研究生: |
黃昱慈 Huang, Yu-Tzu |
---|---|
論文名稱: |
國中生網路正向效果預期、拒網自我效能與網路成癮之相關研究 Positive Outcome Expectancy, Internet Refusal Self-Efficacy and Internet Addiction among Junior High School Students in Taiwan. |
指導教授: |
林旻沛
Lin, Min-Pei |
學位類別: |
碩士 Master |
系所名稱: |
教育心理與輔導學系 Department of Educational Psychology and Counseling |
論文出版年: | 2020 |
畢業學年度: | 108 |
語文別: | 中文 |
論文頁數: | 76 |
中文關鍵詞: | 國中生 、網路正向效果預期 、拒網自我效能 、網路成癮 |
英文關鍵詞: | junior high school students, positive outcome expectancy, refusal self-efficacy, Internet addiction |
DOI URL: | http://doi.org/10.6345/NTNU202000788 |
論文種類: | 學術論文 |
相關次數: | 點閱:311 下載:49 |
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 |
本研究旨在瞭解國中生網路正向效果預期與拒網自我效能之建構,並編製出適合測量國中生網路正向效果預期與拒網自我效能之量表,進而探究網路正向效果預期和拒網自我效能與網路成癮之關聯性,並以Bandura(1986)社會認知理論為基礎,檢驗國中生拒網自我效能是否能中介網路正向效果預期對網路成癮的預測關係。
本研究以臺灣地區的國中生為研究對象,並以立意取樣方式進行問卷施測,且研究過程分為兩部分。第一部分為發展國中生之「網路正向效果預期原始量表」與「拒網自我效能原始量表」;本研究針對372位國中生進行開放式問卷之施測,結果共有339人接受問卷施測、問卷回收率為90.86%,並收集到987個上網好處面的認知內容,以及1,054個難以抗拒或停止上網的高危險情境,經歸納整理後編製出「網路正向效果預期原始量表(共71題)」與「拒網自我效能原始量表(共70題)」。
第二部分為編製出國中生之「網路正向效果預期量表」與「拒網自我效能量表」,並針對研究假設進行考驗。本研究針對1,142位國中生進行問卷施測,問卷回收後有效問卷為906份,因此有效樣本回收率為79.33%,且經探索性因素分析後,編製出「網路正向效果預期量表」(三個主要因素分量表:「紓壓增趣」、「社交聯繫」及「獲取新知」)與「拒網自我效能量表」(四個主要因素分量表:「負向情緒調適」、「資訊查閱」、「遊戲成就」及「人際連結」);此外本研究也發現:(1)國中生最近一年每週平均的上網時數為20.93小時、標準差為18.55小時,且在各類型網路活動之使用時間中,國中生使用線上遊戲之活動時數最多、網路人際互動次之;(2)網路正向效果預期能顯著且正向預測網路成癮;(3)拒網自我效能可顯著且負向預測網路成癮;(4)拒網自我效能可中介網路正向效果預期對網路成癮之預測關係。
本研究發現國中生之網路正向效果預期與拒網自我效能皆可顯著預測網路成癮,且拒網自我效能扮演中介角色,因此本研究建議諮商輔導或教育專業人員在進行國中生網路成癮的防制時,可協助國中生調整其對於網路好處面的過高期待,更要重要的是可增加其拒用或停止上網的方式與技巧、增強其拒網的自我效能感,以減少國中生網路成癮的可能性。
This research is devoted to understanding the construction of junior high school students' positive outcome expectancy and refusal self-efficacy of Internet use, and then to develop a scale suitable for measuring junior high school students' positive outcome expectancy and refusal self-efficacy. Further, this study explored the relationship between positive outcome expectancy, refusal self-efficacy and Internet addiction. Moreover, based on the social cognitive theory proposed by Bandura (1986), the author tested whether the refusal self-efficacy of junior high school students can mediate the predictive relationship of positive outcome expectancy to Internet addiction. Taking junior high school students in Taiwan as the research object, purposive sampling is used to conduct the questionnaire survey. The research process is divided into two parts. The first part is to establish the "Positive outcome expectancy original scale" and "Refusal self-efficacy original scale" belonging to junior high school students. In this study, a total of 372 junior high school students were surveyed with an open questionnaire. A total of 339 people were tested and the questionnaire recovery rate was 90.86%. The author also collected 987 cognitive contents about the benefits of Internet access and 1,054 high-risk situations that are difficult to refuse or stop online behaviors. After summarizing the above contents, the original scale was compiled.
In the second part, this research compiled "Positive outcome expectancy scale" and "Refusal self-efficacy scale", and tested the hypothesis. The author conducted a questionnaire survey on a total of 1,142 junior high school students. After the questionnaires were collected, there were 906 valid questionnaires. The effective sample recovery rate was 79.33%. Moreover, after exploratory factor analysis, this study developed the "Positive outcome expectancy scale" (Three main factors: "Relief and Fun", "Social Connections" and "Access to New Information and Knowledge") and "Refusal self-efficacy scale" (Four main factors: "Adjustment of Negative Emotions", "Information Access", "Achievements from Games" and "Interpersonal Links"). In addition, results showed that (a) the average time of weekly Internet use among junior high school students was 20.93 hours (SD=18.55). Playing Internet games was the most often activities of Internet use, and Internet interpersonal interaction was second most; (b) Positive outcome expectancy significantly and positively predicted Internet addiction; (c) Refusal self-efficacy significantly and negatively predicted Internet addiction; (d) Refusal self-efficacy mediated the predictive relationship of positive outcome expectancy to Internet addiction.
This research found that positive outcome expectancy and refusal self-efficacy of Internet use significantly predicted Internet addiction, and refusal self-efficacy was the mediator of the others. Therefore, this research provide recommends for counselors and teachers about prevention of Internet addiction of junior high school students. Helping junior high school students to adjust their high expectations of Internet use, and improve their refusal self-efficacy by enhancing their refusing Internet use skills to prevent junior high school students from Internet addiction.
王南棟、劉育伶、王慈襄、張紫婷、黃政昌(2020):「網路遊戲成癮評估」的內涵與方式之探討。諮商與輔導,412,47–52。
中華白絲帶關懷協會(2016):2016年台灣青少兒網路社群 、陌生網友分辨調查報告。引自網站:http://www.cyberangel.org.tw/thesis/Study/001.pdf,2019年3月12日。
古欣卉(2006):國中生飲用含糖飲料行為、網路成癮及其相關影響因素之關聯探討(未發表)。國立雲林科技大學技術及職業教育研究所碩士論文。
吳家齊(2009):免費線上遊戲周邊虛擬道具之消費行為研究(未發表)。國立中央大學企業管理研究所碩士論文。
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。https://doi.org/10.6844/NCKU.2011.02246
林旻沛(2011):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)。國立成功大學行為醫學研究所博士論文。https://doi.org/10.6844/NCKU.2011.02246
林欣諭(2017):青年族群使用 Instagram 之心理需求與持續使用意圖研究(未發表)。國立臺灣師範大學圖書資訊學研究所碩士論文。
花茂修、徐如維(2020):網路遊戲成癮。臨床醫學,85(4),206–210。https://doi.org/10.6666/ClinMed.202004_85(4).0037
林青穎、王智弘、陳淑惠、劉淑慧、柯志鴻(2015):國小家長版網路成癮量表之編製及其信效度分析。教育心理學報,46(4),517–539。https://doi.org/10.6251/BEP.20140814
許韶玲, & 施香如(2013):網路成癮是一種心理疾病嗎? 從實證與論述文獻的脈絡檢視。教育心理學報,44(4),773–792。
施香如、許韶玲(2016):網路成癮的診斷準則與評估工具:發展歷史與未來方向。教育心理學報,48(1),53–75。https://doi.org/10.6251/BEP.20150915
徐西森、連廷嘉(2001):大專學生網路沈迷行為及其徑路模式之驗証研究。中華輔導學報,10,119-150。https://doi.org/10.7082/CARGC.200109.0119
國家發展委員會(2017):106年持有手機民眾數位機會調查。引自網站:https://reurl.cc/MvG4qp,2018年12月20日。
國家發展委員會(2017):106年個人家戶數位機會調查報告。引自網站:https://reurl.cc/pdVrOZ,2018年12月20日。
國家發展委員會(2017):106年網路沉迷研究調查。引自網站:https://reurl.cc/QdOe89,2018年12月20日。
陳珏汝(2019):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系碩士論文。https://doi.org/10.6345/NTNU201900041
陳雨鑫(2008,3月10日):網路遊戲成癮跟進WHO列精神疾病。聯合報。引自網站:https://udn.com/news/story/11319/3022861,2019年4月3日。
陳冠名(2007):青少年網路沈迷之研究。實踐博雅學報,7,53–101。https://doi.org/10.7041/SCJLA.200701.0053
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系碩士論文。
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製與心理計量特性研究。中華心理學刊,45(3),279–294。https://doi.org/10.6129/CJP.2003.4503.05
陳麗秋、董貞吟(2010):預防網路沉迷主題課程介入之研究。健康促進與衛生教育學報,33,47–65。https://doi.org/10.7022/JHPHE.201006.0047
黃姮儀(2012):智慧型手機網路成癮之實證研究-從社會心理角度出發的模式(未發表)。國立中正大學資訊管理學系暨研究所博士論文。
黃莉君、吳筱琦、陳盈治(2018):以計畫行為理論及體驗價值探討使用者之手機遊戲行為。企業管理學報,117,49–76。
張瑋庭(2018):用阿德勒心理學觀點談人際關係— 以網路社群軟體「Instagram」為例。諮商與輔導,391,49–52。
創市際市場研究顧問公司(2008年9月17日):社群服務篇與社交媒體類別使用概況。引自網站:https://www.ixresearch.com/wp-content/uploads/report/InsightXplorer%20Biweekly%20Report_20180917.pdf,2020年5月7日。
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。https://doi.org/10.29805/JHM.200806.0007
劉雨涵(2018):你follow她了嗎?Instagram網紅的人類學觀察。中央研究院民族學研究所資料彙編,26,1–34。
趙傑夫、楊一鳴(2009):大學校園網路沉迷防治策略研究-以聯結服務學習課程為例。朝陽人文社會學刊,7(2),207–238。https://doi.org/10.30110/CJHSS.200912.0008
盧浩權(2007):青少年網路沉迷的心理分析與因應。社區發展季刊,119,206–221。
盧永欽(2009):憂鬱程度與正向線上遊戲效果預期對臺灣男女大學生線上遊戲使用與網路成癮程度之預測(未發表)。國立成功大學行為醫學研究所碩士論文。
賴亞岐(2011):青少年網路遊戲成癮傾向與心理需求、自我概念、同儕關係之相關研究(未發表)。國立台北教育大學心理與諮商學系碩士論文。https://doi.org/10.6344/NTUE.2011.00480
蘇素美(2017):大學生的害羞和網路使用時間及網路成癮之關係研究: 以潛在成長模式進行分析。清華教育學報,34(2),95–130。
蕭博諺(2010):青年線上遊戲使用動機、網路社會支持與線上遊戲成癮傾向之研究:以台中縣私立弘文高級中學為例(未發表)。朝陽科技大學休閒事業管理系碩士論文。
Aas, H., Klepp, K., Laberg, J. C., & Aaro, L. E. (1995). Predicting adolescents’ intentions to drink alcohol: Outcome expectancies and self-efficacy. Journal of Studies on Alcohol, 56, 392–399. https://doi.org/10.15288/jsa.1995.56.293
Adiele, I., & Olatokun, W. (2014). Prevalence and determinants of Internet addiction among adolescents. Computers in Human Behavior, 31, 100–110. https://doi.org/10.1016/j.chb.2013.10.028
Andri Efstathiou, R. N., Christina Ioannidou, R. N., Simeou Mikaella, R. N., Samartzis, L., Ioannis Dimitrakopoulos, R. N., & Alexis Samoutis, M. D. (2017). Assessment of the Effect of Online Addiction in Cyprus. International Journal of Caring Sciences, 10(3), 1232–1239.
American Society of Addiction Medicine (2011). A Description of Addiction. Retrieved Febuary 15, 2020, from https://www.asam.org/Quality-Science/resource-links/a-description-of-addiction.
Armstrong, L., Phillips, J. G., & Saling, L. L. (2000). Potential determinants ofheavier intemet usage. International Journal ofHuman-Computer Studies, 53(4), 537–550. https://doi.org/10.1006/ijhc.2000.0400
Baldwin, A. R., Oei, T. P., & Young, R. (1993). To drink or not to drink: The differential role of alcohol expectancies and drinking refusal self-efficacy in quantity and frequency of alcohol consumption. Cognitive Therapy and Research, 17(6), 511–530. https://doi.org/ 10.1007/BF01176076
Bandura, A. (1976). Social learning theory. Prentice-hall.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122–147. https://doi.org/10.1037/0003-066X.37.2.122
Bandura, A. (1986). Social foundations of thought and action. Prentice Hall.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of ersonality and social psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Beard, K. W. (2005). Internet addiction: a review of current assessment techniques and potential assessment questions. CyberPsychology and Behavior, 8(1), 7–14. https://doi.org/10.1089/cpb.2005.8.7
Cao, F., Su, L., Liu, T., & Gao, X. (2007). The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents. European Psychiatry, 22(7), 466–471. https://doi.org/10.1016/j.eurpsy.2007.05.004
Chang, C.-H., Ko, H.-C., Wu, J.-Y., Cheng, C.-P. (2007). Social cognitive determinants of betel quid chewing among college students in southern Taiwan: A revised attitudes-social influence-efficacy model. Addictive Behaviors, 32(10), 2345–2350. https://doi.org/10.1016/j.addbeh.2007.02.001
Choi, H. J., Krieger, J. L., & Hecht, M. L. (2013). Reconceptualizing efficacy in substance use prevention research: refusal response efficacy and drug resistance self-efficacy in adolescent substance use. Health communication, 28(1), 40–52. https://doi.org/ 10.1080/10410236.2012.720245
Connor, J. P., George, S. M., Gullo, M. J., Kelly, A. B., & Young, R. M. (2011). A prospective study of alcohol expectancies and self-efficacy as predictors of young adolescent alcohol misuse. Alcohol and alcoholism, 46(2), 161–169. https://doi.org/10.1093/alcalc/agr004
Connor, J. P., Gullo, M. J., Feeney, G. F., Kavanagh, D. J., & Young, R. M. (2014). The relationship between cannabis outcome expectancies and cannabis refusal self‐efficacy in a treatment population. Addiction, 109(1), 111–119. https://doi.org/10.1111/add.12366
Connor, J. P., Young, R., Williams, R. J., & Ricciardelli, L. A. (2000). Drinking restraint versus alcohol expectancies: which is the better indicator of alcohol problems?. Journal of Studies on Alcohol, 61(2), 352–359. https://doi.org/10.15288/jsa.2000.61.352
Czincz, J., & Hechanova, R. (2009). Internet addiction: Debating the diagnosis. Journal of Technology in Human Services, 27(4), 257–272. https://doi.org/10.1080/15228830903329815
Davis, R. A. (2001). A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior, 17, 187–195. https://doi.org/10.1016/S0747-5632(00)00041-8
Demir, M., Özdemir, M., & Weitekamp, L. A. (2007). Looking to happy tomorrows with friends: Best and close friendships as they predict happiness. Journal of Happiness Studies, 8(2), 243–271. https://doi.org/10.1007/s10902-006-9025-2
Dolan, S. L., Martin, R. A., & Rohsenow, D. J. (2008). Self-efficacy for cocaine abstinence: Pretreatment correlates and relationship to outcomes. Addictive behaviors, 33(5), 675–688. https://doi.org/10.1016/j.addbeh.2007.12.001
Dong, G., & Potenza, M. N. (2014). A cognitive-behavioral model of Internet gaming disorder: theoretical underpinnings and clinical implications. Journal of psychiatric research, 58, 7–11. https://doi.org/10.1016/j.jpsychires.2014.07.005
Dunn, M. E., Fried-Somerstein, A., Flori, J. N., Hall, T. V., & Dvorak, R. D. (2020). Reducing alcohol use in mandated college students: A comparison of a Brief Motivational Intervention (BMI) and the Expectancy Challenge Alcohol Literacy Curriculum (ECALC). Experimental and Clinical Psychopharmacology, 28(1), 87–98. https://doi.org/10.1037/pha0000290
Erikson, E. H. (1968). Identity: Youth and crisis. New York: Norton.
Evans, D. M., & Dunn, N. J. (1995). Alcohol expectancies, coping responses and self-efficacy judgements: A replication and extension of Copper et al.’s 1988 study in a college sample. Journal of Studies on Alcohol, 56, 186–193. https://doi.org/10.15288/jsa.1995.56.186
Goldberg, I. (1996). Internet addictive disorder (IAD) diagnostic criteria. Retrieved January 15, 2019, from http://www.psycom.net/iadcriteria.html
Griffiths, M. (2000). Does Internet and computer" addiction" exist? Some case study evidence. CyberPsychology and Behavior, 3(2), 211–218. https://doi.org/10.1089/109493100316067
Gullo, M. J., Dawe, S., Kambouropoulos, N., Staiger, P. K., & Jackson, C. J. (2010). Alcohol expectancies and drinking refusal self‐efficacy mediate the association of impulsivity with alcohol misuse. Alcoholism: Clinical and Experimental Research, 34(8), 1386–1399. https://doi.org/10.1111/j.1530-0277.2010.01222.x
Gullo, M. J., Matveeva, M., Feeney, G. F., Young, R. M., & Connor, J. P. (2017). Social cognitive predictors of treatment outcome in cannabis dependence. Drug and alcohol dependence, 170, 74–81. https://doi.org/10.1016/j.drugalcdep.2016.10.030
Marlatt, G. A., & Gordon, J. R. (Eds.). (1985). Relapse Prevention: Maintenance Strategies in the Treatment of Addictive Behaviors. Guilford Press.
Hasking, P., & Oei, T. P. S. (2002). The differential role of AE, DRSE and coping resources n predicting alcohol consumption in community and clinical samples. Addiction Research and Theory, 10, 465–494. https://doi.org/10.1080/1606635021000034049
Hasking, P., Oei, T. P. (2007). Alcohol expectancies, self-efficacy and coping in an alcoholdependent sample. Addictive Behaviors, 32(1), 99–113. https://doi.org/10.1016/j.addbeh.2006.03.024
International Data Corporation (2012). Worldwide Mobile Phone Market Maintain Its Growth Trajectory in the Fourth Quarter Despite Soft Demand for Feature Phones. Retrieved April 10, 2020, from https://www.businesswire.com/news/home/20120201007094/en/Worldwide-Mobile-Phone-Market-Maintains-Growth-Trajectory.
Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351–354. https://doi.org/10.1016/j.chb.2013.10.059
Kardefelt-Winther, D. (2014). Problematizing excessive online gaming and its psychological predictors. Computers in Human Behavior, 31, 118–122. https://doi.org/10.1016/j.chb.2013.10.017
King, D., Delfabbro, P., Zwaans, T., & Kaptsis, D. (2013). Clinical features and axis I comorbidity of Australian adolescent pathological Internet and video game users. Australian and New Zealand Journal of Psychiatry, 47(11), 1058–1067. https://doi.org/10.1177/0004867413491159
King, D., & Delfabbro, P. (2018). Internet gaming disorder: Theory, assessment, treatment, and prevention. Academic Press. https://doi.org/10.1016/B978-0-12-812924-1.00007-1
Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in human behavior, 26(6), 1237–1245. https://doi.org/10.1016/j.chb.2010.03.024
Ko, C.-H., Yen, J.-Y., Yen, C.-F., Lin, H.-C., & Yang, M.-J. (2007). Factors predictive for incidence and remission of internet addiction in young adolescents: a prospective study. CyberPsychology & Behavior, 10(4), 545–551. https://doi.org/10.1089/cpb.2007.9992
Larimer, M. E., Palmer, R. S., & Marlatt, G. A. (1999). Relapse prevention: An overview of Marlatt’s cognitive-behavioral model. Alcohol Research & Health, 23(2), 151–160.
Law, B., Gullo, M. J., Daglish, M., Kavanagh, D. J., Feeney, G. F., Young, R. M., & Connor, J. P. (2016). Craving mediates stress in predicting lapse during alcohol dependence treatment. Alcoholism: Clinical and Experimental Research, 40(5), 1058–1064. https://doi.org/10.1111/acer.13034
Lee, C.-K., Corte, C., Stein, K. F., Feng, J.-Y., & Liao, L.-L. (2020). Alcohol-related cognitive mechanisms underlying adolescent alcohol use and alcohol problems: Outcome expectancy, self-schema, and self-efficacy. Addictive behaviors, 105, 106349. https://doi.org/10.1016/j.addbeh.2020.106349
Lee, N. K., & Oei, T. P. S. (1993). The importance of alcohol expectancies and drinking refusal self-efficacy in the quantity and frequency of alcohol consumption. Journal of Substance Abuse, 5, 379–390. https://doi.org/10.1016/0899-3289(93)90006-W
Lee, N. K., Oei, T. P., & Greeley, J. D. (1999). The interaction of alcohol expectancies and drinking refusal self-efficacy in high and low risk drinkers. Addiction Research, 7(2), 91–102. https://doi.org/10.3109/16066359909004377
Lin, M. P., Ko, H. C., & Wu, J. Y. W. (2008). The role of positive/negative outcome expectancy and refusal self-efficacy of Internet use on Internet addiction among college students in Taiwan. CyberPsychology & Behavior, 11(4), 451–457. https://doi.org/10.1089/cpb.2007.0121
Lin, M. P., Wu, J. Y. W., You, J., Hu, W. H., & Yen, C. F. (2018). Prevalence of internet addiction and its risk and protective factors in a representative sample of senior high school students in Taiwan. Journal of Adolescence, 62, 38–46. https://doi.org/10.1016/j.adolescence.2017.11.004
McNicol, M. L., Einar, B. (2017). Internet Addiction, Psychological Distress, and Coping Responses Among Adolescents and Adults. Cyberpsychology, Behavior and Social Networking, 20(5), 296–304. https://doi.org/10.1089/cyber.2016.0669
Newton, N. C., Andrews, G., Teesson, M., & Vogl, L. E. (2009). Delivering prevention for alcohol and cannabis using the internet: A cluster randomised controlled trial. Preventive medicine, 48(6), 579–584. https://doi.org/10.1016/j.ypmed.2009.04.009
Norman, P., Bennett, P., & Lewis, H. (1998). Understanding binge drinking among young people: An application of the theory of planned behaviour. Health education research, 13(2), 163–169. https://doi.org/10.1093/her/13.2.163-a
Oei, T. P. S., & Baldwin, A. R. (1994). Expectancy theory: A two-process model of alcohol use and abuse. Journal of Studies on Alcohol, 55, 525–534. https://doi.org/10.15288/jsa.1994.55.525
Oei, T. P., & Burrow, T. (2000). Alcohol expectancy and drinking refusal self-efficacy: a test of specificity theory. Addictive behaviors, 25(4), 499–507. https://doi.org/10.1016/S0306-4603(99)00044-1
Oei, T. P., & Morawska, A. (2004). A cognitive model of binge drinking: The influence of alcohol expectancies and drinking refusal self-efficacy. Addictive behaviors, 29(1), 159–179. https://doi.org/10.1016/S0306-4603(03)00076-5
Oei, T. P., Hasking, P. A., & Young, R. M. (2005). Drinking refusal self-efficacy questionnaire-revised (DRSEQ-R): a new factor structure with confirmatory factor analysis. Drug and alcohol dependence, 78(3), 297–307. https://doi.org/10.1016/j.drugalcdep.2004.11.010
Oei, T. P. , Jardim, C. L. (2007). Alcohol expectancies, drinking refusal self-efficacy and drinking behaviour in Asian and Australian students. Drug and Alcohol Depend, 87(2-3), 281–287. https://doi.org/10.1016/j.drugalcdep.2006.08.019
Papinczak, Z. E., Connor, J. P., Feeney, G. F., Young, R. M., & Gullo, M. J. (2017). Treatment seeking in cannabis dependence: The role of social cognition. Drug and alcohol dependence, 170, 142–146. https://doi.org/10.1016/j.drugalcdep.2016.11.005
Papinczak, Z. E., Connor, J. P., Harnett, P., & Gullo, M. J. (2018). A biosocial cognitive model of cannabis use in emerging adulthood. Addictive behaviors, 76, 229–235. https://doi.org/10.1016/j.addbeh.2017.08.011
Papinczak, Z. E., Connor, J. P., Feeney, G. F., Harnett, P., Young, R. M., & Gullo, M. J. (2019). Testing the biosocial cognitive model of substance use in cannabis users referred to treatment. Drug and alcohol dependence, 194, 216–224. https://doi.org/10.1016/j.drugalcdep.2018.09.032
Patton, K. A., Connor, J. P., Rundle‐Thiele, S., Dietrich, T., Young, R. M., & Gullo, M. J. (2018). Validation of the Adolescent Drinking Expectancy Questionnaire and development of a short form. Drug and alcohol review, 37(3), 396–405. https://doi.org/10.1111/dar.12567
Pawlikowski, M., Nader, I. W., Burger, C., Stieger, S., & Brand, M. (2014). Pathological Internet use–It is a multidimensional and not a unidimensional construct. Addiction Research & Theory, 22(2), 166–175. https://doi.org/10.3109/16066359.2013.793313
Ramo, D. E., Anderson, K. G., Tate, S. R., & Brown, S. A. (2005). Characteristics of relapse to substance use in comorbid adolescents. Addictive Behaviors, 30, 1811–1823. https://doi.org/10.1016/j.addbeh.2005.07.021
Pontes, H. M., & Griffiths, M. D. (2014). Assessment of internet gaming disorder in clinical research: Past and present perspectives. Clinical Research and Regulatory Affairs, 31(2-4), 35–48. https://doi.org/10.3109/10601333.2014.962748
Schuck, K., Otten, R., Kleinjan, M., Bricker, J. B., & Engels, R. C. (2014). Self-efficacy and acceptance of cravings to smoke underlie the effectiveness of quitline counseling for smoking cessation. Drug and alcohol dependence, 142, 269–276. https://doi.org/10.1016/j.drugalcdep.2014.06.033
Shen, C., & Williams, D. (2011). Unpacking time online: Connecting internet and massively multiplayer online game use with psychosocial well-being. Communication Research, 38(1), 123–149. https://doi.org/10.1177/0093650210377196
Siomos, K., Floros, G., Fisoun, V., Evaggelia, D., Farkonas, N., Sergentani, E., Lamprou, M, & Geroukalis, D. (2012). Evolution of Internet addiction in Greek adolescent students over a two-year period: the impact of parental bonding. European child & adolescent psychiatry, 21(4), 211–219. https://doi.org/10.1007/s00787-012-0254-0
Tokunaga, R. S., & Rains, S. A. (2010). An evaluation of two characterizations of the relationships between problematic Internet use, time spent using the Internet, and psychosocial problems. Human Communication Research, 36(4), 512–545. https://doi.org/10.1111/j.1468-2958.2010.01386.x
Van Rooij, A. J., Schoenmakers, T. M., & Van de Mheen, D. (2017). Clinical validation of the C-VAT 2.0 assessment tool for gaming disorder: A sensitivity analysis of the proposed DSM-5 criteria and the clinical characteristics of young patients with ‘video game addiction’. Addictive Behaviors, 64, 269–274. https://doi.org/10.1016/j.addbeh.2015.10.018
Wan, C. S., & Chiou, W. B. (2006). Why are adolescents addicted to online gaming? An interview study in Taiwan. Cyberpsychology & behavior, 9(6), 762–766. https://doi.org/10.1089/cpb.2006.9.762
Widyanto, L. & McMurran, M. (2004). The psychometric properties of the Internet Addiction Test. Cyberpsychology, Behavior, and Social Networking, 7(4), 443–450. https://doi.org/10.1089/cpb.2004.7.443
Widyanto, L., Griffiths, M. D. & Brunsden, V. (2011). A psychometric comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and self-diagnosis. Cyberpsychology, Behavior, and Social Networking, 14(3), 141–149. https://doi.org/10.1089/cyber.2010.0151
Wu, J. Y. W., Ko, H. C., Wong, T. Y., Wu, L. A., & Oei, T. P. (2016). Positive outcome expectancy mediates the relationship between peer influence and Internet gaming addiction among adolescents in Taiwan. Cyberpsychology, Behavior, and Social Networking, 19(1), 49–55. https://doi.org/10.1089/cyber.2015.0345
Young, R. McD., Oei, T. P. S., & Crook, G. M. (1991). Development of a drinking self-efficacy questionnaire. Journal of Psychopathology and Behavioral Assessment, 13(1), 1–15. https://doi.org/10.1007/BF00960735
Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. Cyberpsychology & Behavior, 1(3), 237–244. https://doi.org/10.1089/cpb.1998.1.237
Young, K. S. (2004). Internet Addiction: A New Clinical Phenomenon and Its Consequences. American Behavioral Scientist, 48(4), 402–415. https://doi.org/10.1177/0002764204270278
Zhang, Y., Mei, S., Li, L., Chai, J., Li, J., & Du, H. (2015). The relationship between impulsivity and internet addiction in Chinese college students: a moderated mediation analysis of meaning in life and self-esteem. PLoS One, 10(7), e0131597. https://doi.org/10.1371/journal.pone.0131597