聊天機器人的興起給企業和消費者帶來可觀的變化，大多主流通訊軟體平台（如Facebook Messenger，LINE，WhatsApp等）已經發布了他們的聊天機器人應用程序編程接口（API），並繼續鼓勵企業透過聊天機器人傳遞服務、販賣商品。然而，由於性質的差異，並不是每個公司或產業都適合透過聊天機器人來傳遞服務。 過去研究較少提供給公司哪種產業或哪種使用情境較適合透過聊天機器人傳遞服務的相關策略和準則，甚至是，如何在聊天機器人中傳遞自己的服務。本研究探討聊天機器人中使用情境的重要課題。由形成不同使用情境的兩個設計要素：產業和任務複雜性。產業的定義為使用者為何種服務付費，在本研究中根據前導訪談結果，以餐飲業和交通運輸業作為產業因素。任務複雜度定義為用戶在購買決策過程中考慮的產品屬性數量，包括簡單、複雜單人、複雜雙人。本研究以執行實驗的方式，比較傳統App和Chatbot之間的使用者感知和使用者意圖，以及產業和任務複雜性的交互效應。本研究目的為找出在聊天機器人商務中導致更好的使用者感知和使用者意圖之關鍵因​​素，更重要的是，探索使用者在每個具體情境下採用chatbot而不採用傳統App的原因和要素。而根據本研究的結果希望對於未來的聊天機器人使用者體驗設計提供實質上的建議與相關設計準則。

The emergence of chatbot has brought substantial changes to both businesses and consumers. Most of dominant messenger Apps platforms, such as Facebook Messenger, LINE, WhatsApp…etc., have already released their messaging bot Application Programming Interface (API) and keep encouraging business to move merchandise via chats and bots. However, not every firms or industry is suitable for chatbot commerce due to characteristics difference in nature. There are limited studies providing strategies and guidance on which industry and what scenarios are suitable for chatbot commerce., and more, how to provides their own service in chatbot. This study explores the important topic of usage scenarios in chatbot. It investigates two design components forming different usage scenarios: industry and task complexity. Industry indicates what kind of service users pay for, and we take Food and Transportation as our industry factors from results of pilot interviews. Task complexity means the number of product attributes users consider during the decision-making process, including simple, complex & single, and complex & pair. We conduct a lab experiment to compare user perception and user intentions between traditional App and Chatbot, with interaction effect of industry and task complexity. The objective of this research is to find key factors that results in better user perception and user intentions in chatbot commerce, and more importantly, explore reasons why users adopt chatbot, rather than App, in each specific scenario. We believe that these findings can provide a useful guideline for future chatbot user experience design.

Abstract 6
1 Introduction 8
2 Theoretical Background 13
2.1 Technology Acceptance Model (TAM) 13
2.2 Conversational User Interface (CUI) 13
2.3 6 features of chatbots 14
2.4 Related literatures on chatbot 16
3 Conceptual Development and Hypotheses 18
3.1 Manipulated Variable: Traditional App vs. Chatbot 18
3.1.1 Application Adoption (Independent Variable) 18
3.2 Dependent Variable 18
3.2.1 Use Intention 18
3.2.2 User Perception 18
3.3 Moderation Variable 19
3.3.1 Industry 19
3.3.2 Task Complexity 19
4 Methodology 22
4.1 Experiment Design 22
4.1.1 Experimental environment 22
4.1.2 Participants 22
4.2 Manipulation Descriptions 23
4.2.1 Independent Variables 23
4.2.2 Dependent Variables 23
4.2.3 Moderation Variables 24
4.3 Experiment Tasks and Procedure 26
5 Data Analysis and Results 29
5.1 Overall User perception & User intentions 29
5.2 Moderation Effects (Industry) 32
5.3 Moderation Effects (task complexity) 36
5.4 Interaction Effects (Between subjects & Within subjects) 43
6 Discussion, Conclusions and Implications 47
7 Limitation and Opportunity for future researches 49
8 Reference 51
9 Appendix 53
9.1 Post-Experimental Questionnaire 53
9.2 Pre-Experimental Questionnaire 56

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