線上遊戲廠商的價值決定於玩家對遊戲的持續消費。關於玩家對遊戲的持續消費行為，本研究從心理學上所謂成癮行為的角度切入，並從線上遊戲的歷程紀錄中擷取資料，進行統計分析與探討，研究目的為提供線上遊戲經營廠商衡量玩家成癮程度的模型，作為瞭解玩家成癮行為的途徑；另外，並針對研究結果進行詮釋與論證，嘗試提出改善的建議，以作為廠商調整目前經營策略的依據，或者作為未來開發自製遊戲時，在遊戲參數設計上的參考。本研究對成癮程度的探討內容以及使用之研究方法敘述如下：
一、線上遊戲玩家之成癮程度分析—衡量玩家的成癮程度，並探討玩家的成癮程度受到哪些因素影響，使用資料包絡分析法及Tobit迴歸模型進行分析。
二、線上遊戲玩家之參與次數分析—探討玩家登入遊戲的次數受到哪些因素影響，使用負二項迴歸模型進行分析。
三、線上遊戲玩家之存活時間分析—探討玩家退出遊戲的風險高低受到哪些因素影響、以及受影響的程度，使用事件歷史分析法之比例危險函數模型進行分析。
研究結果發現，在以資料包絡分析法衡量出線上遊戲玩家的成癮程度（效率值）之後，可再利用Pearson相關分析，推估出等級與效率值的迴歸方程式。而玩家的成癮程度會受到教育程度、得知遊戲網站之管道、角色是否加入軍團這三個變數影響。其中，角色若有加入軍團，其成癮程度（效率值）將較高。
在線上遊戲玩家之參與次數分析的部份，研究結果發現教育程度、職業、最常使用之連線方式、角色等級、角色擁有錢幣總數這五個變數對於登入總次數有顯著的影響效果。其中，角色等級愈高者，其登入次數將愈多。
最後，在線上遊戲玩家之存活時間分析的部份，研究結果發現角色等級、角色是否加入軍團、角色擁有錢幣總數、效率值對於此模式有顯著的影響效果。其中，角色等級愈高者、角色有加入軍團者，其退出遊戲的風險會較低；而角色擁有錢幣總數太多、成癮程度（效率值）太高者，其退出遊戲的風險則較高。除了上述結果之外，本研究並推測出所分析之線上遊戲的壽命週期。

Players’ continuous consuming determines the value of online game firms. Concerning to players’ continuous consuming, this study starts from the point of view of addictive behavior derived from Psychology and gets data from online game’s log. By statistical analysis, this study wants to provide online game firms a model to measure players’ degree of addiction. Besides, base on statistical results, this study addresses interpretations and explanatory comments, and attempts to provide online game firms suggestions to improve their strategy of administration and design features of games. Contents and research methods of this study are listed as follows:
First, analyzing online game players’ degree of addiction. In this section, this study measures online game players’ degree of addiction and discusses factors that affect the degree. Research methods here are Data Envelopment Analysis (DEA) and Tobit Regression Model.
Second, analyzing online game players’ number of participation. In this section, this study discusses factors that affect players’ number of logins. Research method here is Negative Binomial Regression Model.
Third, analyzing online game players’ survival duration. In this section, this study discusses factors that affect players’ hazard rate of withdrawal and the extent of the effect. Research method here is Proportional Hazard Model of Event History Analysis (EHA).
After measuring online game players’ degree of addiction (represented as efficiency), this study speculates a regression equation between level and efficiency by utilizing Pearson correlation analysis. Players’ degree of addiction is affected by level of education, channel of knowing the online game’s website, and whether the playing role joins in the army group. Among these factors, if one’s playing role joins in the army group, he or she will have higher degree of addiction.
In the section of analyzing online game players’ number of participation, factors that have significant effect on it are level of education, occupation, the way connecting to the online game most often, level of the playing role, and the amounts of money the playing role owns. Among these factors, if one’s playing role has higher level, he or she will participate more in the online game.
Finally, in the section of analyzing online game players’ survival duration, factors that have significant effect on it are level of the playing role, whether the playing role joins in the army group, the amount of money the playing role owns, and the efficiency. Among these factors, if one’s playing role has higher level or joins in the army group, the hazard rate of quitting the online game will be lower. On the contrary, if one’s playing role owns too much amounts of money or one’s degree of addiction is too high, the hazard rate of quitting the online game will be higher. In addition to results stated above, this study also estimates the online game’s life cycle.

中文部份：
1.Henry Gleitman著，洪蘭譯（1997），心理學，台北市：遠流。
2.J. Scott Long著，鄭旭智、張育哲、潘倩玉、林克明譯（2002），類別與受限依變數的迴歸統計模式，台北市：弘智文化。
3.Kazuo Yamaguchi著，杜素豪、黃俊龍譯（2001），事件史分析，台北市：弘智文化。
4.元大京華投顧研究部（2003），「軟體產業—線上遊戲業展望」，元大京華投資資訊，7，pp. 39-46。
5.汪宗憲（2003），「產業調查報導：線上遊戲產業發展概況」，產業經濟，261，pp. 1-15。
6.林于勝、許瓊予（2003），「2003年我國線上遊戲發展現況分析」，產業透析：電子商務透析，6，pp. 7-16。
7.高強、黃旭男、Toshiyuki Sueyoshi（2003），管理績效評估：資料包絡分析法，台北市：華泰。
8.張智超、虞孝成（2001），網咖、連線遊戲e軍突起，台北市：聯經。
9.張雅雯（2002），醫療利用可近性：台灣老人之實證研究，碩士論文，國立中央大學產業經濟研究所。
10.莊忠柱、王子湄（2002），「基金經理人存活時間的計量模型—台灣的經驗」，管理與系統，9（2），pp. 195-222。
11.傅鏡暉（2003），線上遊戲產業HAPPY書：帶領你深入瞭解On-Line Game產業，台北市：遠流。
12.盧貞吟（2003），強化線上遊戲吸引力之策略研究—以線上遊戲《天堂》為例，碩士論文，國立成功大學工業設計研究所。
13.戴久永（1991），統計概念與方法，台北市：三民。
14.顏月珠（1995），商用統計學，台北市：三民。
英文部份：
1.Banker, R.D., Charnes, A.S., and Cooper, W.W. (1984), “Some models for estimating technical and scale inefficiencies in data envelopment analysis,” Management Science, 30, pp. 1078-1092.
2.Charnes, A., Cooper, W.W., and Rhodes, E. (1978), “Measuring the efficiency of decision making units,” European Journal of Operational Research, 2, pp. 429-444.
3.Choi, D.S., and Kim, J.W. (2004), “Why People Continue to Play Online Games: In Search of Critical Design Factors to Increase Customer Loyalty to Online Contents,” CyberPsychology & Behavior, 7(1), pp. 11-24.
4.Cooper, W.W., Seiford, L.M., Tone, K. (2000), Data Envelopment Analysis—A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publishers.
5.Egger, O., and Rauterberg, M. (1996), “Internet Behavior and Addiction,” http://www.ifap.bepr.ethz.ch/~egger/ibq/res.html
6.Farrell, M.J. (1957), “The Measurement of Productive Efficiency,” Journal of Royal Statistical Society, Series A, General 120, Part 3, pp. 253-281.
7.Fattah, H., and Paul, P. (2002), “Gaming Gets Serious,” American Demographics, 24(5), pp. 3-43.
8.Goldberg, I. (1996), “Internet Addiction Disorder,” http://wwwphysics.wisc.edu/~shaizi/internet_addiction_criteria.html
9.Griffiths, M.D. (1996), “Gambling on the Internet: A Brief Note,” Journal of Gambling Studies, 12(4), pp. 471-473.
10.Griffiths, M.D. (1997), “Computer Game Playing in Early Adolescence,” Youth and Society, 29(2), pp. 223-237.
11.Griffiths, M.D., and Hunt, N. (1998), “Dependence on Computer Games by Adolescents,” Psychological Reports, 82(2), pp. 475-480.
12.Hatterer, L.J. (1994), Addictive Processes, New York: Encyclopedia of Psychology.
13.Kim, K.H., Park, J.Y., Kim, D.Y., Moon, H.I., and Chun, H.C. (2002), “E-lifestyle and Motives to Use Online Games,” Irish Marketing Review, 15(2), pp. 71-77.
14.McAuliffe, W.E., and Gordon, R.A. (1980), “Reinforcement and the Combination of Effects: Summary of a Theory of Opiate Addiction,” In D.J. Lettieri, M. Sayers, and H. Wallenstein Pearson.
15.Morahan-Martin, J., and Schumacher, P. (2000), “Incidence and Correlates of Pathological Internet Use among College Students,” Computers in Human Behavior, 16(1), pp. 13-29.
16.Novak, T.P., and Hoffman, D.L. (1997), “Diversity on the Internet: The Relationship of Race to Access and Usage,” Aspen Institute''s Forum on Diversity and the Media Queenstown, Maryland, November 5-7.
17.O’Brien J.M. (2000), “The Games Women Like to Play,” Computer Dealer News, 16(5), p. 43.
18.Orford, J. (2001), “Addiction as Excessive Appetite,” Addiction, 96(1), pp. 15-31.
19.Thombs, D.L. (1994), Introduction to Addictive Behaviors, New York: The Guilford Press.
20.Young, K.S. (1997), “What Makes the Internet Addictive: Potential Explanations for Pathological Internet Use,” Paper Presented at the 105th Annual Conference of the American Psychological Association, August 15, 1997, Chicago, IL.