政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152407

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152407

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題名:	人機互動之間的初始信任感對於生成式人工智慧使用意圖之影響：以社會交換理論為框架 The impact of initial trust on usage intention of generative artificial intelligence: A social exchange perspective on human-automation interaction
作者:	李芷瑄 LEE, CHIH-HSUAN
貢獻者:	周致遠 Chou, Chih-Yuan 李芷瑄 LEE, CHIH-HSUAN
關鍵詞:	生成式人工智慧初始信任社會交換理論使用意圖人機互動 Generative AI Initial trust Social exchange theory Usage intention Human-automation interaction
日期:	2024
上傳時間:	2024-08-05 12:06:21 (UTC+8)
摘要:	社會交換理論（SET）是一種廣泛應用於分析或解釋人類行為及關係的理。然而，由於人類在使用科技或電腦時會表現出社會行為，因此社會交換理論可以應用在人機互動（HAI）的領域。本研究採用社會交換理論來研究人類與生成式人工智慧之間的互動。而因生成式人工智慧的日益普及，加上將科技整合在工作場域的重要性日益上升，本研究的背景著重在人們於工作中使用生成式人工智慧。不過，儘管生成式人工智慧可以在工作中協助人，生成式人工智慧也存在缺點，這使得初始信任在個人承擔生成式人工智慧缺點與風險的過程中發揮了重要作用。本研究將生成式人工智慧的使用視為社會交換行為的一種形式，同時在研究模型納入了會影響社會交換過程的三個調節因素，旨在探討初始信任對生成式人工智慧使用意圖的影響。研究方法採用量化研究的統計分析，從具有科技製造業工作經驗的個人中收集問卷資料。數據分析後的研究結果顯示，根據社會交換理論之研究模型的各個構面都有直接或調節效果，控制變數也發揮了效用。本研究的結果可以為人機互動領域提供有價值的學術貢獻，並為希望應用生成式人工智慧技術於工作場合的組織提供實用的見解。 Social exchange theory (SET) is frequently utilized to analyze human behaviors and relationships. With the increasing social interaction between humans and technology, SET has found application in the realm of human-automation interaction (HAI). This study adopts SET to explore interactions between humans and generative artificial intelligence (GenAI), particularly within workplace contexts, given the growing integration of technology in professional settings. While GenAI offers assistance, its drawbacks underscore the importance of initial trust in facilitating its adoption. Framing GenAI use as a form of social exchange behavior, this study investigates the influence of initial trust on GenAI usage intention, incorporating three moderating factors related to social exchange dynamics. Employing statistical analysis, the study gathers data from individuals with experience in technology manufacturing sectors. Findings reveal direct and moderate effects among constructs in the research model, with control variables also exerting influence. Finally, this work can make a valuable contribution to the literature on HAI and can offer practical insights for organizations seeking to employ GenAI technologies.
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