Estimating the innovation benefits of first-mover and second-mover strategies when micro-businesses adopt artificial intelligence and machine learning

Abstract

Digital technologies have the potential to transform all aspects of firms’ operations. The emergence of advanced digital technologies such as Artificial Intelligence and Machine Learning raises questions about whether and when micro-businesses should adopt these technologies. In this paper we focus on how firms’ adoption decisions on Artificial Intelligence and Machine Learning influence their innovation capabilities. Using survey data for over 6,000 micro-businesses in the UK, we identify two groups of adopters based on the timing of their adoption of Artificial Intelligence and Machine Learning. ‘first movers’ – early adopters of the new technologies - and ‘second movers’- later adopters of the new technology. Probit models are used to investigate the innovation benefits of first and second mover adoption strategies. Our results suggest strong and positive impacts of adopting Artificial Intelligence and Machine Learning on micro-businesses’ innovation outcomes and innovation processes. We highlight the differential benefits of first mover and second mover strategies and highlight the role of technology characteristics as the differentiating factor. Our results emphasize both the innovation enabling role of digital technologies and the importance of an appropriate strategic approach to adopting advanced digital technologies.

Using machine learning to determine factors affecting product and product–service innovation

“…For example, building upon this foundation, Eom et al (2022) conducted a comprehensive analysis to ascertain the most suitable ML techniques in terms of their predictive accuracy concerning specific innovation outcomes, such as innovation performance. In a similar vein, Lim et al (2020) utilized ML tools and analytical methodologies to investigate the synergistic Nafizah et al (2023) conducted an evaluation of the innovation benefits resulting from diverse strategies adopted by micro-businesses when implementing AI and machine learning techniques. The results obtained in our study align with those of previous studies in terms revealing the relationships between determinant factors and innovation outcomes.…”