本研究旨在檢驗國中生的憂鬱情緒、現實與虛擬社會支持、拒網自我效能與網路成癮間之關聯性，並釐清拒網自我效能是否能中介憂鬱情緒對網路成癮之預測關係，以及現實社會支持與虛擬社會支持是否能分別調節憂鬱情緒對拒網自我效能的預測關係。本研究採橫斷研究法，以臺灣國中生作為研究對象，共取得有效問卷1,129份，並使用「個人資料表」、「憂鬱焦慮壓力量表之憂鬱分量表」、「社會支持量表」、「拒網自我效能量表」及「陳氏網路成癮量表」等作為研究工具。本研究採用描述統計、皮爾森相關分析及結構方程模式等統計方法進行資料分析。研究結果顯示：（1）國中生平均每週上網時間為20.03小時（標準差為17.16小時）且有90.6%之國中生擁有自己的智慧型手機；（2）國中生之時間管理能力與網路成癮傾向呈顯著負相關；（3）憂鬱情緒可顯著且正向預測網路成癮；（4）拒網自我效能可顯著且負向預測網路成癮；（5）拒網自我效能可部分中介憂鬱情緒對網路成癮之正向預測關係；（6）現實與虛擬社會支持能調節憂鬱情緒對拒網自我效能之負向預測關係。本研究檢驗了國中生憂鬱情緒、現實與虛擬社會支持、拒網自我效能與網路成癮之調節式中介模型，並建議若欲預防網路成癮，可同時降低憂鬱情緒與提升拒網自我效能，且若能提升現實社會支持與降低虛擬社會支持，將能更有效預防國中生沉迷於網路之風險。

Based on the theory of triadic influence (TTI) and buffering hypothesis, the study aimed to investigate the tendency of Internet addiction (IA) in junior high school stu-dents in Taiwan, and to further examine moderated mediation effects of refusal self-efficacy of internet use, actual social support and virtual social support on the relation-ship between depression and IA.
Depression Anxiety Stress Scale (DASS), Refusal Self-Efficacy of Internet Use Scale, Social Support Scale, Virtual Social Support Scale and The Chen Internet Addic-tion Scale (CIAS) were administrated among 1,127 junior high school students (552 fe-males) in Taiwan. Structural equation modeling (SEM) was used to examine the media-tion effect of refusal self-efficacy of internet use on the relation between depression and IA, whereas moderated mediation model was conducted by using the PROCESS macro for SPSS.
The findings showed that refusal self-efficacy of internet use could partially medi-ate the effect of depression on IA. Actual and virtual social support moderated the indi-rect relationship between depression and IA via refusal self-efficacy of internet use. The results not only corresponded with TTI, and were also consistent with the buffering hy-pothesis.
The study aimed to improve junior high school students’ ability to avoid IA when they were suffering from depression. The Moderated mediation model provided a com-prehensive understanding of how depression, actual social support, virtual social support and refusal self-efficacy of internet use work together to affect IA. Theoretical and practical implications were discussed.

王齡竟、陳毓文（2010）：家庭衝突，社會支持與青少年憂鬱情緒：檢視同儕，專業與家外成人支持的緩衝作用。中華心理衛生學刊，23（1），65–97。
林旻沛（2004）：大專校院學生網路成癮盛行率及認知因子之研究（未發表）。國立成功大學行為醫學研究所碩士論文。
林旻沛（2010）：性格與認知因素對大學生網路成癮之影響：一年追蹤研究（未發表）。國立成功大學健康照護科學研究所博士論文。
林旻沛（2014）。臺灣高中職學生網路成癮心理社會成因與預防教育及其成效評估研究。行政院科技部專題研究計畫（編號：MOST 102-2511-S-003- 016-MY3）。
林旻沛（2019）：國中生網路成癮預防教育之課程與教材開發及其成效評估研究：以社會認知理論為基礎。行政院科技部專題研究計畫（編號：MOST 106-2511-S-003-034-MY2）。
朱群芳、巫梓豪、李潼蕙、賴苓蕙（2019）。負面情緒、自我效能、治療同盟與同儕支持對毒癮戒治處遇效果影響程度之探討。藥物濫用防治，4（2），75–103。
財團法人臺灣網路資訊中心（2019）：2019台灣網路報告。引自網站：https://report.twnic.tw/2019/
國家發展委員會（2017）：106年網路沉迷研究調查報告。引自網站：https://ws.ndc.gov.tw/Download.ashx?u=LzAwMS9hZG1pbmlzdHJhdG9yLzEwL2NrZmlsZS83MjVlOTBlOC1iZWVjLTQ0NzgtODA0MC1hNjBlYzEyYjMwNDkucGRm&n=MTA25bm057ay6Lev5rKJ6L%2B3LnBkZg%3D%3D
陳珏汝（2018）：高中職學生自尊、因應型態、拒網自我效能與網路成癮之相關研究（未發表）。國立臺灣師範大學教育心理與輔導學研究所碩士論文。
陳昭瑞（2017）：高中職學生社會影響、上網正向效果預期與網路成癮之相關研究（未發表）。國立臺灣師範大學教育心理與輔導學研究所碩士論文。
陳淑惠、翁儷禎、蘇逸人、吳和懋、楊品鳳（2003）：中文網路成癮量表之編制與心理計量特性研究。中華心理學刊，45（3），279–294。
黃芳銘（2004）：社會科學統計方法學：結構方程模式。五南。
張桂綿（2018）：高中職學生現實和虛擬社會支持、自尊與網路成癮之相關研究（未發表）。國立臺灣師範大學教育心理與輔導學研究所碩士論文。
楊正誠（2003）：大學生網路成癮，社會支持與生活適應之相關研究（未發表）。私立東海大學教育研究所碩士論文。
褚志鵬、林珍如、陳國文（2008）：高中學生綱路使用行為、成癮狀況及戒減自我效能之調查研究。健康管理學刊，6(1)，73–93。
衛生福利部（2017）：聰明上網拒絕沉迷：2016全國網路使用行為調查報告。引自網站：https://www.mohw.gov.tw/cp-2621-567-1.html 

Akbari, M. (2017). Metacognitions or distress intolerance: The mediating role in the rela-tionship between emotional dysregulation and problematic internet use. Addictive Behaviors Reports, 6, 128–133. https://doi.org/10.1016/j.abrep.2017.10.004
Akhtar, M. (2008). What is self-efficacy? Bandura’s 4 sources of efficacy beliefs. Positive psychology UK. https://doi.org/10.17051/io.2015.44466
Alrekebat, A. F. (2016). Internet addiction and its relationship with self-Efficacy level among Al-Hussein Bin Talal University students. Journal of Education and Practice, 7(32), 123–131. https://doi.org/10.7176/JEP/10-30-05
American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders-Text Revision (5th Edition). American Psychiatric Association. https://doi.org/10.1176/appi.books.9780890423349.7336
Aneta, S. T., Kostov, J., & Perevska, Z. (2019). Depression in Survivors of Acute Myo-cardial Infarction. Materia Socio-Medica. https://doi.org/10.5455/msm.2019.31.110-114
Assunção, R., & Matos, P. M. (2017). Adolescents' profiles of problematic Facebook use and associations with developmental variables. Computers in Human Behav-ior, 75, 396–403. https://doi.org/10.1016/j.chb.2017.05.034
Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Jour-nal of Social and Clinical Psychology, 4(3), 359–373. https://doi.org/10.1521/jscp.1986.4.3.359
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinctions in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Bavarian, N., Flay, B. R., Ketcham, P. L., & Smit, E. (2013). Illicit use of prescription stimulants in a college student sample: A theory-guided analysis. Drug and Al-cohol Dependence, 132(3), 665–673. http://doi.org/10.1016/j.drugalcdep.2013.04.024
Bavarian, N., Flay, B. R., Ketcham, P. L., Smit, E., Kodama, C., Martin, M., & Saltz, R. F. (2014). Using structural equation modeling to understand prescription stimu-lant misuse: A test of the Theory of Triadic Influence. Drug and Alcohol De-pendence, 138, 193–201. https://doi.org/10.1016/j.drugalcdep.2014.02.700
Beedie, A., & Kennedy, P. (2002). Quality of social support predicts hopelessness and depression post spinal cord injury. Journal of Clinical Psychology in Medical Set-tings, 9(3), 227–234. https://doi.org/10.1023/A:1016003428370
Bricker, J. B., Rajan, K. B., Zalewski, M., Andersen, M. R., Ramey, M., & Peterson, A. V. (2009). Psychological and social risk factors in adolescent smoking transitions: A population-based longitudinal study. Health Psychology, 28(4), 439. http://doi.org/10.1037/a0014568
Cantrell, P. J. (1993). The Relationship between Relapse Prevention Treatment Outcome and Self-Efficacy. https://files.eric.ed.gov/fulltext/ED370065.pdf
Carli, V., Durkee, T., Wasserman, D., Hadlaczky, G., Despalins, R., Kramarz, E., Was-serman, C., Sarchiapone, M., Hoven, C.W.,Brunner, R., Kaess, M. (2013). The association between pathological internet use and comorbid psychopathology: a systematic review. Psychopathology, 46(1), 1–13. http://doi.org/10.1159/000337971
Cheung, J. C. S., Chan, K. H. W., Lui, Y. W., Tsui, M. S., & Chan, C. (2018). Psycho-logical well-being and adolescents’ internet addiction: A school-based cross-sectional study in Hong Kong. Child and Adolescent Social Work Journal, 35(5), 477–487. http://doi.org/10.1007/s10560-018-0543-7
Chiu, S. I. (2014). The relationship between life stress and smartphone addiction on Taiwanese university student: A mediation model of learning self-efficacy and social self-efficacy. Computers in Human Behavior, 34, 49–57. http://doi.org/ 10.1016/j.chb.2014.01.024
Chun, J. (2015). Determinants of tobacco use among Korean female adolescents: Longi-tudinal test of the theory of triadic influence. Children and Youth Services Re-view, 50, 83–87. http://doi.org/10.1016/j.childyouth.2015.01.009
Cobb, S. (1976). Social support as a moderator of life stress. Psychosomatic Medicine, 38(5), 300–314. http://doi.org/10.1097/00006842-197609000-00003
Cohen, S. E., & Syme, S. L. (1985). Social support and health. Academic Press. http://doi.org/10.1093/geront/29.5.709
Davison, G. C., Neale, J. M., & Kring, A. M. (2004). Abnormal psychology. Wiley. http://doi.org/10.4324/9781315660271-2
Defoe, I. N., Semon Dubas, J., Somerville, L. H., Lugtig, P., & van Aken, M. A. (2016). The unique roles of intrapersonal and social factors in adolescent smoking devel-opment. Developmental Psychology, 52(12), 2044–2056. http://doi.org/10.1037/dev0000198
Demirci, K., Akgönül, M., & Akpinar, A. (2015). Relationship of smartphone use severi-ty with sleep quality, depression, and anxiety in university students. Journal of Behavioral Addictions, 4(2), 85–92. http://doi.org/10.1556/2006.4.2015.010
De Postis, P. (2009). Dossier: addictions avec ou sans substances: Paris. Le Courrier des Addiction, 3. http://doi.org/10.3917/oute.007.0119
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911. http://doi.org/ 10.1037/0003-066X.34.10.906
Flay, B. R., & Petraitis, J. (1994). The theory of triadic influence: A new theory of health behavior with implications for preventive interventions. Medical Sociology, 4, 19–44. http://doi.org/10.4135/9781412952576.n210
Flay, B. R. (1999). Understanding environmental, situational and intrapersonal risk and protective factors for youth tobacco use: the Theory of Triadic Influence. Nico-tine Tob Res, 1(Suppl 2), S111–S114. http://doi.org/10.1080/14622299050011911
Gao, T., Li, J., Zhang, H., Gao, J., Kong, Y., Hu, Y., & Mei, S. (2018). The influence of alexithymia on mobile phone addiction: The role of depression, anxiety and stress. Journal of Affective Disorders, 225, 761–766. http://doi.org/10.1016/j.jad.2017.08.020
Goldberg, I. (1996). Internet addiction disorder. Psychom.net. https://www.psycom.net/iadcriteria.html
Grigsby, T. J., Forster, M., Unger, J. B., & Sussman, S. (2016). Predictors of alcohol-related negative consequences in adolescents: A systematic review of the litera-ture and implications for future research. Journal of Adolescence, 48, 18–35. http://doi.org/10.1016/j.adolescence.2016.01.006
Henderson, N. L., & Dressler, W. W. (2017). Medical disease or moral defect? Stigma attribution and cultural models of addiction causality in a university population. Culture, Medicine, and Psychiatry, 41(4), 480–498. http:/doi.org/10.1007/s11013-017-9531-1
Hovey, J. D. (1999). Moderating influence of social support on suicidal ideation in a sample of Mexican immigrants. Psychological Reports, 85(1), 78–79. http://doi.org/10.2466/pr0.1999.85.1.78
Hovey, J. D. (2000). Acculturative stress, depression, and suicidal ideation among Cen-tral American immigrants. Suicide and Life‐Threatening Behavior, 30(2), 125–139. http://doi.org/10.1111/j.1943-278X.2000.tb01071.x
International Telecommunication Union (2019). New ITU data reveal growing Internet uptake but a widening digital gender divide. https://www.itu.int/en/mediacentre/Pages/2019-PR19.aspx
Khantzian, E. J. (1997). The self-medication hypothesis of substance use disorders: A reconsideration and recent applications. Harvard Review of Psychiatry, 4(5), 231–244. http://doi.org/10.3109/10673229709030550
Kim, K., Ryu, E., Chon, M. Y., Yeun, E. J., Choi, S. Y., Seo, J. S., & Nam, B. W. (2006). Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: A questionnaire survey. International Journal of Nursing Studies, 43(2), 185–192. http://doi.org/10.1016/j.ijnurstu.2005.02.005
Kouimtsidis, C., Drabble, K., & Ford, L. (2012). Implementation and evaluation of a three-stage community treatment programme for alcohol dependence: A short report. Drugs: Education, Prevention and Policy, 19(1), 81–83. http://doi.org/10.3109/09687637.2011.562938
LaRocco, J. M., House, J. S., & French Jr, J. R. (1980). Social support, occupational stress, and health. Journal of Health and Social Behavior, 202–218. http://doi.org/10.2307/2136616
Lau, J. T., Walden, D. L., Wu, A. M., Cheng, K. M., Lau, M. C., & Mo, P. K. (2018). Bidirectional predictions between Internet addiction and probable depression among Chinese adolescents. Journal of Behavioral Addictions, 7(3), 633–643. http://doi.org/10.1556/2006.7.2018.87
Lin, M. P., Ko, H. C., & Wu, J. Y. (2008). The role of positive/ negative outcome expec-tancy and refusal self-efficacy of internet use on Internet addiction among col-lege students in Taiwan. Cyber Psychology& Behavior, 11, 451–457. http://doi.org/10.1089/cpb.2007.0121
Lin, M. P., Wu, J. Y. W., Chen, C. J., & You, J. (2018a). Positive outcome expectancy mediates the relationship between social influence and Internet addiction among senior high-school students. Journal of Behavioral Addictions, 7(2), 292–300. http://doi.org/10.1556/2006.7.2018.56
Lin, M. P., Wu, J. Y. W., You, J., Hu, W. H., & Yen, C. F. (2018b). Prevalence of inter-net addiction and its risk and protective factors in a representative sample of sen-ior high school students in Taiwan. Journal of Adolescence, 62, 38–46. http://doi.org/10.1016/j.adolescence.2017.11.004
Lippke, S., Nigg, C. R., & Maddock, J. E. (2012). Health-promoting and health-risk be-haviors: Theory-driven analyses of multiple health behavior change in three in-ternational samples. International Journal of Behavioral Medicine, 19(1), 1–13. http://doi.org/10.1007/s12529-010-9135-4
Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck De-pression and Anxiety Inventories. Behaviour Research and Therapy, 33(3), 335–343. http://doi.org/10.1016/0005-7967(94)00075-u.
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable ef-fects. Psychological Methods, 7(1), 83–104. http://doi.org/10.1037/1082-989X.7.1.83
Marino, C., Mazzieri, E., Caselli, G., Vieno, A., & Spada, M. M. (2018). Motives to use Facebook and problematic Facebook use in adolescents. Journal of Behavioral Addictions, 7(2), 276–283. http://doi.org/10.1556/2006.7.2018.32
Martens, M. P. (2005). The use of structural equation modeling in counseling psycholo-gy research. The Counseling Psychologist, 33, 269–298. http://doi.org/10.1177/0011000004272260
MPh, M. M. (2015). Prevalence of facebook addiction and related factors among Thai high school students. J Med Assoc Thai, 98(3), S51–S60. http://doi.org/10.6558/MTJM.2008.13(4).6
Petraitis, J., Flay, B. R., & Miller, T. Q. (1995). Reviewing theories of adolescent sub-stance use: organizing pieces in the puzzle. Psychological Bulletin, 117(1), 67. http://doi.org/10.1037/0033-2909.117.1.67
Przepiorka, A., Blachnio, A., & Cudo, A. (2019). The role of depression, personality, and future time perspective in internet addiction in adolescents and emerging adults. Psychiatry Research, 272, 340–348. http://doi.org/10.1016/j.psychres.2018.12.086
Shapira, N. A., Lessig, M. C., Goldsmith, T. D., Szabo, S. T., Lazoritz, M., Gold, M. S., Stein, D. J. (2003). Problematic Internet use: proposed classification and diag-nostic criteria. Depression and Anxiety, 17, 207–216. http://doi.org/10.1002/da.10094
Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychological Methods, 7(4), 422. http://doi.org/10.1037/1082-989X.7.4.422
Siddiqi, S., & Memon, Z. A. (2016). Internet addiction impacts on time management that results in poor academic performance. In 2016 International Conference on Frontiers of Information Technology (FIT) (pp. 63–68). IEEE. http://doi.org/ 10.1109/FIT.2016.020
Spada, M. M., Nikčević, A. V., Moneta, G. B., & Wells, A. (2007). Metacognition as a mediator of the relationship between emotion and smoking dependence. Addic-tive Behaviors, 32(10), 2120–2129. http://doi.org/10.1016/j.addbeh.2007.01.012
Spada, M. M., Langston, B., Nikčević, A. V., & Moneta, G. B. (2008). The role of met-acognitions in problematic Internet use. Computers in Human Behavior, 24(5), 2325–2335. http://doi.org/10.1016/j.chb.2007.12.002
Stavropoulos, V., Gomez, R., Steen, E., Beard, C., Liew, L., & Griffiths, M. D. (2017). The longitudinal association between anxiety and Internet addiction in adoles-cence: The moderating effect of classroom extraversion. Journal of Behavioral Addictions, 6(2), 237–247. http://doi.org/10.1556/2006.6.2017.026
Sussman, S., Dent, C. W., & Leu, L. (2000). The one-year prospective prediction of sub-stance abuse and dependence among high-risk adolescents. Journal of Substance Abuse, 12(4), 373–386. http://doi.org/10.1016/S0899-3289(01)00053-0
Tanis, M. (2007). Online social support groups. In A. Joinson, K. McKenna, T. Postmes, & U. D. Reips (Eds.), The Oxford handbook of internet psychology (pp. 139–154). Oxford University Press. http://doi.org/ 10.1093/oxfordhb/9780199933815.013.003
Taouk, M. L. P. F. L. R., Lovibond, P. F., & Laube, R. (2001). Psychometric properties of a Chinese version of the short Depression Anxiety Stress Scales (DASS21). Re-port for new South Wales transcultural mental health centre, Cumberland Hospi-tal, Sydney. http://doi.org/10.1016/j.ajp.2013.11.001
Tsai, C. C., & Lin, S. S. (2003). Internet addiction of adolescents in Taiwan: An inter-view study. Cyber Psychology & Behavior, 6(6), 649–652. http://doi.org/ 10.1089/109493103322725432
Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23(1), 3–43. http://doi.org/10.1177/009365096023001001
Wang, W. C. (2019). Exploring the relationship among free-time management, leisure boredom, and internet addiction in undergraduates in Taiwan. Psychological Re-ports, 122(5), 1651–1665. http://doi.org/10.1177/0033294118789034
Yeh, Y. C., Ko, H. C., Wu, J. Y. W., & Cheng, C. P. (2008). Gender differences in rela-tionships of actual and virtual social support to Internet addiction mediated through depressive symptoms among college students in Taiwan. Cyberpsychol-ogy & Behavior, 11(4), 485–487. http://dx.doi.org/10.1089/cpb.2007.0134
Young, K. S., & Rogers, R. C. (1998). The relationship between depression and internet addiction. Cyber Psychology and Behavior, 1 (1) , 25–28. http://doi.org/10.1089/cpb.1998.1.25
Younes, F., Halawi, G., Jabbour, H., El Osta, N., Karam, L., Hajj, A., & Rabbaa Khabbaz, L. (2016). Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: A cross-sectional de-signed study. PloS one, 11(9), e0161126. http://doi.org/10.1371/journal.pone.0161126