the euro area candidate countries. Entropy, 23(9), 1213.

Czarnitzki, D., Fern´andez, G. P., and Rammer, C. (2023). Artificial intelligence and firm-level productivity. Journal of Economic Behavior & Organization, 211:188–205.

Czeczeli, V., Kolozsi, P. P., Kutasi, G., & Marton, Á. (2024). Exposure and preparedness for wartime inflation in the EU: Retrospective cluster analysis. Society and Economy, 46(3), 236-255.

Da Silva, L. E. B., Melton, N. M., & Wunsch, D. C. (2020). Incremental cluster validity indices for online learning of hard partitions: Extensions and comparative study. IEEE Access, 8, 22025-22047.

Dirican, C. (2015). The impacts of robotics, artificial intelligence on business and economics. Procedia-Social and Behavioral Sciences, 195, 564-573.

Doran, N. M., Badareu, G., & Puiu, S. (2025). Automation Systems Implications on Economic Performance of Industrial Sectors in Selected European Union Countries. Systems, 13(1), 26.

Dritsaki, M., Dritsaki, C., & Tsianaka, E. (2023). The effect of macroeconomic and environmental factors on innovation in EU member countries. Journal of Infrastructure, Policy and Development, 7(3), 2560.

Dudzevičiūtė, G. (2021). Does causality exist between exports and economic development? Evidence from selected EU countries. Journal of International Studies, 14(4), 56-66.

Elkahlout, G. R., & Elkahlout, M. A. (2024). Employing cluster analysis in defining groundwater wells patterns in Rafah. Edelweiss Applied Science and Technology, 8(6), 3542-3555.

Elnaeem Balila, A., & Shabri, A. B. (2024). Comparative analysis of machine learning algorithms for predicting Dubai property prices. Frontiers in Applied Mathematics and Statistics, 10, 1327376.

Eloundou, T., Manning, S., Mishkin, P., and Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. Papers 2303.10130, arXiv.org.

Erdoğan, S., Yıldırım, D. Ç., & Gedikli, A. (2020). Dynamics and determinants of inflation during the COVID-19 pandemic period in European countries: A spatial panel data analysis. Duzce Medical Journal, 22(Special Issue), 61-67.

Ernst, E., Merola, R., & Samaan, D. (2019). Economics of artificial intelligence: Implications for the future of work. IZA Journal of Labor Policy, 9(1), 1-35.

Felten, E. W., Raj, M., and Seamans, R. (2018). A method to link advances in artificial intelligence to occupational abilities. AEA Papers and Proceedings, 108:54–57.

Ferraro, S., Männasoo, K., & Tasane, H. (2023). How the EU Cohesion Policy targeted at R&D and innovation impacts the productivity, employment and exports of SMEs in Estonia. Evaluation and Program Planning, 97, 102221.

Gagolewski, M., Bartoszuk, M., & Cena, A. (2021). Are cluster validity measures (in) valid?. Information Sciences, 581, 620-636.

Gazzani, A. G., & Natoli, F. (2024). The macroeconomic effects of AI innovation. Available at SSRN 4938014.

Giannini, M., & Martini, B. (2024). Regional disparities in the European Union. A machine learning approach. Papers in Regional Science, 103(4), 100033.

Gonzalez, D. A. (2025). Exploring the Impact of Machine Learning and AI on Inflation Prediction: A Bibliometric Approach. Studies of Applied Economics, 43(1).

Grennan, J. and Michaely, R. (2020). Artificial intelligence and high-skilled work: Evidence from analysts. Swiss Finance Institute Research Paper, (20-84).

Gries, T., & Naudé, W. (2022). Modelling artificial intelligence in economics. Journal for labour market research, 56(1), 12.

GUALANDRI, F., & KUZIOR, A. (2024). Drivers of AI adoption in enterprises: A European-wide analysis. Materials Research Proceedings, 45.

Guarascio, D., Reljic, J., & Stöllinger, R. (2025). Diverging paths: AI exposure and employment across European regions. Structural Change and Economic Dynamics, 73, 11-24.

Hatzius, J. (2023). The potentially large effects of artificial intelligence on economic growth (briggs/kodnani). Goldman Sachs, 1.

Hoffmann, M., & Nurski, L. (2021). What is holding back artificial intelligence adoption in Europe?. Bruegel.

Hui, X., Reshef, O., and Zhou, L. (2023). The short-term effects of generative artificial intelligence on employment: Evidence from an online labor market. Available at SSRN 4527336.

Ilkka, T. (2018). The impact of artificial intelligence on learning, teaching, and education. European Union.

Ilzetzki, E. and Jain, S. (2023). The impact of artificial intelligence on growth and employment. Technical report, VoxEU.

Iuga, I. C., & Socol, A. (2024). Government Artificial Intelligence readiness and brain drain: influencing factors and spatial effects in the European Union member states. Journal of Business Economics and Management, 25(2), 268-296.

Jenifel, M. G., Jasmine, R. A., & Umanandhini, D. (2024, June). Bitcoin Price Predictive Dynamics Using Machine Learning Models. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.

Kaliappan, J., Srinivasan, K., Qaisar, S. M., Sundararajan, K., Chang, C. Y., & Suganthan, C. (2021). Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate. Frontiers in Public Health, 9.

Kezlya, E. M., Glushchenko, A. M., & Kulikovskiy, M. S. (2024). Diatom Diversity from Watercourses of North-Eastern Kamchatka with Description of One New Species. Diversity, 16(9), 592.

Kochkina, N., Andriushchenko, I., & Gatto, G. (2024, October). Strategic AI Adoption: Economic Impact, Case Studies from Handy. ai, and Industry Readiness. In 2024 IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE) (pp. 1-6). IEEE.

Korinek, A., & Stiglitz, J. E. (2021). Artificial intelligence, globalization, and strategies for economic development (No. w28453). National Bureau of Economic Research.

Kotrachai, C., Chanruangrat, P., Thaipisutikul, T., Kusakunniran, W., Hsu, W. C., & Sun, Y. C. (2023, November). Explainable AI supported Evaluation and Comparison on Credit Card Fraud Detection Models. In 2023 7th International Conference on Information Technology (InCIT) (pp. 86-91). IEEE.

Kumah, M. S. K., Fiakumah, W. K., Gozah, E. K. A., & Edekor, L. K. Key Factors That Predict Students’ Mathematics Competence in A College of Education in Hohoe, Volta Region of Ghana.

Licchetta, M., & Meyermans, E. (2022). Gross fixed capital formation in the euro area during the COVID-19 pandemic. Intereconomics, 57(4), 238-246.

Lu, C. H. (2021). The impact of artificial intelligence on economic growth and welfare. Journal of Macroeconomics, 69, 103342.

Ma, X., Hao, Y., Li, X., Liu, J., & Qi, J. (2023). Evaluating global intelligence innovation: An index based on machine learning methods. Technological Forecasting and Social Change, 194, 122736.

Maccarrone, G., Morelli, G., & Spadaccini, S. (2021). GDP forecasting: Machine learning, linear or autoregression?. Frontiers in Artificial Intelligence, 4, 757864.

Maestro, S., & Rana, P. (2024). Variables Impacting the AI Adoption in Organizations. International Journal of Science and Research Archive.

Manshur Al Ahmad, A. S., Judijanto, L., Tooy, D., Putra, P., Hermansyah, M., Kumalasanti, M., & Agit, A. (2024). Integration of Artificial Intelligence and Macro-Economic Analysis: A Novel Approach with Distributed Information Systems. EAI Endorsed Transactions on Scalable Information Systems, 11(2).

Marčeta, M., & Bojnec, Š. (2023). Trade openness, global competitiveness, and catching up between the European Union countries. Review of International Business and Strategy, 33(4), 691-714.

Marwala, T., & Hurwitz, E. (2017). Artificial intelligence and economic theory: Skynet in the market (Vol. 1). Cham: Springer International Publishing.

Mateos-Garcia, J. C. (2018). The complex economics of artificial intelligence. Available at SSRN 3294552.

Merkulova, T., & Nikolaeva, O. (2022). Cluster analysis of tax indicators in Europien countries. Bulletin of VN Karazin Kharkiv National University Economic Series, (102), 69-81.

Morikawa, M. (2016). The Effects of AI and Robotics on Business and Employment: Evidence from a Survey on Japanese Firms. Technical Report 16-E-066, RIETI Discussion Paper.

Morina, F., Misiri, V., & Alijaj, S. (2024). Examining the relationship between public debt and private consumption in European OECD countries (2011–2020). Quantitative Finance and Economics, 8(1), 75-102.

Navigating the AI Wave: Overcoming Barriers and Unleashing the Potential of Artificial Intelligence in Transforming European Public Health. European Journal of Public Health, 34(Supplement_3), ckae144-001.

Nenov, I., Simeonova-Ganeva, R., Ganev, K., & Mengov, G. (2023, October). A Neural Model Forecasts Macroeconomic Indicators. In 2023 International Conference Automatics and Informatics (ICAI) (pp. 156-159). IEEE.

Nguyen, D., & Santarelli, E. (2024). Trade Openness and the Diffusion of Artificial Intelligence Technologies in Europe: A Panel Data Approach. European Economic Review, 154, 104218.

Okoye, K. (2023, October). Technology-mediated method for prediction of global government investment in education toward sustainable development and aid using machine learning and classification. In 2023 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 151-158). IEEE.

Olasiuk, H., Kumar, S., Singh, S., & Ganushchak, T. (2023, December). Mapping research clusters of artificial intelligence for financial services using topic modelling: A machine learning insight. In 2023 Global Conference on Information Technologies and Communications (GCITC) (pp. 1-6). IEEE.

Ozkan-Okay, M., Akin, E., Aslan, Ö., Kosunalp, S., Iliev, T., Stoyanov, I., & Beloev, I. (2024). A comprehensive survey: Evaluating the efficiency of artificial intelligence and machine learning techniques on cyber security solutions. IEEe Access, 12, 12229-12256.

Papagiannis, F., Gazzola, P., Burak, O., & Pokutsa, I. (2021). A European household waste management approach: Intelligently clean Ukraine. Journal of environmental management, 294, 113015.

Parkes, D. C., & Wellman, M. P. (2015). Economic reasoning and artificial intelligence. Science, 349(6245), 267-272.

Pehlivan, C. N. (2024). Report: The EU Artificial Intelligence (AI) Act: An Introduction. Global Privacy Law Review, 5(1).

Popescu, R., Corboș, R.-A., & Bunea, O. (2024). From bytes to insights through a bibliometric journey into AI's influence on public services. Applied Research in Administrative Sciences.

Popović, A., Todorović, A., & Milijić, A. ARTIFICIAL INTELLIGENCE ADOPTION AND ITS INFLUENCE ON CIRCULAR MATERIAL USE: AN EU CROSS-COUNTRY ANALYSIS.

Qin, Y., Xu, Z., Wang, X., & Skare, M. (2024). Artificial intelligence and economic development: An evolutionary investigation and systematic review. Journal of the Knowledge Economy, 15(1), 1736-1770.

Rađenović, Ž., Janjić, I., & Talić, M. (2024). Targeting eco-innovation performance among EU countries: Cluster outcome. SCIENCE International Journal, 3(1), 189-194.

Raj, D. K., & Gopikrishnan, T. (2024). Soft computing techniques for predicting vegetation dynamics in Delhi. Journal of Earth System Science, 133(4), 241.

Ruiz-Real, J. L., Uribe-Toril, J., Arriaza Torres, J. A., & de Pablo Valenciano, J. (2021). Artificial intelligence in business and economics research: Trends and future. Business Economics and Management (JBEM), 22(1), 98-117.

Sarmas, E., Fragkiadaki, A., & Marinakis, V. (2024). Explainable AI-Based Ensemble Clustering for Load Profiling and Demand Response. Energies, 17(22), 5559.

Schulz, M., Pehl, M., & Trinitis, C. (2024, May). The European Chips Act and its Impact on Teaching. In Proceedings of the 21st ACM International Conference on Computing Frontiers: Workshops and Special Sessions (pp. 146-146).

Sehgal, D., Kaur, I., Sharma, V., Gautam, B., Singh, A., & Kumar, N. (2024). AI-Driven Early Diabetes Prediction. 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC), 40–48.

Shokouhifar, M., Fanian, F., Rafsanjani, M. K., Hosseinzadeh, M., & Mirjalili, S. (2024). AI-driven cluster-based routing protocols in WSNs: A survey of fuzzy heuristics, metaheuristics, and machine learning models. Computer Science Review, 54, 100684.

Siddik, A. B., Forid, M. S., Yong, L., Du, A. M., & Goodell, J. W. (2025). Artificial intelligence as a catalyst for sustainable tourism growth and economic cycles. Technological Forecasting and Social Change, 210, 123875.

Stokman, A. (2023). Accelerating inflation expectations of households in the euro area: sources and macroeconomic spending consequences. Applied Economics Letters.

Szczepanski, M. (2019). Economic impacts of artificial intelligence (AI).

Tapeh, A. T. G., & Naser, M. Z. (2023). Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices. Archives of Computational Methods in Engineering, 30(1), 115-159.

Thamrin, N., & Wijayanto, A. W. (2021). Comparison of Soft and Hard Clustering: A Case Study on Welfare Level in Cities on Java Island: Analisis cluster dengan menggunakan hard clustering dan soft clustering untuk pengelompokkan tingkat kesejahteraan kabupaten/kota di pulau Jawa. Indonesian Journal of Statistics and Its Applications, 5(1), 141-160.

Tiutiunyk, I., Drabek, J., Antoniuk, N., Navickas, V., & Rubanov, P. (2021). The impact of digital transformation on macroeconomic stability: Evidence from EU countries. Journal of International Studies (2071-8330), 14(3).

Trabelsi, M. A. (2024). The impact of artificial intelligence on economic development. Journal of Electronic Business & Digital Economics, 3(2), 142-155.

Tudor, C., Sova, R., Stamatiou, P., Vlachos, V., & Polychronidou, P. (2025). Future-Proofing EU-27 Energy Policies with AI: Analyzing and Forecasting Fossil Fuel Trends. Electronics, 14(3), 631.

Übellacker, T. (2025). Making Sense of AI Limitations: How Individual Perceptions Shape Organizational Readiness for AI Adoption. arXiv preprint arXiv:2502.15870.

Uren, V., & Edwards, J. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management, 68, 102588.

Usman, M., Saeed, A., Latif, A., & Zakir, A. (2024). Energy Consumption and Trade Liberalization: Investigating Performance of Economic Growth for French Economy. Journal of Asian Development Studies, 13(1), 402-411.

Varian, H. R. (2018). Artificial intelligence, economics, and industrial organization (Vol. 24839). Cambridge, MA, USA:: National Bureau of Economic Research.

Vijayalakshmi, V., Selvakumar, A., & Panimalar, K. (2023, January). Implementation of medical insurance price prediction system using regression algorithms. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1529-1534). IEEE.

Vujović, D. (2025). Generative AI: A new general-purpose technology for growth and research. Ekonomika preduzeća, 73(1-2), 97-114.

WAGAN, S. M., & SIDRA, S. (2024). EXPLORING THE IMPACT OF AI RESEARCH, VENTURE CAPITAL INVESTMENT, AND ADOPTION ON PRODUCTIVITY: A MULTI-COUNTRY PANEL DATA ANALYSIS. Journal of European Economy, 23(4), 688-713.

Wagner, D. N. (2020). Economic patterns in a world with artificial intelligence. Evolutionary and Institutional Economics Review, 17(1), 111-131.

Wang, L., Sarker, P., Alam, K., & Sumon, S. (2021). Artificial intelligence and economic growth: A theoretical framework. Scientific Annals of Economics and Business, 68(4), 421-443.

Wang, X., He, T., Wang, S., & Zhao, H. (2025). The impact of artificial intelligence on economic growth from the perspective of population external system. Social Science Computer Review, 43(1), 129-147.

Wang, Y., Xie, Y., Wu, Y., & Yang, Y. (2024). Improved KNN-based Stock Price Prediction. Academic Journal of Computing & Information Science, 7(6), 38-43.

Webb, M. (2019). The Impact of Artificial Intelligence on the Labor Market. Available at SSRN: https://ssrn.com/abstract=3482150.

Wolff, J., Pauling, J., Keck, A., & Baumbach, J. (2020). The economic impact of artificial intelligence in health care: systematic review. Journal of medical Internet research, 22(2), e16866.

Žarković, M., Ćetković, J., & Cvijović, J. (2025). Economic growth determinants in old and new EU countries. Panoeconomicus, 72(2), 183-210.

Zekos, G. I. (2021). Economics and law of artificial intelligence. Finance, Economic Impacts, Risk Management and Governance.

Zeleke, A. J., Palumbo, P., Tubertini, P., Miglio, R., & Chiari, L. (2023). Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis. Frontiers in Artificial Intelligence, 6.