This study investigates the effects of AI-mediated communication (AMC) on trust-building and negotiation outcomes in professional remote collaboration settings. Through a mixed-methods approach combining experimental ...This study investigates the effects of AI-mediated communication (AMC) on trust-building and negotiation outcomes in professional remote collaboration settings. Through a mixed-methods approach combining experimental design and qualitative analysis (N = 120), we examine how AI intermediaries influence communication dynamics, relationship building, and decision-making processes. Results indicate that while AMC initially creates barriers to trust formation, it ultimately leads to enhanced communication outcomes and stronger professional relationships when implemented with appropriate transparency and support. The study revealed a 31% improvement in cross-cultural understanding and a 24% increase in negotiation satisfaction rates when using AI-mediated channels with proper transparency measures. These findings contribute to the theoretical understanding of technology-mediated communication and practical applications for organizations implementing AI communication tools.展开更多
基于文献计量学方法,以2020-2024年Web of Science核心数据库的1044篇文献为样本,借助CiteSpace软件从发文趋势、机构合作网络、作者网络图谱及关键词聚类等维度系统分析全球“AI+信任”研究的演进脉络。研究发现,首先,核心作者群体已...基于文献计量学方法,以2020-2024年Web of Science核心数据库的1044篇文献为样本,借助CiteSpace软件从发文趋势、机构合作网络、作者网络图谱及关键词聚类等维度系统分析全球“AI+信任”研究的演进脉络。研究发现,首先,核心作者群体已形成紧密合作网络,有效驱动了该领域学术进展;其次,教育领域作为“AI+信任”应用的核心场景特征显著,高频关键词“generative AI”“trust”的共现揭示了当前研究热点;最后,跨领域合作不足与数据隐私风险是当前主要瓶颈,研究可为构建可信AI技术体系提供理论支撑。展开更多
In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing custome...In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.展开更多
文摘This study investigates the effects of AI-mediated communication (AMC) on trust-building and negotiation outcomes in professional remote collaboration settings. Through a mixed-methods approach combining experimental design and qualitative analysis (N = 120), we examine how AI intermediaries influence communication dynamics, relationship building, and decision-making processes. Results indicate that while AMC initially creates barriers to trust formation, it ultimately leads to enhanced communication outcomes and stronger professional relationships when implemented with appropriate transparency and support. The study revealed a 31% improvement in cross-cultural understanding and a 24% increase in negotiation satisfaction rates when using AI-mediated channels with proper transparency measures. These findings contribute to the theoretical understanding of technology-mediated communication and practical applications for organizations implementing AI communication tools.
文摘基于文献计量学方法,以2020-2024年Web of Science核心数据库的1044篇文献为样本,借助CiteSpace软件从发文趋势、机构合作网络、作者网络图谱及关键词聚类等维度系统分析全球“AI+信任”研究的演进脉络。研究发现,首先,核心作者群体已形成紧密合作网络,有效驱动了该领域学术进展;其次,教育领域作为“AI+信任”应用的核心场景特征显著,高频关键词“generative AI”“trust”的共现揭示了当前研究热点;最后,跨领域合作不足与数据隐私风险是当前主要瓶颈,研究可为构建可信AI技术体系提供理论支撑。
文摘In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.
