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

  • Mark Anthony D. Libunao Technological University of the Philippines


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.


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How to Cite
D. LIBUNAO, Mark Anthony. Adoption of Artificial Intelligence in the Games and Amusements Board: A Stepwise Multiple Linear Regression Analysis. International Journal of Business and Technology Management, [S.l.], v. 5, n. 2, p. 30-48, june 2023. ISSN 2682-7646. Available at: <>. Date accessed: 22 sep. 2023.