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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/158580
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/158580


    Title: 大型語言模型內外向性格對用戶選擇閉合與風險決策的影響
    Impact of LLM Introversion and Extraversion on User Choice Closure and Risk-Based Decision-Making
    Authors: 張庭綺
    Chang, Ting-Chi
    Contributors: 簡士鎰
    Chien, Shih-Yi
    張庭綺
    Chang, Ting-Chi
    Keywords: 大型語言模型 (LLM)
    選擇閉合
    風險決策
    AI 輔助決策
    Large Language Models (LLM)
    Choice closure
    Risk-based decision-making
    AI-assisted decision-making
    Date: 2025
    Issue Date: 2025-08-04 14:27:56 (UTC+8)
    Abstract: 隨著大型語言模型(LLM)在決策輔助領域的應用愈發普及,其擬人化對話風格對使用者行為的影響受到學界與業界的高度關注。然而,迄今尚缺乏針對不同人格風格的 LLM 在多元風險情境下影響機制的系統性研究。本研究基於 Prompt Induction post Supervised Fine-Tuning(PISF)技術,將 Meta-Llama-3-8B-Instruct 微調為「內向型」與「外向型」兩種對話風格,並設計高風險(投資基金分配)與低風險(留學保險方案選擇)兩種情境,邀請 32 名受試者完成行為任務與問卷測量。研究聚焦於「選擇閉合」(choice closure)與「建議採納」兩大行為指標,並檢驗外向性、風險承受度、技術不安感與宜人性等個人特質的調節效應。結果發現:外向型 LLM 顯著提升使用者在投資情境下嘗試高風險資產的意願,並於保險情境中加速決策閉合;內向型 LLM 則穩健地引導受試者集中於中等風險選項;在高風險的投資環境中,兩種對話風格受高認知負荷的影響而不顯著,兩者對選擇閉合效果的差異趨於平緩。此外,選擇閉合與決策滿意度呈現高度正相關,且使用者個人特質顯著調節以上關係。本研究不僅驗證了 PISF 技術在 LLM 人格操控上的可行性,還從認知負荷與說服理論的視角,深度闡明了擬人化對話風格如何影響使用者決策過程,並提出在不同風險場景下選擇最適對話風格的設計原則。最終,本研究為金融與保險等高風險決策領域的 AI 支援系統開發提供了具體的實證依據與操作建議,並期待透過擴大樣本與多元任務場景的後續研究,進一步驗證人格化 LLM 的廣泛適用性與潛在機制。
    As Large Language Models (LLMs) become increasingly prevalent in decision-making assistance, their anthropomorphic conversational styles’ impact on user behavior has attracted significant academic and industry attention. However, systematic research on how different LLM personality styles influence users across diverse risk contexts remains lacking. This study used Prompt Induction post Supervised Fine-Tuning (PISF) to fine-tune Meta-Llama-3-8B-Instruct into "introverted" and "extroverted" personality, testing 32 participants across high-risk (investment fund allocation) and low-risk (study abroad insurance) scenarios.
    Focusing on "choice closure" and "recommendation adoption" behaviors, results showed extroverted LLMs significantly enhanced users’ willingness to attempt high-risk assets in investment scenarios and achieve higher closure in insurance contexts, while introverted LLMs consistently guided participants toward moderate-risk options. In high-risk investment scenario, both personalities became non-significant due to high cognitive load, with differences in choice closure effects becoming attenuated. Choice closure showed strong positive correlation with decision satisfaction, with personal traits significantly moderating these relationships. This study validates PISF technology’s feasibility for LLM personality manipulation and elucidates how anthropomorphic conversational styles influence decision-making through cognitive load and persuasion theory perspectives. It provides design principles for optimal conversational style selection across risk scenarios and empirical evidence for developing AI support systems in financial domains.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    112356041
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112356041
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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