Research Article
BibTex RIS Cite

Are We Ready for Artificial Intelligence? Comparative Causal Relationship of Origins Variables and Local Policy Implications

Year 2025, Volume: 10 Issue: 1, 409 - 421

Sergen Gürsoy

Abstract

This study aims to reveal the causal relationship between the stated value of 174 countries' readiness for artificial intelligence and its origin variables, to obtain asymmetric results and to identify local context-specific results. The method of the research is fsQCA (fuzzy-set Qualitative Comparative Analysis). According to the general findings, it has been determined that the presence/high level of all the condition variables for preparation for artificial intelligence is absolutely necessary and sufficient in the set-theoretical context. On the other hand, using the advantage of asymmetric findings, it was determined that the absence of DI and RE is absolutely necessary in the theoretical sense, while the other conditions are sufficient. According to the configuration findings, it has been determined that AI readiness is not dependent on a single variable but is determined by a combination of various factors according to the local context for countries.

Keywords

Labor Economics and Industrial Relations Artificial Intelligence Human Capital Digital Infrastructure

References

  • Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation, and work. In The economics of artificial intelligence: An agenda (pp. 197-236). University of Chicago Press.
  • Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of political economy, 128(6), 2188-2244.
  • Adigwe, C. S., Olaniyi, O. O., Olabanji, S. O., Okunleye, O. J., Mayeke, N. R., & Ajayi, S. A. (2024). Forecasting the future: The interplay of artificial intelligence, innovation, and competitiveness and its effect on the global economy. Asian journal of economics, business and accounting, 24(4), 126-146.
  • Athey, S. (2018). The impact of machine learning on economics. The Economics of Artificial Intelligence: An Agenda. University of Chicago Press. ISBN 978-0-226-61333-8
  • Bhowmick, A., & Seetharaman, A. (2024). Influence of digital infrastructure, data integration & ethical dimension on artificial intelligence in product development. In 2024 10th International Conference on Mechatronics and Robotics Engineering (ICMRE) (pp. 201-208). IEEE.
  • Borges, D., (2024). Digital Infrastructure: Benefits and How to Build It. Codence.
  • Brey, B., & Van der Marel, E. (2023). Artificial Intelligence and the clustering of human capital: The risks for Europe (No. 05/2023). ECIPE Occasional Paper.
  • Brey, B., & van der Marel, E. (2024). The role of human-capital in artificial intelligence adoption. Economics Letters, 244, 111949.
  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & company.
  • Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., ... & Trench, M. (2018). Artificial Intelligence: The Next Digital Frontier? McKinsey Global Institute.
  • Bughin, J., Hazan, E., Sree Ramaswamy, P., DC, W., & Chu, M. (2017). Artificial intelligence the next digital frontier.
  • Cangialosi, N. (2023). Fuzzy-set qualitative comparative analysis (fsQCA) in organizational psychology: Theoretical overview, research guidelines, and a step-by-step tutorial using R software. The Spanish journal of psychology, 26, e21.
  • Carbonero, F., Davies, J., Ernst, E., Fossen, F. M., Samaan, D., & Sorgner, A. (2023). The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam. Journal of Evolutionary Economics, 33(3), 707-736.
  • Carrillo, M. R. (2020). Artificial intelligence: From ethics to law. Telecommunications policy, 44(6), 101937.
  • Carter, D. (2020). Regulation and ethics in artificial intelligence and machine learning technologies: Where are we now? Who is responsible? Can the information professional play a role? Business Information Review, 37(2), 60-68.
  • Cazzaniga, M., Jaumotte, M. F., Li, L., Melina, M. G., Panton, A. J., Pizzinelli, C., ... & Tavares, M. M. M. (2024). Gen-AI: Artificial intelligence and the future of work. International Monetary Fund.
  • Cockburn, I., Henderson, R. & Stern, S. (2019). 4. The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis. In A. Agrawal, J. Gans & A. Goldfarb (Ed.), The Economics of Artificial Intelligence: An Agenda (pp. 115-148). Chicago: University of Chicago Press. https://doi.org/10.7208/9780226613475-006
  • Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.
  • Dağlı, İ. (2022). Yapay Zekâ Teknolojilerinde Etkili Faktörler Üzerine Bir Model Denemesi: En Başarılı Ülkelerle Panel Veri Analizi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 17(2), 368-386.
  • de Almeida, P. G. R., dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: a framework for governance. Ethics and Information Technology, 23(3), 505-525.
  • Deaton, A. (2010). Price indexes, inequality, and the measurement of world poverty. American Economic Review, 100(1), 5–34.
  • Deshmukh, R., & Pasumarti, S. S. (2023). Digital Infrastructure and Applications for Smart Cities and Societies—Role of Artificial Intelligence. In International Conference on Smart Trends in Computing and Communications (pp. 549-561). Singapore: Springer Nature Singapore.
  • Duşa, A. (2018). QCA with R: A comprehensive resource. Springer. ISBN 978-3-319-75668-4
  • E. G. M’hamed, O. Rachid, & C. Houda. (2024). Redefining Human Capital in the Age of Artificial Intelligence: Challenges and Opportunities. Revue Internationale De La Recherche Scientifique Et De l’Innovation (Revue-IRSI), 2(4), 933–946. https://doi.org/10.5281/zenodo.13893186
  • Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of management journal, 54(2), 393-420.
  • Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393–420.
  • Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design, 535-545.
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
  • Frey, C. B., & Osborne, M. A. (2017). " The Future of Employment: How Susceptible Are Jobs to Computerisation?" Technological Forecasting and Social Change. 114 (C): 254-280.
  • Furman, J., & Seamans, R. (2019). AI and the Economy. Innovation policy and the economy, 19(1), 161-191.
  • Goldin, C. (2024). Human Capital. In: Diebolt, C., Haupert, M. (eds) Handbook of Cliometrics. Springer, Cham. https://doi.org/10.1007/978-3-031-35583-7_23
  • Greenstein, S. (2021). 8. Digital Infrastructure. In E. Glaeser & J. Poterba (Ed.), Economic Analysis and Infrastructure Investment (pp. 409-452). Chicago: University of Chicago Press. https://doi.org/10.7208/chicago/9780226800615-011
  • Günbayi, I., & Sorm, S. (2018). Social paradigms in guiding social research design: The functional, interpretive, radical humanist and radical structural paradigms. Online Submission, 9(2), 57-76.
  • Huy, T., Hong, T., Cuong, N. T., & Tran, P. (2024). AI Innovation and Economics Growth: A Global Evidence. WSB Journal of Business and Finance, (58), 198-216.
  • Huynh-The, T., Pham, Q. V., Pham, X. Q., Nguyen, T. T., Han, Z., & Kim, D. S. (2023). Artificial intelligence for the metaverse: A survey. Engineering Applications of Artificial Intelligence, 117, 105581.
  • IMF. (2024). AI Preparedness Index (AIPI). https://www.imf.org/external/datamapper/datasets/AIPI
  • International Science Council. (2024). Preparing National Research Ecosystems for AI: strategies and progress in 2024. https://council.science/publications/ai-science-systems
  • Iphofen, R., & Kritikos, M. (2021). Regulating artificial intelligence and robotics: ethics by design in a digital society. Contemporary Social Science, 16(2), 170-184.
  • Jerven, M. (2013). Poor numbers: How we are misled by African development statistics and what to do about it. Cornell University Press.
  • Jiang, Y. (2022). Prediction model of the impact of innovation and entrepreneurship on China's digital economy based on neural network integration systems. Neural Computing and Applications, 34(4), 2661-2675.
  • Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence?. Discover Artificial Intelligence, 2(1), 4.
  • Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational researcher, 33(7), 14-26.
  • Komninos, N. (2006). The architecture of intelligent clities: Integrating human, collective and artificial intelligence to enhance knowledge and innovation. In 2006 2nd IET International Conference on Intelligent Environments-IE 06 (Vol. 1, pp. 13-20). IET.
  • Kraus, N., Kraus, K., Shtepa, O., Hryhorkiv, M., & Kuzmuk, I. (2022). Artificial intelligence in established of industry 4.0. WSEAS Transactions on Business and Economics, (19), 1884-1900.
  • Larsson, S. (2020). On the governance of artificial intelligence through ethics guidelines. Asian Journal of Law and Society, 7(3), 437-451.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • Magrani, E. (2019). New perspectives on ethics and the laws of artificial intelligence. Internet policy review, 8(3).
  • McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
  • Minh, D., Wang, H.X., Li, Y.F. et al. Explainable artificial intelligence: a comprehensive review. Artif Intell Rev. 55, 3503–3568 (2022). https://doi.org/10.1007/s10462-021-10088-y
  • Misangyi, V. F., Greckhamer, T., Furnari, S., Fiss, P. C., Crilly, D., & Aguilera, R. (2017). Embracing causal complexity: The emergence of a neo-configurational perspective. Journal of Management, 43(1), 255–282.
  • Mishel, L., & Bivens, J. (2017). The zombie robot argument lurches on: There is no evidence that automation leads to joblessness or inequality. Economic Policy Institute Working Papers.
  • Navruz-Zoda, B. N., & Shomiev, G. U. (2017). The different approaches of human capital formation. International Journal of Innovative Technologies in Economy, 5 (11), 6-10.
  • Newel, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113-126.
  • OECD (2021). OECD AI Principles and Recommendations. OECD Publishing. https://www.oecd.org/going-digital/ai/principles/
  • OECD. (2024). OECD Digital Economy Outlook. Organisation for Economic Co-operation and Development. Biennial. ISSN: 3008-1750 (Online).
  • Okoroma, F. N. (2024). Artificial intelligence and libraries: Import, risks and prospects. Journal of ICT Development, Applications and Research. 6(2), 30 – 40.
  • Pençe, I., Tunç, H., Şişeci Çeşmeli, M., & Kalkan, A. (2019). Forecasting Imports And Exports Of Turkey Using Artificial Intelligence Methods. European Proceedings of Social and Behavioural Sciences, 54.
  • Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press.
  • Ragin, C. C. (2009). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press.
  • Ragin, C. C. (2014). The comparative method: Moving beyond qualitative and quantitative strategies. Univ of California Press.
  • Raikov, A., & Abrosimov, V. (2018). Import countries ranking with econometric and artificial intelligence methods. In International Conference on Digital Transformation and Global Society (pp. 402-414). Cham: Springer International Publishing.
  • Roberts, H., Cowls, J., Morley, J., Taddeo, M., Wang, V., & Floridi, L. (2021). The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation (pp. 47-79). Springer International Publishing.
  • Russell, S., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach, 4th Global ed.
  • Sandefur, J., & Glassman, A. (2015). The political economy of bad data: Evidence from African survey and administrative statistics. The Journal of Development Studies, 51(2), 116–132.
  • Schneider, C. Q., & Wagemann, C. (2010). Qualitative comparative analysis (QCA) and fuzzy-sets: Agenda for a research approach and a data analysis technique. Comparative Sociology, 9(3), 376-396.
  • Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis (QCA). Cambridge University Press.
  • Serrano, W. (2018). Digital systems in smart city and infrastructure: Digital as a service. Smart cities, 1(1), 134-154.
  • Smuha, N. A. (2019). The EU approach to ethics guidelines for trustworthy artificial intelligence. Computer Law Review International, 20(4), 97-106.
  • Strusani, D., & Houngbonon, G. V. (2019). The role of artificial intelligence in supporting development in emerging markets. International Finance Corporation, Washington, DC.
  • Susskind, D. (2020). A world without work: Technology, automation and how we should respond. Penguin UK.
  • UNCTAD (2021). Technology and Innovation Report 2021: Catching Technological Waves. United Nations Conference on Trade and Development.
  • Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of business research, 122, 889-901.
  • Wang, H., & Li, H. (2019). Research on theoretical analysis of human capital of labor economics based on artificial intelligence. Journal of Intelligent & Fuzzy Systems, 37(3), 3257-3265.
  • WEF. (2024). AI for Impact: Strengthening AI Ecosystems for Social Innovation. INSIGHT REPORT SEPTEMBER 2024. World Economic Forum.
  • Wong, A. (2021). Ethics and regulation of artificial intelligence. In Artificial Intelligence for Knowledge Management: 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Yokohama, Japan, January 7–8, 2021, Revised Selected Papers 8 (pp. 1-18). Springer International Publishing.
  • World Bank (2022). Digital Development Global Practice: Digital Economy for Africa Initiative. https://www.worldbank.org/en/programs/all-africa-digital-transformation-initiative
  • Yi, Z., & Ayangbah, S. (2024). The Impact of AI Innovation Management on Organizational Productivity and Economic Growth: An Analytical Study. International Journal of Business Management and Economic Review.

Yapay Zekâya Hazır mıyız? Köken Değişkenlerin Karşılaştırmalı Nedensel İlişkisi ve Yerel Politik Öneriler

Year 2025, Volume: 10 Issue: 1, 409 - 421

Sergen Gürsoy

Abstract

Bu çalışma, 174 ülkenin yapay zekaya hazır olmasına yönelik belirtilen değerin, köken değişkenleri ile nedensel ilişkisini ortaya koymayı, asimetrik sonuçlar elde etmeyi ve buna yönelik olarak yerel bağlama özgü sonuçlar belirlemeyi amaçlamaktadır. Araştırmanın yöntemi fsQCA (fuzzy-set Qualitative Comparative Analysis) olarak seçilmiştir. Elde edilen genel bulgulara göre yapay zekaya hazırlık için koşul değişkenlerin tamamının varlığının/yüksek seviyede olmasının küme-teorik bağlamda mutlak gerekli ve yeterli olduğu belirlenmiştir. Öte yandan asimetrik bulguların avantajını kullanarak yapay zekaya hazır olmama durumlarına yönelik ortaya çıkarılan sonuca göre DI’nın ve RE’nin olmamasının teorik anlamda mutlak gerekli olduğu diğer koşulların ise yeterli olduğu belirlenmiştir. Konfigürasyon bulgularına göre ise yapay zekaya hazırlığın tek bir değişkene bağlı olmadığı ülkeler için yerel bağlama göre çeşitli etkenlerin bir kombinasyonu tarafından belirlendiği tespit edilmiştir.

Keywords

Çalışma Ekonomisi ve Endüstri İlişkileri Yapay Zeka Beşeri Sermaye Dijital Altyapı

Thanks

Nitel araştırma yöntemlerinde bana büyük katkılar sağlayan ve ailemizden olduğunu hissettiğimiz Prof. Dr. İlhan GÜNBAYI ve her anımda bana desteğini esirgemeyen eşim Ebru GÜRSOY'a teşekkürlerimi borç bilirim.

References

  • Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation, and work. In The economics of artificial intelligence: An agenda (pp. 197-236). University of Chicago Press.
  • Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of political economy, 128(6), 2188-2244.
  • Adigwe, C. S., Olaniyi, O. O., Olabanji, S. O., Okunleye, O. J., Mayeke, N. R., & Ajayi, S. A. (2024). Forecasting the future: The interplay of artificial intelligence, innovation, and competitiveness and its effect on the global economy. Asian journal of economics, business and accounting, 24(4), 126-146.
  • Athey, S. (2018). The impact of machine learning on economics. The Economics of Artificial Intelligence: An Agenda. University of Chicago Press. ISBN 978-0-226-61333-8
  • Bhowmick, A., & Seetharaman, A. (2024). Influence of digital infrastructure, data integration & ethical dimension on artificial intelligence in product development. In 2024 10th International Conference on Mechatronics and Robotics Engineering (ICMRE) (pp. 201-208). IEEE.
  • Borges, D., (2024). Digital Infrastructure: Benefits and How to Build It. Codence.
  • Brey, B., & Van der Marel, E. (2023). Artificial Intelligence and the clustering of human capital: The risks for Europe (No. 05/2023). ECIPE Occasional Paper.
  • Brey, B., & van der Marel, E. (2024). The role of human-capital in artificial intelligence adoption. Economics Letters, 244, 111949.
  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & company.
  • Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., ... & Trench, M. (2018). Artificial Intelligence: The Next Digital Frontier? McKinsey Global Institute.
  • Bughin, J., Hazan, E., Sree Ramaswamy, P., DC, W., & Chu, M. (2017). Artificial intelligence the next digital frontier.
  • Cangialosi, N. (2023). Fuzzy-set qualitative comparative analysis (fsQCA) in organizational psychology: Theoretical overview, research guidelines, and a step-by-step tutorial using R software. The Spanish journal of psychology, 26, e21.
  • Carbonero, F., Davies, J., Ernst, E., Fossen, F. M., Samaan, D., & Sorgner, A. (2023). The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam. Journal of Evolutionary Economics, 33(3), 707-736.
  • Carrillo, M. R. (2020). Artificial intelligence: From ethics to law. Telecommunications policy, 44(6), 101937.
  • Carter, D. (2020). Regulation and ethics in artificial intelligence and machine learning technologies: Where are we now? Who is responsible? Can the information professional play a role? Business Information Review, 37(2), 60-68.
  • Cazzaniga, M., Jaumotte, M. F., Li, L., Melina, M. G., Panton, A. J., Pizzinelli, C., ... & Tavares, M. M. M. (2024). Gen-AI: Artificial intelligence and the future of work. International Monetary Fund.
  • Cockburn, I., Henderson, R. & Stern, S. (2019). 4. The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis. In A. Agrawal, J. Gans & A. Goldfarb (Ed.), The Economics of Artificial Intelligence: An Agenda (pp. 115-148). Chicago: University of Chicago Press. https://doi.org/10.7208/9780226613475-006
  • Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.
  • Dağlı, İ. (2022). Yapay Zekâ Teknolojilerinde Etkili Faktörler Üzerine Bir Model Denemesi: En Başarılı Ülkelerle Panel Veri Analizi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 17(2), 368-386.
  • de Almeida, P. G. R., dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: a framework for governance. Ethics and Information Technology, 23(3), 505-525.
  • Deaton, A. (2010). Price indexes, inequality, and the measurement of world poverty. American Economic Review, 100(1), 5–34.
  • Deshmukh, R., & Pasumarti, S. S. (2023). Digital Infrastructure and Applications for Smart Cities and Societies—Role of Artificial Intelligence. In International Conference on Smart Trends in Computing and Communications (pp. 549-561). Singapore: Springer Nature Singapore.
  • Duşa, A. (2018). QCA with R: A comprehensive resource. Springer. ISBN 978-3-319-75668-4
  • E. G. M’hamed, O. Rachid, & C. Houda. (2024). Redefining Human Capital in the Age of Artificial Intelligence: Challenges and Opportunities. Revue Internationale De La Recherche Scientifique Et De l’Innovation (Revue-IRSI), 2(4), 933–946. https://doi.org/10.5281/zenodo.13893186
  • Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of management journal, 54(2), 393-420.
  • Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393–420.
  • Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design, 535-545.
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
  • Frey, C. B., & Osborne, M. A. (2017). " The Future of Employment: How Susceptible Are Jobs to Computerisation?" Technological Forecasting and Social Change. 114 (C): 254-280.
  • Furman, J., & Seamans, R. (2019). AI and the Economy. Innovation policy and the economy, 19(1), 161-191.
  • Goldin, C. (2024). Human Capital. In: Diebolt, C., Haupert, M. (eds) Handbook of Cliometrics. Springer, Cham. https://doi.org/10.1007/978-3-031-35583-7_23
  • Greenstein, S. (2021). 8. Digital Infrastructure. In E. Glaeser & J. Poterba (Ed.), Economic Analysis and Infrastructure Investment (pp. 409-452). Chicago: University of Chicago Press. https://doi.org/10.7208/chicago/9780226800615-011
  • Günbayi, I., & Sorm, S. (2018). Social paradigms in guiding social research design: The functional, interpretive, radical humanist and radical structural paradigms. Online Submission, 9(2), 57-76.
  • Huy, T., Hong, T., Cuong, N. T., & Tran, P. (2024). AI Innovation and Economics Growth: A Global Evidence. WSB Journal of Business and Finance, (58), 198-216.
  • Huynh-The, T., Pham, Q. V., Pham, X. Q., Nguyen, T. T., Han, Z., & Kim, D. S. (2023). Artificial intelligence for the metaverse: A survey. Engineering Applications of Artificial Intelligence, 117, 105581.
  • IMF. (2024). AI Preparedness Index (AIPI). https://www.imf.org/external/datamapper/datasets/AIPI
  • International Science Council. (2024). Preparing National Research Ecosystems for AI: strategies and progress in 2024. https://council.science/publications/ai-science-systems
  • Iphofen, R., & Kritikos, M. (2021). Regulating artificial intelligence and robotics: ethics by design in a digital society. Contemporary Social Science, 16(2), 170-184.
  • Jerven, M. (2013). Poor numbers: How we are misled by African development statistics and what to do about it. Cornell University Press.
  • Jiang, Y. (2022). Prediction model of the impact of innovation and entrepreneurship on China's digital economy based on neural network integration systems. Neural Computing and Applications, 34(4), 2661-2675.
  • Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence?. Discover Artificial Intelligence, 2(1), 4.
  • Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational researcher, 33(7), 14-26.
  • Komninos, N. (2006). The architecture of intelligent clities: Integrating human, collective and artificial intelligence to enhance knowledge and innovation. In 2006 2nd IET International Conference on Intelligent Environments-IE 06 (Vol. 1, pp. 13-20). IET.
  • Kraus, N., Kraus, K., Shtepa, O., Hryhorkiv, M., & Kuzmuk, I. (2022). Artificial intelligence in established of industry 4.0. WSEAS Transactions on Business and Economics, (19), 1884-1900.
  • Larsson, S. (2020). On the governance of artificial intelligence through ethics guidelines. Asian Journal of Law and Society, 7(3), 437-451.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • Magrani, E. (2019). New perspectives on ethics and the laws of artificial intelligence. Internet policy review, 8(3).
  • McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
  • Minh, D., Wang, H.X., Li, Y.F. et al. Explainable artificial intelligence: a comprehensive review. Artif Intell Rev. 55, 3503–3568 (2022). https://doi.org/10.1007/s10462-021-10088-y
  • Misangyi, V. F., Greckhamer, T., Furnari, S., Fiss, P. C., Crilly, D., & Aguilera, R. (2017). Embracing causal complexity: The emergence of a neo-configurational perspective. Journal of Management, 43(1), 255–282.
  • Mishel, L., & Bivens, J. (2017). The zombie robot argument lurches on: There is no evidence that automation leads to joblessness or inequality. Economic Policy Institute Working Papers.
  • Navruz-Zoda, B. N., & Shomiev, G. U. (2017). The different approaches of human capital formation. International Journal of Innovative Technologies in Economy, 5 (11), 6-10.
  • Newel, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113-126.
  • OECD (2021). OECD AI Principles and Recommendations. OECD Publishing. https://www.oecd.org/going-digital/ai/principles/
  • OECD. (2024). OECD Digital Economy Outlook. Organisation for Economic Co-operation and Development. Biennial. ISSN: 3008-1750 (Online).
  • Okoroma, F. N. (2024). Artificial intelligence and libraries: Import, risks and prospects. Journal of ICT Development, Applications and Research. 6(2), 30 – 40.
  • Pençe, I., Tunç, H., Şişeci Çeşmeli, M., & Kalkan, A. (2019). Forecasting Imports And Exports Of Turkey Using Artificial Intelligence Methods. European Proceedings of Social and Behavioural Sciences, 54.
  • Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press.
  • Ragin, C. C. (2009). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press.
  • Ragin, C. C. (2014). The comparative method: Moving beyond qualitative and quantitative strategies. Univ of California Press.
  • Raikov, A., & Abrosimov, V. (2018). Import countries ranking with econometric and artificial intelligence methods. In International Conference on Digital Transformation and Global Society (pp. 402-414). Cham: Springer International Publishing.
  • Roberts, H., Cowls, J., Morley, J., Taddeo, M., Wang, V., & Floridi, L. (2021). The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation (pp. 47-79). Springer International Publishing.
  • Russell, S., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach, 4th Global ed.
  • Sandefur, J., & Glassman, A. (2015). The political economy of bad data: Evidence from African survey and administrative statistics. The Journal of Development Studies, 51(2), 116–132.
  • Schneider, C. Q., & Wagemann, C. (2010). Qualitative comparative analysis (QCA) and fuzzy-sets: Agenda for a research approach and a data analysis technique. Comparative Sociology, 9(3), 376-396.
  • Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis (QCA). Cambridge University Press.
  • Serrano, W. (2018). Digital systems in smart city and infrastructure: Digital as a service. Smart cities, 1(1), 134-154.
  • Smuha, N. A. (2019). The EU approach to ethics guidelines for trustworthy artificial intelligence. Computer Law Review International, 20(4), 97-106.
  • Strusani, D., & Houngbonon, G. V. (2019). The role of artificial intelligence in supporting development in emerging markets. International Finance Corporation, Washington, DC.
  • Susskind, D. (2020). A world without work: Technology, automation and how we should respond. Penguin UK.
  • UNCTAD (2021). Technology and Innovation Report 2021: Catching Technological Waves. United Nations Conference on Trade and Development.
  • Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of business research, 122, 889-901.
  • Wang, H., & Li, H. (2019). Research on theoretical analysis of human capital of labor economics based on artificial intelligence. Journal of Intelligent & Fuzzy Systems, 37(3), 3257-3265.
  • WEF. (2024). AI for Impact: Strengthening AI Ecosystems for Social Innovation. INSIGHT REPORT SEPTEMBER 2024. World Economic Forum.
  • Wong, A. (2021). Ethics and regulation of artificial intelligence. In Artificial Intelligence for Knowledge Management: 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Yokohama, Japan, January 7–8, 2021, Revised Selected Papers 8 (pp. 1-18). Springer International Publishing.
  • World Bank (2022). Digital Development Global Practice: Digital Economy for Africa Initiative. https://www.worldbank.org/en/programs/all-africa-digital-transformation-initiative
  • Yi, Z., & Ayangbah, S. (2024). The Impact of AI Innovation Management on Organizational Productivity and Economic Growth: An Analytical Study. International Journal of Business Management and Economic Review.
There are 77 citations in total.

Details

Primary Language Turkish
Subjects Labor Economics, Policy and Administration (Other)
Journal Section Research Article
Authors

Sergen Gürsoy 0000-0002-9032-2999

Early Pub Date May 27, 2025
Publication Date
Submission Date January 28, 2025
Acceptance Date May 18, 2025
Published in Issue Year 2025 Volume: 10 Issue: 1

Cite

APA Gürsoy, S. (2025). Yapay Zekâya Hazır mıyız? Köken Değişkenlerin Karşılaştırmalı Nedensel İlişkisi ve Yerel Politik Öneriler. JOEEP: Journal of Emerging Economies and Policy, 10(1), 409-421.
 Download Cover Image

JOEEP is published as two issues per year June and December and all publication policies and processes are conducted according to the international standards. JOEEP accepts and publishes the research articles in the fields of economics, political economy, fiscal economics, applied economics, business economics, labour economics and econometrics. JOEEP, without depending on any institution or organization, is a non-profit journal that has an International Editorial Board specialist on their fields. All “Publication Process” and “Writing Guidelines” are explained in the related title and it is expected from authors to Show a complete match to the rules. JOEEP is an open Access journal.