A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide

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dc.authoridDursun Delen / 0000-0001-8857-5148
dc.authorscopusidDursun Delen / 55887961100
dc.authorwosidDursun Delen / AGA-9892-2022
dc.contributor.authorSun, Ying-Chih
dc.contributor.authorCoşgun, Özlem
dc.contributor.authorSharman, Raj
dc.contributor.authorMulgund, Pavankumar
dc.contributor.authorDelen, Dursun
dc.date.accessioned2025-04-18T08:46:55Z
dc.date.available2025-04-18T08:46:55Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractAs artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study. © 2024 The Author(s)
dc.identifier.citationSun, Y. C., Cosgun, O., Sharman, R., Mulgund, P., & Delen, D. (2024). A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide. Decision Analytics Journal, 12, 100504.
dc.identifier.doi10.1016/j.dajour.2024.100504
dc.identifier.issn27726622
dc.identifier.scopus2-s2.0-85200149919
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.dajour.2024.100504
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6613
dc.identifier.volume12
dc.indekslendigikaynakScopus
dc.institutionauthorDelen, Dursun
dc.institutionauthoridDursun Delen / 0000-0001-8857-5148
dc.language.isoen
dc.publisherElsevier Inc.
dc.relation.ispartofDecision Analytics Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial İntelligence
dc.subjectCobb–Douglas Function
dc.subjectInnovation
dc.subjectProduction Efficiency
dc.subjectStochastic Production Frontier
dc.titleA stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide
dc.typeArticle

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