| Title: | Statistically significant differences in AI support levels for project management between SMEs and large enterprises |
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| Authors: | ID Tominc, Polona (Author)
ID Oreški, Dijana (Author)
ID Čančer, Vesna (Author)
ID Rožman, Maja (Author) |
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| Files: |  Tominc_2024_Statistically_Significant_Differences.pdf (1,10 MB)
MD5: 6DA955899667E4FACEC667475BE3AC4C
 https://doi.org/10.3390/ai5010008
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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: | Background: This article delves into an in-depth analysis of the statistically significant differences in AI support levels for project management between SMEs and large enterprises. The research was conducted based on a comprehensive survey encompassing a sample of 473 SMEs and large Slovenian enterprises.
Methods: To validate the observed differences, statistical analysis, specifically the Mann–Whitney U test, was employed.
Results: The results confirm the presence of statistically significant differences between SMEs and large enterprises across multiple dimensions of AI support in project management. Large enterprises exhibit on average a higher level of AI adoption across all five AI utilization dimensions. Specifically, large enterprises scored significantly higher (p < 0.05) in AI adopting strategies and in adopting AI technologies for project tasks and team creation. This study’s findings also underscored the significant differences (p < 0.05) between SMEs and large enterprises in their adoption and utilization of AI technologies for project management purposes. While large enterprises scored above 4 for several dimensions, with the highest average score assessed (mean value 4.46 on 1 to 5 scale) for the usage of predictive Analytics Tools to improve the work on the project, SMEs’ average levels, on the other hand, were all below 4. SMEs in particular may lag in incorporating AI into various project activities due to several factors such as resource constraints, limited access to AI expertise, or risk aversion.
Conclusions: The results underscore the need for targeted strategies to enhance AI adoption in SMEs and leverage its benefits for successful project implementation and strengthen the company’s competitiveness. |
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| Keywords: | enterprises, project management, leadership, artificial intelligence |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 25.11.2023 |
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| Article acceptance date: | 02.01.2024 |
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| Publication date: | 05.01.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | Str. 136-157 |
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| Numbering: | Letn. 5, št. 1 |
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| PID: | 20.500.12556/DKUM-92391  |
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| UDC: | 005.8 |
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| ISSN on article: | 2673-2688 |
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| COBISS.SI-ID: | 180289027 |
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| DOI: | 10.3390/ai5010008 |
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| Publication date in DKUM: | 04.04.2025 |
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| Views: | 260 |
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| Downloads: | 9 |
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| Metadata: |    |
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| Categories: | Misc.
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