Teoh Ai Ni, Dillon Roberto, Kaur Divjyot
School of Social and Health Sciences, James Cook University, Singapore 387380, Singapore.
School of Science and Technology, James Cook University, Singapore 387380, Singapore.
Eur J Investig Health Psychol Educ. 2023 Sep 15;13(9):1895-1908. doi: 10.3390/ejihpe13090137.
Being able to quantify gaming motivation in a valid, systematic way has important implications for game designers and gaming user experience researchers. In the present study, we aimed to develop and validate a 30-item Gaming Instinctual Motivation Scale (GIMS) based on Dillon's 6-11 Framework on instinctual gaming motivation and Lazzaro's gaming experience model. To validate the scale, we recruited 194 regular gamers ( = 22.70 years old, = 4.38) to complete the GIMS based on their general gaming experience and their experience playing role-laying games (RPGs), first-person shooters (FPSs), real-time strategy, puzzle, and action games. We used a cross-validation approach and performed exploratory factor analysis and confirmatory factor analysis to test the structure of the scale and the reliability and validity of the scale, respectively. The final version of the GIMS had a one-dimensional structure with 15 items. It also had good construct validity, χ ( = 117, = 86) = 126.28, = 0.003, CFI = 0.93, TLI = 0.92, and RMSEA = 0.064 (90% CI [0.04, 0.09]), and reliability (CR = 0.89), and an acceptable convergent validity (AVE = 0.35). The one-dimensional structure was generalizable to RPG and FPS games, demonstrating the applicability of the scale to these two gaming genres. Higher scores on the GIMS were also associated with a greater intention to play games.
能够以有效、系统的方式量化游戏动机，对游戏设计师和游戏用户体验研究人员具有重要意义。在本研究中，我们旨在基于狄龙关于本能游戏动机的6 - 11框架和拉扎罗的游戏体验模型，开发并验证一个包含30个条目的游戏本能动机量表（GIMS）。为了验证该量表，我们招募了194名普通游戏玩家（平均年龄 = 22.70岁，标准差 = 4.38），让他们根据自己的一般游戏体验以及玩角色扮演游戏（RPG）、第一人称射击游戏（FPS）、即时战略游戏、益智游戏和动作游戏的体验来完成GIMS。我们采用交叉验证方法，分别进行探索性因素分析和验证性因素分析，以检验量表的结构以及量表的信度和效度。GIMS的最终版本具有一个包含15个条目的一维结构。它还具有良好的结构效度，χ²（自由度 = 117，样本量 = 86） = 126.28，p = 0.003，CFI = 0.93，TLI = 0.92，RMSEA = 0.064（90%置信区间[0.04, 0.09]），以及信度（CR = 0.89）和可接受的聚合效度（AVE = 0.35）。这种一维结构可推广到RPG和FPS游戏，证明了该量表对这两种游戏类型的适用性。GIMS得分越高，玩游戏的意愿也越强。
