| Title: | Agility and artificial intelligence adoption : small vs. large enterprises |
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| Authors: | ID Rožman, Maja (Author)
ID Oreški, Dijana (Author)
ID Crnogaj, Katja (Author)
ID Tominc, Polona (Author) |
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| Files: |  https://journals.um.si/index.php/oe/article/view/3390
https://journals.um.si/index.php/oe/article/view/3390
 RAZ_Rozman_Maja_2023.pdf (455,61 KB)
MD5: C6CE4B9C0E4247D022189A7FE4BF07A0
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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 article presents the findings of a survey conducted in Slovenia, encompassing
a random sample of 275 enterprises, to analyze the factors influencing
the transition to an agile approach, the AI-supported organizational
culture, AI-enabled workload reduction, and AI-enabled performance enhancement
in small and large enterprises. The study investigates whether
there are statistically significant differences between small and large
enterprises in Slovenia regarding these aspects. These findings provide
valuable insights into the distinct perspectives and priorities of small and
large enterprises in Slovenia regarding agility and the adoption of AI technologies.
The results highlight areas where small businesses may need
additional support or targeted strategies to fully leverage the benefits of
agility and AI. Policymakers and industry leaders can utilize these findings
to promote tailored approaches that enhance agility and facilitate effective
AI integration in both small and large enterprises, ultimately contributing
to the growth and competitiveness of the Slovenian business landscape. |
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| Keywords: | firm performance, IT management, agility, artificial intelligence, Slovenia |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 25.12.2023 |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 26-37 |
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| Numbering: | Vol. 69, no. 4 |
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| PID: | 20.500.12556/DKUM-92942-cc41adfd-d6a2-2d7b-534e-2ba63c9d36e7  |
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| UDC: | 005.7:004.8 |
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| ISSN on article: | 0547-3101 |
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| COBISS.SI-ID: | 181962499 |
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| DOI: | 10.18690/10.2478/ngoe-2023-0021 |
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| Publication date in DKUM: | 28.05.2025 |
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| Views: | 193 |
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| Downloads: | 9 |
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| Metadata: |    |
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| Categories: | Misc.
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