EXPLORING THE IMPACT OF AI RESEARCH, VENTURE CAPITAL INVESTMENT, AND ADOPTION ON PRODUCTIVITY: A MULTI-COUNTRY PANEL DATA ANALYSIS
DOI:
https://doi.org/10.35774/jee2024.04.688Keywords:
total factor productivity, venture capital, AI research, AI adoption, technological innovation, panel data, fixed effects model.Abstract
Artificial intelligence is the most important technological development of the 21st century, which is transforming businesses and economies. This paper investigates how AI venture capital investment, AI research publications, and AI adoption affect total factor productivity (TFP). The study utilizes fixed effects econometric modeling on panel data from 14 countries over the period from 2013 to 2023. Results indicate that total factor productivity is being positively affected by AI venture capital investment, AI research output, and AI adoption, with the highest contribution coming from AI adoption. These findings show that a strong ecosystem of venture capital, research, and diffusion of artificial intelligence technologies within industries have to be fostered for innovation in artificial intelligence.
JEL: O33, G24, C23, O47.
References
Boavida, N., & Candeias, M. (2021). Recent Automation Trends in Portugal: Implications on Industrial Productivity and Employment in Automotive Sector. Societies, 11(3), 101. https://doi.org/10.3390/soc11030101
Cho, J., DeStefano, T., Kim, H., Kim, I., & Paik, J. H. (2023). What's driving the diffusion of next-generation digital technologies? Technovation, 119, 102477. https://doi.org/10.1016/j.technovation.2022.102477
Davoyan, A. (2023). The Impact of Artificial Intelligence on Economy. Proceedings of the Future Technologies Conference (FTC) 2023, 1, 371–376, Springer. https://doi.org/10.1007/978-3-031-47454-5_28
Dixon, J., Hong, B., & Wu, L. (2021). The Robot Revolution: Managerial and Employment Consequences for Firms. Management Science, 67(9), 5586- 5605. https://doi.org/10.1287/mnsc.2020.3812
Domini, G., Grazzi, M., Moschella, D., & Treibich, T. (2022). For whom the bell tolls: The firm-level effects of automation on wage and gender inequality. Research Policy, 51(7), 104533. https://doi.org/10.1016/j.respol.2022.104533
Galdino Martinez-Garcia, C., Dorward, P., & Rehman, T. (2016). FACTORS INFLUENCING ADOPTION OF CROP AND FORAGE RELATED AND ANIMAL HUSBANDRY TECHNOLOGIES BY SMALL-SCALE DAIRY FARMERS IN CENTRAL MEXICO. Experimental Agriculture, 52(1), 87- 109. https://doi.org/10.1017/s001447971400057x
Gandia, J. A. G., Gavrila, S. G., Ancillo, A. d. L., & Nunez, M. T. d. V. (2024). RPA as a Challenge Beyond Technology: Self-Learning and Attitude Needed for Successful RPA Implementation in the Workplace. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-024-01865-5
Goldburgh, M., LaChance, M., Komissarchik, J., Patriarche, J., Chapa, J., Chen, O., Deshpande, P., Geeslin, M., Kottler, N., Sommer, J., Ayers, M., & Vujic, V. (2024). 2023 Industry Perceptions Survey on AI Adoption and Return on Investment. Journal of Imaging Informatics in Medicine. https://doi.org/ 10.1007/s10278-024-01147-1
Huang, X., Yang, F., Zheng, J., Feng, C., & Zhang, L. (2023). Personalized human resource management via HR analytics and artificial intelligence: Theory and implications. Asia Pacific Management Review, 28(4), 598-610. https://doi.org/10.1016/j.apmrv.2023.04.004
Hwang, W.-S., & Kim, H.-S. (2022). Does the adoption of emerging technologies improve technical efficiency? Evidence from Korean manufacturing SMEs. Small Business Economics, 59(2), 627-643. https://doi.org/10.1007/ s11187-021-00554-w
Jacobs, M., Remus, A., Gaillard, C., Menendez, H. M., Tedeschi, L. O., Neethirajan, S., & Ellis, J. L. (2022). ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences. Journal of Animal Science, 100(6), skac132. https://doi.org/10.1093/jas/skac132
Jaiwani, M., & Gopalkrishnan, S. (2022). Adoption of RPA and AI to Enhance the Productivity of Employees and Overall Efficiency of Indian Private Banks: An Inquiry. 2022 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, 191-197. https://doi.org/10.1109/iSemantic55962.2022.9920383
Kaufman, D. (September 05-06, 2019; 2020). Deep Learning: A Brazilian Case. Intelligent Systems and Applications [Intelligent systems and applications, vol 1, eds.: Bi, Y., Bhatia, R., & Kapoor, S.]. Intelligent Systems Conference (IntelliSys), London, ENGLAND, 832-847. https://doi.org/10.1007/978-3- 030-29516-5
Khalifa, N., Abd Elghany, M., & Abd Elghany, M. (2021). Exploratory research on digitalization transformation practices within supply chain management context in developing countries specifically Egypt in the MENA region. Cogent Business & Management, 8(1), 1965459. https://doi.org/10.1080/ 23311975.2021.1965459
Mamela, T. L., Sukdeo, N., & Mukwakungu, S. C. (2020). The Integration of AI on Workforce Performance for a South African Banking Institution. 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Durban, South Africa, 1-8, https://doi.org/10.1109/icABCD49160.2020.9183834
Musaeva, K., Vyachina, I., & Aliyeva, M. (2024). Smart factories and their impact on modern manufacturing enterprises: Prospects and challenges in the era of the digital economy [Conference Paper]. E3S Web of Conferences, 537, 07010. https://doi.org/10.1051/e3sconf/202453707010
Nimkar, P., Kanyal, D., & Sabale, S. R. (September 18, 2024). Increasing Trends of Artificial Intelligence With Robotic Process Automation in Health Care: A Narrative Review. Cureus Journal of Medical Science, 16(9), e69680. https://doi.org/10.7759/cureus.69680
Nucci, F., Puccioni, C., & Ricchi, O. (2023). Digital technologies and productivity: A firm-level investigation. Economic Modelling, 128, 106524. https://doi.org/10.1016/j.econmod.2023.106524
Owino, A. (2023). Challenges of Computer Vision Adoption in the Kenyan Agricultural Sector and How to Solve Them: A General Perspective. Advances in Agriculture, 2023, 1530629. https://doi.org/10.1155/2023/1530629
Pham, P., Zhang, H., Gao, W., & Zhu, X. (2024). Determinants and performance outcomes of artificial intelligence adoption: Evidence from US Hospitals. Journal of Business Research, 172, 114402. https://doi.org/10.1016/ j.jbusres.2023.114402
Rademakers, E., & Zierahn-Weilage, U. (2024). New Technologies: End of Work or Structural Change? Economists Voice. https://doi.org/10.1515/ev-2024- 0046
Rana, A., Sarkar, B., Parida, R. K., Adhikari, S., Anandha Lakshmi, R., Akila, D., & Pal, S. (2024). A Data-Driven Analytical Approach on Digital Adoption and Digital Policy for Pharmaceutical Industry in India. Micro-Electronics and Telecommunication Engineering [eds: Sharma, D.K., Peng, SL., Sharma, R., Jeon, G.], ICMETE 2023, 894. Springer, Singapore. https://doi.org/10.1007/978-981-99-9562-2_42
Romao, M., Costa, J., & Costa, C. J. (2019). Robotic Process Automation: A Case Study in the Banking Industry. 14th Iberian Conference on Information Systems and Technologies (CISTI) 19 – 22 June 2019, Coimbra, Portugal, 1-6. https://doi.org/10.23919/CISTI.2019.8760733
Bhaskaran, S. (2024). Analysis of an Intelligent and Cybersecurity Optimization Model for Financial Applications. 2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC), Bengaluru, India, 1-6. https://doi.org/10.1109/ICECCC61767.2024.10593867
Serban, A. C., & Lytras, M. D. (2020). Artificial Intelligence for Smart Renewable Energy Sector in Europe - Smart Energy Infrastructures for Next Generation Smart Cities. Ieee Access, 8, 77364-77377. https://doi.org/10.1109/ access.2020.2990123
Sweeney, D., Nair, S., & Cormican, K. (2023). Scaling AI-based industry 4.0 projects in the medical device industry: An exploratory analysis. Procedia Computer Science, 219, 759-766. https://doi.org/10.1016/j.procs.2023.01.349
Szalavetz, A. (2019). Artificial Intelligence-Based Development Strategy in Dependent Market Economies – Any Room amidst Big Power Rivalry? Central European Business Review, 8(4), 40-54. https://doi.org/10.18267/j.cebr.219
Tawil, A.-R. H., Mohamed, M., Schmoor, X., Vlachos, K., & Haidar, D. (2024). Trends and Challenges towards Effective Data-Driven Decision Making in UK Small and Medium-Sized Enterprises: Case Studies and Lessons Learnt from the Analysis of 85 Small and Medium-Sized Enterprises. Big Data and Cognitive Computing, 8(7), 79. https://doi.org/10.3390/ bdcc8070079
Wimpfheimer, O., & Kimmel, Y. (2024). Artificial Intelligence in Medical Imaging: An Overview of a Decade of Experience. Israel Medical Association Journal, 26(2), 122-125. https://pubmed.ncbi.nlm.nih.gov/38420986/.
Wu, L., Hitt, L., & Lou, B. (2019). Data Analytics, Innovation, and Firm Productivity. Management Science, 66(5), 2017-2039. https://doi.org/10.1287/ mnsc.2018.3281
Received: October 15, 2024.
Reviewed: November 18, 2024.
Accepted: November 21, 2024.
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