簡易檢索 / 詳目顯示

研究生: 林漢東
Lin, Han-Tung
論文名稱: Facebook韓國流行音樂(K-pop)粉絲社團中意見兩極化現象對社團成員行為的影響與心情按鈕的調節作用:社會認同理論觀點
The Impact of Opinion Polarization on Member Behavior in Facebook K-pop Fan Group and the Moderating Effect of Facebook Reaction Icons: A Social Identity Theory Perspective
指導教授: 侯建任
Hou, Jian-Ren
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理研究所
Institute of Information Management
論文出版年: 2025
畢業學年度: 113
語文別: 中文
論文頁數: 77
中文關鍵詞: 韓國流行音樂(K-pop)粉絲社群意見兩極化Facebook心情按鈕社會認同理論
外文關鍵詞: Korean popular music(K-pop), Fan community, Opinion polarization, Facebook reaction icons, Social identity theory
相關次數: 點閱:21下載:0
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
  • 隨著社群媒體的迅速發展,人們可以突破時空的限制在線上互動,並促成各式主題的線上社群興起。以Facebook「社團」功能為例,許多以韓國流行音樂(K-pop)為主題、由K-pop偶像的粉絲組成的K-pop粉絲社團,提供了諸多志同道合的用戶進行交流與互動的空間,使其在互動上更為集中且便捷。然而,社群媒體為人們帶來便利的同時,也加劇了意見兩極化現象。意見兩極化係指人們分裂為對立群體,針對同一事件抱持對立態度的現象。據本研究觀察,意見兩極化現象在粉絲社群中十分常見,於K-pop粉絲社團中尤為頻繁且嚴重。儘管社團成員擁有許多共通點,鑒於社群媒體可容納不同意見的用戶的特性,結合推薦演算法以及人們喜愛與志同道合的人互動的傾向,仍容易導致意見兩極化現象發生,並伴隨著惡性爭論、激辯、攻擊性言語等,將影響社團氛圍,甚至導致社團分裂。故本研究聚焦於Facebook上的K-pop粉絲社團,透過社會認同理論觀點探討粉絲社團中的意見兩極化現象對社團成員行為的影響,以及探究Facebook心情按鈕除了作為多樣化的情感表達工具外,是否也能調節意見兩極化現象對社團成員的影響,為社團管理與長期發展提供具體建議。
    本研究透過問卷收集資料,分析結果發現,在發生意見兩極化現象的貼文情境下,負面效價的Facebook心情按鈕表情符號可正向調節韓國流行音樂認同;粉絲社群認同正向影響粉絲社群成員信任與粉絲社群滿意度,韓國流行音樂認同正向影響粉絲社群成員信任;粉絲社群成員信任正向影響粉絲社群滿意度;粉絲社群滿意度負向影響引戰行為意圖,並正向影響社群合作;最後,引戰行為意圖負向影響社群合作。上述結果可為粉絲社團管理者提供意見兩極化現象發生時的有效應對方案,以及制定管理決策時的參考依據。

    With the rapid development of social media, people can interact online without the constraints of time and space, leading to the emergence of various topic-based online communities. Taking Facebook "Groups" function as an example, many K-pop fan groups—composed of fans of Korean popular music idols—have become spaces for like-minded users to communicate and interact more conveniently and intensively. However, while social media brings convenience, it also intensifies the phenomenon of opinion polarization. Opinion polarization refers to the division of people into opposing groups with conflicting attitudes toward the same issue. According to this study's observations, opinion polarization is highly prevalent—and particularly severe—in K-pop fan communities. Although community members often share many similarities, the nature of social media in accommodating diverse opinions, combined with recommendation algorithms and users’ tendency to interact with like-minded individuals, easily contributes to opinion polarization. This is often accompanied by hostile arguments, heated debates, and aggressive language, which can negatively affect community atmosphere and even lead to community fragmentation.
    Therefore, this study focuses on K-pop fan groups on Facebook, exploring the influence of opinion polarization on K-pop fan community members’ behavior through the Social Identity Theory perspective. It also investigates whether Facebook reaction icons, aside from serving as diverse tools for emotional expression, can moderate the impact of opinion polarization on community members—providing practical insights for management and long-term development.
    Using survey to collect data, the results show that in posts involving opinion polarization, negative emotional valence Facebook reaction icons can positively moderate the impact of opinion polarization on K-pop identification. Fan community identification positively influences fan community member trust and fan community satisfaction, while K-pop identification positively influences fan community member trust. Fan community member trust, in turn, positively affects fan community satisfaction. Fan community satisfaction negatively impacts Flaming behaviour intention and positively affects cooperation. Finally, flaming behaviour intention negatively affects cooperation. These findings offer actionable recommendations for K-pop fan community administrators in managing opinion polarization and formulating effective management strategies.

    摘要 ii 誌謝 viii 目錄 ix 表目錄 xi 圖目錄 xii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 5 第二章 文獻探討 7 2.1 社會認同理論 7 2.2 韓國流行音樂粉絲社群中的認同 8 2.3 社群媒體上的意見兩極化現象 8 2.4 表情符號情緒效價 10 2.5 粉絲社群成員信任與粉絲社群滿意度 12 2.6 粉絲社群中的引戰行為 13 2.7 粉絲社群成員間的合作 14 第三章 研究方法 16 3.1 研究設計 16 3.2 實驗設計 16 3.3 問卷設計 21 3.3.1 粉絲社群認同 21 3.3.2 韓國流行音樂認同 21 3.3.3 粉絲社群成員信任 21 3.3.4 粉絲社群滿意度 22 3.3.5 引戰行為意圖 22 3.3.6 社群合作 22 3.4 前測與資料收集 25 3.5 資料分析方法 26 第四章 資料分析與結果 28 4.1 敘述性統計分析 28 4.2 信效度分析 32 4.2.1 信度分析與收斂效度 32 4.2.2 因素分析 34 4.2.3 區別效度 34 4.2.4 共線性診斷 35 4.3 研究檢定分析 35 4.3.1 操弄檢定 36 4.3.2 多變量變異數分析 37 4.3.3 結構方程模型 39 第五章 結論 43 5.1 結論與討論 43 5.2 學術貢獻 46 5.3 實務貢獻 47 5.4 研究限制與未來研究方向 47 參考文獻 50 附錄A 正式問卷 55

    Algesheimer, R., Dholakia, U. M., & Herrmann, A. (2005). The Social Influence of Brand Community: Evidence from European Car Clubs. Journal of Marketing, 69(3), 19–34. https://doi.org/10.1509/jmkg.69.3.19.66363
    Alonzo, M., & Aiken, M. (2004). Flaming in electronic communication. Decision Support Systems, 36(3), 205–213. https://doi.org/10.1016/S0167-9236(02)00190-2
    Anwar, S., & Giglietto, F. (2024). Facebook reactions in the context of politics and social issues: A systematic literature review. Frontiers in Sociology, 9, 1379265. https://doi.org/10.3389/fsoc.2024.1379265
    Apostolou, B., Bélanger, F., & Schaupp, L. C. (2017). Online communities: Satisfaction and continued use intention. Information Research, 22(4), 1–27.
    Arora, S. D., Singh, G. P., Chakraborty, A., & Maity, M. (2022). Polarization and social media: A systematic review and research agenda. Technological Forecasting and Social Change, 183, 121942. https://doi.org/10.1016/j.techfore.2022.121942
    Ashforth, B. E., & Mael, F. (1989). Social Identity Theory and the Organization. The Academy of Management Review, 14(1), 20. https://doi.org/10.2307/258189
    Baldassarri, D., & Gelman, A. (2008). Partisans without Constraint: Political Polarization and Trends in American Public Opinion. American Journal of Sociology, 114(2), 408–446. https://doi.org/10.1086/590649
    Bettencourt, L. (1997). Customer voluntary performance: Customers as partners in service delivery. Journal of Retailing, 73(3), 383–406. https://doi.org/10.1016/S0022-4359(97)90024-5
    Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214. https://doi.org/10.1016/S0167-9236(01)00111-7
    Bitner, M. J., Booms, B. H., & Mohr, L. A. (1994). Critical Service Encounters: The Employee’s Viewpoint. Journal of Marketing, 58(4), 95–106. https://doi.org/10.1177/002224299405800408
    Byron, K., & Baldridge, D. C. (2007). E-Mail Recipients’ Impressions of Senders’ Likability: The Interactive Effect of Nonverbal Cues and Recipients’ Personality. Journal of Business Communication, 44(2), 137–160. https://doi.org/10.1177/0021943606297902
    Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: A trust transfer perspective. Internet Research, 28(2), 456–476. https://doi.org/10.1108/IntR-11-2016-0359
    Chapman, G. (1995). Cranks, fetishists and monomaniacs - flamers. The New Republic, 212(15), 13–15.
    Derks, D., Bos, A. E. R., & Von Grumbkow, J. (2008a). Emoticons and Online Message Interpretation. Social Science Computer Review, 26(3), 379–388. https://doi.org/10.1177/0894439307311611
    Derks, D., Bos, A. E. R., & Von Grumbkow, J. (2008b). Emoticons in Computer-Mediated Communication: Social Motives and Social Context. CyberPsychology & Behavior, 11(1), 99–101. https://doi.org/10.1089/cpb.2007.9926
    Derks, D., Fischer, A. H., & Bos, A. E. R. (2008c). The role of emotion in computer-mediated communication: A review. Computers in Human Behavior, 24(3), 766–785. https://doi.org/10.1016/j.chb.2007.04.004
    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
    Giuntini, F. T., Ruiz, L. P., Kirchner, L. D. F., Passarelli, D. A., Dos Reis, M. D. J. D., Campbell, A. T., & Ueyama, J. (2019). How Do I Feel? Identifying Emotional Expressions on Facebook Reactions Using Clustering Mechanism. IEEE Access, 7, 53909–53921. https://doi.org/10.1109/ACCESS.2019.2913136
    Hair, J. (2009). Multivariate Data Analysis. Faculty Articles. https://digitalcommons.kennesaw.edu/facpubs/2925
    Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. In Multivariate data analysis (pp. 785–785). https://pesquisa.bvsalud.org/portal/resource/pt/biblio-1074274
    Hong, S., & Kim, S. H. (2016). Political polarization on twitter: Implications for the use of social media in digital governments. Government Information Quarterly, 33(4), 777–782. https://doi.org/10.1016/j.giq.2016.04.007
    Hou, J.-R., & Kankham, S. (2022). More than feelings? How Facebook reaction icons affect online users’ behavioral intentions toward online health rumor posts. Internet Research, 32(6), 1978–2002. https://doi.org/10.1108/INTR-04-2021-0236
    Hsieh, S. H., Lee, C. T., & Tseng, T. H. (2022). Psychological empowerment and user satisfaction: Investigating the influences of online brand community participation. Information & Management, 59(1), 103570. https://doi.org/10.1016/j.im.2021.103570
    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. https://doi.org/10.1016/j.chb.2017.06.006
    Huang, A. H., Yen, D. C., & Zhang, X. (2008). Exploring the potential effects of emoticons. Information & Management, 45(7), 466–473. https://doi.org/10.1016/j.im.2008.07.001
    Hwang, J., Lee, H., Kim, K., Zo, H., & Ciganek, A. P. (2016). Cyber neutralisation and flaming. Behaviour & Information Technology, 35(3), 210–224. https://doi.org/10.1080/0144929X.2015.1135191
    Iyengar, S., & Westwood, S. J. (2015). Fear and Loathing across Party Lines: New Evidence on Group Polarization. American Journal of Political Science, 59(3), 690–707. https://doi.org/10.1111/ajps.12152
    Janssen, J. H., IJsselsteijn, W. A., & Westerink, J. H. D. M. (2014). How affective technologies can influence intimate interactions and improve social connectedness. International Journal of Human-Computer Studies, 72(1), 33–43. https://doi.org/10.1016/j.ijhcs.2013.09.007
    Joyce, E., & Kraut, R. E. (2006). Predicting Continued Participation in Newsgroups. Journal of Computer-Mediated Communication, 11(3), 723–747. https://doi.org/10.1111/j.1083-6101.2006.00033.x
    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. https://doi.org/10.1016/j.dss.2007.07.001
    Kim, J. W., Choi, J., Qualls, W., & Han, K. (2008). It takes a marketplace community to raise brand commitment: The role of online communities. Journal of Marketing Management, 24(3–4), 409–431. https://doi.org/10.1362/026725708X306167
    Kim, M.-S., & Kim, H.-M. (2017). The effect of online fan community attributes on the loyalty and cooperation of fan community members: The moderating role of connect hours. Computers in Human Behavior, 68, 232–243. https://doi.org/10.1016/j.chb.2016.11.031
    Kitchens, B., Johnson, S. L., & Gray, P. (2020). Understanding Echo Chambers and Filter Bubbles: The Impact of Social Media on Diversification and Partisan Shifts in News Consumption. MIS Quarterly, 44(4), 1619–1649. https://doi.org/10.25300/MISQ/2020/16371
    Kleiner, T. (2018). Public opinion polarisation and protest behaviour. European Journal of Political Research, 57(4), 941–962. https://doi.org/10.1111/1475-6765.12260
    Koorank Beheshti, M., Gopinath, M., Ashouri, S., & Zal, S. (2023). Does polarizing personality matter in influencer marketing? Evidence from Instagram. Journal of Business Research, 160, 113804. https://doi.org/10.1016/j.jbusres.2023.113804
    Korea JoongAng Daily (2022, December 12). Female fans become driving force of K-pop girl groups. Korea JoongAng Daily. https://koreajoongangdaily.joins.com/2022/12/12/entertainment/kpop/girl-group-kpop-female-kpop-fan/20221212152033239.html
    Kuo, Y.-F., & Hou, J.-R. (2017). Oppositional Brand Loyalty in Online Brand Communities: Perspectives on Social Identity Theory and Consumer-Brand Relationship. Journal of Electronic Commerce Research, 18(3), 254–268.
    Laroche, M., Habibi, M. R., Richard, M.-O., & Sankaranarayanan, R. (2012). The effects of social media based brand communities on brand community markers, value creation practices, brand trust and brand loyalty. Computers in Human Behavior, 28(5), 1755–1767. https://doi.org/10.1016/j.chb.2012.04.016
    Lee, Y.-I., Phua, J., & Wu, T.-Y. (2020). Marketing a health Brand on Facebook: Effects of reaction icons and user comments on brand attitude, trust, purchase intention, and eWOM intention. Health Marketing Quarterly, 37(2), 138–154. https://doi.org/10.1080/07359683.2020.1754049
    Lin, H., Fan, W., & Chau, P. Y. K. (2014). Determinants of users’ continuance of social networking sites: A self-regulation perspective. Information & Management, 51(5), 595–603. https://doi.org/10.1016/j.im.2014.03.010
    Lin, H.-F. (2008). Determinants of successful virtual communities: Contributions from system characteristics and social factors. Information & Management, 45(8), 522–527. https://doi.org/10.1016/j.im.2008.08.002
    Ma, R., & Wang, W. (2021). Smile or pity? Examine the impact of emoticon valence on customer satisfaction and purchase intention. Journal of Business Research, 134, 443–456. https://doi.org/10.1016/j.jbusres.2021.05.057
    McAlexander, J. H., Schouten, J. W., & Koenig, H. F. (2002). Building Brand Community. Journal of Marketing, 66(1), 38–54. https://doi.org/10.1509/jmkg.66.1.38.18451
    Mikal, J. P., Rice, R. E., Kent, R. G., & Uchino, B. N. (2014). Common voice: Analysis of behavior modification and content convergence in a popular online community. Computers in Human Behavior, 35, 506–515. https://doi.org/10.1016/j.chb.2014.02.036
    Moor, P. J., Heuvelman, A., & Verleur, R. (2010). Flaming on YouTube. Computers in Human Behavior, 26(6), 1536–1546. https://doi.org/10.1016/j.chb.2010.05.023
    Quattrociocchi, W., Scala, A., & Sunstein, C. R. (2016). Echo Chambers on Facebook. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2795110
    Rabbani, K., Mehmood, M. A., & Areej, A. (2024). Speech act of flaming: A pragmatic analysis of Twitter trolling in Pakistan. Discourse & Society, 35(4), 499–517. https://doi.org/10.1177/09579265231222589
    Sandoval-Almazan, R., & Valle-Cruz, D. (2020). Sentiment Analysis of Facebook Users Reacting to Political Campaign Posts. Digital Government: Research and Practice, 1(2), 1–13. https://doi.org/10.1145/3382735
    Sun, R., Zhu, H., & Guo, F. (2023). Impact of content ideology on social media opinion polarization: The moderating role of functional affordances and symbolic expressions. Decision Support Systems, 164, 113845. https://doi.org/10.1016/j.dss.2022.113845
    Tajfel, H. (1979). An integrative theory of intergroup conflict. The social psychology of intergroup relations/Brooks/Cole.
    Tang, Y., & Hew, K. F. (2019). Emoticon, Emoji, and Sticker Use in Computer-Mediated Communication: A Review of Theories and Research Findings. International Journal of Communication, 13(0), Article 0.
    Thompsen, P. A., & Foulger, D. A. (1996). Effects of pictographs and quoting on flaming in electronic mail. Computers in Human Behavior, 12(2), 225–243. https://doi.org/10.1016/0747-5632(96)00004-0
    Trepte, S., & Loy, L. S. (2017). Social Identity Theory and Self‐Categorization Theory. In P. Rössler, C. A. Hoffner, & L. Zoonen (Eds.), The International Encyclopedia of Media Effects (1st ed., pp. 1–13). Wiley. https://doi.org/10.1002/9781118783764.wbieme0088
    Varanasi, R. A., Dicicco, E., & Gambino, A. (2018). Facebook Reactions: Impact of Introducing New Features of SNS on Social Capital. In C. Stephanidis (Ed.), HCI International 2018 – Posters’ Extended Abstracts (Vol. 850, pp. 444–451). Springer International Publishing. https://doi.org/10.1007/978-3-319-92270-6_64
    World Economic Forum. (2021). The global risks report 2021 (16th ed.). World Economic Forum. http://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2021.pdf
    Yardi, S., & Boyd, D. (2010). Dynamic Debates: An Analysis of Group Polarization Over Time on Twitter. Bulletin of Science, Technology & Society, 30(5), 316–327. https://doi.org/10.1177/0270467610380011
    Yuqing Ren, Kraut, R., & Kiesler, S. (2007). Applying Common Identity and Bond Theory to Design of Online Communities. Organization Studies, 28(3), 377–408. https://doi.org/10.1177/0170840607076007
    Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091. https://doi.org/10.1016/j.dss.2012.10.034
    Zhu, D. H., Sun, H., & Chang, Y. P. (2016). Effect of social support on customer satisfaction and citizenship behavior in online brand communities: The moderating role of support source. Journal of Retailing and Consumer Services, 31, 287–293. https://doi.org/10.1016/j.jretconser.2016.04.013
    中央通訊社(2024年7月11日)。NewJeans出任韓國觀光宣傳大使 瞄準年輕市場。中央社。取自https://www.cna.com.tw/news/aopl/202407110078.aspx

    無法下載圖示 校內:2030-07-14公開
    校外:2030-07-14公開
    電子論文尚未授權公開,紙本請查館藏目錄
    QR CODE