利用線上遊戲歷程紀錄探討影響玩家成癮程度之因素; Utilization of Online Game Log for Investigating Factors Affecting Players' Degree of Addiction

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Title:	利用線上遊戲歷程紀錄探討影響玩家成癮程度之因素;Utilization of Online Game Log for Investigating Factors Affecting Players' Degree of Addiction
Authors:	黃韻竹;Yun-Chu Huang
Contributors:	企業管理研究所
Keywords:	成癮行為;歷程紀錄;線上遊戲;資料包絡分析法;事件歷史分析;Log;DEA;EHA;Addictive Behavior;Online Game
Date:	2004-06-04
Issue Date:	2009-09-22 14:27:34 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	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.
Appears in Collections:	[Graduate Institute of Business Administration] Electronic Thesis & Dissertation

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