近年來直播平台蓬勃發展，直播平台要長久經營最主要是觀眾能持續觀看該平台，而訂閱則是直播平台及直播主的收益來源。本研究探討持續觀看直播意圖及訂閱的影響因素，並利用共同體驗、群體認同及潮流效應的理論及變數，了解這些因素是否會影響持續觀看意圖及訂閱。研究方法採用問卷調查法，利用SPSS及AMOS分析了418份有效問卷。研究結果顯示，僅有潮流效應中的一致性對訂閱不成立，共同體驗對群體認同有顯著關係，群體認同對持續意圖及訂閱亦皆具有顯著關係，潮流效應中的地位消費及獨特的需求對訂閱也呈正向顯著。

Recently, the livestream platform has been booming.The success of a livestream platform hinges on audiences’ following, while the revenues of the platform and its streamers mainly hinge on audiences’ subscription.This study applies the theories and variables of co-experience, group identification, and bandwagon effects to explore the determinants of continuous following intentions and subscriptions toward a livestream platform.This study conducts a questionnaire survey and uses SPSS and AMOS to analyze data received from 418 respondents.Our results show that except for the hypothesis between conformity and subscription, most of our proposed hypotheses are empirically supported.Implications for practices and theory are briefly discussed.

目錄
第一章	緒論	1
第一節 研究背景與動機	1
第二節 研究目的與問題	5
第三節 預期之研究貢獻	6
第四節 論文架構	7
第二章 文獻探討	8
第一節 Twitch直播	8
第二節 共同體驗	9
第三節 群體認同	11
第四節 潮流效應	13
第五節 訂閱	15
第六節 持續觀看意圖	16
第三章 研究方法	18
第一節 研究假說	18
第二節 研究架構	22
第三節 研究變數與衡量	23
第四節 研究對象與問卷蒐集方法	31
第四章 實證分析與討論	32
第一節 樣本基本資料分析	32
第二節 信度分析	35
第三節 效度分析	36
第四節 敘述性統計分析	45
第五節 結構方程模式分析	51
第六節 中介效果之驗證	58
第五章 結論與建議	61
第一節 研究意涵	61
第二節 管理意涵	62
第三節 研究限制與後續研究建議	64
參考文獻	66
一、中文文獻	66
二、英文文獻	66

 
圖目錄
圖1-4-1 論文架構	7
圖3-2-1 研究架構	22
圖4-2-1 信度分析表	35
圖4-3-1 區別效度	44
圖4-5-1 結構方程模式之路徑係數	58

 
表目錄
表3-3-1 參與之問項	23
表3-3-2 認知共融之問項	24
表3-3-3 共鳴傳染之問項	24
表3-3-4 群體認同之問項	25
表3-3-5 地位消費之問項	25
表3-3-6 規範性之問項	26
表3-3-7 資訊性之問項	26
表3-3-8 獨特的創意之問項	27
表3-3-9 與眾不同之問項	27
表3-3-10 避免相似之問項	28
表3-3-11 持續觀看意圖之問項	28
表3-3-12 金錢支持之問項	29
表3-3-13 吸引注意之問項	29
表3-3-14 個人關係之問項	30
表3-3-15 利益需求之問項	30
表4-1-1樣本基本資料	33
表4-3-1 參與構面收斂效度分析	36
表4-3-2 認知共融構面收斂效度分析	37
表4-3-3 共鳴傳染構面收斂效度分析	37
表4-3-4 群體認同構面收斂效度分析	37
表4-3-5 地位消費構面收斂效度分析	38
表4-3-6 規範性構面收斂效度分析	38
表4-3-7 資訊性構面收斂效度分析	39
表4-3-8 獨特的創意構面收斂效度分析	39
表4-3-9 與眾不同構面收斂效度分析	40
表4-3-10 避免相似構面收斂效度分析	40
表4-3-11 持續觀看意圖構面收斂效度分析	41
表4-3-12 金錢支持構面收斂效度分析	41
表4-3-13 吸引注意構面收斂效度分析	41
表4-3-14 個人關係構面收斂效度分析	42
表4-3-15 利益需求構面收斂效度分析	42
表4-4-1 參與構面敘述性統計分析	45
表4-4-2 認知共融構面敘述性統計分析	45
表4-4-3 共鳴傳染構面敘述性統計分析	46
表4-4-4 群體認同構面敘述性統計分析	46
表4-4-5 地位消費構面敘述性統計分析	46
表4-4-6 規範性構面敘述性統計分析	47
表4-4-7 資訊性構面敘述性統計分析	47
表4-4-8 獨特的創意構面敘述性統計分析	48
表4-4-9 與眾不同構面敘述性統計分析	48
表4-4-10 避免相似構面敘述性統計分析	48
表4-4-11 持續觀看意圖構面敘述性統計分析	49
表4-4-12 金錢支持構面敘述性統計分析	49
表4-4-13 吸引注意構面敘述性統計分析	50
表4-4-14 個人關係構面敘述性統計分析	50
表4-4-15 利益需求構面敘述性統計分析	50
表4-5-1 結構方程模式分析結果	52
表4-5-2 結構方程模式之適配度指標	54
表4-5-3 假說之結果	57
表4-6-1 中介效果之迴歸分析	60
表4-6-2 Sobel Test與Bootstrap法之中介效果分析	60

參考文獻
一、中文文獻
吳明隆.（2007）。結構方程模式AMOS的操作與應用，台北:五南圖書出版公司。
蘇冠餘.（2019）。遊戲直播平台使用者對於贊助與訂閱意圖之研究：以Twitch.TV為例（碩士）。國立雲林科技大學，雲林縣。取自: https://hdl.handle.net/11296/8b2b62
二、英文文獻
Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44.
Ashforth, B. E., & Mael, F. (1989). Social identity theory and the organization. Academy of management review, 14(1), 20-39.
Badrinarayanan, V. A., Sierra, J. J., & Martin, K. M. (2015). A dual identification framework of online multiplayer video games: The case of massively multiplayer online role playing games (MMORPGs). Journal of Business Research, 68(5), 1045-1052.
Bagozzi, R. P., Yi, Y., & Nassen, K. D. (1998). Representation of measurement error in marketing variables: Review of approaches and extension to three-facet designs. Journal of Econometrics, 89(1-2), 393-421.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.
Battarbee, K. (2003, April). Co-experience: the social user experience. In CHI'03 extended abstracts on Human factors in computing systems (pp. 730-731). ACM.
Battarbee, K. (2003, June). Defining co-experience. In Proceedings of the 2003 international conference on Designing pleasurable products and interfaces (pp. 109-113). ACM.
Bearden, W. O., Netemeyer, R. G., & Teel, J. E. (1989). Measurement of consumer susceptibility to interpersonal influence. Journal of consumer research, 15(4), 473-481.
Bhattacharya, C. B., & Sen, S. (2003). Consumer–company identification: A framework for understanding consumers’ relationships with companies. Journal of marketing, 67(2), 76-88.
Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, 351-370.
Burroughs, B., & Rama, P. (2015). The eSports Trojan horse: Twitch and streaming futures. Journal For Virtual Worlds Research, 8(2).
Chan, W. Y., To, C. K., & Chu, W. C. (2015). Materialistic consumers who seek unique products: how does their need for status and their affective response facilitate the repurchase intention of luxury goods?. Journal of Retailing and Consumer Services, 27, 1-10.
Chang, C. C. (2013). Examining users′ intention to continue using social network games: A flow experience perspective. Telematics and Informatics, 30(4), 311-321.
Chen, Y., Gao, Q., & Rau, P. L. P. (2017). Watching a movie alone yet together: Understanding reasons for watching Danmaku videos. International Journal of Human–Computer Interaction, 33(9), 731-743.
Coyne, S. M., Padilla-Walker, L. M., Stockdale, L., & Day, R. D. (2011). Game on… girls: Associations between co-playing video games and adolescent behavioral and family outcomes. Journal of Adolescent Health, 49(2), 160-165.
Deng, J., Cuadrado, F., Tyson, G., & Uhlig, S. (2015, December). Behind the game: Exploring the twitch streaming platform. In 2015 International Workshop on Network and Systems Support for Games (NetGames) (pp. 1-6). IEEE.
Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS quarterly, 453-461.
Doughty, M., Rowland, D., & Lawson, S. (2011, June). Co-viewing live TV with digital backchannel streams. In Proceedings of the 9th European Conference on Interactive TV and Video (pp. 141-144). ACM.
Eastman, J. K., Goldsmith, R. E., & Flynn, L. R. (1999). Status consumption in consumer behavior: Scale development and validation. Journal of marketing theory and practice, 7(3), 41-52.
Forlizzi, J., & Battarbee, K. (2004, August). Understanding experience in interactive systems. In Proceedings of the 5th conference on Designing interactive systems: processes, practices, methods, and techniques (pp. 261-268). ACM.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study–A case of China. Computers in Human Behavior, 53, 249-262.
Gros, D., Hackenholt, A., Zawadzki, P., & Wanner, B. (2018, July). Interactions of Twitch users and their usage behavior. In International Conference on Social Computing and Social Media (pp. 201-213). Springer, Cham.
Gwebu, K. L., Wang, J., & Guo, L. (2014). Continued usage intention of multifunctional friend networking services: A test of a dual-process model using Facebook. Decision Support Systems, 67, 66-77.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6).
Hamilton, E. (2014). Entrepreneurial narrative identity and gender: a double epistemological shift. Journal of Small Business Management, 52(4), 703-712.
Haridakis, P., & Hanson, G. (2009). Social interaction and co-viewing with YouTube: Blending mass communication reception and social connection. Journal of Broadcasting & Electronic Media, 53(2), 317-335.
Hilvert-Bruce, Z., Neill, J. T., Sjöblom, M., & Hamari, J. (2018). Social motivations of live-streaming viewer engagement on Twitch. Computers in Human Behavior, 84, 58-67.
Hu, M., Zhang, M., & Wang, Y. (2017). Why do audiences choose to keep watching on live video streaming platforms? An explanation of dual identification framework. Computers in Human Behavior, 75, 594-606.
Ifinedo, P. (2017). Examining students' intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior, 72, 189-199.
Johnson, M. R., & Woodcock, J. (2018). Conversation, Discourse and Play: Interaction and Moderation in Twitch. tv Live Streaming.
Kang, Y. S., Hong, S., & Lee, H. (2009). Exploring continued online service usage behavior: The roles of self-image congruity and regret. Computers in Human Behavior, 25(1), 111-122.
Kastanakis, M. N., & Balabanis, G. (2012). Between the mass and the class: Antecedents of the “bandwagon” luxury consumption behavior. Journal of Business Research, 65(10), 1399-1407.
Leibenstein, H. (1950). Bandwagon, snob, and Veblen effects in the theory of consumers' demand. The quarterly journal of economics, 64(2), 183-207.
Lertwannawit, A., & Mandhachitara, R. (2012). Interpersonal effects on fashion consciousness and status consumption moderated by materialism in metropolitan men. Journal of Business Research, 65(10), 1408-1416.
Lim, S., Cha, S. Y., Park, C., Lee, I., & Kim, J. (2012). Getting closer and experiencing together: Antecedents and consequences of psychological distance in social media-enhanced real-time streaming video. Computers in Human Behavior, 28(4), 1365-1378.
Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce?. Internet Research, 24(2), 134-159.
Luo, N., Zhang, M., Hu, M., & Wang, Y. (2016). How community interactions contribute to harmonious community relationships and customers’ identification in online brand community. International Journal of Information Management, 36(5), 673-685.
MacCallum, R. C., & Hong, S. (1997). Power analysis in covariance structure modeling using GFI and AGFI. Multivariate Behavioral Research, 32(2), 193-210.
Mouakket, S. (2015). Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human Behavior, 53, 102-110.
Nabavi, A., Taghavi-Fard, M. T., Hanafizadeh, P., & Taghva, M. R. (2016). Information technology continuance intention: a systematic literature review. International Journal of E-Business Research (IJEBR), 12(1), 58-95.
Ruvio, A., Shoham, A., & Makovec Brenčič, M. (2008). Consumers' need for uniqueness: short-form scale development and cross-cultural validation. International Marketing Review, 25(1), 33-53.
Schmitt‐Beck, R. (2015). Bandwagon effect. The international encyclopedia of political communication, 1-5.
Shaikh, S., Malik, A., Akram, M. S., & Chakrabarti, R. (2017). Do luxury brands successfully entice consumers? The role of bandwagon effect. International Marketing Review, 34(4), 498-513.
Sjöblom, M., & Hamari, J. (2017). Why do people watch others play video games? An empirical study on the motivations of Twitch users. Computers in Human Behavior, 75, 985-996.
Sjöblom, M., Törhönen, M., Hamari, J., & Macey, J. (2019). The ingredients of Twitch streaming: Affordances of game streams. Computers in Human Behavior, 92, 20-28.
Stolwijk, S. B., Schuck, A. R., & de Vreese, C. H. (2016). How anxiety and enthusiasm help explain the bandwagon effect. International Journal of Public Opinion Research, 29(4), 554-574.
Sundar, S. S., Oeldorf-Hirsch, A., & Xu, Q. (2008, April). The bandwagon effect of collaborative filtering technology. In CHI'08 extended abstracts on Human factors in computing systems (pp. 3453-3458). ACM.
Tajfel, H. (1974). Social identity and intergroup behaviour. Information (International Social Science Council), 13(2), 65-93.
Tajfel, H., Turner, J. C., Austin, W. G., & Worchel, S. (1979). An integrative theory of intergroup conflict. Organizational identity: A reader, 56-65.
Thorson, E., Hawthorne, J., Swasy, A., & McKinney, M. S. (2015). Co-viewing, tweeting, and Facebooking the 2012 presidential debates. Electronic News, 9(3), 195-214.
Tian, K. T., Bearden, W. O., & Hunter, G. L. (2001). Consumers' need for uniqueness: Scale development and validation. Journal of consumer research, 28(1), 50-66.
Tsai, W. S., Yang, Q., & Liu, Y. (2013). Young Chinese consumers’ snob and bandwagon luxury consumption preferences. Journal of International Consumer Marketing, 25(5), 290-304.
Turner, J. C. (1984). Social identification and psychological group formation. The social dimension: European developments in social psychology, 2, 518-538.
Turner, J. C., & Tajfel, H. (1986). The social identity theory of intergroup behavior. Psychology of intergroup relations, 5, 7-24.
Vernuccio, M., Pagani, M., Barbarossa, C., & Pastore, A. (2015). Antecedents of brand love in online network-based communities. A social identity perspective. Journal of Product & Brand Management, 24(7), 706-719.
Wohn, D. Y., Jough, P., Eskander, P., Siri, J. S., Shimobayashi, M., & Desai, P. (2019, October). Understanding Digital Patronage: Why Do People Subscribe to Streamers on Twitch?. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play (pp. 99-110). ACM.
Xu, Q., Schmierbach, M., Bellur, S., Ash, E., Oeldorf-Hirsch, A., & Kegerise, A. (2012). The effects of “friend” characteristics on evaluations of an activist group in a social networking context. Mass Communication and Society, 15(3), 432-453.