中小型企业和大型企业在项目管理人工智能支持水平上的显著统计差异

AI Pub Date : 2024-01-05 DOI:10.3390/ai5010008
P. Tominc, D. Oreški, Vesna Čančer, M. Rožman
{"title":"中小型企业和大型企业在项目管理人工智能支持水平上的显著统计差异","authors":"P. Tominc, D. Oreški, Vesna Čančer, M. Rožman","doi":"10.3390/ai5010008","DOIUrl":null,"url":null,"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.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Statistically Significant Differences in AI Support Levels for Project Management between SMEs and Large Enterprises\",\"authors\":\"P. Tominc, D. Oreški, Vesna Čančer, M. Rožman\",\"doi\":\"10.3390/ai5010008\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":503525,\"journal\":{\"name\":\"AI\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/ai5010008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/ai5010008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

背景:本文深入分析了中小型企业和大型企业在项目管理人工智能支持水平上的显著差异。这项研究是在对斯洛文尼亚 473 家中小型企业和大型企业进行抽样调查的基础上开展的。研究方法为验证观察到的差异,采用了统计分析,特别是曼-惠特尼 U 检验。结果结果证实,中小型企业和大型企业在项目管理人工智能支持的多个方面存在显著的统计学差异。在所有五个人工智能利用维度上,大型企业平均表现出更高的人工智能采用水平。具体而言,大型企业在采用人工智能战略以及在项目任务和团队创建中采用人工智能技术方面的得分明显更高(P < 0.05)。本研究的结果还强调了中小型企业和大型企业在采用和利用人工智能技术进行项目管理方面的显著差异(p < 0.05)。大型企业在多个维度上的得分都超过了 4 分,其中在使用预测分析工具改进项目工作方面的平均得分最高(1 至 5 分制的平均值为 4.46),而中小企业的平均得分则全部低于 4 分。结论:研究结果表明,有必要制定有针对性的战略,以促进中小型企业采用人工智能,并利用其优势成功实施项目,增强公司的竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Statistically Significant Differences in AI Support Levels for Project Management between SMEs and Large Enterprises
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
AI
AI
自引率
0.00%
发文量
0
期刊最新文献
Recent Advances in 3D Object Detection for Self-Driving Vehicles: A Survey A Model for Feature Selection with Binary Particle Swarm Optimisation and Synthetic Features Dynamic Programming-Based White Box Adversarial Attack for Deep Neural Networks Computer Vision for Safety Management in the Steel Industry Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1