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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 楊銘欽(Ming-Chin Yang) | |
| dc.contributor.author | CHIN-YUAN HUANG | en |
| dc.contributor.author | 黃慶原 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:22:38Z | - |
| dc.date.available | 2030-03-17 | |
| dc.date.copyright | 2020-08-26 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-03-17 | |
| dc.identifier.citation | 1. Kalvin, B. and S. Stefano, The State of Mobile Internet Connectivity 2019. 2019, GSMA Intelligence: London.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63905 | - |
| dc.description.abstract | 對基於行動醫療應用程式(mHealth APP)的介入研究已引起學者的極大關注，然而過去大多數的研究都集中在以研究為主導的應用程式(APP)，以及其使用於過重與肥胖成年人之有效性，少有研究針對一般消費者對採用mHealth APP進行體重管理之態度進行探討。本研究以延伸整合型科技接受理論(UTAUT2)為理論基礎，提出一個整合個人創新性(personal innovativeness)和網路外部性(network externality)之創新綜合模型以探討最顯著影響消費者使用人工智慧(AI)驅動的健康聊天機器人(health chatbot)進行減重和健康管理的因素。本研究所開發在Line™ APP平台上運行的健康聊天機器人，能以近乎即時的方式促進準確分析以及健康諮詢。
本研究制定了一份自填式問卷，並於2019年11月23日至12月30日期間對20歲以上的台灣成年人進行了線上調查，其後使用結構方程模型(structural equation modeling)對研究假說進行檢驗。針對415份的有效問卷進行分析，結果顯示本研究模型可解釋87.1%的行為意圖差異；習慣是預測使用者意圖方面表現最強的自變數，其次是績效預期、社會影響力、網路外部性以及個人創新性。社會影響力與個人創新性是透過績效預期來影響使用者意圖。多群組分析(multi-group analysis)結果顯示使用者的性別與APP使用經驗對研究模型中某些假設的關係具有調節作用(moderating influence)，然而使用者的教育程度、慢性病、身體質量指數(BMI)與年齡並未具有類似的調節作用。 本研究以實證資料驗證UTAUT2模型中影響使用者採用健康聊天機器人以進行減重與健康管理的主要因素。這些理論架構和實證資料有助於尋求將UTAUT2模型的應用性(applicability)擴展到健康聊天機器人的研究人員，以及尋求促進此類聊天機器人的採用的業者。未來研究者可擴展本模型以探討行為意圖對實際使用行為的影響。 關鍵字：行動醫療、UTAUT2模型、人工智慧、網路外部性、個人創新性 | zh_TW |
| dc.description.abstract | Research into interventions based on mobile health (mHealth) APPs has attracted considerable attention among researchers; however, most previous studies have focused on research-led APPs and their effectiveness when applied to overweight/obese adults. There remains a paucity of research on the attitudes of typical consumers toward the adoption of mHealth APPs for weight management. This study adopted the tenets of the extended unified theory of acceptance and use of technology (UTAUT2) as the theoretical foundation in developing an innovative and comprehensive model. This model integrates personal innovativeness and network externality in seeking to identify the factors with the most pronounced effect on one’s intention to use an artificial intelligence (AI)-powered health chatbot for weight loss and health management. The health chatbot that runs on Line™ APP platform features AI technology to facilitate accurate analysis/health consultations in near real-time.
This study developed a self-administered questionnaire and conducted an online survey for Taiwanese participants aged ≥20 years from 23 November to 30 December 2019. Totally, this study received 415 valid responses. Hypotheses were tested using structural equation modeling. The proposed research model explained 87.1% of variance in behavioral intention. Habit was the independent variable with the strongest performance in predicting user intention, followed by performance expectancy, social influence, network externality, and personal innovativeness. Social influence and personal innovativeness influence user intention through performance expectancy. In multi-group analysis, gender and APP usage experience were shown to exert a moderating influence on some of the relationships hypothesized in the model, whereas education, chronic conditions, BMI, and age did not exert the similar moderating influence. The empirically validated model in this study provides insights into the primary determinants of user intention toward the adoption of AI-powered health chatbot for weight loss and health management. The theoretical and practical implications are relevant to researchers seeking to extend the applicability of the UTAUT2 model to health chatbot as well as APP providers seeking to promote the adoption of such chatbot. In the future, researchers could extend the model to assess the effects of behavioral intention on actual use behavior. Keywords – mobile health, UTAUT2 model, artificial intelligence, network externality, personal innovativeness | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:22:38Z (GMT). No. of bitstreams: 1 ntu-109-D03848009-1.pdf: 4399718 bytes, checksum: 94c34150d2e8ce9d2f6e7eba198fa46e (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 國立臺灣大學博士學位論文口試委員會審定書 i
誌謝 ii 中文摘要 iv Abstract vi Figure Contents 4 Table Contents 6 List of abbreviations 7 Chapter 1 Introduction 10 1.1 Research Background 10 1.2 Research Motives 11 1.3 Research Objectives 12 Chapter 2 Literature Review 14 2.1 mHealth Interventions for Weight Management 14 2.2 UTAUT2 Model 16 2.3 Personal Innovativeness 18 2.4 Network Externality 20 2.5 Summary of Literature Review 22 Chapter 3 Methodology 24 3.1 Theoretical Framework and System Architecture of SWITCHes 24 3.1.1 Health Chatbot 29 3.1.2 Server Application 31 3.2 Research Model and Hypotheses Development 33 3.3 Questionnaire Development 40 3.4 Survey Method 44 3.5 Data Processing and Statistical Methods 45 Chapter 4 Results 47 4.1 Sample Profile and Descriptive Analysis 47 4.2 Structural Equation Modeling 49 4.2.1 Measurement Model 49 4.2.2 Structural Model 52 4.3 Multi-Group Analysis 54 4.3.1 Gender 54 4.3.2 Experience 56 4.3.3 Education 58 4.3.4 Chronic Conditions 60 4.3.5 BMI 62 4.3.6 Age 64 Chapter 5 Discussion 67 5.1 Research Findings 67 5.1.1 Determinants of Adoption of Health Chatbot (Main Effect) 68 5.1.2 Role of Gender Difference (Moderating Effect) 70 5.1.3 Role of Experience Difference (Moderating Effect) 71 5.1.4 Results of Hypotheses Testing 72 5.2 Theoretical Contributions 73 5.3 Practical Implications 73 5.4 Limitations 74 Chapter 6 Conclusions 75 6.1 Conclusions 75 6.2 Suggestions 76 6.2.1 Suggestions for APP Developers 76 6.2.2 Suggestions for APP Providers 76 6.2.3 Suggestions for Future Studies 77 Appendix A: Taiwan Patent Granted for SWITCHes 91 Appendix B: Survey Questionnaire in Chinese 95 Appendix C: National Taiwan University Hospital IRB Approval Letter 102 Appendix D: Measurement Model in SEM (Using AMOS) 105 Appendix E: Structural Model in SEM (Using AMOS) 106 Appendix F: Summary of Publications 107 | |
| dc.language.iso | en | |
| dc.subject | UTAUT2模型 | zh_TW |
| dc.subject | 行動醫療 | zh_TW |
| dc.subject | 個人創新性 | zh_TW |
| dc.subject | 網路外部性 | zh_TW |
| dc.subject | 人工智慧 | zh_TW |
| dc.subject | personal innovativeness | en |
| dc.subject | mobile health | en |
| dc.subject | artificial intelligence | en |
| dc.subject | network externality | en |
| dc.subject | UTAUT2 model | en |
| dc.title | 台灣消費者採用AI健康聊天機器人之意圖建模：實證觀點 | zh_TW |
| dc.title | Modeling Consumer Adoption Intention of an AI-Powered Health Chatbot in Taiwan: An Empirical Perspective | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 郭冠良(Kuan-Liang Kuo),陳富莉(Fu-Li Chen),張瑞益(Ray-I Chang),鍾國彪(Kuo-Piao Chung) | |
| dc.subject.keyword | 行動醫療,UTAUT2模型,人工智慧,網路外部性,個人創新性, | zh_TW |
| dc.subject.keyword | mobile health,UTAUT2 model,artificial intelligence,network externality,personal innovativeness, | en |
| dc.relation.page | 107 | |
| dc.identifier.doi | 10.6342/NTU202000694 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-03-18 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 健康政策與管理研究所 | zh_TW |
| Appears in Collections: | 健康政策與管理研究所 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-109-1.pdf Restricted Access | 4.3 MB | Adobe PDF |
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