AI and ML patent intensity and firm performance: A machine learning-based lagged analysis
This study investigates the long-term impact of artificial intelligence (AI) and machine learning (ML) patent intensity on firms’ performance, focusing on innovation-driven competitive advantage. Using a panel of 20 technology-intensive firms from 2013 to 2023, this study employs eXtreme gradient bo...
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| Format: | Article |
| Language: | Spanish |
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Elsevier
2025-09-01
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| Series: | European Research on Management and Business Economics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2444883425000233 |
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| author | Melih Sefa Yavuz Hilal Çalik |
| author_facet | Melih Sefa Yavuz Hilal Çalik |
| author_sort | Melih Sefa Yavuz |
| collection | DOAJ |
| description | This study investigates the long-term impact of artificial intelligence (AI) and machine learning (ML) patent intensity on firms’ performance, focusing on innovation-driven competitive advantage. Using a panel of 20 technology-intensive firms from 2013 to 2023, this study employs eXtreme gradient boosting (XGBoost) and random forest algorithms to capture nonlinear relationships between AI and ML patent intensity and key financial indicators, including return on assets (ROA), operating margin, and net profit margin. The results indicate that AI and ML patents significantly enhance ROA and operating margins, particularly with a five-year lag, highlighting the delayed but positive influence of such innovations. However, the effect on net profit margin remains limited. These findings underscore the strategic value of AI and ML innovation in driving sustainable firm performance while also emphasizing the importance of long-term planning and complementary investments for maximizing financial returns. |
| format | Article |
| id | doaj-art-a4bad0f0acd44975bd0dbaa24fe2d38b |
| institution | Matheson Library |
| issn | 2444-8834 |
| language | Spanish |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Research on Management and Business Economics |
| spelling | doaj-art-a4bad0f0acd44975bd0dbaa24fe2d38b2025-08-02T04:47:19ZspaElsevierEuropean Research on Management and Business Economics2444-88342025-09-01313100291AI and ML patent intensity and firm performance: A machine learning-based lagged analysisMelih Sefa Yavuz0Hilal Çalik1Department of Banking and Finance, Istanbul Beykent University, Cihangir District, S..raselviler St. No:65, Istanbul, Beyoglu, Turkey; Corresponding author.Department of International Trade and Finance, Istanbul Beykent University, Cumhuriyet District, Beykent, Istanbul, Büyükcekmece, TurkeyThis study investigates the long-term impact of artificial intelligence (AI) and machine learning (ML) patent intensity on firms’ performance, focusing on innovation-driven competitive advantage. Using a panel of 20 technology-intensive firms from 2013 to 2023, this study employs eXtreme gradient boosting (XGBoost) and random forest algorithms to capture nonlinear relationships between AI and ML patent intensity and key financial indicators, including return on assets (ROA), operating margin, and net profit margin. The results indicate that AI and ML patents significantly enhance ROA and operating margins, particularly with a five-year lag, highlighting the delayed but positive influence of such innovations. However, the effect on net profit margin remains limited. These findings underscore the strategic value of AI and ML innovation in driving sustainable firm performance while also emphasizing the importance of long-term planning and complementary investments for maximizing financial returns.http://www.sciencedirect.com/science/article/pii/S2444883425000233O33O34L25M21 |
| spellingShingle | Melih Sefa Yavuz Hilal Çalik AI and ML patent intensity and firm performance: A machine learning-based lagged analysis European Research on Management and Business Economics O33 O34 L25 M21 |
| title | AI and ML patent intensity and firm performance: A machine learning-based lagged analysis |
| title_full | AI and ML patent intensity and firm performance: A machine learning-based lagged analysis |
| title_fullStr | AI and ML patent intensity and firm performance: A machine learning-based lagged analysis |
| title_full_unstemmed | AI and ML patent intensity and firm performance: A machine learning-based lagged analysis |
| title_short | AI and ML patent intensity and firm performance: A machine learning-based lagged analysis |
| title_sort | ai and ml patent intensity and firm performance a machine learning based lagged analysis |
| topic | O33 O34 L25 M21 |
| url | http://www.sciencedirect.com/science/article/pii/S2444883425000233 |
| work_keys_str_mv | AT melihsefayavuz aiandmlpatentintensityandfirmperformanceamachinelearningbasedlaggedanalysis AT hilalcalik aiandmlpatentintensityandfirmperformanceamachinelearningbasedlaggedanalysis |