Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives

知识管理 任务(项目管理) 资源(消歧) 组织绩效 计算机科学 心理学 工程类 业务 系统工程 计算机网络
作者
Aleksandra Przegalińska,Tamilla Triantoro,Anna Kovbasiuk,Leon Ciechanowski,Richard B. Freeman,Konrad Sowa
出处
期刊:International Journal of Information Management [Elsevier BV]
卷期号:81: 102853-102853 被引量:82
标识
DOI:10.1016/j.ijinfomgt.2024.102853
摘要

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. • Firms can utilize AI to gain competitive advantages. • Firms can deploy AI to automate routine tasks and enhance decision support tasks. • Firms should focus on AI and human collaboration to boost creative and innovative tasks. • Firms benefit when employees upskill to improve performance with AI. • Generative AI tool in our study had a more positive sentiment and used simpler language than humans.
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