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

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.