AI Adoption in Technology Sector SMEs in Kainuu, Finland
Maddumage, Jayani; Madhushani, Kandage (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025053018557
https://urn.fi/URN:NBN:fi:amk-2025053018557
Tiivistelmä
This study investigates the factors influencing the adoption of Artificial Intelligence (AI) technologies among Small and Medium-sized Enterprises (SMEs) in Kainuu, Finland, using the Unified Theory of Ac-ceptance and Use of Technology (UTAUT) framework. Adoption of AI is increasingly relevant due to its potential to enhance efficiency, support decision-making, and drive innovation. However, SMEs in pe-ripheral regions like Kainuu often face unique barriers that hinder adoption. This study examines four UTAUT constructs: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Condi-tions, to assess their impact on behavioral intention to adopt AI.
We distributed a structured online survey to 50 SMEs, achieving 17 valid responses, resulting in a 34% response rate. It targeted SME decision-makers to capture their perceptions on AI adoption. The re-sults reveal that Performance Expectancy and Social Influence strongly influence SMEs’ intention to adopt AI, emphasizing perceived benefits and peer influence. Interestingly, Effort Expectancy did not significantly affect adoption intention, suggesting ease of use may be less critical in this context. Facili-tating Conditions, including access to infrastructure, resources, and organizational support, were posi-tively associated with adoption intentions.
The study also identifies major barriers such as lack of technical expertise, data security concerns, and limited strategic direction. These insights underline the importance of addressing both technological readiness and contextual challenges. These findings provide actionable recommendations for SMEs planning AI adoption and enhance understanding of AI integration challenges and opportunities in regional business contexts. Limitations include a small sample size and regional scope; future studies should broaden the context and explore long-term adoption outcomes.
We distributed a structured online survey to 50 SMEs, achieving 17 valid responses, resulting in a 34% response rate. It targeted SME decision-makers to capture their perceptions on AI adoption. The re-sults reveal that Performance Expectancy and Social Influence strongly influence SMEs’ intention to adopt AI, emphasizing perceived benefits and peer influence. Interestingly, Effort Expectancy did not significantly affect adoption intention, suggesting ease of use may be less critical in this context. Facili-tating Conditions, including access to infrastructure, resources, and organizational support, were posi-tively associated with adoption intentions.
The study also identifies major barriers such as lack of technical expertise, data security concerns, and limited strategic direction. These insights underline the importance of addressing both technological readiness and contextual challenges. These findings provide actionable recommendations for SMEs planning AI adoption and enhance understanding of AI integration challenges and opportunities in regional business contexts. Limitations include a small sample size and regional scope; future studies should broaden the context and explore long-term adoption outcomes.