工作场所的协作式人工智能:从基于资源和任务-技术契合的角度提升组织绩效

IF 20.1 1区 管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Aleksandra Przegalinska , Tamilla Triantoro , Anna Kovbasiuk , Leon Ciechanowski , Richard B. Freeman , Konrad Sowa
{"title":"工作场所的协作式人工智能:从基于资源和任务-技术契合的角度提升组织绩效","authors":"Aleksandra Przegalinska ,&nbsp;Tamilla Triantoro ,&nbsp;Anna Kovbasiuk ,&nbsp;Leon Ciechanowski ,&nbsp;Richard B. Freeman ,&nbsp;Konrad Sowa","doi":"10.1016/j.ijinfomgt.2024.102853","DOIUrl":null,"url":null,"abstract":"<div><div>This research examines how artificial intelligence, human capabilities, and task types influence organizational outcomes. By leveraging the frameworks of the Resource-Based View and Task Technology Fit theories, we executed two distinct studies to assess the effectiveness of a generative AI tool in aiding task performance across a spectrum of task complexities and creative demands. The initial study tested the utility of generative AI across diverse tasks and the significance of AI-related skills enhancement. The subsequent study explored interactions between humans and AI, analyzing emotional tone, sentence structure, and word choice. Our results indicate that incorporating AI can significantly improve organizational task performance in areas such as automation, support, creative endeavors, and innovation processes. We also observed that generative AI generally presents more positive sentiment, utilizes simpler language, and has a narrower vocabulary than human counterparts. These insights contribute to a broader understanding of AI's strengths and weaknesses in organizational settings and guide the strategic implementation of AI systems.</div></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"81 ","pages":"Article 102853"},"PeriodicalIF":20.1000,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives\",\"authors\":\"Aleksandra Przegalinska ,&nbsp;Tamilla Triantoro ,&nbsp;Anna Kovbasiuk ,&nbsp;Leon Ciechanowski ,&nbsp;Richard B. Freeman ,&nbsp;Konrad Sowa\",\"doi\":\"10.1016/j.ijinfomgt.2024.102853\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This research examines how artificial intelligence, human capabilities, and task types influence organizational outcomes. By leveraging the frameworks of the Resource-Based View and Task Technology Fit theories, we executed two distinct studies to assess the effectiveness of a generative AI tool in aiding task performance across a spectrum of task complexities and creative demands. The initial study tested the utility of generative AI across diverse tasks and the significance of AI-related skills enhancement. The subsequent study explored interactions between humans and AI, analyzing emotional tone, sentence structure, and word choice. Our results indicate that incorporating AI can significantly improve organizational task performance in areas such as automation, support, creative endeavors, and innovation processes. We also observed that generative AI generally presents more positive sentiment, utilizes simpler language, and has a narrower vocabulary than human counterparts. These insights contribute to a broader understanding of AI's strengths and weaknesses in organizational settings and guide the strategic implementation of AI systems.</div></div>\",\"PeriodicalId\":48422,\"journal\":{\"name\":\"International Journal of Information Management\",\"volume\":\"81 \",\"pages\":\"Article 102853\"},\"PeriodicalIF\":20.1000,\"publicationDate\":\"2024-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Information Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0268401224001014\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0268401224001014","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0

摘要

本研究探讨了人工智能、人类能力和任务类型如何影响组织成果。通过利用基于资源的观点和任务技术契合理论框架,我们进行了两项不同的研究,以评估生成式人工智能工具在帮助不同任务复杂性和创造性需求的任务执行方面的有效性。最初的研究测试了生成式人工智能在不同任务中的实用性,以及人工智能相关技能提升的意义。随后的研究探讨了人类与人工智能之间的互动,分析了情感基调、句子结构和词语选择。我们的研究结果表明,在自动化、支持、创造性工作和创新流程等领域,融入人工智能可以显著提高组织的任务绩效。我们还观察到,与人类同行相比,生成式人工智能通常会表达更积极的情感,使用更简单的语言,词汇量也更小。这些见解有助于更广泛地了解人工智能在组织环境中的优缺点,并为人工智能系统的战略实施提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives
This research examines how artificial intelligence, human capabilities, and task types influence organizational outcomes. By leveraging the frameworks of the Resource-Based View and Task Technology Fit theories, we executed two distinct studies to assess the effectiveness of a generative AI tool in aiding task performance across a spectrum of task complexities and creative demands. The initial study tested the utility of generative AI across diverse tasks and the significance of AI-related skills enhancement. The subsequent study explored interactions between humans and AI, analyzing emotional tone, sentence structure, and word choice. Our results indicate that incorporating AI can significantly improve organizational task performance in areas such as automation, support, creative endeavors, and innovation processes. We also observed that generative AI generally presents more positive sentiment, utilizes simpler language, and has a narrower vocabulary than human counterparts. These insights contribute to a broader understanding of AI's strengths and weaknesses in organizational settings and guide the strategic implementation of AI systems.
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
International Journal of Information Management
International Journal of Information Management INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
53.10
自引率
6.20%
发文量
111
审稿时长
24 days
期刊介绍: The International Journal of Information Management (IJIM) is a distinguished, international, and peer-reviewed journal dedicated to providing its readers with top-notch analysis and discussions within the evolving field of information management. Key features of the journal include: Comprehensive Coverage: IJIM keeps readers informed with major papers, reports, and reviews. Topical Relevance: The journal remains current and relevant through Viewpoint articles and regular features like Research Notes, Case Studies, and a Reviews section, ensuring readers are updated on contemporary issues. Focus on Quality: IJIM prioritizes high-quality papers that address contemporary issues in information management.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信