Adoption of Artificial Intelligence in the Games and Amusements Board: A Stepwise Multiple Linear Regression Analysis

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Abstract

The Games and Amusements Board (GAB) of the Philippines was the subject of this study which aimed to determine the factors that have the most impact on the adoption of artificial intelligence (AI). In accordance with the combined constructs of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), a survey questionnaire was administered to a sample of 99 GAB officials and employees. To analyze the data, stepwise multiple linear regression and related statistical tools were performed using the IBM Statistical Package for Social Sciences (SPSS) and Microsoft Excel. Correlation analysis revealed that the attitude toward using AI and the behavioral intention to use AI showed a very strong positive correlation with the adoption of AI in the GAB. Meanwhile, social influence, effort expectancy, and performance expectancy exhibited a strong positive correlation with the AI adoption. On the other hand, facilitating conditions showed a moderate positive correlation while the perceived risk is the lone variable which exhibited a weak negative correlation. The stepwise multiple linear regression analysis revealed that out of the seven independent variables, the attitude toward using AI and the effort expectancy are the strongest factors that influence the adoption of AI. Further, the model revealed that 63.6% of the variance of the dependent variable can be explained by the predictor variables. This means that the 36.4% unexplained variance can be explained by the variables that are not included in the conceptualization of this research study. This paper makes a contribution to the growing body of research on how government agencies are governing, accepting, and adopting AI.
人工智能在游戏和娱乐板中的应用:逐步多元线性回归分析
菲律宾游戏和娱乐委员会(GAB)是这项研究的主题,旨在确定对人工智能(AI)采用影响最大的因素。根据技术接受模型(TAM)和技术接受与使用统一理论(UTAUT)的组合构建,对99名政府审计局官员和员工进行问卷调查。为了分析数据,采用逐步多元线性回归和相关的统计工具,使用IBM社会科学统计软件包(SPSS)和Microsoft Excel。相关分析显示,使用人工智能的态度和使用人工智能的行为意图与GAB中人工智能的采用呈非常强的正相关。同时,社会影响力、努力期望和绩效期望与人工智能的采用表现出强烈的正相关。另一方面,便利条件表现出适度的正相关,感知风险是唯一的变量,表现出弱的负相关。逐步多元线性回归分析显示,在7个自变量中,使用人工智能的态度和努力预期是影响人工智能采用的最强因素。此外,模型显示,63.6%的因变量方差可以被预测变量解释。这意味着36.4%的未解释方差可以用本研究概念化中未包含的变量来解释。本文对政府机构如何管理、接受和采用人工智能的研究做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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