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An investigation on trust in AI-enabled collaboration: Application of AI-Driven chatbot in accommodation-based sharing economy.
Cheng, Xusen; Zhang, Xiaoping; Yang, Bo; Fu, Yaxin.
  • Cheng X; School of Information, Renmin University of China, Beijing, China.
  • Zhang X; School of Information, Renmin University of China, Beijing, China.
  • Yang B; School of Information, Renmin University of China, Beijing, China.
  • Fu Y; School of Information, Renmin University of China, Beijing, China.
Electron Commer Res Appl ; 54: 101164, 2022.
Article in English | MEDLINE | ID: covidwho-1881973
ABSTRACT
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, AI-Driven, 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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Qualitative research Language: English Journal: Electron Commer Res Appl Year: 2022 Document Type: Article Affiliation country: J.elerap.2022.101164

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Qualitative research Language: English Journal: Electron Commer Res Appl Year: 2022 Document Type: Article Affiliation country: J.elerap.2022.101164