由於現今網路與智慧行動裝置的普及與發展迅速，現代人的觀影習慣逐漸從舊有的有線電視或是無線電視轉移到網路影音平台上，而在此浪潮之下，OTT影音服務(Over-The-Top Media Services)也隨之蓬勃發展，而在OTT影音服務的主要三種不同的收費模式：SVOD (Subscription video on demand) 訂閱隨選視訊、AVOD (Advertisement video on demand) 廣告隨選視訊以及TVOD (Transactional video on demand) 交易型隨選視訊，其中又以SVOD的成長幅度最為顯著。目前在台灣市場中影音串流服務相對於國外還未成熟，因此希望藉由本研究窺探更多消費者使用SVOD平台之行為。
本研究根據Viswanath Venkatesh et al. (2012) 所建立之延伸性整合科技接受模型UTAUT2，為主要的理論基礎，同時參考過去多個與科技接受相關的理論文獻，建構出以SVOD平台為研究對象的模型，而其中自變數包含：付出預期、社會影響、價格價值、習慣、產品多樣化，以此分析消費者在使用SVOD平台的使用意願或是行為意圖，同時根據本研究之目的加入產品多樣化作為調節變數對整體進行討論。
本研究希望透過量化的實證分析，探討消費者使用SVOD平台的心理因素，以了解其持續使用之行為意圖，本研究透過網路進行問卷的發放，總共蒐集了368份問卷，而有效問卷共355份，題項內容藉由結構方程進行信度與效度分析後，再由路徑分析和迴歸分析得出研究結果。而從研究中發現，UTAUT2對於消費者使用SVOD平台的行為意圖有很高的解釋能力。
另外，除了社會影響之外，付出預期、價格價值、習慣以及產品多樣化皆對消費者持續使用SVOD平台的行為意圖具有正向顯著之影響。同時，根據本研究之結果，在所有自變數中，產品多樣化作為調節變數只能夠對價格價值產生調節影響 。

Due to the rapid popularization and development of the Internet and smart mobile devices, people’s viewing habits have gradually shifted from the old cable TV or wireless TV to the online audio and video platform nowadays. Under this wave, OTT (Over-The-Top) media services are also booming. Among them, SVOD (Subscription video on demand) has the most significant growth. Therefore, this study hopes to explore the behavior of customers using SVOD platforms.
This study using the Extend Unified Theory of Acceptance and Use of Technology (UTAUT2) as the main theoretical basis. As the same time, this study referring to numbers of theoretical literatures which related to technology acceptance in the past and constructed a model with the SVOD platform as the research target.
This study distributed questionnaires through the Internet and collected 368 questionnaires in total, of which 355 were valid. After the reliability and validity of the items were analyzed by SEM, the results were obtained by path analysis and regression analysis. From the research, it is found that the UTAUT2 has a high ability to explain customers’ behavioral intention of continuous use the SVOD platform.
In addition, the independent variables effort expectancy, price value, habits and product variety all have a positive and significant impact on costumers’ behavioral intention of continuous use the SVOD platform, except social influence. Meanwhile, according to the results of this study, among all the independent variables, product variety as a moderator can only have a moderating effect on price value.

一、英文文獻
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control (pp. 11-39): Springer.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of experimental social psychology, 22(5), 453-474.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23-28).
Bhawan, M. D., & Marg, J. L. N. (2015). Regulatory Framework for Over-the-top (OTT) services. Telecom Regulatory Authority of India, 1-118.
Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 399-426.
Celik, H. (2016). Customer online shopping anxiety within the Unified Theory of Acceptance and Use Technology (UTAUT) framework. Asia Pacific journal of Marketing and logistics.
Chan, K. Y., Gong, M., Xu, Y., & Thong, J. (2008). Examining user acceptance of SMS: An empirical study in China and Hong Kong. PACIS 2008 Proceedings, 294.
Charng, H.-W., Piliavin, J. A., & Callero, P. L. (1988). Role identity and reasoned action in the prediction of repeated behavior. Social psychology quarterly, 303-317.
Chen, Y.-N. K. (2019). Competitions between OTT TV platforms and traditional television in Taiwan: A Niche analysis. Telecommunications Policy, 43(9), 101793.
Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. In (pp. vii-xvi): JSTOR.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
Cronbach, L. J. (1947). Test “reliability”: Its meaning and determination. Psychometrika, 12(1), 1-16.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Mis Quarterly, 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
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.
Decharms, R., & Carpenter, V. (1968). Measuring motivation in culturally disadvantaged school children. The Journal of Experimental Education, 37(1), 31-41.
Dodds, W. B., & Monroe, K. B. (1985). The effect of brand and price information on subjective product evaluations. ACR North American Advances.
Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of marketing research, 28(3), 307-319.
Fishbein, M. Ajzen, I.(1985) From intentions to actions: A theory of planned behavior. In: Action control from cognition to behavior. Eds: Khul, J. and Beckmann, J. Berlin: Verlag. 10-39. Ajzen, I.(1988) Attitudes, personality and behavior. Chicago: Dorsey Press. Ajzen, I.(1991) The theory of planned behavior. Organitational Behavior and Human Decision Proceses 50, 179-211. Ajzen, I.(1996) The directive influence of attitudes on behavior. In: The psychology of action. Linking cognition and motivation to behavior. Eds: Gollwitzer, PM and Bargh, JA NewYork: Guilford Press. 385-454. Ajzen, I.(2001) Perceived behavioral control, self-Efficacy, locus of control, and the Theory of Planned Behavior. Journal of Applied Social Psychology 32, 11-20.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Hair, J. F. (2009). Multivariate data analysis.
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.
Kaiser, H. F. (1970). A second generation little jiffy.
Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the academy of marketing science, 29(4), 374-390.
Kim, J., Kim, S., & Nam, C. (2016). Competitive dynamics in the Korean video platform market: Traditional pay TV platforms vs. OTT platforms. Telematics and Informatics, 33(2), 711-721.
Kim, J. O., Forsythe, S., Gu, Q., & Moon, S. J. (2002). Cross‐cultural consumer values, needs and purchase behavior. Journal of Consumer marketing.
Kim, S., Baek, H., & Kim, D. H. (2021). OTT and live streaming services: Past, present, and future. In (Vol. 45, pp. 102244): Elsevier.
Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management science, 51(5), 741-755.
Kraemer, H. C., & Blasey, C. M. (2004). Centring in regression analyses: a strategy to prevent errors in statistical inference. International journal of methods in psychiatric research, 13(3), 141-151.
Kwateng, K. O., Atiemo, K. A. O., & Appiah, C. (2018). Acceptance and use of mobile banking: an application of UTAUT2. Journal of enterprise information management.
Lepper, M. R. (1985). Microcomputers in education: Motivational and social issues. American psychologist, 40(1), 1.
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 705-737.
Michaelidou, N., & Dibb, S. (2009). Brand switching in clothing: the role of variety‐seeking drive and product category‐level characteristics. International Journal of Consumer Studies, 33(3), 322-326.
Mishra, D., Akman, I., & Mishra, A. (2014). Theory of Reasoned Action application for Green Information Technology acceptance. Computers in Human Behavior, 36, 29-40. Retrieved from <Go to ISI>://WOS:000338387300004. doi:10.1016/j.chb.2014.03.030
Mitchell, T. R., & Biglan, A. (1971). Instrumentality theories: Current uses in psychology. Psychological Bulletin, 76(6), 432.
Mittal, B. (1988). Achieving higher seat belt usage: The role of habit in bridging the attitude‐behavior gap 1. Journal of applied social psychology, 18(12), 993-1016.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
Moore, G. C., & Benbasat, I. (1996). Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by end-users. In Diffusion and adoption of information technology (pp. 132-146): Springer.
Murray, K. B., Liang, J., & Häubl, G. (2010). ACT 2.0: the next generation of assistive consumer technology research. Internet Research.
Niininen, O., Szivas, E., & Riley, M. (2004). Destination loyalty and repeat behaviour: An application of optimum stimulation measurement. International Journal of Tourism Research, 6(6), 439-447.
Norouzi, S., Moghaddam, F., & Sharafi, F. (2018). Effectiveness of Trainings Based on the Theory of Reasoned Action in the Choice of Natural Delivery by Pregnant Women. International Journal of Womens Health and Reproduction Sciences, 6(2), 181-186. Retrieved from <Go to ISI>://WOS:000438919600014. doi:10.15296/ijwhr.2018.30
Raju, P. S. (1980). Optimum stimulation level: Its relationship to personality, demographics, and exploratory behavior. Journal of consumer research, 7(3), 272-282.
Ramdas, K. (2003). Managing product variety: An integrative review and research directions. Production and operations management, 12(1), 79-101.
Ramdas, K., & Sawhney, M. S. (2001). A cross-functional approach to evaluating multiple line extensions for assembled products. Management science, 47(1), 22-36.
Roberts, C., & Muscarella, V. (2015). Defining Over-The-Top (OTT) Digital Distribution. The Entertainment Merchants Association, 3-4.
Rogers, E. M. (1995). Diffusion of Innovations: modifications of a model for telecommunications. In Die diffusion von innovationen in der telekommunikation (pp. 25-38): Springer.
Schifter, D. E., & Ajzen, I. (1985). Intention, perceived control, and weight loss: an application of the theory of planned behavior. Journal of personality and social psychology, 49(3), 843.
Sheng, T.-W. (2020). Product Differentiation, Customer Equity, and Purchase Intention: An Empirical Study of Original Content on Video Streaming Service.
Steenkamp, J.-B. E., & Baumgartner, H. (1992). The role of optimum stimulation level in exploratory consumer behavior. Journal of consumer research, 19(3), 434-448.
Sumak, B., Hericko, M., Polancic, G., & Pusnik, M. (2010). Investigation of E-Learning System Acceptance using UTAUT. International Journal of Engineering Education, 26(6), 1327-1342. Retrieved from <Go to ISI>://WOS:000285646700002.
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS quarterly, 125-143.
Thong, J. Y., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of human-computer studies, 64(9), 799-810.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. Mis Quarterly, 27(3), 425-478. Retrieved from <Go to ISI>://WOS:000185196400005. doi:10.2307/30036540
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.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). CONSUMER ACCEPTANCE AND USE OF INFORMATION TECHNOLOGY: EXTENDING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY. Mis Quarterly, 36(1), 157-178. Retrieved from <Go to ISI>://WOS:000300480200011.
Verplanken, B. (2006). Beyond frequency: Habit as mental construct. British Journal of Social Psychology, 45(3), 639-656.
Vroom, V. H. (1964). Work and motivation.
Yang, K. (2010). Determinants of US consumer mobile shopping services adoption: implications for designing mobile shopping services. Journal of consumer marketing, 27(3), 262-270.

二、中文文獻
王郁雅. (2018). 以UTAUT探討消費者對安全監視系統接受度之研究. (碩士), 國立高雄應用科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/ea9js8
朱莉芳. (2010). 探討影響電子書使用意圖之因素－計劃性行為理論與科技接受模式觀點. (碩士), 國立成功大學, 台南市. Retrieved from https://hdl.handle.net/11296/7hw5gx
江志卿, 黃興進, & 嚴紀中. (2005). 中小企業採用網路科技之比較: 創新擴散通用模式的整合觀點. 資訊管理學報, 12(2), 103-139.
何胤呈. (2019). 線上影音串流平台 4C 策略行銷分析: 以愛奇藝, NETFLIX, LINE TV 為例.
李佩君. (2016). 以UTAUT與TTF整合模式探討醫師與藥師 使用健保雲端藥歷系統意圖之研究. (碩士), 國立高雄應用科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/m7syym
李靜芳. (2019). 以UTAUT模式進行Beacon微定位技術採用探討. [Using UTAUT Model to Explore Beacon Micro-Positioning Technology Adoption]. 管理資訊計算, 8(2), 11-19. doi:10.6285/mic.201909_8(2).0002
周英奇. (2016). 以UTAUT2模式理論探討銀髮族對社群軟體接受度. (碩士), 國立高雄應用科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/my425g
林昱豪. (2018). 內容供應商與 OTT 串流影音平台的交易模式研究.
林勝蒝. (2020). 消費者對美食外送平台使用意圖之探討—以台灣市場為例. 淡江大學, Available from Airiti AiritiLibrary database. (2020年)
胡庠卉. (2011). 影響高等教育學生使用數位學習系統意願因素之研究—應用 UTAUT 模型分析.
翁晨語, & 黃惠萍. (2017). 以延伸整合型科技接受模式和數位生活型態探討LINE TV的使用行為. [A Study on the Use Behavior of LINE TV Based on the UTAUT2 and Digital Lifestyle Scale]. 資訊社會研究(33), 17-63. doi:10.29843/jccis.201707_(33).0002
張巧真, & 陳筠惠. (2014). 應用延伸型整合科技接受模式探討線上購買意願-以雙媒介之觀點. Electronic Commerce Studies, 12(2), 143-168.
張志銘, 甯晏然, 許子淵, & 張世沛. (2019). 以健康信念模式結合計畫行為理論探討國小教師運動參與行為意圖之影響. [The study of the influence of health belief model combined with theory of planned behavior on the sports behavior intention participated by Changhua elementary school teachers]. 管理實務與理論研究, 13(2), 40-56. doi:10.29916/jmpp.201912_13(2).0003
張維熊. (2016). 行動銀行知覺易用性、使用者體驗與忠誠度之關聯性研究. 國立交通大學, Available from Airiti AiritiLibrary database. (2016年)
曹金成. (2008). 物業管理系統使用行為之研究－UTAUT模型之應用. 國立臺北大學, Available from Airiti AiritiLibrary database. (2008年)
黃璿吉. (2010). 影響消費者採用創新服務之因素研究—以提供雲端閱讀服務網站為例. (碩士), 國立交通大學, 新竹市. Retrieved from https://hdl.handle.net/11296/rkjvk7
楊雅婷. (2018). OTT影音平台的使用者研究：以整合科技接受與資訊系統成功模式探討行為意圖與付費意願. 國立政治大學, Available from Airiti AiritiLibrary database. (2018年)
葉宗樺. (2017). 消費者對智慧型手環穿戴式科技產品使用意願之研究－UTAUT2模型之應用. (碩士), 國立臺北大學, 新北市. Retrieved from https://hdl.handle.net/11296/ppt33e
賴俊杉. (2017). 擴充UTAUT理論探討高齡者使用行動掛號App的意圖. (碩士), 國立高雄應用科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/pmmwet
賴禹心, 鄭宇庭, & 蘇志雄. (2020). 使用 OTT 影音平台的關鍵因素與行為模式-以 YouTube 為例. Journal of Data Analysis, 15(5), 21-43.