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Author: 呂佳蓉
Lu, Chia-Jung
Thesis Title: 客服聊天機器人與自我揭露程度在外送情境下對消費者行為之影響 ---以咖啡外送為例
The Effect of Different Types of Customer Service Chatbots and Self-Disclosure on Customer Behavior in Delivery Service---An Example for Coffee Delivery Service
Advisor: 蔣旭政
Chiang, Hsu-Cheng
Committee: 伍柏翰 宋涵鈺 蔣旭政
Approval Date: 2021/10/12
Degree: 碩士
Master
Department: 大眾傳播研究所
Graduate Institute of Mass Communication
Thesis Publication Year: 2021
Academic Year: 110
Language: 中文
Number of pages: 150
Keywords (in Chinese): 聊天機器人AI外送咖啡客服自我揭露消費者行為品牌態度購買意願口碑推薦意願品牌忠誠度
Keywords (in English): Chatbot, AI, delivery, coffee, customer service, self-disclosure, consumer behavior, brand attitude, purchase intention, word-of-mouth recommendation willingness, brand loyalty
Research Methods: 實驗設計法內容分析法
DOI URL: http://doi.org/10.6345/NTNU202101617
Thesis Type: Academic thesis/ dissertation
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  • 隨著客戶服務成為品牌與消費者間的重要連結,客服系統的自動化越來越重要,因此聊天機器人成為客服系統自動化的首要選擇,而隨著疫情的升溫,外送的需求大幅增加,使得客服問題層出不窮,加上台灣對於咖啡飲品的需求增加,因此適當的客服平台能為品牌方提升效益、節省成本。本研究以咖啡外送平台之客服聊天機器人為背景,實驗設計為2客服聊天機器人(AI式聊天機器人vs.規則式聊天機器人)× 2自我揭露程度(自我揭露程度高 vs. 自我揭露程度低),探討客服聊天機器人搭配自我揭露程度對消費者行為是否具有差異,並以年齡作為調節變項,探討年齡(數位移民vs.數位原民)是否會調節客服聊天機器人並對消費者行為產生差異,將消費者行為分為四個依變項(品牌態度、購買意願、口碑推薦意願以及品牌忠誠度)。本研究主要採用實驗法作為驗證研究假設分析之依據,以此驗證受測者在與聊天機器人互動過後,所產生的消費者行為是否與研究假設相符,再輔以滯後序列分析以了解消費者在與AI式聊天機器人互動過程中的行為,會為品牌方帶來哪些優勢。
    本研究結果證實(1)「AI式聊天機器人」相較於「規則式聊天機器人」有較佳的消費者行為、(2)「自我揭露程度高」相較於「自我揭露程度低」有較佳的 消費者行為、(3)「與AI式聊天機器人互動」,相較於「與規則式聊天機器人互動」, 「數位原民」能產生較佳的消費者行為、(4)「與AI式聊天機器人互動」相較於「與規則式聊天機器人互動」,「自我揭露程度高」能產生較佳的消費者行為、(5)消費者與AI式聊天機器人互動過程中,所產生的消費者行為,能提高客戶服務效率與品質進而減少人力成本。

    As customer service has become an important link between brands and consumers, the automation of customer service systems has become important. Therefore, chatbots have become the first choice for customer service system automation.
    A 2 customer service chatbot(Rule-Based Chatbot vs. AI-Based Chatbot)× 2 self-disclosure degree(high vs. low), to discuss whether customer service chatbot with self-disclosure levels have any difference in consumer behavior, and consider age as an adjustment variable, to discuss whether age(digital immigrants vs. digital natives) will adjust customer service chatbots and make differences in consumer behavior. Consumer behavior is divided into four dependent variables(brand attitude, purchase intention, intention to recommend word of mouth, and brand loyalty).
    The result of this research shows that:(1)compared to the Rule-Based Chatbot, AI-Based Chatbot leads to a better consumer behavior. ;(2)compared to the low self-disclosure, high self-disclosure degree leads to a better consumer behavior. ; (3)compared to Rule-Based Chatbot, AI-Based Chatbot with digital natives leads to a better consumer behavior. ;(4)compared to Rule-Based Chatbot, AI-Based Chatbot with high self-disclosure degree leads to a better consumer behavior. ;(5)Consumer behaviors generated during the interaction between consumers and AI-Based chatbots can improve the efficiency, quality of customer service and reduce labor costs.

    第壹章、緒論 1 第一節、研究動機、背景與目的 1 第二節、研究問題 8 第三節、研究流程 10 第四節、名詞解釋 11 一、聊天機器人(Chatbot) 11 二、人工智慧(Artificial Intelligence, AI) 11 三、人機互動(Human-Computer Interaction, HCI) 12 四、自我揭露(Self-Disclosure) 12 第貳章、文獻探討 13 第一節、AI聊天機器人 13 一、聊天機器人 13 二、客戶服務與人工智慧 15 三、人機互動(Human-Computer Interaction, HCI) 16 第二節、社會訊息處理理論(SOCIAL INFORMATION PROCESSING , SIP) 18 第三節、自我揭露(SELF-DISCLOSURE) 19 一、自我揭露定義 19 二、社會交換理論(Social Exchange Theory, SET) 20 第四節、數位原民與數位移民 21 第五節、消費者行為 22 一、品牌態度 22 二、購買意願 23 三、口碑推薦意願 25 四、品牌忠誠度 26 第六節、假設推導 28 一、客服聊天機器人與消費者行為的關係 28 二、自我揭露與消費者行為的關係 30 三、年齡調節客服聊天機器人與消費者行為間的關係 31 四、客服聊天機器人、自我揭露程度與消費者行為的交互關係 33 第參章、研究方法 36 第一節、研究架構、研究工具與分析方法 36 一、研究架構 36 二、研究工具 37 三、分析方法 38 第二節、聊天機器人系統建立 43 一、使用系統 43 二、系統建置 43 三、LINE Bot 建構 48 四、建立QnA Maker資源 50 五、Heroku介紹與部署 52 第三節、實驗情境 54 第四節、自變項操弄檢定與前測結果 54 一、自我揭露操弄檢定 54 二、前測結果 56 第五節、依變項 57 一、依變項測量 57 二、量表信度分析 61 第六節、主實驗刺激物呈現與實驗流程 63 一、主實驗刺激物之呈現 63 二、實驗流程 67 第肆章、研究結果 69 第一節、樣本結構與敘述統計 69 第二節、量表信度分析 71 第三節、研究假設之驗證 72 一、客服聊天機器人對消費者行為之影響 75 二、自我揭露程度對消費者行為之影響 76 三、客服聊天機器人、年齡對消費者行為之影響 77 四、客服聊天機器人、自我揭露程度對消費者行為之影響 81 第四節、滯後序列分析 85 一、行為序列之次數分配 85 二、消費者行為序列 87 三、序列數據分析 88 四、顯著數據分析 90 第五節、研究假設檢定結果與滯後序列分析結果發現 94 一、研究假設檢定結果 94 二、滯後序列分析結果發現 96 第伍章、結論與建議 97 第一節、研究結果討論 97 第二節、學術貢獻與實務建議 104 一、學術貢獻 104 二、實務建議 105 第三節、研究限制 107 參考文獻 108 英文文獻 108 中文文獻 140 附錄一、前測問卷 142 附錄二、主實驗問卷 146

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