{"title":"人工智能和机器学习专利强度与企业绩效:基于机器学习的滞后分析","authors":"Melih Sefa Yavuz , Hilal Çalik","doi":"10.1016/j.iedeen.2025.100291","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":45796,"journal":{"name":"European Research on Management and Business Economics","volume":"31 3","pages":"Article 100291"},"PeriodicalIF":6.4000,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI and ML patent intensity and firm performance: A machine learning-based lagged analysis\",\"authors\":\"Melih Sefa Yavuz , Hilal Çalik\",\"doi\":\"10.1016/j.iedeen.2025.100291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":45796,\"journal\":{\"name\":\"European Research on Management and Business Economics\",\"volume\":\"31 3\",\"pages\":\"Article 100291\"},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2025-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Research on Management and Business Economics\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2444883425000233\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Research on Management and Business Economics","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2444883425000233","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
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 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.
期刊介绍:
European Research on Management and Business Economics (ERMBE) was born in 1995 as Investigaciones Europeas de Dirección y Economía de la Empresa (IEDEE). The journal is published by the European Academy of Management and Business Economics (AEDEM) under this new title since 2016, it was indexed in SCOPUS in 2012 and in Thomson Reuters Emerging Sources Citation Index in 2015. From the beginning, the aim of the Journal is to foster academic research by publishing original research articles that meet the highest analytical standards, and provide new insights that contribute and spread the business management knowledge