Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda

Marcello M. Mariani, Isa Machado and Satish Nambisan

Journal of Business Research, 2023, vol. 155, issue PB

Abstract: This study provides a systematic overview of innovation research strands revolving around AI. By adopting a Systematic Quantitative Literature Review (SQLR) approach, we retrieved articles published in academic journals, and analysed them using bibliometric techniques such as keyword co-occurrences and bibliographic coupling. The findings allow us to offer an up-to-date outline of existing literature that are embedded into an interpretative framework allowing to disentangle the key antecedents and consequences of AI in the context of innovation. Among the antecedents, we identify technological, social, and economic reasons leading firms to embrace AI to innovate. In addition to detecting the disciplinary foci, we also identify firms' product innovation, process innovation, business model innovation and social innovation, as key consequences of AI deployment. Drawing on the key findings from this study, we offer research directions for further investigation in relation to different types of innovation.

Keywords: Types of innovation; Systematic quantitative literature review; Artificial intelligence; Bibliometric techniques (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:155:y:2023:i:pb:s0148296322008293

DOI: 10.1016/j.jbusres.2022.113364

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