| Title: | Multi-criteria measurement of ai support to project management |
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| Authors: | ID Čančer, Vesna (Author)
ID Tominc, Polona (Author)
ID Rožman, Maja (Author) |
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| Files: |  Cancer-2023-Multi-Criteria_Measurement_of_AI_S.pdf (4,18 MB)
MD5: 63802703739DDDC1D5CD36763E200CA2
 https://ieeexplore.ieee.org/document/10355961
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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 paper aims to measure the level of artificial intelligence (AI) support to project management (PM) in selected service sector activities. The exploratory factor analysis was employed based on the extensive survey on AI in Slovenian companies and the multi-criteria measurement with an emphasis on value functions and pairwise comparisons in the analytic hierarchy process. The synthesis and performance sensitivity analysis results show that in the service sector, concerning all criteria, PM is with the level 0.276 best supported with AI in services of professional, scientific, and technical activities, which also stand out concerning the first-level goals in using AI solutions in a project with the value 0.284, and in successful project implementation using AI with the value 0.301. Although the lowest level of AI support to PM, which is 0.220, is in services of wholesale and retail trade and repair of motor vehicles and motorcycles, these services excel in adopting AI technologies in a project with a value of 0.277. Services of financial and insurance activities, with the level 0.257 second-ranked concerning all criteria, have the highest value of 0.269 concerning the first-level goal of improving the work of project leaders using AI. The paper, therefore, contributes to the comparison of AI support to PM in service sector activities. The results can help AI development policymakers determine which activities need to be supported and which should be set as an example. The presented methodological frame can serve to perform measurements and benchmarking in various research fields. |
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| Keywords: | artificial intelligence, factor analysis, multiple criteria, performance sensitivity, project management |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 17.11.2023 |
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| Article acceptance date: | 07.12.2023 |
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| Publication date: | 13.12.2023 |
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| Publisher: | IEEE |
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| Year of publishing: | 2023 |
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| Number of pages: | Str. 142816-142828 |
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| Numbering: | Letn. 11 |
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| PID: | 20.500.12556/DKUM-87045  |
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| UDC: | 005.8 |
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| ISSN on article: | 2169-3536 |
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| COBISS.SI-ID: | 179926787 |
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| DOI: | 10.1109/ACCESS.2023.3342276 |
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| Publication date in DKUM: | 12.02.2024 |
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| Views: | 657 |
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| Downloads: | 81 |
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| Metadata: |    |
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| Categories: | Misc.
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