AI and ML patent intensity and firm performance: A machine learning-based lagged analysis

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dc.authorid0000-0003-1085-5304
dc.contributor.authorYavuz, Melih Sefa
dc.contributor.authorCalik, Hilal
dc.date.accessioned2026-01-31T15:08:18Z
dc.date.available2026-01-31T15:08:18Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThis 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.
dc.identifier.doi10.1016/j.iedeen.2025.100291
dc.identifier.issn2444-8834
dc.identifier.issn2444-8842
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105012039128
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org./10.1016/j.iedeen.2025.100291
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10650
dc.identifier.volume31
dc.identifier.wosWOS:001544240600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofEuropean Research on Management And Business Economics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260128
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectPatent intensity
dc.subjectFirm performance
dc.titleAI and ML patent intensity and firm performance: A machine learning-based lagged analysis
dc.typeArticle

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