Optimizing financial success: The synergistic impact of artificial intelligence and R&D investments in U.S. firms

Sonia Kumari ,Raja Shaikh ,Mujeeb-u-Rehman Bhayo ,Sharmila Devi ,Shengjie Cao

doi:10.24294/jipd.v8i9.6985

Home - JIPD - Volume 8 - Issue 9

Submit to JIPD

Journal Browser

Volume | Year

Issue

• Current lssue

View All

News and Announcements

View All

Optimizing financial success: The synergistic impact of artificial intelligence and R&D investments in U.S. firms

Sonia Kumari

Raja Shaikh

Mujeeb-u-Rehman Bhayo

Sharmila Devi

Shengjie Cao

Journal of Infrastructure Policy and Development 2024, 8(9), 6985; https://doi.org/10.24294/jipd.v8i9.6985

Submitted：07 Jun 2024

Accepted：24 Jun 2024

Published：03 Sept 2024

Download PDF(558.85KB)

Cite

XML

Abstract

The use of artificial intelligence (AI) and intellectual machines can support businesses in performing various activities. Therefore, it is necessary to examine the performance outcomes by assessing the concentration of AI technologies. To create a quantifiable score of AI concentration, AI-related terms are identified in the annual reports of all listed firms in the U.S. For analysis purposes, a fixed effects model is employed, using firms’ panel data from 2003 to 2022. The analysis reveals that AI concentration is beneficial for a company’s financial success. Additional analysis examines the moderating role of research and development (R&D). Firms with higher R&D spending experience increased financial benefits from concentrating on AI technologies. The uniqueness of this study lies in analyzing the financial success through the AI and R&D parameters. The findings support a higher concentration on AI, combined with higher R&D spending, to attain greater financial success. The main insights suggest that management must evaluate their existing focus on AI and R&D spending to improve their financial position.

Keywords

References

Acemoglu, D., & Restrepo, P. (2019). Artificial Intelligence, Automation and Work. National Bureau of Economic Research.

Aguirre, S., & Rodriguez, A. (2017). Automation of a business process using robotic process automation (RPA): A case study. In: Applied Computer Sciences in Engineering. Springer.

Andreou, P. C., Harris, T., & Philip, D. (2020). Measuring Firms’ Market Orientation Using Textual Analysis of 10-K Filings. British Journal of Management, 31(4), 872–895. https://doi.org/10.1111/1467-8551.12391

Arumugam, T., Arun, R., Natarajan, S., et al. (2024). Unlocking the Power of Artificial Intelligence and Machine Learning in Transforming Marketing as We Know It. Data-Driven Intelligent Business Sustainability, 60–74. https://doi.org/10.4018/979-8-3693-0049-7.ch00

Babina, T., Fedyk, A., He, A., et al. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, 103745. https://doi.org/10.1016/j.jfineco.2023.103745

Babolmorad, N., & Massoud, N. (2020). When sentiment is news: The polarity pattern approach. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3706522

Bharadiya, J. P. (2023). A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics. American Journal of Artificial Intelligence.

Charroud, A., El Moutaouakil, K., Palade, V., et al. (2024). Localization and Mapping for Self-Driving Vehicles: A Survey. Machines, 12(2), 118. https://doi.org/10.3390/machines12020118

Chhaidar, A., Abdelhedi, M., & Abdelkafi, I. (2022). The Effect of Financial Technology Investment Level on European Banks’ Profitability. Journal of the Knowledge Economy, 14(3), 2959–2981. https://doi.org/10.1007/s13132-022-00992-1

EDGAR. (2024). company reports. Available online: https://www.sec.gov/edgar/search/# (accessed on 15 May 2024).

Feigenbaum, E. A., & McCorduck, P. (1983). The Fifth Generation: Artificial Intelligence and Japan’s Computer Challenge to the World. Addison-Wesley Pub.

Finlay, S. (2021). Artificial intelligence and machine learning for business: A no-nonsense guide to data driven technologies. Relativistic.

Forbes. (2024). Top AI Statistics and Trends. Available online: https://www.forbes.com/advisor/in/business/ai-statistics (accessed on 15 May 2024).

Greene, W. H. (2012). Econometric Analysis 7th ed (International). Available online: https://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm (accessed on 15 May 2024).

Gupta, R., Nair, K., Mishra, M., et al. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4(1), 100232. https://doi.org/10.1016/j.jjimei.2024.100232

Gurcan, F., Boztas, G. D., Dalveren, G. G. M., et al. (2023). Digital Transformation Strategies, Practices, and Trends: A Large-Scale Retrospective Study Based on Machine Learning. Sustainability, 15(9), 7496. https://doi.org/10.3390/su15097496

Haque, M. R., Siddique, M. A., & Kumar, A. (2024). Research and development intensity, inventory leanness, and firm performance. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), 100263. https://doi.org/10.1016/j.joitmc.2024.100263

Henry, E. (2008). Are Investors Influenced by How Earnings Press Releases Are Written? Journal of Business Communication, 45(4), 363–407. https://doi.org/10.1177/0021943608319388

Hoque, A., Le, D. T., & Le, T. (2024). Does digital transformation reduce bank’s risk-taking? evidence from vietnamese commercial banks. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), 100260. https://doi.org/10.1016/j.joitmc.2024.100260

Huang, Z., Che, C., Zheng, H., et al. (2024). Research on Generative Artificial Intelligence for Virtual Financial Robo-Advisor. Academic Journal of Science and Technology, 10(1), 74–80. https://doi.org/10.54097/30r2kk80

IBM. (2022). Advancing AI in defense organizations. Available online: https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/advancing-ai-in-defense (accessed on 20 May 2023).

Jiang, S., Li, Y., & You, N. (2023). Corporate digitalization, application modes, and green growth: Evidence from the innovation of Chinese listed companies. 10. doi:10.3389/fenvs.2022.1103540

Johnson, P. C., Laurell, C., Ots, M., et al. (2022). Digital innovation and the effects of artificial intelligence on firms’ research and development—Automation or augmentation, exploration or exploitation? Technological Forecasting and Social Change, 179, 121636. https://doi.org/10.1016/j.techfore.2022.121636

Jones, C. I. (2019). Paul Romer: Ideas, Nonrivalry, and Endogenous Growth. The Scandinavian Journal of Economics, 121(3), 859–883. https://doi.org/10.1111/sjoe.12370

Khogali, H. O., & Mekid, S. (2023). The blended future of automation and AI: Examining some long-term societal and ethical impact features. Technology in Society, 73, 102232. https://doi.org/10.1016/j.techsoc.2023.102232

Kim, T., Park, Y., & Kim, W. (2022). The Impact of Artificial Intelligence on Firm Performance. IEEE.

Kiron, D., & Schrage, M. (2019). Strategy for and with AI. MIT Sloan Management Review.

Laborda, J., Salas-Fumás, V., & Suárez, C. (2020). An Endogenous Approach to the Cyclicality of R&D Investment under Credit Constraints: Firms’ Cash Flow Matters! Journal of Open Innovation: Technology, Market, and Complexity, 6(2), 33. https://doi.org/10.3390/joitmc6020033

Leung, T. Y., & Sharma, P. (2021). Differences in the impact of R&D intensity and R&D internationalization on firm performance—Mediating role of innovation performance. Journal of Business Research, 131, 81–91. https://doi.org/10.1016/j.jbusres.2021.03.060

Li, C., He, S., Tian, Y., et al. (2022). Does the bank’s FinTech innovation reduce its risk-taking? Evidence from China’s banking industry. Journal of Innovation & Knowledge, 7(3), 100219. https://doi.org/10.1016/j.jik.2022.100219

Li, J., Ma, S., Qu, Y., et al. (2023). The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China. Resources Policy, 82, 103507. https://doi.org/10.1016/j.resourpol.2023.103507

Lin, B., & Xie, Y. (2023). Positive or negative? R&D subsidies and green technology innovation: Evidence from China’s renewable energy industry. Renewable Energy, 213, 148–156. https://doi.org/10.1016/j.renene.2023.06.011

Lu, X., & Wang, J. (2024). Is innovation strategy a catalyst to solve social problems? The impact of R&D and non-R&D innovation strategies on the performance of social innovation-oriented firms. Technological Forecasting and Social Change, 199, 123020. https://doi.org/10.1016/j.techfore.2023.123020

Lui, A. K. H., Lee, M. C. M., & Ngai, E. W. T. (2022). Impact of artificial intelligence investment on firm value. Annals of Operations Research, 308(1–2), 373–388. https://doi.org/10.1007/s10479-020-03862-8

Lundvall, B. Å., & Rikap, C. (2022). China’s catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems. Research Policy, 51(1), 104395. https://doi.org/10.1016/j.respol.2021.104395

Macanovic, A. (2022). Text mining for social science—The state and the future of computational text analysis in sociology. Social Science Research, 108, 102784. https://doi.org/10.1016/j.ssresearch.2022.102784

Martínez-Alonso, R., Martínez-Romero, M. J., Rojo-Ramírez, A. A., et al. (2023). Process innovation in family firms: Family involvement in management, R&D collaboration with suppliers, and technology protection. Journal of Business Research, 157, 113581. https://doi.org/10.1016/j.jbusres.2022.113581

Meske, C., Bunde, E., Schneider, J., et al. (2022). Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities. Information Systems Management, 39(1), 53–63. https://doi.org/10.1080/10580530.2020.1849465

Mishra, S., Ewing, M. T., & Cooper, H. B. (2022). Artificial intelligence focus and firm performance. Journal of the Academy of Marketing Science, 50(6), 1176–1197. https://doi.org/10.1007/s11747-022-00876-5

Ng, A. (2017). Artificial intelligence is the new electricity. Available online: https://www.gsb.stanford.edu/insights/andrew-ng-why-ai-new-electricity (accessed on 15 January 2024).

Nguyen-Van, D., & Chang, C. H. (2020). Foreign technology licensing and firm innovation in Asian: the moderating role of employee training and R&D. The Singapore Economic Review, 69(01), 269–296. https://doi.org/10.1142/s0217590820500393

Parker, J. (2009). Theories of Investment Expenditures. Available online: https://www.reed.edu/economics/parker/s10/314/book/Ch15.pdf (accessed on 13 May 2023).

Pathak, A., Dixit, C. K., Somani, P., et al. (2023). Prediction of Employees’ Performance using Machine Learning (ML) Techniques. Designing Workforce Management Systems for Industry 4.0, 177–196. https://doi.org/10.1201/9781003357070-11

Qalati, S. A., Kumari, S., Soomro, I. A., et al. (2022). Green Supply Chain Management and Corporate Performance Among Manufacturing Firms in Pakistan. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.873837

Qinqin, W., Qalati, S. A., Hussain, R. Y., et al. (2023). The effects of enterprises’ attention to digital economy on innovation and cost control: Evidence from A-stock market of China. Journal of Innovation & Knowledge, 8(4), 100415. https://doi.org/10.1016/j.jik.2023.100415

Ramirez, J. G. C. (2024). AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems. Journal of Artificial Intelligence General Science (JAIGS), 1(1), 31–37. https://doi.org/10.60087/jaigs.v1i1.p37

Richards, G., Yeoh, W., Chong, A. Y. L., et al. (2019). Business Intelligence Effectiveness and Corporate Performance Management: An Empirical Analysis. Journal of Computer Information Systems, 59(2), 188–196. https://doi.org/10.1080/08874417.2017.1334244

Romer, P. M. (1994). The Origins of Endogenous Growth. Journal of Economic Perspectives, 8(1), 3–22. https://doi.org/10.1257/jep.8.1.3

Sarpong, D., Boakye, D., Ofosu, G., et al. (2023). The three pointers of research and development (R&D) for growth-boosting sustainable innovation system. Technovation, 122, 102581. https://doi.org/10.1016/j.technovation.2022.102581

Secinaro, S., Calandra, D., Secinaro, A., et al. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01488-9

Statista. (2023). Artificial intelligence (AI) investment growth from 2015 to 2025. Available online: https://www.statista.com/statistics/1424667/ai-investment-growth-worldwide/ (accessed on 2 June 2023).

Tung, L. T., & Binh, Q. M. Q. (2022). The impact of R&D expenditure on firm performance in emerging markets: evidence from the Vietnamese listed companies. Asian Journal of Technology Innovation, 30(2), 447–465. https://doi.org/10.1080/19761597.2021.1897470

Turing, A. M., & Haugeland, J. (1950). Computing machinery and intelligence. In: The Turing Test: Verbal Behavior as the Hallmark of Intelligence. Springer.

Vinuesa, R., Azizpour, H., Leite, I., et al. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1). https://doi.org/10.1038/s41467-019-14108-y

Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: theory and practice. The Knowledge Engineering Review, 10(2), 115–152. https://doi.org/10.1017/s0269888900008122

WRDS. (2024). Wharton Research Data Services. Available online: https://wrds-www.wharton.upenn.edu/ (accessed on 10 January 2024).

Xiong, R., Wei, P., Yang, J., et al. (2023). Tax incentive, R&D manipulation and enterprises’ innovation performance: the moderating role of political connections. International Journal of Technology Management, 91(3/4), 264. https://doi.org/10.1504/ijtm.2023.128795

Yang, C. H. (2022). How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan. Research Policy, 51(6), 104536. https://doi.org/10.1016/j.respol.2022.104536

Yuanyuan, Z., Kumari, S., Ilyas, M., et al. (2023). Media coverage and stock market returns: Evidence from China Pakistan economic corridor (CPEC). Heliyon, 9(3), e14204. https://doi.org/10.1016/j.heliyon.2023.e14204

Zhang, N., & Liu, B. (2019). Alignment of business in robotic process automation. International Journal of Crowd Science, 3(1), 26–35. https://doi.org/10.1108/ijcs-09-2018-0018

Previous
article in this issue

Next
article in this issue