近年來傳統中心化YouTube平台制定許多不合理的政策，包括榨取創作者的利潤，以及以廣告為主要收益等，導致越來越多使用者轉移至去中心化影音分享平台，其中又以LBRY公司開發的Odysee平台(舊版為LBRY平台)使用人口最多。本研究目的除了探討使用者是否會因為兩平台性質相似，將信任從原本中心化YouTube平台轉移至去中心化Odysee平台，並且是否因為信任該平台進而引起使用意圖，除此之外還探討究竟是哪些因素影響使用Odysee平台的意圖。
本研究透過發放線上問卷進行資料蒐集，共收回215份有效問卷。研究結果顯示，在正效價框架內的創新擴散理論元素中的能見度和結果明顯度，以及在負效價框架內的慣性和感知處理速度會影響Odysee平台的使用意圖；信任確實會在相似的平台間產生移轉，但並沒有影響使用者使用新平台的意圖，因此本研究進行分組探討，發現負效價比正效價更強烈影響使用意圖，並且在低轉換成本以及低感知處理速度中，對Odysee平台的信任會影響使用意圖。最後根據研究結果提出理論與實務面之建議。

In recent years, the traditional centralized YouTube platform has formulated many unreasonable policies, including extracting the profits of creators and taking advertising as the main income, etc., which has led to more and more users transferring to the decentralized video sharing platform. Among them, LBRY company developed Odysee platform (the old version is the LBRY platform) has the largest population. The purpose of this study is to explore whether users will transfer their trust from the original centralized YouTube platform to the decentralized Odysee platform because of the similar nature of the two platforms, and whether they will use the platform because of their trust in the platform. In addition to exploring what factors influence the intention to use Odysee platform.
This study collected data by distributing online questionnaires, and a total of 215 valid questionnaires were returned. The findings show that visibility and result demonstrability within the theoretical elements of innovation diffusion theory within the positive valence framework, and inertia and perceived processing speed within the negative valence framework can influence behavior intention of the Odysee platform. There is a trust transfer between two similar platforms, but it does not affect the intention of users to use the new platform. Therefore, this study conducted group discussions and found that negative valence has a stronger impact on behavior intention than positive valence, and in low switching costs and low perceived processing speed , trust in Odysee platform affects behavior intention. Finally, the theoretical and practical suggestions are put forward according to the research results.

論文審定書i
摘要ii
Abstractiii
第一章 緒論1
第一節 研究背景1
第二節 研究動機2
第三節 研究問題與目的4
第四節 研究方法與流程4
第二章 文獻回顧6
第一節 效價框架(The Valence Framework)6
第二節 創新擴散理論(Innovation Diffusion Theory)7
第三節 信任(Trust)與信任轉移(Trust Transfer Theory)8
第三章 研究方法11
第一節 研究模型11
第三節 操作型定義18
第四節 研究設計19
第四章 資料分析22
第一節 敘述性統計22
第二節 衡量模型23
第三節 結構模型及假說驗證30
第四節 分組分析33
第五節 討論40
第五章 結論44
第一節 結論44
第二節 理論及實務意涵44
第三節 研究限制與未來研究方向46
參考文獻48
附錄：本研究正式問卷54

關鍵評論網媒體集團.(2020).預言YouTube沒落？「好和弦」看好去中心化服務LBRY看見創作者對YouTube的愛很情仇. Retrieved from https://www.inside.com.tw/article/21916-youtube-lbry-nicechord
Agag, G., & El-Masry, A. A. (2016). Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust. Computers in Human Behavior, 60, 97-111.
Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision sciences, 28(3), 557-582.
Aladwani, A. M., & Palvia, P. C. (2002). Developing and validating an instrument for measuring user-perceived web quality. Information & Management, 39(6), 467-476.
Ali, M., Zhou, L., Miller, L., & Ieromonachou, P. (2016). User resistance in IT: A literature review. International Journal of Information Management, 36(1), 35-43.
Ball, J., Ogletree, R., Asunda, P., Miller, K., & Jurkowski, E. (2014). DIFFUSION OF INNOVATION ELEMENTS THAT INFLUENCE THE ADOPTION AND DIFFUSION OF DISTANCE EDUCATION IN HEALTH. American Journal of Health Studies, 29(3).
Benbasat, I., Gefen, D., & Pavlou, P. A. (2010). Introduction to the special issue on novel perspectives on trust in information systems. Mis Quarterly, 34(2), 367-371.
Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International journal of accounting information systems, 4(3), 205-225.
Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs: A typology, antecedents, and consequences. Journal of the Academy of marketing Science, 31(2), 109-126.
Buss, D. M. (1987). Selection, evocation, and manipulation. Journal of personality and social psychology, 53(6), 1214.
Chang, I. C., Liu, C. C., & Chen, K. (2014). The push, pull and mooring effects in virtual migration for social networking sites. Information Systems Journal, 24(4), 323-346.
Chen, L., & Wang, R. (2016a). Trust Development and Transfer from Electronic Commerce to Social Commerce: An Empirical Investigation. American Journal of Industrial and Business Management, 06, 568-576.
Chen, L., & Wang, R. (2016b). Trust development and transfer from electronic commerce to social commerce: an empirical investigation. American Journal of Industrial and Business Management, 6(05), 568.
Chuah, S. H.-W., Rauschnabel, P. A., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016). Wearable technologies: The role of usefulness and visibility in smartwatch adoption. Computers in Human Behavior, 65, 276-284.
Corritore, C. L., Kracher, B., & Wiedenbeck, S. (2003). On-line trust: concepts, evolving themes, a model. International journal of human-computer studies, 58(6), 737-758.
Eckhardt, A., Laumer, S., & Weitzel, T. (2009). Who influences whom? Analyzing workplace referents’ social influence on IT adoption and non-adoption. Journal of information technology, 24(1), 11-24.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. Mis Quarterly, 51-90.
Ghasemaghaei, M. (2020). The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage. International Journal of Information Management, 50, 395-404.
Gong, X., Zhang, K. Z., Chen, C., Cheung, C. M., & Lee, M. K. (2020). Transition from web to mobile payment services: The triple effects of status quo inertia. International Journal of Information Management, 50, 310-324.
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.
Hilz, L. (2000). The informatics nurse specialist as change agent. Application of innovation-diffusion theory. Computers in Nursing, 18(6), 272-278; quiz 279.
Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of information technology, 21(1), 1-23.
Kim, D., & Ammeter, T. (2014). Predicting personal information system adoption using an integrated diffusion model. Information & Management, 51(4), 451-464.
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision support systems, 44(2), 544-564.
Kim, H.-W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. Mis Quarterly, 567-582.
Kitchen, P. J., Martin, R., & Che-Ha, N. (2015). Long term evolution mobile services and intention to adopt: a Malaysian perspective. Journal of Strategic Marketing, 23(7), 643-654.
Kuan, H.-H., & Bock, G.-W. (2007). Trust transference in brick and click retailers: An investigation of the before-online-visit phase. Information & Management, 44(2), 175-187.
Kumar, N., Scheer, L. K., & Steenkamp, J.-B. E. (1995). The effects of perceived interdependence on dealer attitudes. Journal of marketing research, 32(3), 348-356.
Kwon, T. H., & Zmud, R. W. (1987). Unifying the fragmented models of information systems implementation. In Critical issues in information systems research (pp. 227–251). John Wiley & Sons, Inc.
Li, J., Grintsvayg, A., Kauffman, J., & Fleming, C. (2020, 3-6 Aug 2020). LBRY: A Blockchain-Based Decentralized Digital Content Marketplace. 2020 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS),
Li, X., Hess, T. J., & Valacich, J. S. (2008). Why do we trust new technology? A study of initial trust formation with organizational information systems. The Journal of Strategic Information Systems, 17(1), 39-71.
Lin, J., Wang, B., Wang, N., & Lu, Y. (2014). Understanding the evolution of consumer trust in mobile commerce: a longitudinal study. Information Technology and Management, 15(1), 37-49.
Liu, L., Lee, M. K., Liu, R., & Chen, J. (2018). Trust transfer in social media brand communities: The role of consumer engagement. International Journal of Information Management, 41, 1-13.
Lu, Y., Cao, Y., Wang, B., & Yang, S. (2011). A study on factors that affect users’ behavioral intention to transfer usage from the offline to the online channel. Computers in Human Behavior, 27(1), 355-364.
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The Journal of Strategic Information Systems, 11(3-4), 297-323.
Möhlmann, M. (2021). Unjustified trust beliefs: Trust conflation on sharing economy platforms. Research Policy, 50(3), 104173.
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.
Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350-363.
Nunnally, J. C. (1978). An overview of psychological measurement. Clinical diagnosis of mental disorders, 97-146.
Oh, J., & Yoon, S.-J. (2014). Validation of haptic enabling technology acceptance model (HE-TAM): Integration of IDT and TAM. Telematics and Informatics, 31(4), 585-596.
Ozturk, A. B., Bilgihan, A., Salehi-Esfahani, S., & Hua, N. (2017). Understanding the mobile payment technology acceptance based on valence theory. International Journal of Contemporary Hospitality Management, 29(8), 2027-2049.
Park, E., & Kim, K. J. (2013). User acceptance of long‐term evolution (LTE) services: An application of extended technology acceptance model. Program.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134.
Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information systems research, 15(1), 37-59.
Peter, J. P., & Tarpey, L. X., Sr. (1975). A Comparative Analysis of Three Consumer Decision Strategies. Journal of Consumer Research, 2(1), 29-37.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of management, 12(4), 531-544.
Polites, G. L., & Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. Mis Quarterly, 21-42.
Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. In: New York: Free Press.
Premkumar, G., Ramamurthy, K., & Nilakanta, S. (1994). Implementation of electronic data interchange: an innovation diffusion perspective. Journal of Management Information Systems, 11(2), 157-186.
Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.
Rogers Everett, M. (1995). Diffusion of innovations. New York, 12.
Ryals, L. J., & Humphries, A. S. (2007). Managing key business-to-business relationships: what marketing can learn from supply chain management. Journal of Service research, 9(4), 312-326.
Stewart, K. J. (2003). Trust transfer on the world wide web. Organization science, 14(1), 5-17.
Sun, Y., Liu, D., Chen, S., Wu, X., Shen, X.-L., & Zhang, X. (2017). Understanding users' switching behavior of mobile instant messaging applications: An empirical study from the perspective of push-pull-mooring framework. Computers in Human Behavior, 75, 727-738.
Swanson, E. B. (1988). Information system implementation: Bridging the gap between design and utilization. McGraw-Hill/Irwin.
Tarhini, A., Arachchilage, N. A. G., & Abbasi, M. S. (2015). A critical review of theories and models of technology adoption and acceptance in information system research. International Journal of Technology Diffusion (IJTD), 6(4), 58-77.
Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on engineering management(1), 28-45.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Wang, M., Wang, T., Kang, M., & Sun, S. (2014). Understanding perceived platform trust and institutional risk in peer-to-peer lending platforms from cognition-based and affect-based perspectives.
Wang, W.-T., Ou, W.-M., & Chen, W.-Y. (2019). The impact of inertia and user satisfaction on the continuance intentions to use mobile communication applications: A mobile service quality perspective. International Journal of Information Management, 44, 178-193.
Wang, Y. D., & Emurian, H. H. (2005). An overview of online trust: Concepts, elements, and implications. Computers in Human Behavior, 21(1), 105-125.
Wolters, M., Georgila, K., Moore, J. D., Logie, R. H., MacPherson, S. E., & Watson, M. (2009). Reducing working memory load in spoken dialogue systems. Interacting with Computers, 21(4), 276-287.
Wu, K., Vassileva, J., & Zhao, Y. (2017). Understanding users' intention to switch personal cloud storage services: Evidence from the Chinese market. Computers in Human Behavior, 68, 300-314.
Yang, S., Chen, Y., & Wei, J. (2015). Understanding consumers' web-mobile shopping extension behavior: A trust transfer perspective. Journal of computer information systems, 55(2), 78-87.
Yang, Z., Cai, S., Zhou, Z., & Zhou, N. (2005). Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Information & Management, 42(4), 575-589.
Yang, Z., Jun, M., & Peterson, R. T. (2004). Measuring customer perceived online service quality: scale development and managerial implications. International Journal of operations & production Management.
Yoo, B., & Donthu, N. (2001). Developing a scale to measure the perceived quality of an Internet shopping site (SITEQUAL). Quarterly journal of electronic commerce, 2(1), 31-45.
Yuen, K. F., Cai, L., Qi, G., & Wang, X. (2021). Factors influencing autonomous vehicle adoption: an application of the technology acceptance model and innovation diffusion theory. Technology Analysis & Strategic Management, 33(5), 505-519.
Yuen, K. F., Wang, X., Ng, L. T. W., & Wong, Y. D. (2018). An investigation of customers’ intention to use self-collection services for last-mile delivery. Transport Policy, 66, 1-8.