本研究探討影響使用者陷入資訊繭房的主要因素，並進行了深入的分析和研究。背景動機源於社群媒體平台上推薦系統的普及，這些系統通過個性化的推薦，可能導致使用者只接觸到相似的資訊，從而形成資訊繭房。研究目的在於解析使用者資訊繭房的形成機制，並提出相應的對策，以促進資訊多元化和公共討論的健康發展。
研究發現顯示，當推薦內容越趨同質化時，使用者更容易陷入資訊繭房。此外，渴望探索新知的使用者較不容易受到資訊繭房的影響。資訊繭房與群體極化之間存在顯著關聯，長期接觸同質化資訊的使用者其觀點會變得更加極端，並加劇與持不同觀點者之間的分歧。
研究方法採用問卷調查法，針對社群媒體平台使用者進行數據收集和分析，並運用結構方程模型驗證研究假說。研究結果不僅填補了資訊繭房研究中的多項空白，也為未來的研究指明了新的方向。

This study explores the major factors affecting users' immersion in information cocoons, providing an in-depth analysis and research. The background motivation stems from the widespread use of recommendation systems on social media platforms. These systems, through personalized recommendations, may lead users to only access similar information, thus forming information cocoons. The research aims to analyze the formation mechanisms of users' information cocoons and propose corresponding countermeasures to promote information diversity and healthy public discussions.
The research findings indicate that when recommended content becomes more homogeneous, users are more likely to fall into information cocoons. Additionally, users with a strong desire to explore new knowledge are less likely to be affected by information cocoons. There is a significant correlation between information cocoons and group polarization, where users exposed to homogeneous information over time become more extreme in their views and increase disagreements with those holding different perspectives.
The research methodology involves a questionnaire survey, targeting users of social media platforms for data collection and analysis, and employing structural equation modeling to test the hypotheses. The results not only fill several gaps in the research on information cocoons but also point to new directions for future studies.

目錄
論文審定書 ............................................................................................................................................ i
致謝 ....................................................................................................................................................... ii
摘要 ...................................................................................................................................................... iii
Abstract ................................................................................................................................................ iv
目錄 ....................................................................................................................................................... v
圖次 ..................................................................................................................................................... vii
表次 .................................................................................................................................................... viii
第壹章 緒論..................................................................................................................................... 1
研究背景 ............................................................................................................................ 1
研究動機 ............................................................................................................................ 4
研究目的 ............................................................................................................................ 5
研究流程 ............................................................................................................................ 6
第貳章 文獻探討 ............................................................................................................................. 7
資訊繭房(INFORMATION COCOON) .......................................................................................... 7
群體極化(GROUP POLARIZATION) ........................................................................................... 8
使用者個人對資訊的態度構面 ...................................................................................... 10
使用者個人獲取資訊的態度構面 .................................................................................. 13
使用者個人社交環境構面 .............................................................................................. 14
第參章 研究方法 ........................................................................................................................... 17
研究架構 .......................................................................................................................... 17
研究假說 .......................................................................................................................... 17
vi
...................................................................................................................... 24
研究設計 .......................................................................................................................... 24
第肆章 資料分析 ........................................................................................................................... 29
描述性統計 ...................................................................................................................... 29
衡量模型 .......................................................................................................................... 33
結構模型與假說檢定 ...................................................................................................... 43
討論(DISCUSSION) ............................................................................................................... 46
第伍章 結論與建議 ....................................................................................................................... 47
研究結論 .......................................................................................................................... 47
管理意涵(理論與實務貢獻)............................................................................................ 49
研究限制與建議 .............................................................................................................. 50
參考文獻 ............................................................................................................................................. 51
中文參考文獻 .................................................................................................................. 51
英文參考文獻 .................................................................................................................. 52
附錄：正式問卷 .................................................................................................................................. 59

中文參考文獻
1.
Cheng, H. F. (2023). 一般年輕族群使用社群媒體的心理經驗: 以國立大學學生為例. 教育心理學報, 54(3), 663-683.簡體版
2. 劉友芝、胡青山(2022)。基于算法推薦模式的社會性反思:個體困境、群體極化與媒體公共性。傳播經紀與管理研究，(1)，192-212。簡體版
3. 吳旻純. (2019). Seeing is Believing:[同溫層] 效應. 清流雙月刊, (20), 25-30.
4.
張海、徐红昌(2022)。S-O-R理論視角下網路用戶資訊繭房成因要素研究。新世纪圖書館，(12)，15-22。簡體版
5.
段薈,袁勇志,張海. 大數據環境下網路用户資訊繭房形成機制的實證研究 [J]. 情報雜誌, 2020, 39 (11): 158-164.簡體版
6. 陳怡璇. (2009). 影響電視新聞同質化研究─ 組織層次因素的分析 (Doctoral dissertation).
7. 彭藍(2020)。導致資訊繭房的多重因素及“破繭”路徑。新闻界，(1)，30-38。簡體版
8. 谢佩峰, 葉青, & 吴磊. (2019). 社群营销與運營實戰. 華中科技大學出版社有限责任公司.簡體版
9. 蔡淑芬.TWNIC(2020，12月30日)。「社群媒體同溫層之傳播內容對社會建構之和諧與對立之挑戰」。財團法人台灣網路資訊中心。https://blog.twnic.tw/2020/12/25/16495/
10. 薛堯云(2019)。算法推薦機制下的短影音“過濾氣泡”問題研究——以抖音为例。收藏，14。簡體版
51
英文參考文獻
1. Abrams, D., Wetherell, M., Cochrane, S., Hogg, M. A., & Turner, J. C. (1990). Knowing what to think by knowing who you are: Self‐categorization and the nature of norm formation, conformity and group polarization. British journal of social psychology, 29(2), 97-119.
2. Arceneaux, K., & Johnson, M. (2010). Does media fragmentation produce mass polarization? Selective exposure and a new era of minimal effects. Selective Exposure and a New Era of Minimal Effects.
3.
Benkler, Y., Faris, R., & Roberts, H. (2018). Network propaganda: Manipulation, disinformation, and radicalization in American politics. Oxford University Press.
4.
Bodó, B., Helberger, N., Eskens, S., & Möller, J. (2019). Interested in diversity: The role of user attitudes, algorithmic feedback loops, and policy in news personalization. Digital journalism, 7(2), 206-229.
5. Brewer, M. B. (1979). In-group bias in the minimal intergroup situation: A cognitive-motivational analysis. Psychological bulletin, 86(2), 307.
6. Bright, J. (2018). Explaining the emergence of political fragmentation on social media: The role of ideology and extremism. Journal of Computer-Mediated Communication, 23(1), 17-33.
7. Chambers, D. (2013). Social media and personal relationships: Online intimacies and networked friendship. Springer.
8. Chaney, A. J., Stewart, B. M., & Engelhardt, B. E. (2018, September). How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In Proceedings of the 12th ACM conference on recommender systems (pp. 224-232).Hou, L., Pan, X., Liu, K., Yang, Z., Liu, J., & Zhou, T. (2023). Information cocoons in online navigation. IScience, 26(1), 105893.
52
https://doi.org/10.1016/j.isci.2022.105893
9.
Chatterjee, S., & Price, B. (1991). Regression Analysis by Example (2nd ed.). Wiley.
10.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 295-336). Lawrence Erlbaum Associates.
11.
Clark, L. (2022). User Agency and Control in Algorithmic Information Cocoon. Algorithm Information Journal.
12. Dai, B., Ali, A., & Wang, H. (2020). Exploring information avoidance intention of social media users: A cognition–affect–conation perspective. Internet Research, 30(5), 1455-1478.
13. Das, B., & Sahoo, J. S. (2011). Social networking sites–a critical analysis of its impact on personal and social life. International Journal of Business and Social Science, 2(14), 222-228.
14.
DataReportal. (2024). Digital 2024: Taiwan. Retrieved from https://datareportal.com/reports/digital-2024-taiwan
15.
DeVellis, R. F. (2012). Scale development: Theory and applications (3rd ed.). Sage Publications.
16. Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192.
17.
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/10.2307/3151312
53
18.
Green, S. (2024). Future Directions in Information Cocoon Research. Information Cocoon Review.
19.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
20.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2012). Multivariate Data Analysis (7th ed.). Pearson.
21.
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.
22. Hansen, S. S., Lee, J. K., & Lee, S. Y. (2014). Consumer-generated ads on YouTube: Impacts of source credibility and need for cognition on attitudes, interactive behaviors, and eWOM. Journal of Electronic Commerce Research, 15(3), 254.
23. Harrigan, M., Feddema, K., Wang, S., Harrigan, P., & Diot, E. (2021). How trust leads to online purchase intention founded in perceived usefulness and peer communication. Journal of Consumer Behaviour, 20(5), 1297-1312.
24.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
25.
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204.
26. Isbulan, O. (2011). Opinions of University Graduates about Social Networks According to Their Personal Characteristics. Turkish Online Journal of Educational Technology-TOJET, 10(2), 184-189.
27. Jiang, T., & Xu, Y. (2021). Narrowed information universe: A review of research
54
on information cocoons, selective exposure, and echo chambers. Documentation, Information & Knowledge in China, 38(5), 134-144.
28.
Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). Springer.
29.
Kelman, H. C. (1958). Compliance, identification, and internalization: Three processes of attitude change. Journal of Conflict Resolution, 2(1), 51–60. https://doi.org/10.1177/002200275800200106
30.
King, P. (2021). Information Cocoon and Political Polarization in Digital Age. Political Studies in Digital Age.
31.
Kong, Z., Zhang, X., & Wang, R. (2021, November). Review of the research on the relationship between algorithmic news recommendation and information cocoons. In 2021 3rd International Conference on Literature, Art and Human Development (ICLAHD 2021) (pp. 341-345). Atlantis Press.
32. Mancini, P. (2013). Media fragmentation, party system, and democracy. The International Journal of Press/Politics, 18(1), 43-60.
33. O’malley, A. J., Arbesman, S., Steiger, D. M., Fowler, J. H., & Christakis, N. A. (2012). Egocentric social network structure, health, and pro-social behaviors in a national panel study of Americans. PloS one, 7(5), e36250.
34. O'neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
35. Pan, Z., Lu, Y., Wang, B., & Chau, P. Y. (2017). Who do you think you are? Common and differential effects of social self-identity on social media usage. Journal of Management Information Systems, 34(1), 71-101.
36. Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.
37.
Peng, H., & Liu, C. (2021). Breaking the information cocoon: When do people
55
actively seek conflicting information? Proceedings of the Association for
Information Science and Technology, 58(1), 801–803.
38.
Perez, X. (2021). Analyzing Information Cocoon through Big Data Analytics. Big Data Analytics Journal.
39. Petty, R. E., Briñol, P., Loersch, C., McCaslin, M. J., Leary, M. R., & Hoyle, R. H. (2009). The need for cognition. Handbook of individual differences in social behavior, 318, 329.
40.
Ren, S., Liu, L., Yang, S., & Jiang, J. (2022). Investigating Information Cocoon Attitudes in Short-Form Video Applications. International Conference on Human-Computer Interaction, 89–96.
41.
Robinson, N. (2021). AI-driven Solutions to Counteract Information Cocoon. AI Solutions Review.
42.
Scott, R. (2023). Information Cocoon and Digital Well-being. Digital Well-being Journal.
43.
Shen, J., & Jiuhua Zhu, C. (2011). Effects of socially responsible human resource management on employee organizational commitment. International Journal of Human Resource Management, 22(15), 3020-3035. https://doi.org/10.1080/09585192.2011.599951
44. Shivaram, K., Liu, P., Shapiro, M., Bilgic, M., & Culotta, A. (2022, September). Reducing cross-topic political homogenization in content-based news recommendation. In Proceedings of the 16th ACM conference on Recommender Systems (pp. 220-228).
45.
Song, J., & Kim, Y. J. (2006). Social influence process in the acceptance of a virtual community service. Information Systems Frontiers, 8, 241–252. https://doi.org/10.1007/s10796-006-0011-y
56
46. Spies Shapiro, L. A., & Margolin, G. (2014). Growing up wired: Social networking sites and adolescent psychosocial development. Clinical child and family psychology review, 17, 1-18.
47. Sunstein, C. R. (2001). Republic. com. Princeton university press.
48. Sunstein, C. R. (2001). Republic. com. Princeton university press.
49.
Tewksbury, D., & Rittenberg, J. (2012). News on the Internet: Information and Citizenship in the 21st Century. OUP USA.
50. Trepte, S. (2013). Social identity theory. In Psychology of entertainment (pp. 255-271). Routledge.
51. Tsfati, Y., & Cappella, J. N. (2005). Why do people watch news they do not trust? The need for cognition as a moderator in the association between news media skepticism and exposure. Media psychology, 7(3), 251-271.
52.
Turner, J. C. (1991). Social influence. Milton-Keynes, UK: Open University Press.
53. Van Swol, L. M. (2009). Extreme members and group polarization. Social Influence, 4(3), 185-199.
54. 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.
55.
Walker, O. (2021). The Role of Digital Literacy in Mitigating Information Cocoon. Digital Literacy Quarterly.
56.
Walker, O. (2023). Ethical Implications of Information Cocoon in AI Systems. AI Systems Review.
57. Wang, T. (2017). Social identity dimensions and consumer behavior in social media. Asia Pacific Management Review, 22(1), 45-51.
58. Yang, C. C., & Brown, B. B. (2015). Factors involved in associations between
57
Facebook use and college adjustment: Social competence, perceived usefulness, and use patterns. Computers in Human Behavior, 46, 245-253.
59. 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
60.
Yuan, X., & Wang, C. (2022). Research on the formation mechanism of information cocoon and individual differences among researchers based on information ecology theory. Frontiers in Psychology, 13, 1055798.
61.
Yuan, X., & Wang, C. (2022). Research on the formation mechanism of information cocoon and individual differences among researchers based on information ecology theory. Frontiers in Psychology, 13, 1055798. https://doi.org/10.3389/fpsyg.2022.1055798