請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張仁和 | zh_TW |
| dc.contributor.advisor | Jen-Ho Chang | en |
| dc.contributor.author | 張詠 | zh_TW |
| dc.contributor.author | Yong Zhang | en |
| dc.date.accessioned | 2025-07-11T16:13:42Z | - |
| dc.date.available | 2025-07-12 | - |
| dc.date.copyright | 2025-07-11 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-06-30 | - |
| dc.identifier.citation | 中文部分
江宜諭、廖御圻、張瀚云（2024）：〈大學生網路成癮、睡眠品質和身心健康之關聯研究〉。《教育心理學報》，55（3），557-576。https://doi.org/10.6251/BEP.202403_55(3).0006 林宗弘（2015）：〈再探臺灣的世代政治：交叉分類隨機效應模型的應用，1995–2010〉。《人文及社會科學集刊》，27（2），395-436。 周玉慧、朱瑞玲（2008）：〈變遷中的台灣民眾心理需求、疏離感與身心困擾〉。《台灣社會學刊》，41，59-96。 陶宏麟、鄭輝培（2019）：〈快樂在年齡上的變化：U型的爭辯與臺灣兩性的差異〉。《人口學刊》，（59），35-88。 陶振超（2025）：〈從奢侈品到日用品：臺灣民眾的網路使用行為〉。張卿卿、陶振超（主編），《傳播潮什麼：臺灣傳播行為變遷與理論對話》，頁53-69。國立陽明交通大學出版社。 陳瑋婷、黃瓊蓉（2015）：〈臺灣學生網路成癮性別差異之後設分析〉。《中華心理衛生學刊》，28（3），351-387。https://doi.org/10.30074/FJMH.201509_28(3).0001 陳鴻嘉（2020）：〈台灣民眾媒體使用的變遷（1993~2018）：交叉分類隨機效應模型的應用〉。2020年臺灣傳播年會。台北：銘傳大學。 數位發展部（2024）：〈113 年數位近用調查報告及摘要〉。https://moda.gov.tw/digital-affairs/digital-service/dv-survey/14516 劉蓉果、朱瑞玲（2020）：〈臺灣民衆幸福感變動趨勢：世代、年齡及人格的影響〉。《人口學刊》，（60），43-94。https://doi.org/10.6191/JPS.202006_(60).0002 Twenge, J. M.（2020）：《i世代報告：更包容、沒有叛逆期，卻也更憂鬱不安，且遲遲無法長大的一代》（林哲安、程道民譯）。大家出版。（原著出版年：2017） Twenge, J. M.（2024）：《跨世代報告：從出生率到工作、政治、經濟、科技、心理健康，世代差異如何影響百年來的人類軌跡？》（朱怡康譯）。大家出版。（原著出版年：2023） TWNIC財團法人台灣網路資訊中心（2003）：〈2003年7月台灣地區寬頻網路使用調查報告全文〉。https://www.twnic.tw/doc/twrp/200307f.shtml TWNIC財團法人台灣網路資訊中心（2024）：〈2024年台灣網路報告〉。https://twnic.tw/stat_n.php 西文部分 Aalbers, G., McNally, R. J., Heeren, A., De Wit, S., & Fried, E. I. (2019). Social media and depression symptoms: A network perspective. Journal of Experimental Psychology: General, 148(8), 1454. Appel, M., Marker, C., & Gnambs, T. (2020). Are social media ruining our lives? A review of meta-analytic evidence. Review of General Psychology, 24(1), 60-74. Augner, C., Vlasak, T., & Barth, A. (2023). The relationship between problematic internet use and attention deficit, hyperactivity and impulsivity: A meta-analysis. Journal of Psychiatric Research, 168, 1-12. Bayer, J. B., Triệu, P., & Ellison, N. B. (2020). Social media elements, ecologies, and effects. Annual review of psychology, 71(1), 471-497. Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British journal of Educational Technology, 39(5), 775-786. Benge, J. F., & Scullin, M. K. (2025). A meta-analysis of technology use and cognitive aging. Nature Human Behaviour, 1-15. Berlyne, D. E. (1960). Conflict, arousal, and curiosity. McGraw-Hill Book Company. https://doi.org/10.1037/11164-000 Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and adolescent wellbeing: A systematic narrative review. Children and Youth Services Review, 41, 27-36. Cai, Z., Mao, P., Wang, Z., Wang, D., He, J., & Fan, X. (2023). Associations between problematic internet use and mental health outcomes of students: A meta-analytic review. Adolescent Research Review, 8(1), 45-62. Carstensen, L. L. (1992). Social and emotional patterns in adulthood: Support for socioemotional selectivity theory. Psychology and Aging, 7(3), 331. Cheng, C., Chen, S., & Chen, S. (2024). Impact of national lockdown measures on the association between social media use and sleep disturbance during COVID-19: A meta-analysis of 21 nations. Cyberpsychology, Behavior, and Social Networking, 27(8), 527-538. DataReportal (2024). Digital 2024: Taiwan. https://datareportal.com/reports/digital-2024-taiwan De Coninck, D., Waechter, N., & d'Haenens, L. (2023). Predicting self-reported depression and health among adolescents: Time spent online mediated by digital skills and digital activities. Cyberpsychology, Behavior, and Social Networking, 26(10), 747-754. Dienlin, T., & Johannes, N. (2020). The impact of digital technology use on adolescent well-being. Dialogues in Clinical Neuroscience, 22(2), 135-142. Duplaga, M. (2021). The association between Internet use and health-related outcomes in older adults and the elderly: A cross-sectional study. BMC Medical Informatics and Decision Making, 21(1), 150. Gecaite-Stonciene, J., Saudargiene, A., Pranckeviciene, A., Liaugaudaite, V., Griskova-Bulanova, I., Simkute, D., ... & Burkauskas, J. (2021). Impulsivity mediates associations between problematic internet use, anxiety, and depressive symptoms in students: A cross-sectional COVID-19 study. Frontiers in Psychiatry, 12, 634464. Ghersetti, M., & Westlund, O. (2018). Habits and generational media use. Journalism Studies, 19(7), 1039-1058. Greitemeyer, T., & Osswald, S. (2010). Effects of prosocial video games on prosocial behavior. Journal of Personality and Social Psychology, 98(2), 211. Godard, R., & Holtzman, S. (2024). Are active and passive social media use related to mental health, wellbeing, and social support outcomes? A meta-analysis of 141 studies. Journal of Computer-Mediated Communication, 29(1), zmad055. Jensen, M., George, M. J., Russell, M. R., & Odgers, C. L. (2019). Young adolescents’ digital technology use and mental health symptoms: Little evidence of longitudinal or daily linkages. Clinical Psychological Science, 7(6), 1416-1433. Johannes, N., Dienlin, T., Bakhshi, H., & Przybylski, A. K. (2022). No effect of different types of media on well-being. Scientific Reports, 12(1), 61. Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2012). The Goldilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex. PloS One, 7(5), e36399. Kroencke, L., Harari, G. M., Back, M. D., & Wagner, J. (2023). Well-being in social interactions: Examining personality-situation dynamics in face-to-face and computer-mediated communication. Journal of Personality and Social Psychology, 124(2), 437. Kross, E., Verduyn, P., Sheppes, G., Costello, C. K., Jonides, J., & Ybarra, O. (2021). Social media and well-being: Pitfalls, progress, and next steps. Trends in Cognitive Sciences, 25(1), 55-66. LaRose, R. (2010). The problem of media habits. Communication Theory, 20(2), 194-222. Liu, S., Zou, S., Zhang, D., Wang, X., & Wu, X. (2022). Problematic Internet use and academic engagement during the COVID-19 lockdown: The indirect effects of depression, anxiety, and insomnia in early, middle, and late adolescence. Journal of Affective Disorders, 309, 9-18. Liu, Y., & Marciano, L. (2024). Appnome analysis reveals small or no associations between social media app-specific usage and adolescent well-being. Scientific Reports, 14(1), 30836. Luo, Y., Yip, P. S. F., & Zhang, Q. (2025). Positive association between Internet use and mental health among adults aged≥ 50 years in 23 countries. Nature Human Behaviour, 9(1), 90-100. Ma, J. Q., & Sheng, L. (2023). Internet use time and mental health among rural adolescents in China: A longitudinal study. Journal of Affective Disorders, 337, 18-26. Madigan, S., Eirich, R., Pador, P., McArthur, B. A., & Neville, R. D. (2022). Assessment of changes in child and adolescent screen time during the COVID-19 pandemic: a systematic review and meta-analysis. JAMA Pediatrics, 176(12), 1188-1198. Metzler, H., & Garcia, D. (2024). Social drivers and algorithmic mechanisms on digital media. Perspectives on Psychological Science, 19(5), 735-748. Midgley, C., Thai, S., Lockwood, P., Kovacheff, C., & Page-Gould, E. (2021). When every day is a high school reunion: Social media comparisons and self-esteem. Journal of Personality and Social Psychology, 121(2), 285. Mikami, A. Y., Khalis, A., & Karasavva, V. (2025). Logging out or leaning in? Social media strategies for enhancing well-being. Journal of Experimental Psychology: General, 154(1), 171–189. Nagata, J. M., Chu, J., Ganson, K. T., Murray, S. B., Iyer, P., Gabriel, K. P., Garber, A. K., Bibbins-Domingo, K., Baker, FC. (2023). Contemporary screen time modalities and disruptive behavior disorders in children: A prospective cohort study. Journal of Child Psychology and Psychiatry, 64(1), 125-135. Nguyen, C. T. T., Yang, H. J., Lee, G. T., Nguyen, L. T. K., & Kuo, S. Y. (2022). Relationships of excessive internet use with depression, anxiety, and sleep quality among high school students in northern Vietnam. Journal of Pediatric Nursing, 62, e91-e97. Nguyen, M. H., & Hargittai, E. (2024). Digital disconnection, digital inequality, and subjective well-being: A mobile experience sampling study. Journal of Computer-Mediated Communication, 29(1), zmad044. Nowland, R., Necka, E. A., & Cacioppo, J. T. (2018). Loneliness and social internet use: pathways to reconnection in a digital world?. Perspectives on Psychological Science, 13(1), 70-87. O’Day, E. B., & Heimberg, R. G. (2021). Social media use, social anxiety, and loneliness: A systematic review. Computers in Human Behavior Reports, 3, 100070. Orben, A., Przybylski, A. K., Blakemore, S. J., & Kievit, R. A. (2022). Windows of developmental sensitivity to social media. Nature Communications, 13(1), 1649. Pew Research Center surveys. (2023). Teens and social media: Key findings from Pew Research Center surveys. https://www.pewresearch.org/short-reads/2023/04/24/teens-and-social-media-key-findings-from-pew-research-center-surveys/ Prensky, M. (2001). Digital natives, digital immigrants part 2: Do they really think differently?. On the Horizon, 9(6), 1-6. Przybylski, A. K., & Weinstein, N. (2017). A large-scale test of the goldilocks hypothesis: Quantifying the relations between digital-screen use and the mental well-being of adolescents. Psychological Science, 28(2), 204-215. Przybylski, A. K., Orben, A., & Weinstein, N. (2020). How much is too much? Examining the relationship between digital screen engagement and psychosocial functioning in a confirmatory cohort study. Journal of the American Academy of Child & Adolescent Psychiatry, 59(9), 1080-1088. Rifkin, J. R., Chan, C., & Kahn, B. E. (2025). Anxiety about the social consequences of missed group experiences intensifies fear of missing out (FOMO). Journal of Personality and Social Psychology, 128(2), 300–313. Sanders, T., Noetel, M., Parker, P., Del Pozo Cruz, B., Biddle, S., Ronto, R., Hulteen, R., Parker, R., Thomas, G., De Cocker, K., Salmon, J., Hesketh, K., Weeks, N., Arnott, H., Devine, E., Vasconcellos, R., Pagano, R., Sherson, J., Conigrave, J., & Lonsdale, C. (2024). An umbrella review of the benefits and risks associated with youths’ interactions with electronic screens. Nature Human Behaviour, 8(1), 82-99. Schemer, C., Masur, P. K., Geiß, S., Müller, P., & Schäfer, S. (2021). The impact of internet and social media use on well-being: A longitudinal analysis of adolescents across nine years. Journal of Computer-Mediated Communication, 26(1), 1-21. Sen, K., Prybutok, G., & Prybutok, V. (2022). The use of digital technology for social wellbeing reduces social isolation in older adults: A systematic review. SSM-Population Health, 17, 101020. Shi, J. G., Liu, M., Fu, G., & Dai, X. (2023). Internet use among older adults: determinants of usage and impacts on individuals’ well-being. Computers in Human Behavior, 139, 107538. Stavrova, O., & Denissen, J. (2021). Does using social media jeopardize well-being? The importance of separating within-from between-person effects. Social Psychological and Personality Science, 12(6), 964-973. Stockwell, S., Stubbs, B., Jackson, S. E., Fisher, A., Yang, L., & Smith, L. (2021). Internet use, social isolation and loneliness in older adults. Ageing & Society, 41(12), 2723-2746. Stuart, J., O'Donnell, K., O'Donnell, A., Scott, R., & Barber, B. (2021). Online social connection as a buffer of health anxiety and isolation during COVID-19. Cyberpsychology, Behavior, and Social Networking, 24(8), 521-525. Szabo, A., Allen, J., Stephens, C., & Alpass, F. (2019). Longitudinal analysis of the relationship between purposes of internet use and well-being among older adults. The Gerontologist, 59(1), 58-68. Tao, S., Reichert, F., Law, N. W., & Rao, N. (2023). Digital technology use and adolescent mental health before and during the COVID-19 pandemic: The roles of Internet addiction and digital competence. Cyberpsychology, Behavior, and Social Networking, 26(10), 739-746. Tkaczyk, M., Tancoš, M., Smahel, D., Elavsky, S., & Plhák, J. (2024). (In) accuracy and convergent validity of daily end-of-day and single-time self-reported estimations of smartphone use among adolescents. Computers in Human Behavior, 158, 108281. Valkenburg, P. M. (2022). Social media use and well-being: What we know and what we need to know. Current Opinion in Psychology, 45, 101294. Verduyn, P., Lee, DS., Park, J., Shablack, H., Orvell, A., Bayer, J ., Ybarra, O., Jonides, J., Kross, E. (2015). Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. Journal of Experimental Psychology: General, 144(2), 480-488. Vuorre, M., Orben, A., & Przybylski, A. K. (2021). There is no evidence that associations between adolescents’ digital technology engagement and mental health problems have increased. Clinical Psychological Science, 9(5), 823-835. Vuorre, M., & Przybylski, A. K. (2024). Global well-being and mental health in the internet age. Clinical Psychological Science, 12(5), 917-935. Yang, Y., & Land, K. C. (2013). Age-period-cohort analysis: New models, methods, and empirical applications. Taylor & Francis. Young, K. S., & Rogers, R. C. (1998). The relationship between depression and Internet addiction. Cyberpsychology & Behavior, 1(1), 25-28. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97696 | - |
| dc.description.abstract | 科技進步推動數位設備普及，深刻影響人們的生活與社會互動。自1998年以來，台灣民眾的網路使用隨時代演變，並與社會背景和科技發展密切相關。過往研究多關注年齡對網路使用時間與幸福感關係的調節效果，但對於時期與世代效應的影響仍缺乏探討。因此，本研究旨在檢驗網路使用時間對幸福感的影響，並進一步探討年齡、時期與世代的調節作用。本研究整合1998至2023年間共19期《台灣社會變遷基本調查》資料，超過兩萬名參與者，運用「階層年齡—時期—世代交叉分類隨機效應模型」（Hierarchical APC-Cross Classified Random Effects Models, HAPC-CCREM）進行分析。結果顯示：（1）控制年齡、時期與世代後，網路使用時間與幸福感呈倒U型趨勢關係。（2）年齡效應在網路使用時間對幸福感之間具顯著調節效果；其中，高齡者對網路使用時間的敏感度較高，倒U型趨勢更為明顯。（3）然而，時期與世代效應在網路使用時間對幸福感之間皆未達顯著調節效果。（4）以快樂感作為替代指標進一步分析，顯示倒U型趨勢及APC效果得到驗證。根據研究發現，網路使用時間對幸福感的影響存在最佳範圍，過度／少皆可能帶來負面效果，且此關係主要受年齡調節。研究結果對於理解數位時代變遷下的心理福祉提供了實證支持，為未來的政策制定與心理健康促進提供參考依據。 | zh_TW |
| dc.description.abstract | Technological progress has promoted the popularization of digital devices and deeply influenced people's life and social interaction patterns. Since 1998, the use of Internet in Taiwanese people has evolved with the times and is closely related to social background and technological development. Previous studies have mostly focused on the moderating effect of age on the relationship between internet use time and well-being, while the influences of period and cohort effects have received limited attention. Therefore, this study aims to examine the effect of internet use time on well-being and further explore the moderating roles of age, period, and cohort. This study integrates data from 19 waves of the "Taiwan Social Change Survey" conducted between 1998 and 2023, over 20,000 participants, this study adopts the Hierarchical Age–Period–Cohort Cross-Classified Random Effects Model (HAPC-CCREM) for analysis. The results show that: (1) After controlling for age, period, and cohort, internet use time shows an inverted U-shaped relationship with well-being. (2) Age significantly moderates the relationship between internet use time and well-being, with older adults being more sensitive to internet use time and the inverted U-shaped trend is more obvious. (3) Period and cohort effects do not significantly moderate the relationship between internet use time and well-being. (4) A supplementary analysis using happiness as an alternative indicator confirms the inverted U-shaped trend and APC effects. Based on the findings, there appears to be an optimal range of internet use time for well-being, while both excessive and insufficient use lead to negative outcomes. This relationship is primarily moderated by age. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-11T16:13:42Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-11T16:13:42Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
摘要 II ABSTRACT III 目次 IV 表次 VI 圖次 VII 第一章 緒論 1 第一節 研究背景與動機 1 第二節 文獻回顧 2 第三節 小結與本研究目的 11 第二章 研究方法 13 第一節 資料來源與研究參與者 13 第二節 主要分析變項 15 第三章 研究結果 21 第一節 描述性統計 21 第二節 相關分析：網路使用時間對幸福感之關係 25 第三節 HAPC-CCREM：幸福感 26 第四節 HAPC-CCREM：快樂感 40 第四章 結論與討論 57 第一節 一般性綜合討論 57 第二節 研究限制與未來研究 61 第三節 結論 62 參考文獻 65 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 幸福感 | zh_TW |
| dc.subject | HAPC-CCREM | zh_TW |
| dc.subject | APC效應 | zh_TW |
| dc.subject | 網路使用時間 | zh_TW |
| dc.subject | 快樂感 | zh_TW |
| dc.subject | happiness | en |
| dc.subject | well-being | en |
| dc.subject | internet use | en |
| dc.subject | HAPC-CCREM | en |
| dc.subject | APC effects | en |
| dc.title | 幸福感的數位變遷：從年齡、時期與世代探討網路使用時間跟幸福感之關聯 | zh_TW |
| dc.title | An Age–Period–Cohort Analysis of the Relationship Between Internet Use and Well-Being | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 李怡青 | zh_TW |
| dc.contributor.coadvisor | I-Ching Lee | en |
| dc.contributor.oralexamcommittee | 郭郡羽;許詩淇 | zh_TW |
| dc.contributor.oralexamcommittee | Jun-Yu Guo;Shih-Chi Hsh | en |
| dc.subject.keyword | HAPC-CCREM,APC效應,快樂感,幸福感,網路使用時間, | zh_TW |
| dc.subject.keyword | HAPC-CCREM,APC effects,happiness,well-being,internet use, | en |
| dc.relation.page | 71 | - |
| dc.identifier.doi | 10.6342/NTU202501171 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-01 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 心理學系 | - |
| dc.date.embargo-lift | 2025-07-12 | - |
| 顯示於系所單位： | 心理學系 | |
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