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研究生:昝亭瑜
研究生(外文):Ting-Yu Tsan
論文名稱:探討聊天機器人個性化對使用感知與意願的影響
論文名稱(外文):A Study on the Influence of Chatbot Personalization on User Perception and Willingness
指導教授:董芳武董芳武引用關係
指導教授(外文):Fang-Wu Tung
口試委員:唐玄輝張永儒
口試委員(外文):Hsien-Hui TangYung-Ju Chang
口試日期:2020-01-14
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:設計系
學門:設計學門
學類:產品設計學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:76
中文關鍵詞:聊天機器人對話式介面個性化社會臨場感信賴度
外文關鍵詞:ChatbotConversational User InterfacePersonalizationSocial presenceTrust
相關次數:
  • 被引用被引用:5
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  • 下載下載:151
  • 收藏至我的研究室書目清單書目收藏:1
互聯網愈趨成熟之發展與行動裝置之普及,且使用者對於社群媒體與即時通訊軟體的高黏著度,讓許多品牌與企業開始把產品服務,從既定介面操作轉變到人與人最基本的互動方式:「交談」上,繼而開啟聊天機器人之對話式介面的契機與商機。
如何建構符合使用情境之聊天機器人個性,繼而提高對話式互動體驗之使用意願,為一重要議題。本研究以「外放」--「內斂」人格維度之語言風格的相關文獻作為聊天機器人個性語句的參考,設計與建構個性不同的腳本式聊天機器人實驗原型,而機器人的使用情境為基礎理財知識查詢之「資訊查詢」情境。再根據文獻探討與統整,提出四項影響對話式互動體驗之相關構面:聊天機器人功能性、社會臨場感、信賴度與聊天機器人性別,作為本研究架構與評估聊天機器人個性和使用感知的相關依據。實驗以年輕族群為研究對象,採用觀察、問卷調查與訪談等研究方法收集資料。
透過文獻探討、量表調查與訪談、實驗分析,彙整歸納本研究之結論為:(1)受測者的確能透過不同個性之聊天機器人的對話語句,感知到其個性差異,因此當研究者需要建構聊天機器人個性特徵時,找到合適之人格模型的語言風格作為語句的參考,是非常有效率與適宜使用的工具;(2)影響對話式互動體驗之相關構面中,以信賴度影響感知與使用意願之程度最高。內斂機器人語句的語言風格,可讓使用者感知到較高的功能性與信賴度;外放機器人語句的語言風格,則讓使用者感知到較高的社會臨場感;(3)聊天機器人個性化確實會影響使用感知與使用意願,且以個性特徵之對話語句與功能性、社會臨場感、信賴度一同探討感知與意願時,可得出在理財知識查詢的使用情境下,內斂機器人的個性特徵,較適合作為此情境下可運用於機器人個性化的人格模型。
根據研究成果,本研究可作為在建構聊天機器人個性化時的設計參考架構,為聊天機器人之對話式互動體驗提供有用的建議。
Because of the advancement of Internet technology and wide diffusion of mobile devices, smartphone users are strongly bound to social media and instant messaging applications. Under such circumstances, various brands and businesses have transformed the way their products and services work from interface operation to chat—the most fundamental interaction between human beings. This change has fostered the development of conversational user interfaces with numerous business opportunities.
Furthermore, this change highlights the importance of developing favorable personalities for chatbots according to different scenarios to increase users’ willingness to engage in conversational interactions with computers. In this study, texts reflecting the language styles of an extraversion or introversion personality dimension were introduced as reference materials for chatbots. These scripted chatbots were designed to represent either of the two personality dimensions, with dialogues focusing on queries of basic wealth management information. Through literature review, four dimensions influencing conversational interactions were then proposed as references for the framework of the study and the assessment of personality dimensions and user perception: chatbot functionality, social presence, trust, and chatbot gender. The study investigated the young generation with research methods such as observation, questionnaire surveys, and interviews.
The results from literature review, scale questionnaires, and study analysis revealed three findings: (1) Participants were able to perceive personality difference through conversations with chatbots of different personality dimensions. Therefore, texts reflecting the language styles of certain personality dimensions can be a highly efficient and suitable tool for researchers who look to construct the personality traits of a chatbot; (2) Among the dimensions influencing conversational interactions, trust had the most significant impact on perception and willingness of usage. The linguistic output of an introvert chatbot led to higher user perception of functionality, while that of an extravert chatbot contributed to higher user perception of social presence; (3) Personalization of chatbot services does affect user perception and their willingness of usage. Personalized dialogues, functionality, social presence, and trust are relevant to user perception and willingness. The finding indicated the personality traits of an introvert chatbot are ideal for the prototype of a personalized chatbot providing query services of wealth management information.
This study serves as a reference for chatbot personality designs and provides useful suggestions for improving user experience of conversational interaction with chatbots.
摘要 III
Abstract IV
誌謝 V
目錄 VI
圖目錄 VII
表目錄 VIII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究範圍 4
1.5 研究流程 4
第二章 文獻探討 6
2.1 聊天機器人 6
2.1.1 聊天機器人類型與使用情境 6
2.1.2 對話式介面設計 8
2.1.3 聊天機器人個性化 11
2.1.4 聊天機器人評估指標 14
2.2 影響對話式互動體驗之其他構面 15
2.2.1 社會臨場感 15
2.2.2 信賴度 17
2.2.3 性別 19
第三章 研究方法 21
3.1 前導實驗 21
3.2 研究架構 24
3.3 實驗設計 25
3.3.1 實驗設計原型 26
3.3.2 實驗量測工具 32
3.3.3 實驗流程 35
第四章 研究結果分析 37
4.1 樣本資料敘述統計 37
4.2 信度與效度 38
4.3 相關分析與迴歸分析 40
4.4 差異分析 42
4.4.1 感知外放程度之差異分析 43
4.4.2 功能性之差異分析 45
4.4.3 社會臨場感之差異分析 47
4.4.4 信賴度之差異分析 49
4.4.5 滿意度與使用意願之差異分析 51
第五章 結論與建議 57
5.1 研究結論與實務建議 57
5.1.1 聊天機器人個性化語句之規劃與設計 57
5.1.2 對話式互動體驗之四項構面對於使用感知與意願的影響 58
5.1.3 不同個性之聊天機器人對於使用感知與意願的影響 61
5.2 研究限制 62
5.3 未來研究建議 62
參考文獻 63
附錄一:線上問卷 72
附錄二:質性訪談大綱 76

圖目錄
圖2-2-1 對話式介面之選項型介面 8
圖2-1-2 對話式介面之確認型介面 9
圖2-2-2 對話式介面之列表型介面 9
圖3-1 前導性實驗聊天機器人設計框架與流程 21
圖3-2 研究架構 25
圖3-3-1-1 實驗原型設計流程 26
圖3-3-1-2 實驗原型資訊架構 27
圖3-3-1-3 實驗原型角色姓名與圖像 31
圖3-3-1-4 實驗原型視覺化介面 32
圖3-3-1-5 實驗原型對話式介面 32

表目錄
表3-1-1 前導性實驗使用者目標任務 21
表3-1-2 前測實驗腳本外向個性化語句 22
表3-1-3 前測實驗腳本內向個性化語句 23
表3-3-1-1 實驗腳本外放個性化語句 28
表3-3-1-2 實驗腳本內斂個性化語句 29
表3-3-1-3 實驗腳本個性化語句差異對照分析 30
表3-3-2 受測者個性語意差異量表 33
表4-1 受測者敘述統計 38
表4-2 實驗量表信度分析 40
表4-3-1 相關構面之於滿意度與使用意願相關分析 41
表4-3-2 滿意度迴歸分析 41
表4-3-3 使用意願迴歸分析 42
表4-4-1-1 聊天機器人個性敘述統計 44
表4-4-1-2 聊天機器人個性雙因子變異數分析 44
表4-4-1-3 聊天機器人個性訪談分析 45
表4-4-2-1 功能性敘述統計 45
表4-4-2-2 功能性雙因子變異數分析 46
表4-4-2-3 功能性訪談分析 47
表4-4-3-1 社會臨場感敘述統計 47
表4-4-3-2 社會臨場感雙因子變異數分析 48
表4-4-3-3 社會臨場感訪談分析 49
表4-4-4-1 信賴度敘述統計 49
表4-4-4-2 信賴度雙因子變異數分析 50
表4-4-4-3 信賴度之成對樣本T檢定分析 50
表4-4-4-4 信賴度訪談分析 51
表4-4-5-1 滿意度敘述統計 52
表4-4-5-2 滿意度雙因子變異數分析 52
表4-4-5-3 使用意願敘述統計 53
表4-4-5-4 使用意願雙因子變異數分析 53
表4-4-5-5 聊天機器人屬性之於滿意度與使用意願訪談分析 54
表4-4-5-6 外放機器人之於滿意度與使用意願訪談分析 55
表4-4-5-7 內斂機器人之於滿意度與使用意願訪談分析 56
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楊美雪、蔡雯婷,〈智慧型手機通訊軟體使用者之社會臨場感與愉悅感研究--以LINE即時通訊軟體為例〉,國立虎尾科技大學學報,32卷1期 P35-50,2014


-網 路 文 獻-
Chris, K. (2018),〈App使用時間趨飽和、購物型應用大幅成長〉
https://www.smartm.com.tw/article

Chris Klotzbach, Lali Kesiraju. (2017),〈2017 App年度關鍵報告〉,【Flurry】
https://www.smartm.com.tw/article/34363233cea3

Emmet Connolly. (2019),〈8 principles of bot design〉
https://medium.com/intercom-inside/8-principles-of-bot-design-51f03df1d84c

Matt, G. (2016),〈Bot is a hilariously over-simplified buzzword. Let’s fix that〉
https://medium.com

Paul Adams. (2011),〈Grouped: How small groups of friends are the key to influence on the social web〉
https://www.amazon.com
https://www.intercom.com/blog/bots-versus-humans/?utm_medium=article&utm_source=medium&utm_campaign=botdesign

Simon, K.(2018),〈Global Digital Statshot 2018〉,【全球網路報告】
https://www.funp.com›news
http://wearesocial.cn/blog/2019/10/25/the-global-state-of-digital-in-october-2019

Ting, H. (2018),〈如何用豐富的訊息格式,吸引使用者的目光?〉,【Super 8 對話式商務平台】
https://medium.com›8-interactive

〈Market Research Report〉,【GRAND VIEW RESEARCH】
https://www.grandviewresearch.com

〈Customer Experience: Can Chatbots Jump The Uncanny Valley?〉
http://www.brandquarterly.com/customer-experience-can-chatbots-jump-uncanny-valley

〈什麼是Chatbot聊天機器人?它能幫你導入客流量,是行銷自動化的必備工具〉
https://blog.gogopartners.com
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