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AI for the underdogs: Navigating risk and growth in high-tech micro-firms through generative artificial intelligence

Faisal Shahzad et al.

Journal of Strategy & Innovation2026https://doi.org/10.1016/j.jsinno.2026.200566article
ABDC C
Weight
0.41

What the paper says

Generative Artificial Intelligence (Gen-AI) has gained significant traction in larger firms, yet its adoption among micro-firms remains underexplored particularly in contexts marked by resource scarcity and heightened operational risk. This study addresses this gap by investigating how hightech micro-firms adopt Gen-AI for risk management and growth. Drawing on semi-structured interviews with decision-makers from eight Finnish micro-firms, the research applies the Technology-Organization-Environment (TOE) framework to identify critical enablers and barriers. The findings highlight five key dimensions influencing adoption: technological readiness, leadership engagement, regulatory compliance, data-driven decision-making, and competitive pressures. While Gen-AI fosters operational resilience and strategic agility, its impact is constrained by limited data quality and high implementation costs. By offering a holistic and theoretically grounded perspective, this study advances understanding of Gen-AI adoption in microfirms and contributes to literature on digital transformation under resource constraints. The insights also inform policymakers and practitioners aiming to enhance AI accessibility and governance for micro-enterprises.

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https://doi.org/https://doi.org/10.1016/j.jsinno.2026.200566

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@article{faisal2026,
  title        = {{AI for the underdogs: Navigating risk and growth in high-tech micro-firms through generative artificial intelligence}},
  author       = {Faisal Shahzad et al.},
  journal      = {Journal of Strategy & Innovation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jsinno.2026.200566},
}

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AI for the underdogs: Navigating risk and growth in high-tech micro-firms through generative artificial intelligence

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.