| Title: | Artificial Intelligence software adoption in manufacturing companies |
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| Authors: | ID Kovič, Klemen (Author)
ID Tominc, Polona (Author)
ID Prester, Jasna (Author)
ID Palčič, Iztok (Author) |
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| Files: |  https://www.mdpi.com/2076-3417/14/16/6959
 Artificial_Intelligence_Software_Adoption.pdf (476,18 KB)
MD5: 815D9E9E1413C740991CBF62C5EEA63A
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | EPF - Faculty of Business and Economics
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| Abstract: | This study investigates the adoption of artificial intelligence (AI) software in manufacturing companies in Slovenia, Slovakia and Croatia, and across six production areas. This research ad-dresses a gap in the literature regarding AI software implementation in relation to company size, technology intensity and supply chain role, and examines whether Industry 4.0 (I4.0) readiness influences AI adoption. Data from the European Manufacturing Survey 2022 were analyzed, and showed that the use of AI is still relatively low. On average only 18.4% of companies use AI software in at least one production area. Logistic regression analysis revealed that neither company size nor role in the supply chain or technology intensity are statistically significantly related to AI usage. However, a significant positive relationship was found between I4.0 readiness and AI adoption, suggesting that companies with advanced digital infrastructures and integrated cyber-physical systems are more likely to adopt AI. This finding underlines the importance of digital transformation for the integration of AI software. The study concludes that while company characteristics such as size and the role of the company in the supply chain are not statistically significantly related to the use of AI, the level of digital readiness is crucial. |
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| Keywords: | artificial intelligence, manufacturing, company size, company role, technology intensity, Industry 4.0, Industry 4.0 readiness |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 02.07.2024 |
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| Article acceptance date: | 04.08.2024 |
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| Publication date: | 08.08.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | 21 str. |
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| Numbering: | Vol. 14, iss. 16, spec. iss, [article no.] 6959 |
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| PID: | 20.500.12556/DKUM-92135  |
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| UDC: | 004.43 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 205110019 |
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| DOI: | 10.3390/app14166959 |
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| Publication date in DKUM: | 30.06.2025 |
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| Views: | 155 |
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| Downloads: | 8 |
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| Metadata: |    |
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| Categories: | Misc.
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