簡易檢索 / 詳目顯示

研究生: 蔡瓊儀
Tsai, Chiung-Yi
論文名稱: 語音助理感知特質對用戶信任感與使用行為之影響
The Impact of Perceived Traits of Voice Assistants on Users’ Trust and Usage Behavior
指導教授: 蔡欣怡
Tsai, Hsin-Yi
學位類別: 碩士
Master
系所名稱: 管理學院 - 電信管理研究所
Institute of Telecommunications Management
論文出版年: 2025
畢業學年度: 113
語文別: 中文
論文頁數: 123
中文關鍵詞: 智慧語音助理感知特質信任感使用行為人機互動
外文關鍵詞: Smart Voice Assistant, Perceived Traits, Trust, Usage Behavior, Human-Computer Interaction
相關次數: 點閱:21下載:17
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 智慧語音助理(Smart Voice Assistants, SVAs)是運用人工智慧技術,透過語音與人類互動的軟體,近年來,語音助理已融入人們的日常生活。因此,理解驅動使用者互動的心理機制,對於優化設計與促進技術採用,具有重要意義。
    本研究旨在探討使用者對語音助理所感知的特質,包括智慧、有趣與真誠,如何影響其信任感,進而影響後續的使用行為。有別於以往主要著重於科技接受模型的研究,本研究整合跨領域觀點,建構一個專為語音助理研究所設計的整體性模型。該模型納入信任、功利價值與享樂價值、感知風險,以及社會聯繫感等變項。研究透過線上問卷蒐集資料,並以結構方程模型(SEM)進行分析。
    研究結果顯示,使用者感知語音助理特質能顯著提升其信任感,進而增強對語音助理的功能性與情感價值認知,降低感知風險,並提升社會聯繫感。這些因素綜合促進了使用者的滿意度、使用行為與持續使用意圖。此外,本研究也發現語音助理的性別與使用者所賦予的角色認知,會顯著影響其對語音助理特質的感知與互動行為。此結果進一步凸顯「電腦即社會行為者」(CASA)範式與社會實體理論(social entity theory)在人工智慧系統設計中的重要性。
    本研究提供理論與實務上的雙重貢獻。結果指出,開發者除了強化語音助理的技術功能外,也應將社會性與人格特質納入設計,以提升使用者的信任感、滿意度與長期使用意願。

    Smart voice assistants (SVAs), software that utilizes artificial intelligence to interact with human beings through spoken language, have become increasingly important in people’s daily lives. Therefore, understanding the psychological mechanisms driving user interaction has become essential to optimizing design and promoting adoption. This study examines how users’ perceived traits of SVAs—including intelligence, fun, and sincerity—affect trust and subsequent usage behavior. Unlike prior studies that mainly focused on technology acceptance models, this research integrates interdisciplinary perspectives to construct a holistic model tailored for voice assistant studies. The model incorporates trust, utilitarian and hedonic value, perceived risk, and social connectedness. Data were collected through an online survey and analyzed using structural equation modeling (SEM).
    The results indicate that perceived traits significantly enhance user trust, which in turn strengthens perceptions of both functional and emotional value, reduces perceived risk, and increases social connectedness with SVAs. These factors collectively promote user satisfaction, actual usage, and continued usage intention. Moreover, this study finds that both the gender of an SVA and users’ role perceptions significantly influence how users perceive the assistant’s traits and interact with it. These findings further highlight the importance of the CASA (Computers Are Social Actors) paradigm and social entity theory in the design of AI systems.
    This research offers both theoretical and practical contributions. It suggests that developers, in addition to enhancing technical functionality, should also incorporate social and personality-related traits into the design of voice assistants to strengthen user trust, satisfaction, and long-term engagement.

    目錄 vi 表目錄 viii 圖目錄 ix 第一章 緒論 1 第一節 研究背景 1 第二節 研究動機與目的 3 第三節 研究流程 5 第二章 文獻回顧 6 第一節 智慧語音助理介紹 6 一、 智慧語音助理定義 6 二、 智慧語音助理服務 7 三、 熱門語音助理介紹 9 四、 目前語音助理相關研究現況 12 第二節 人機互動(Human-Computer Interaction) 15 一、 CASA典範(Computers Are Social Actors) 15 二、 SVA性別與角色在人機互動中的作用 16 三、 智慧語音助理感知特質(Perceived Traits) 17 四、 智慧語音助理作為社會實體(Social Entity) 18 第三節 AI信任感(AI Trust) 19 一、 信任感(Trust) 19 二、 人機互動中的信任(Trust in human-AI interaction) 20 第四節 社會聯繫感(Social Connectedness) 21 第五節 功利價值與享樂價值(Utilitarian & Hedonic Value) 22 一、 價值的基本概念與相關理論 22 二、 價值的衡量構面 22 第六節 感知風險(Perceived Risk) 24 第七節 滿意度(Satisfaction) 26 第八節 使用行為(Usage) 28 第九節 持續使用意圖(Continuance Intention) 30 第三章 研究方法 31 第一節 問卷調查法 31 第二節 研究架構與研究假設 32 第三節 研究假設 33 第四節 研究變項測量 34 第五節 資料分析方法 40 第六節 前測問卷結果分析 42 第四章 研究結果 45 第一節 樣本特性分析 45 一、 樣本基本資料 45 二、 語音助理用戶使用行為與人機互動分析 47 第二節 研究變項敘述性統計分析 54 第三節 信度分析 57 第四節 結構方程模型-驗證性因素分析 58 一、 因素分析(Factors Analysis) 58 二、 效度分析(Validity Analysis) 60 第五節 結構方程模型-結構模型分析 62 一、 模型解釋力與預測能力 62 二、 假設驗證與路徑分析 65 第六節 獨立樣本t檢定 69 一、 不同性別使用者在語音助理各研究變項上的差異分析 69 二、 語音助理性別與聲音設定對感知特質影響分析 70 三、 語音助理角色認知對人機互動行為的影響分析 72 第七節 ANOVA變異數分析 74 一、 用戶年齡對使用行為與持續使用意圖之影響分析 74 第五章 結果與討論 81 第一節 研究結論 81 一、 研究問題分析 81 二、 假設驗證結果 83 三、 理論意涵 89 第二節 管理實務與建議 90 一、 給語音助理業者的建議 90 第三節 研究限制與未來研究方向 93 一、 研究限制 93 二、 未來研究方向 94 參考文獻 95 英文文獻 95 國外網站資料 105 國內網站資料 106 附錄一 問卷 107

    Adedeji, A., Olawa, B. D., Hanft-Robert, S., Olonisakin, T. T., Akintunde, T. Y., Buchcik, J., & Boehnke, K. (2023). Examining the Pathways from General Trust through Social Connectedness to subjective wellbeing. Applied Research in Quality of Life, 18(5), 2619-2638. https://doi.org/10.1007/s11482-023-10201-z
    Ahmad, N., Omar, A., & Ramayah, T. (2010). Consumer lifestyles and online shopping continuance intention. Business strategy series, 11(4), 227-243. https://doi.org/10.1108/17515631011063767
    Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473
    Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: measuring hedonic and utilitarian shopping value. Journal of consumer research, 20(4), 644-656. https://doi.org/10.1086/209376
    Ball, H. L. (2019). Conducting online surveys. Journal of human lactation, 35(3), 413-417. https://doi.org/10.1177/0890334419848734
    Bauer, R. A. (1967). Consumer behavior as risk taking. Marketing: Critical perspectives on business and management, 593, 13-21.
    Bethlehem, J. (2009). Applied survey methods: A statistical perspective. John Wiley & Sons.
    Benbasat, I., & Wang, W. (2005). Trust in and adoption of online recommendation agents. Journal of the association for information systems, 6(3), 4. doi:10.17705/1jais.00065
    Blau, G., DiMino, J., DeMaria, P. A., Beverly, C., Chessler, M., & Drennan, R. (2016). Social connectedness and life satisfaction: Comparing mean levels for 2 undergraduate samples and testing for improvement based on brief counseling. Journal of American College Health, 64(8), 585–592. https://doi.org/10.1080/07448481.2016.1207645
    Burbach, L., Halbach, P., Plettenberg, N., Nakayama, J., Ziefle, M., & Valdez, A. C. (2019, July). " Hey, Siri"," Ok, Google"," Alexa". Acceptance-Relevant Factors of Virtual Voice-Assistants. In 2019 IEEE international professional communication conference (procomm) (pp. 101-111). IEEE. doi:10.1109/ProComm.2019.00025.
    Callegaro, M., Manfreda, K. L., & Vehovar, V. (2015). Web survey methodology. Sage.
    Chen, Qimei; Rodgers, Shelly (2006). Development of an Instrument to Measure Web Site Personality. Journal of Interactive Advertising, 7(1), 4–46. doi:10.1080/15252019.2006.10722124
    Chen, S. C., Yen, D. C., & Hwang, M. I. (2012). Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers in Human Behavior, 28(3), 933-941. https://doi.org/10.1016/j.chb.2011.12.014
    Choi, T. R., & Drumwright, M. E. (2021). “OK, Google, why do I use you?” Motivations, post-consumption evaluations, and perceptions of voice AI assistants. Telematics and Informatics, 62, 101628. https://doi.org/10.1016/j.tele.2021.101628
    Cleary, P. D., & McNeil, B. J. (1988). Patient satisfaction as an indicator of quality care. Inquiry, 25-36. http://www.jstor.org/stable/29771928
    Craig, S. D., Chiou, E. K., & Schroeder, N. L. (2019, November). The impact of virtual human voice on learner trust. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 63, No. 1, pp. 2272-2276). Sage CA: Los Angeles, CA: SAGE Publications. https://doi.org/10.1177/1071181319631517
    Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), 5
    Deng, L., Turner, D. E., Gehling, R., & Prince, B. (2010). User experience, satisfaction, and continual usage intention of IT. European Journal of Information Systems, 19(1), 60-75. https://doi.org/10.1057/ejis.2009.50
    Dhar, R., & Wertenbroch, K. (2000). Consumer Choice between Hedonic and Utilitarian Goods. Journal of Marketing Research, 37(1), 60-71. https://doi.org/10.1509/jmkr.37.1.60.18718
    Downing, C. E. (1999). System usage behavior as a proxy for user satisfaction: an empirical investigation. Information & Management, 35(4), 203-216. https://doi.org/10.1016/S0378-7206(98)00090-1
    Edwards, C., Edwards, A., Stoll, B., Lin, X., & Massey, N. (2019). Evaluations of an artificial intelligence instructor’s voice: Social identity theory in human-robot interactions. Computers in Human Behavior, 90, 357–362. https://doi.org/10.1016/j.chb.2018.08.027
    El-Adly, M. I. (2019). Modelling the relationship between hotel perceived value, customer satisfaction, and customer loyalty. Journal of Retailing and Consumer Services, 50, 322-332. https://doi.org/10.1016/j.jretconser.2018.07.007
    Elliott, K. M., & Shin, D. (2002). Student Satisfaction: An alternative approach to assessing this important concept. Journal of Higher Education Policy and Management, 24(2), 197–209. https://doi.org/10.1080/1360080022000013518
    Eyssel, F., Kuchenbrandt, D., Bobinger, S., De Ruiter, L., & Hegel, F. (2012, March). 'If you sound like me, you must be more human' on the interplay of robot and user features on human-robot acceptance and anthropomorphism. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction (pp. 125-126). https://doi.org/10.1145/2157689.2157717
    Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.
    Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International journal of human-computer studies, 59(4), 451-474 https://doi.org/10.1016/S1071-5819(03)00111-3
    Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
    Frieling, M., Peach, E. K., & Cording, J. (2018). The measurement of social connectedness and its relationship to wellbeing (p. 69). Kōtātā Insight.
    Gefen, D., Karahanna, E., & Straub, D. W. (2003). Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on engineering management, 50(3), 307-321. doi: 10.1109/TEM.2003.817277.
    Gefen, D., & Straub, D. W. (2004). Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services. Omega, 32(6), 407-424. https://doi.org/10.1016/j.omega.2004.01.006
    Gelderman, M. (1998). The relation between user satisfaction, usage of information systems and performance. Information & management, 34(1), 11-18. https://doi.org/10.1016/S0378-7206(98)00044-5
    George, D., & Mallery, P. (2019). IBM SPSS statistics 26 step by step: A simple guide and reference. Routledge. https://doi.org/10.4324/9780429056765
    Gulati, S., S. Sousa, and D. Lamas. (2018). “Modelling Trust in Human-Like Technologies.” In Proceedings of the 9th Indian Conference on Human Computer Interaction, 1–10. ACM. https://doi.org/10.1145/3297121.3297124
    Gulati, S., Sousa, S., & Lamas, D. (2019). Design, development and evaluation of a human-computer trust scale. Behaviour & Information Technology, 38(10), 1004-1015. https://doi.org/10.1080/0144929X.2019.1656779
    Hair, J. F., Black, B., Babin, B., Anderson, R. E., & Tatham, R. L. (1992). Multivariate Data Analysis (6th ed.). New York: Macmillan.
    Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
    Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2004). The role of social presence and moderating role of computer self efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 139-154.
    Helliwell, J. F., & Wang, S. (2010). Trust and well-being. National Bureau of Economic Research.
    Hendrickson, B., Rosen, D., & Aune, R. K. (2011). An analysis of friendship networks, social connectedness, homesickness, and satisfaction levels of international students. International journal of intercultural relations, 35(3), 281-295. https://doi.org/10.1016/j.ijintrel.2010.08.001
    Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57(3), 407-434. https://doi.org/10.1177/0018720814547570
    Hong, C., Choi, E. K., Joung, H. W., & Kim, H. S. (2023). The impact of customer perceived value on customer satisfaction and loyalty toward the food delivery robot service. Journal of Hospitality and Tourism Technology, 14(5), 908-924. https://doi.org/10.1108/JHTT-11-2022-0305
    Hoy, M. B. (2018). Alexa, Siri, Cortana, and more: an introduction to voice assistants. Medical reference services quarterly, 37(1), 81-88. https://doi.org/10.1080/02763869.2018.1404391
    Hsu, C. L., & Lin, J. C. C. (2023). Understanding the user satisfaction and loyalty of customer service chatbots. Journal of Retailing and Consumer Services, 71, 103211. https://doi.org/10.1016/j.jretconser.2022.103211
    Hu, H. H., Kandampully, J., & Juwaheer, T. D. (2009). Relationships and impacts of service quality, perceived value, customer satisfaction, and image: an empirical study. The service industries journal, 29(2), 111-125. https://doi.org/10.1080/02642060802292932
    Im, H., Sung, B., Lee, G., & Kok, K. Q. X. (2023). Let voice assistants sound like a machine: Voice and task type effects on perceived fluency, competence, and consumer attitude. Computers in Human Behavior, 145, 107791. https://doi.org/10.1016/j.chb.2023.107791
    Jayashankar, P., Nilakanta, S., Johnston, W. J., Gill, P., & Burres, R. (2018). IoT adoption in agriculture: the role of trust, perceived value and risk. Journal of Business & Industrial Marketing, 33(6), 804-821. https://doi.org/10.1108/JBIM-01-2018-0023
    Kamoonpuri, S. Z., & Sengar, A. (2023). Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail. Journal of Retailing and Consumer Services, 72, 103258. https://doi.org/10.1016/j.jretconser.2023.103258
    Kesharwani, A., & Singh Bisht, S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model. International journal of bank marketing, 30(4), 303-322. https://doi.org/10.1108/02652321211236923
    Kim, B., & Han, I. (2011). The role of utilitarian and hedonic values and their antecedents in a mobile data service environment. Expert Systems with Applications, 38(3), 2311-2318. https://doi.org/10.1016/j.eswa.2010.08.019
    Konuk, F. A. (2018). The role of store image, perceived quality, trust and perceived value in predicting consumers’ purchase intentions towards organic private label food. Journal of retailing and consumer services, 43, 304-310. https://doi.org/10.1016/j.jretconser.2018.04.011
    Kowalczuk (2018). Consumer acceptance of smart speakers: A mixed methods approach. Journal of Research in Interactive Marketing, 12(4), 418-431. https://doi.org/10.1108/JRIM-01-2018-0022
    Lau, J., Zimmerman, B., & Schaub, F. (2018). Alexa, are you listening? Privacy perceptions, concerns and privacy-seeking behaviors with smart speakers. Proceedings of the ACM on human-computer interaction, 2(CSCW), 1-31. https://doi.org/10.1145/3274371
    Lee, E. J., Nass, C., & Brave, S. (2000, April). Can computer-generated speech have gender? An experimental test of gender stereotype. In CHI'00 extended abstracts on Human factors in computing systems (pp. 289-290). https://doi.org/10.1145/633292.633461
    Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human factors, 46(1), 50-80. https://doi.org/10.1518/hfes.46.1.50_30392
    Lee, K. Y., Sheehan, L., Lee, K., & Chang, Y. (2021). The continuation and recommendation intention of artificial intelligence-based voice assistant systems (AIVAS): the influence of personal traits. Internet Research, 31(5), 1899-1939. https://doi.org/10.1108/INTR-06-2020-0327
    Lee, R. M., & Robbins, S. B. (1995). Measuring belongingness: The social connectedness and the social assurance scales. Journal of counseling psychology, 42(2), 232. https://doi.org/10.1037/0022-0167.42.2.232
    Lee, S., & Choi, J. (2017). Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies, 103, 95-105. https://doi.org/10.1016/j.ijhcs.2017.02.005
    Lee, S. K., Kavya, P., & Lasser, S. C. (2021). Social interactions and relationships with an intelligent virtual agent. International Journal of Human-Computer Studies, 150, 102608. https://doi.org/10.1016/j.ijhcs.2021.102608
    Lien, C. H., Cao, Y., & Zhou, X. (2017). Service quality, satisfaction, stickiness, and usage intentions: An exploratory evaluation in the context of WeChat services. Computers in human behavior, 68, 403-410. https://doi.org/10.1016/j.chb.2016.11.061
    Liu, Y., Li, Y., Tao, L., & Wang, Y. (2008). Relationship stability, trust and relational risk in marketing channels: Evidence from China. Industrial Marketing Management, 37(4), 432-446. https://doi.org/10.1016/j.indmarman.2007.04.001
    Luger, E., & Sellen, A. (2016, May). " Like Having a Really Bad PA" The Gulf between User Expectation and Experience of Conversational Agents. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 5286-5297). https://doi.org/10.1145/2858036.2858288
    Manikonda, L., Deotale, A., & Kambhampati, S. (2018, December). What's up with privacy? User preferences and privacy concerns in intelligent personal assistants. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 229-235). https://doi.org/10.1145/3278721.3278773
    Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161, 113838. https://doi.org/10.1016/j.jbusres.2023.113838
    Maroufkhani, P., Asadi, S., Ghobakhloo, M., Jannesari, M. T., & Ismail, W. K. W. (2022). How do interactive voice assistants build brands' loyalty? Technological Forecasting and Social Change, 183, 121870. https://doi.org/10.1016/j.techfore.2022.121870
    McKnight, D. H., & Chervany, N. L. (2001). What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology. International journal of electronic commerce, 6(2), 35-59. https://doi.org/10.1080/10864415.2001.11044235
    Mickes, L., Walker, D. E., Parris, J. L., Mankoff, R., & Christenfeld, N. J. (2012). Who’s funny: Gender stereotypes, humor production, and memory bias. Psychonomic Bulletin & Review, 19, 108-112. https://doi.org/10.3758/s13423-011-0161-2
    Mironova, A. A. (2015). Trust, social capital, and subjective individual well-being. Sociological Research, 54(2), 121-133. https://doi.org/10.1080/10610154.2015.1082391
    Mishra, A., Shukla, A., & Sharma, S. K. (2022). Psychological determinants of users’ adoption and word-of-mouth recommendations of smart voice assistants. International Journal of Information Management, 67, 102413. https://doi.org/10.1016/j.ijinfomgt.2021.102413
    Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of marketing, 58(3), 20-38. https://doi.org/10.1177/002224299405800302
    Moussawi, S., Koufaris, M., & Benbunan-Fich, R. (2022). The role of user perceptions of intelligence, anthropomorphism, and self-extension on continuance of use of personal intelligent agents. European Journal of Information Systems, 32(3), 601–622. https://doi.org/10.1080/0960085X.2021.2018365
    Nass, C., Fogg, B. J., & Moon, Y. (1996). Can computers be teammates? International Journal of Human-Computer Studies, 45(6), 669-678. https://doi.org/10.1006/ijhc.1996.0073
    Nass, C., Steuer, J., & Tauber, E. R. (1994, April). Computers are social actors. In Proceedings of the SIGCHI conference human factors in computing systems (pp. 72–78).
    Nomura, T. (2017). Robots and gender. Gender and the Genome, 1(1), 18-26. https://journals.sagepub.com/doi/10.1089/gg.2016.29002.nom
    Oliver, R. L. (1997). Satisfaction: A Behavioral Perspective on the Consumer. https://doi.org/10.4324/9781315700892
    Omrani, N., Rivieccio, G., Fiore, U., Schiavone, F., & Agreda, S. G. (2022). To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts. Technological Forecasting and Social Change, 181, 121763. https://doi.org/10.1016/j.techfore.2022.121763
    Peter, J. P., & Ryan, M. J. (1976). An investigation of perceived risk at the brand level. Journal of marketing research, 13(2), 184-188. https://doi.org/10.1177/002224377601300210
    Ponte, E. B., Carvajal-Trujillo, E., & Escobar-Rodríguez, T. (2015). Influence of trust and perceived value on the intention to purchase travel online: Integrating the effects of assurance on trust antecedents. Tourism management, 47, 286-302. https://doi.org/10.1016/j.tourman.2014.10.009
    Poushneh, A. (2021). Humanizing voice assistant: The impact of voice assistant personality on consumers’ attitudes and behaviors. Journal of Retailing and Consumer Services, 58, 102283. https://doi.org/10.1016/j.jretconser.2020.102283
    Ratan, R. R., & Tsai, H. Y. S. (2014). Dude, where’s my avacar? A mixed-method examination of communication in the driving context. Pervasive and Mobile Computing, 14, 112-128. https://doi.org/10.1016/j.pmcj.2014.05.011
    Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people. Cambridge, UK, 10(10), 19-36.
    Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696. https://doi.org/10.1016/j.ijhcs.2006.01.003
    Schaumburg, H. (2001). Computers as tools or as social actors? —the users’ perspective on anthropomorphic agents. International Journal of Cooperative Information Systems, 10, 217–234. https://doi.org/10.1142/S0218843001000321
    Siegel, M., Breazeal, C., & Norton, M. I. (2009, October). Persuasive robotics: The influence of robot gender on human behavior. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2563-2568). IEEE. doi: 10.1109/IROS.2009.5354116.
    Sousa, S., Lamas, D., & Dias, P. (2014). A model for human-computer trust: contributions towards leveraging user engagement. In Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences: First International Conference, LCT 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014, Proceedings, Part I 1 (pp. 128-137). Springer International Publishing. https://doi.org/10.1007/978-3-319-07482-5_13
    Su, J., Wang, Y., Liu, H., Zhang, Z., Wang, Z., & Li, Z. (2025). Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model. Scientific Reports, 15(1), 1-19. https://doi.org/10.1038/s41598-025-01897-0
    Tzavlopoulos, Ι., Gotzamani, K., Andronikidis, A., & Vassiliadis, C. (2019). Determining the impact of e-commerce quality on customers’ perceived risk, satisfaction, value and loyalty. International Journal of Quality and Service Sciences, 11(4), 576-587. https://doi.org/10.1108/IJQSS-03-2019-0047
    Van Pinxteren, M. M., Wetzels, R. W., Rüger, J., Pluymaekers, M., & Wetzels, M. (2019). Trust in humanoid robots: implications for services marketing. Journal of Services Marketing, 33(4), 507-518. https://doi.org/10.1108/JSM-01-2018-0045
    Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS quarterly, 71-102. https://doi.org/10.2307/3250959
    Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540
    Wang, J., Molina, M. D., & Sundar, S. S. (2020). When expert recommendation contradicts peer opinion: Relative social influence of valence, group identity and artificial intelligence. Computers in Human Behavior, 107, 106278. https://doi.org/10.1016/j.chb.2020.106278
    Waytz, A., Heafner, J., & Epley, N. (2014). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of experimental social psychology, 52, 113-117. https://doi.org/10.1016/j.jesp.2014.01.005
    Whang, C. (2018). Voice Shopping: The effect of the consumer-voice assistant parasocial relationship on the consumer's perception and decision making (Doctoral dissertation, University of Minnesota). https://hdl.handle.net/11299/201041
    Wu, J., & Du, H. (2012). Toward a better understanding of behavioral intention and system usage constructs. European Journal of Information Systems, 21(6), 680-698.https://doi.org/10.1057/ejis.2012.15
    Xiong, Y., Yu, Q., & Liu, N. (2024, June). Influence of Voice Characteristics and Language Style of Intelligent Assistant on User Trust and Intention to Use. In International Conference on Human-Computer Interaction (pp. 375-387). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-60913-8_26
    Yagoda, R. E., & Gillan, D. J. (2012). You want me to trust a ROBOT? The development of a human–robot interaction trust scale. International Journal of Social Robotics, 4, 235-248. https://doi.org/10.1007/s12369-012-0144-0
    Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. Journal of marketing, 52(3), 2-22. https://doi.org/10.1177/002224298805200302
    Zhang, A., & Rau, P. L. P. (2023). Tools or peers? Impacts of anthropomorphism level and social role on emotional attachment and disclosure tendency towards intelligent agents. Computers in Human Behavior, 138, 107415. https://doi.org/10.1016/j.chb.2022.107415
    Zhao, L., Lu, Y., Zhang, L., & Chau, P. Y. (2012). Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decision support systems, 52(3), 645-656. https://doi.org/10.1016/j.dss.2011.10.022
    Zimet, Gregory D.; Dahlem, Nancy W.; Zimet, Sara G.; Farley, Gordon K. (1988). The Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment, 52(1), 30–41. doi:10.1207/s15327752jpa5201_2
    Apple (2011, October 5). Siri: Your wish is its command. https://web.archive.org/web/20111031080134/http://www.apple.com/iphone/features/siri.html
    Bateman, R. (2025, February 16). Voice assistants and privacy issues. TermsFeed. https://www.termsfeed.com/blog/voice-assistants-privacy-issues/
    Bensinger, G. (2025, February 6). Amazon's AI revamp of Alexa assistant nears unveiling. Reuters. https://www.reuters.com/technology/amazon-set-release-long-delayed-alexa-generative-ai-revamp-2025-02-05/
    Businesswire (2020, April 28). Juniper Research: Number of Voice Assistant Devices in Use to Overtake World Population by 2024, Reaching 8.4bn, Led by Smartphones.https://www.businesswire.com/news/home/20200427005609/en/Juniper-Research-Number-Voice-Assistant-Devices-Overtake
    Coughlin, J. (2018, September 23). Alexa, will you be my friend? When artificial intelligence becomes something more. Forbes. https://www.forbes.com/sites/josephcoughlin/2018/09/23/alexa-will-you-be-my-friend-when-artificial-intelligence-becomes-something-more/
    Counterpoint. (2025, March 3). Global Smartphone Market Share: Quarterly. https://www.counterpointresearch.com/insights/global-smartphone-share/
    Google Assistant. (2020, January 7). A More Helpful Google Assistant for Your Every Day. https://blog.google/products/assistant/ces-2020-google-assistant/
    Hay, T. (2010, February 5). Siri Inc. launches ‘Do Engine’application for iPhone. Dow Jones News. https://www.advfn.com/stock-market/NASDAQ/NUAN/stock-news/41425250/siri-inc-launches-do-engine-application-for-iphon
    Kent State University Libraries. (2025, March 31). SPSS Tutorials: Independent samples t-test. https://libguides.library.kent.edu/spss/independentttest
    Shewale, R. (2024, January 11). 67 voice search statistics for 2024. Demandsage. https://www.demandsage.com/voice-search-statistics/
    TCL Guides. (2023, July 29). Google Assistant 101: How To Make It Enjoyable and Useful For You. https://www.tcl.com/global/en/blog/playbooks/google-assistant-101-how-to-make-it-enjoyable-and-useful-for-you
    Thorp, C. (2018, April 16). I tried to make Alexa my best friend. BBC. https://www.bbc.com/bbcthree/article/5a0449e7-1b2b-46f6-bacf-0cdc5f196383
    Verified Market Research (2024, December). Global Voice Assistant Market Size By Product (Chatbot, Smart Speaker), By Technology (Text To Speech, Text-based), By Application (Healthcare, Education), By Geographic Scope And Forecast. https://www.verifiedmarketresearch.com/product/voice-assistant-market/
    Blakemore, E. (2024, July 29). 為什麼虛擬助理清一色都是女性的聲音呢? 國家地理. https://www.natgeomedia.com/science/article/content-17418.html
    Lee, E., & Chung, K. (2022, May 10). Amazon智慧音響Echo正式登台!解鎖Alexa台灣在地新功能、花千元變身智能生活小天才。Bazaar. https://www.harpersbazaar.com/tw/life/3c/g40101077/amazon-echo-alexa-taiwan-2022/
    Mercier, M. (2025, January 16). 什麼是語音助手?Botpress. https://botpress.com/tw/blog/voice-assistant
    財團法人台灣網路資訊中心(2024年10月)。2024年台灣網路報告:台灣民眾 AI 使用經驗:數位語音助理。https://report.twnic.tw/2024/assets/download/TWNIC_TaiwanInternetReport_2024_CH_all.pdf
    李立心(2024年06月14日)。嘿 Siri!請幫 Apple 擺脫 AI 困境。天下雜誌。 https://www.cw.com.tw/article/5130804
    遠傳電信(2024年09月22日)。IPhone 16的Apple Intelligence 是什麼?有什麼亮點?哪些設備支援蘋果AI?https://www.fetnet.net/content/cbu/tw/lifecircle/tech/2024/06/intelligence.html
    優分析產業數據中心(2025年02月06日)。生成式AI為Alexa注入新生機,亞馬遜能否重奪智慧助理市場霸主?https://uanalyze.com.tw/articles/7685910283
    劉祥航(2024年09月25日)。OpenAI新推出ChatGPT高級語音模式 能說中文等逾50種語言。鉅亨網。https://news.cnyes.com/news/id/5723877
    陳寬裕(2018)結構方程模型分析實務:SPSS與SmartPLS的運用

    下載圖示 校內:立即公開
    校外:立即公開
    QR CODE