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组态视角下AIGC应用平台用户中辍行为影响因素研究 被引量:1

A Study on Discontinuance Behavior in the AIGC Application Platform Based on the Perspective of Configuration
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摘要 [目的/意义]从组态视角对AIGC应用平台用户的中辍行为影响因素进行研究,明确人工智能技术服务的痛点与边界,更好地优化服务,以促进技术赋能。[方法/过程]使用问卷收集219份有效数据,采用扎根编码与定性比较分析相结合的方法,对影响AIGC应用平台用户中辍行为的因素进行识别,并构建相应的理论模型,揭示引致AIGC应用平台用户中辍行为发生的条件组合。[结果/结论]将AIGC应用平台用户中辍行为归纳为潜在威胁感知型和技术因素主导型两种模式,发现感知威胁、期望失验、系统质量、内容质量在AIGC应用平台用户中辍行为中发挥着重要作用。基于上述研究结论,对后续的研究视角提出了相关建议。 [Purpose/significance] To study the factors influencing dropout behavior among users of the AIGC application platform from a configuration perspective,clarify the pain points and boundaries of artificial intelligence technology services,optimize services more effectively and promote technological empowerment.[Method/process] A total of 219 valid data were collected using questionnaires,and a combined approach of grounded coding and qualitative comparative analysis was employed to identify the factors influencing dropout behavior among users of the AIGC application platform.Corresponding theoretical models were constructed to reveal the conditional combinations that lead to dropout behavior among users of the AIGC application platform.[Result/conclusion] The dropout behavior among users of the AIGC application platform is categorized into two patterns:perceived threat-driven and technology-driven.It was found that perceived threat,expectation disconfirmation,system quality,and content quality play important roles in the dropout behavior among users of the AIGC application platform.Based on the above research findings,relevant suggestions are proposed for subsequent research perspectives.
作者 张玥 李青宇 刘雨琪 朱庆华 Zhang Yue
出处 《情报理论与实践》 北大核心 2024年第3期130-138,148,共10页 Information Studies:Theory & Application
基金 国家社会科学基金项目“信息茧房催化下群体性迷失形成机制与治理策略研究”(项目编号:23BTQ090) 教育部人文社会科学研究基金项目“移动健康管理中信息回避形成机制与演化规律研究”(项目编号:21YJC870020) 江苏省社会科学基金项目“面向移动健康管理的信息回避行为模式识别与演化规律研究”(项目编号:21TQB003)的成果。
关键词 人工智能生成内容 中辍行为 扎根理论 定性比较分析 artificial intelligence generated content(AIGC) discontinuance behavior grounded theory qualitative comparative analysis
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