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

Aug 3, 2026

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Digital technologies can reshape firm operations, and recent advances in Artificial Intelligence and Machine Learning raise key questions for micro-businesses: whether to adopt and how adoption timing affects innovation. This paper studies how AI and ML adoption decisions influence innovation capabilities by analyzing survey data covering more than 6,000 UK micro-businesses. Respondents are classified into first movers (early adopters) and second movers (later adopters). Probit models evaluate innovation benefits and show strong positive effects on both innovation outcomes and innovation processes, with differential advantages shaped by technology characteristics and the strategic adoption approach.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Introduction

Adoption strategies: first mover and second mover

Why micro-business adoption is complex

Small Bus Econ [https://doi.org/10.1007/s11187-023-00779-x](https://doi.org/10.1007/s11187-023-00779-x) Estimating the innovation benefits of first‑moverand second‑mover strategies when micro‑businesses adopt artificial intelligence and machine learning Ully Y. Nafizah · Stephen Roper · Kevin Mole Accepted: 22 April 2023 © The Author(s) 2023 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 U. Y. Nafizah (*) · S. Roper · K. Mole Warwick Business School, The University of Warwick, Coventry, UK [e-mail: Ully-Yunita.Nafizah@warwick.ac.uk](e-mail: Ully-Yunita.Nafizah@warwick.ac.uk) [S. Roper](S. Roper) e-mail: [Stephen.Roper@wbs.ac.uk](Stephen.Roper@wbs.ac.uk) K. Mole [e-mail: Kevin.Mole@wbs.ac.uk](e-mail: Kevin.Mole@wbs.ac.uk) 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. Plain English Summary Despite the powerful functions offered by advanced digital technologies, such as Artificial Intelligence and Machine Learning, it is unclear whether micro-businesses should adopt these technologies. In addition, micro-businesses are faced with two adoption strategy options: a first mover strategy by becoming an early adopter, or a second mover strategy by becoming a later adopter of the new technologies. Our study suggests that adopting Artificial Intelligence and Machine Learning enhances micro-businesses’ innovation outcomesand innovation processes, highlighting the benefits of technology adoption on micro-businesses with limited financial and human resources. Interestingly, our study suggests the differential benefits of first mover and second mover strategies based on technology characteristics. The principal implication of this study is that micro-businesses should be encouraged to adopt Artificial Intelligence and Machine Learning to compensate for their resources and capabilities in the innovation process. Keywords Advanced digital technology · Artificial Intelligence · Digital adoption · Innovation · Machine Learning · Micro-Business · Timing Adoption JEL Classification O31 · O33 · C12 · C20 1 Introduction The emergence of advanced digital technologies in the last decade promises a fourth industrial revolution, termed Industry 4.0, where firms adopt digital technologies to transform their business processes, product offerings, and inter-organizational relationships, so increasing competitiveness and profitability (Bharadwaj et al., 2013 ; Nambisan, 2017) . Industry 4.0 requires digital adoption throughout the supply chain; yet prior literature highlights the complexity of the decision-making processes surrounding both the adoption of new technology and the timing of new technology adoption (e.g., Hoppe, 2000 ; Suarez & Lanzolla, 2007) . Generally, the literature di

FAQs

What adoption strategies does the study compare for micro-businesses?

The study compares first mover strategies (early adoption of AI and ML) with second mover strategies (later adoption). It examines how the timing of adopting these technologies relates to innovation capabilities.

What data and method are used to analyze innovation benefits?

The analysis uses survey data from over 6,000 UK micro-businesses. Probit models are applied to investigate the innovation benefits associated with first and second mover adoption.

What are the main findings about AI/ML adoption and innovation?

Adopting Artificial Intelligence and Machine Learning has strong and positive impacts on micro-businesses’ innovation outcomes and innovation processes. The study also reports that the differential benefits between first and second mover strategies depend on technology characteristics.

Aug 3, 2026

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