Cheng Xusen, Zhang Xiaoping, Yang Bo, Fu Yaxin
School of Information, Renmin University of China, Beijing, China.
Electron Commer Res Appl. 2022 Jul-Aug;54:101164. doi: 10.1016/j.elerap.2022.101164. Epub 2022 Jun 3.
Several measures taken to control the spread of the COVID-19 pandemic have severely disrupted the accommodation sharing sector. This study attempts to find solutions to aid the recovery of the accommodation sharing sector via team efforts. Accordingly, we focus on the integration of artificial intelligence (AI) and collaboration. Despite the significant developments in AI technologies, there exists no research considering the application of AI in team collaboration. Utilizing the design science research method and collaboration engineering, we developed an AI-driven prototype system, , for collaboration process recommendation. Qualitative results show that the newly developed tool for collaboration process recommendation has achieved satisfactory performance. Furthermore, we investigated the antecedents and outcomes of trust in the AI-driven collaboration context. From a practical perspective, we propose several solutions to the challenges looming over the accommodation sharing sector according to collaboration deliverables. Furthermore, a system prototype was developed to facilitate collaboration process recommendation and provide procedural guidance.
为控制新冠疫情传播所采取的多项措施严重扰乱了共享住宿行业。本研究试图通过团队协作找到帮助共享住宿行业复苏的解决方案。因此，我们专注于人工智能（AI）与协作的整合。尽管AI技术取得了重大进展，但尚无研究考虑AI在团队协作中的应用。利用设计科学研究方法和协作工程，我们开发了一个用于协作过程推荐的AI驱动原型系统。定性结果表明，新开发的协作过程推荐工具取得了令人满意的性能。此外，我们还研究了AI驱动协作环境中信任的前因和后果。从实际角度出发，我们根据协作成果针对共享住宿行业面临的挑战提出了几种解决方案。此外，还开发了一个系统原型，以促进协作过程推荐并提供程序指导。
