隨著人工智慧的潮流，語音助理(voice assistant)在人機互動領域逐漸受到重視，其便利性、直覺性與自然的溝通模式，讓近年來語音助理的使用率逐漸攀升，此創新的互動模式將會帶來人類與機器溝通上的變革，未來語音助理的應用更可能拓展至智慧家庭、語音商務或車載語音系統上，逐漸融入大眾的日常生活當中。本研究針對語音助理之接受因素進行研究與探討，以整合性科技接受模型為基礎(Wixom & Todd, 2005)，加入多種因素進行權衡包括語音助理的「品質」、「操作」、「個性」、「隱私」與「有趣性」。因此，我們針對語音助理的使用者進行問卷調查，有效樣本為214份，並運用 PLS-SEM 結構方程模式進行檢驗。研究結果顯示「品質」對滿意度有正向影響，「操作」、「個性」與「有趣性」均對使用態度有正向影響。另外，我們發現在「品質」當中，資訊品質的影響程度是大於系統品質的；在「個性」當中，語音助理的「人格特質」是影響使用者信任的重要因素，且影響程度最高的人格特質為務實的、樂於助人的與邏輯的。本研究提供一個較為完整的架構探討語音助理之使用意圖，並且適合用於解釋語音助理相關服務之應用，且能供語音助理開發商作為設計依據，以改善使用者體驗創造出良好的互動環境。

With the trend of artificial intelligence, voice assistant has been increasingly valued in the field of human-computer interaction. Due to its convenient, intuitive, and natural communication capacities, the use of voice assistant has gradually gained wider usage in recent years. The mode of innovative interactions will bring about changes in communication between humans and machines. In the future, voice assistant will more than likely be used in smart homes, voice commerce, or in-vehicle voice systems. It will gradually be integrated into the daily lives of the general public. This research studies and explores the acceptance factors of voice assistant. Using the integrated model of user satisfaction and technology acceptance (Wixom & Todd, 2005), a number of factors are added to the trade-offs including “quality,” “operation,” “personality,” “privacy,” and “interestingness.” As a result, we conducted a survey on the users of voice assistant. The valid samples were 214, and the PLS-SEM structural equation model was used for testing. Research results show that “quality” has a positive effect on user satisfaction, and “operation,” “personality,” and “interestingness” have a positive effect on the attitude of use. In addition, we found that in “quality,” the quality of information is more influential than the quality of the system. Among the “personality,” the “personality trait” of the voice assistant is an important factor that affects the users’ trust. And the most influential personality traits are practical, helpful, and logical. This study provides a more complete framework for exploring the intent of use of voice assistant and is suitable for interpreting applications related to voice assistant services. It can be used by voice assistant developers as a design basis to improve the user experience and create a good interactive environment.

英文文獻
Aaker, J. L. (1997). Dimensions of Brand Personality. SSRN Electronic Journal.
Ahn, T., Ryu, S., & Han, I. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. Information & management, 44(3), 263-275.
Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management science, 29(5), 530-545.
Bauer, R. A. (1960). Consumer behavior as risk taking. Chicago, IL, 384-398.
Burbach, L., Halbach, P., Plettenberg, N., Nakayama, J., Ziefle, M., & Valdez, A. C. (2019). "Hey, Siri", "Ok, Google", "Alexa". Acceptance-Relevant Factors of Virtual Voice-Assistants. 2019 IEEE International Professional Communication Conference (ProComm).
Cardozo, R. N. (1965). An experimental study of customer effort, expectation, and satisfaction. Journal of marketing research, 2(3), 244-249.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical strategies for small sample research, 1(1), 307-341.
Chung, H., Iorga, M., Voas, J., & Lee, S. (2017). “Alexa, Can I Trust You?” Computer, 50(9), 100–104.
Cote, J. A., & Buckley, M. R. (1988). Measurement error and theory testing in consumer research: An illustration of the importance of construct validation. Journal of Consumer Research, 14(4), 579-582.
Crampton, S. M., & Wagner III, J. A. (1994). Percept-percept inflation in microorganizational research: An investigation of prevalence and effect. Journal of applied psychology, 79(1), 67.
Dasgupta, R. (2018). Voice User Interface Design Moving from Gui to Mixed Modal Interaction. New York: Apress.
Davis, F. D. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111-1132.
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application (Vol. 1). Cambridge university press.
Deutsch, M. (1962). Cooperation and trust: Some theoretical notes. In M. R. Jones (Ed.), Nebraska Symposium on Motivation, 1962 (p. 275–320). Univer. Nebraska Press.
Dickinger, A., Arami, M., & Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17(1), 4-11.
Dirks, K. T. (1999). The effects of interpersonal trust on work group performance. Journal of Applied Psychology, 84(3), 445–455.
Dwyer, C., Hiltz, S., & Passerini, K. (2007). Trust and privacy concern within social networking sites: A comparison of Facebook and MySpace. AMCIS 2007 proceedings, 339.
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.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 382-388.
Funder, David Charles. The Personality Puzzle. W. W. Norton & Company, 2019.
Garcia, D. M. P., Lopez, S. S., & Donis, H. (2018). Everybody is talking about Virtual Assistants, but how are people really using them?
Garcia, D. M. P., Lopez, S. S., & Donis, H. (2018). Voice Activated Virtual Assistants Personality Perceptions and Desires: Comparing Personality Evaluation Frameworks.
Garcia, M. P., & Lopez, S. S. (2018). Building Trust Between Users and Telecommunications Data Driven Virtual Assistants. IFIP Advances in Information and Communication Technology Artificial Intelligence Applications and Innovations, 628–637.
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7).
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.
Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 80, 197–206.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems, 116(10), 2-20
Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using partial least squares path modeling in advertising research: basic concepts and recent issues. In Handbook of research on international advertising. Edward Elgar Publishing.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
Hui, B. S., & Wold, H. (1982). Consistency and consistency at large of partial least squares estimates. Systems under indirect observation, part II, 119-130.
Ives, B., Olson, M. H., & Baroudi, J. J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26(10), 785-793.
Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk. ACR Special Volumes.
Joreskog, K. G. (1982). The ML and PLS techniques for modeling with latent variables: historical and comparative aspects. Systems under indirect observation, part I, 263-270.
Kinsella, B. & Mutchler, A. (2018). Voice Assistant Consumer Adoption Report 2018. Washington, DC: Voicebot.ai.
Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic commerce research and applications, 8(3), 130-141.
Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & management, 42(8), 1095-1104.
Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002). AIMQ: a methodology for information quality assessment. Information & management, 40(2), 133-146.
Lewis, J. R., & Hardzinski, M. L. (2015). Investigating the psychometric properties of the Speech User Interface Service Quality questionnaire. International Journal of Speech Technology, 18(3), 479-487.
McKay, E. (2017) Delightful user experience: How to design UIs that are polite and forgiving, and have a great personality. Conference Interaction South America, Floripa, Brazil, November 9-11 2017.
Metzger, M. J. (2004). Privacy, trust, and disclosure: Exploring barriers to electronic commerce. Journal of computer-mediated communication, 9(4), JCMC942.
Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and system quality: an empirical examination within the context of data warehousing. Journal of management information systems, 21(4), 199-235.
Nguyen, Q. N., Ta, A., & Prybutok, V. (2018). An Integrated Model of Voice-User Interface Continuance Intention: The Gender Effect. International Journal of Human–Computer Interaction, 35(15), 1362–1377.
Normark, C. J. (2015). Design and Evaluation of a Touch-Based Personalizable In-Vehicle User Interface. International Journal of Human-Computer Interaction, 31(11), 731–745.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychological theory.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of management, 12(4), 531-544.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
Reilly, W. (1996) Believable Social and Emotional Agents (PhD Thesis). School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of research in Marketing, 26(4), 332-344.
Saarinen, T. (1996). An expanded instrument for evaluating information system success. Information & management, 31(2), 103-118.
Scherer, K. R. (1978). Personality inference from voice quality: The loud voice of extroversion. European Journal of Social Psychology, 8(4), 467–487.
Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144–176.
The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. (2003). Journal of Management Information Systems, 19(4), 9–30.
Tulshan, A. S., & Dhage, S. N. (2019). Survey on Virtual Assistant: Google Assistant, Siri, Cortana, Alexa. Communications in Computer and Information Science Advances in Signal Processing and Intelligent Recognition Systems, 190–201.
Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS quarterly, 695-704.
Vance, A., Elie-Dit-Cosaque, C., & Straub, D. W. (2008). Examining Trust in Information Technology Artifacts: The Effects of System Quality and Culture. Journal of Management Information Systems, 24(4), 73–100.
Wang, J., & Wang, X. (2012). Structural equation modeling: Methods and applications. Wiley.
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 12(4), 5-33.
Wixom, B. H., & Todd, P. A. (2005). A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, 16(1), 85–102.
Xu, H., & Gupta, S. (2009). The effects of privacy concerns and personal innovativeness on potential and experienced customers’ adoption of location-based services. Electronic Markets, 19(2-3), 137-149.
Xu, J., Benbasat, I., & Cenfetelli, R. T. (2013). Integrating service quality with system and information quality: an empirical test in the e-service context. MIS quarterly, 777-794.
Yang, X., Aurisicchio, M., & Baxter, W. (2019). Understanding Affective Experiences with Conversational Agents. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI 19.

中文文獻
湯家偉(2016)。結構方程模式：偏最小平方法PLS-SEM。台北市：高等教育。

網路文獻
Griffin, A. (2017). Google is quietly recording everything you say. Here's how to hear it. Retrieved March 3, 2020, from https://www.independent.co.uk/life-style/gadgets-and-tech/news/google-voice-search-records-stores-conversation-people-have-around-their-phones-but-files-can-be-a7059376.html
Johnson, K. (2017). People, not tech companies, should pick their AI assistant's personality. Retrieved March 3, 2020, from https://venturebeat.com/2017/10/02/tech-giants-should-let-people-pick-their-ai-assistants-personality
Olmstead, K. (2017). Nearly half of Americans use digital voice assis- tants, mostly on their smartphones. Retrieved March 3, 2020, from https://www.pewresearch.org/fact-tank/2017/12/12/nearly-half-of-americans-use-digital-voice-assistants-mostly-on-their-smartphones/
Siegel, B., & Siegel, B. (2016). How voice recognition will affect privacy in the Internet of Things. Retrieved March 3, 2020, from https://www.csoonline.com/article/3140633/how-voice-recognition-will-affect-privacy-and-the-internet-of-things.html
科技新報 (2019)。微軟為什麼要「殺死」Cortana？。2020年3月20日，取自https://technews.tw/2019/12/27/why-microsoft-kill-cortana/。