Adoption of Artificial Intelligence in the Games and Amusements Board: A Stepwise Multiple Linear Regression Analysis
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.
References
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Guadagnoli, E., & Velicer, W. F. (1988). Relation of sample size to the stability of component patterns. Psychological Bulletin, 103(2), 265-275.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
Heo, J., Lee, S. M., & Park, Y. J. (2021). Factors influencing government AI adoption: A comparative study of the US and South Korea. Government Information Quarterly, 38(1), 101506.
Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioral sciences (Vol. 663). Houghton Mifflin college division.
Huck, S. W. (2012). Reading statistics and research (6th ed.). Boston, MA: Pearson.
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Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2022). What Factors Contribute to Acceptance of Artificial Intelligence? A Systematic Review. Telematics and Informatics, 101925.
Kirlidog, M., Kaynak, A. (2013). Technology acceptance model and determinants of technology rejection. In Information Systems and Modern Society: Social Change and Global Development (pp. 226-238). IGI Global. https://doi.org/10.4018/jissc.2011100101.
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Moon, M. J. (2019). Public attitudes toward artificial intelligence and the future of government. Public Administration Review, 79(6), 841-844.
Nasrallah, R. (2014). Learning outcomes’ role in higher education teaching. Education, Business and Society: Contemporary Middle Eastern Issues, 7(4), 257-276.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.
Raykov, T. (2009). Evaluation of scale reliability for unidimensional measures using latent variable modeling. Measurement and Evaluation in Counseling and Development, 42(3), 223-232.
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Park, J., Hong, E., & Le, H. T. (2021). Adopting autonomous vehicles: The moderating effects of demographic variables. Journal of Retailing and Consumer Services, 63, 102687.
Pattinson, E. (2018). An Investigation into the Relationship Between Diving, Self-Efficacy and Performance in Competitive Diving (Doctoral dissertation, University of Winchester).
Perry, B., & Uuk, R. (2019). AI governance and the policymaking process: key considerations for reducing AI risk. Big data and cognitive computing, 3(2), 26.
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Wang, H., Xie, Y., & Song, R. (2020). An empirical study of factors influencing government intention to adopt artificial intelligence. Government Information Quarterly, 37(3), 101428.
Warkentin, M., Gefen, D., Pavlou, P. A., & Rose, G. M. (2002). Encouraging citizen adoption of e-government by building trust. Electronic markets, 12(3), 157-162.
Yigitcanlar, T., Corchado, J. M., Mehmood, R., Li, R. Y. M., Mossberger, K., & Desouza, K. (2021). Responsible urban innovation with local government artificial intelligence (AI): A conceptual framework and research agenda. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 71.
Yu, S., & Carroll, F. (2022). Implications of AI in National Security: Understanding the Security issues and Ethical challenges. In Artificial Intelligence in Cyber Security: Impact and Implications: Security Challenges, Technical and Ethical Issues, Forensic Investigative Challenges (pp. 157-175). Cham: Springer International Publishing.
Vasileiou, K., Sachini, E., & Stergioulas, L. K. (2020). The impact of trust and attitude on the intention to use AI in the public sector. Government Information Quarterly, 37(3), 101453.
Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 1-12
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Zhang, J., Liu, Y., & Xue, L. (2020). Exploring the factors influencing government adoption of artificial intelligence in China. Government Information Quarterly, 37(1), 101395.
Zhang, W., & Gutierrez, O. (2007). Information technology acceptance in the social services sector context: An exploration. Social Work, 52(3), 221-231.
Zhang, X., & Maruping, L. M. (2008). Household technology adoption in a global marketplace: Incorporating the role of espoused cultural values. Information systems frontiers, 10, 403-413.
Artificial Intelligence in Sports Market Statistics: Forecast - 2030. Allied Market Research. (n.d.). Retrieved April 2, 2023, from https://www.alliedmarketresearch.com/artificial-intelligence-in-sports-market-A12905
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2021). Artificial intelligence adoption in government services: A qualitative study of perceived benefits and barriers. Government Information Quarterly, 38(1), 101502.
Alhashmi, S. F., Salloum, S. A., & Abdallah, S. (2019, October). Critical success factors for implementing artificial intelligence (AI) projects in Dubai Government United Arab Emirates (UAE) health sector: applying the extended technology acceptance model (TAM). In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 (pp. 393-405). Cham: Springer International Publishing.
Andrews, J. E., Ward, H., & Yoon, J. (2021). UTAUT as a model for understanding intention to adopt AI and related technologies among librarians. The Journal of Academic Librarianship, 47(6), 102437.
Babbie, E. R. (2016). The basics of social research (7th ed.). Boston, MA: Cengage Learning.
Bangdiwala, S. I. (2018). Regression: binary logistic. International journal of injury control and safety promotion, 25(3), 336-338.
Bostrom, R. P., & Heinen, J. S. (2019). Artificial intelligence governance in public administration: An exploratory study of challenges and opportunities. Public Administration Review, 79(6), 799-809.
Chang, H.-C., Chou, C.-H., & Kuo, F.-Y. (2020). The effects of attitude, subjective norm, and perceived behavioral control on intention to adopt AI in government: An empirical study. International Journal of Public Administration, 43(1), 44-54.
Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1-9.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Los Angeles, CA: Sage Publications.
Dickson, B. (2017, May 12). What is narrow, general and super artificial intelligence. TechTalks. https://bdtechtalks.com/2017/05/12/ what-is-narrow-general-and-super-artificial-intelligence/
Does AI spoil the naturalness of sports? Dataconomy. (2022, November 3). Retrieved April 2, 2023, from https://dataconomy.com/2022/11/artificial-intelligence-in-sports-examples/
Dutta, S., Lanvin, B., León, L. R., & Wunsch-Vincent, S. (Eds.). (2021). Global innovation index 2021: tracking innovation through the covid-19 crisis. WIPO.
Elahi, E., Weijun, C., Zhang, H., & Nazeer, M. (2019). Agricultural intensification and damages to human health in relation to agrochemicals: Application of artificial intelligence. Land use policy, 83, 461-474.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Addison-Wesley.
Floyd, F. J., & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7(3), 286-299.
Gasser, U., & Almeida, V. A. (2017). A layered model for AI governance. IEEE Internet Computing, 21(6), 58-62.
Ghorayeb, A., Comber, R., & Gooberman-Hill, R. (2021). Older adults' perspectives of smart home technology: Are we developing the technology that older people want? International journal of human-computer studies, 147, 102571.
Girasa, R. (2020). Applications of AI and projections of AI impact. In Artificial intelligence as a disruptive technology (pp. 23–67). Palgrave Macmillan.
Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330.
Government AI readiness index 2022. Oxford Insights. (n.d.). Retrieved April 1, 2023, from https://www.oxfordinsights.com/government-ai-readiness-index-2022
Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340-2350.
Guadagnoli, E., & Velicer, W. F. (1988). Relation of sample size to the stability of component patterns. Psychological Bulletin, 103(2), 265-275.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
Heo, J., Lee, S. M., & Park, Y. J. (2021). Factors influencing government AI adoption: A comparative study of the US and South Korea. Government Information Quarterly, 38(1), 101506.
Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioral sciences (Vol. 663). Houghton Mifflin college division.
Huck, S. W. (2012). Reading statistics and research (6th ed.). Boston, MA: Pearson.
Johnson, M., Jain, R., Brennan-Tonetta, P., Swartz, E., Silver, D., Paolini, J., ... & Hill, C. (2021). Impact of big data and artificial intelligence on industry: developing a workforce roadmap for a data driven economy. Global Journal of Flexible Systems Management, 22(3), 197-217.
Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2022). What Factors Contribute to Acceptance of Artificial Intelligence? A Systematic Review. Telematics and Informatics, 101925.
Kirlidog, M., Kaynak, A. (2013). Technology acceptance model and determinants of technology rejection. In Information Systems and Modern Society: Social Change and Global Development (pp. 226-238). IGI Global. https://doi.org/10.4018/jissc.2011100101.
Kupper, D., Lorenz, M., Kuhlman, K., Bouffault, O., Lim, Y., Van Wyck, J., & Schlageter, J. (2018). AI in the factory of the future: The ghost in the machine. Boston Consulting Group.
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human factors, 46(1), 50-80.
Meyers, L. S., Gamst, G., & Guarino, A. J. (2016). Applied multivariate research: Design and interpretation. Sage publications.
Moon, M. J. (2019). Public attitudes toward artificial intelligence and the future of government. Public Administration Review, 79(6), 841-844.
Nasrallah, R. (2014). Learning outcomes’ role in higher education teaching. Education, Business and Society: Contemporary Middle Eastern Issues, 7(4), 257-276.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.
Raykov, T. (2009). Evaluation of scale reliability for unidimensional measures using latent variable modeling. Measurement and Evaluation in Counseling and Development, 42(3), 223-232.
United Nations. (2020). E-government survey 2020: Digital government in the decade of action for sustainable development. United Nations Digital Library. https://digitallibrary.un.org/record/3884686?ln=en
Park, J., Hong, E., & Le, H. T. (2021). Adopting autonomous vehicles: The moderating effects of demographic variables. Journal of Retailing and Consumer Services, 63, 102687.
Pattinson, E. (2018). An Investigation into the Relationship Between Diving, Self-Efficacy and Performance in Competitive Diving (Doctoral dissertation, University of Winchester).
Perry, B., & Uuk, R. (2019). AI governance and the policymaking process: key considerations for reducing AI risk. Big data and cognitive computing, 3(2), 26.
Santhika, E., & Ocampo, Y. (2023, January 13). The Philippines collaborating to enhance AI and Cloud - OpenGov asia. OpenGov Asia -. Retrieved April 1, 2023, from https://opengovasia.com/the-philippines-collaborating-to-enhance-ai-and-cloud/
Siau, K., Wang, W., & Chua, A. (2020). Exploring the determinants of artificial intelligence adoption in government: A case study of Singapore. Journal of Organizational Computing and Electronic Commerce, 30(1), 72-90.
Taeihagh, A. (2021). Governance of artificial intelligence. Policy and society, 40(2), 137-157.
The impact of rapid technological change on sustainable development. UNCTAD. (2020, February 17). Retrieved April 1, 2023, from https://unctad.org/publication/impact-rapid-technological-change-sustainable-development
Uyanik, G. K., & Güler, N. (2013). A study on multiple linear regression analysis. Procedia-Social and Behavioral Sciences, 106, 234-240.
Valle-Cruz, D., Alejandro Ruvalcaba-Gomez, E., Sandoval-Almazan, R., & Ignacio Criado, J. (2019, June). A review of artificial intelligence in government and its potential from a public policy perspective. In Proceedings of the 20th Annual International Conference on Digital Government Research (pp. 91-99).
Wang, H., Xie, Y., & Song, R. (2020). An empirical study of factors influencing government intention to adopt artificial intelligence. Government Information Quarterly, 37(3), 101428.
Warkentin, M., Gefen, D., Pavlou, P. A., & Rose, G. M. (2002). Encouraging citizen adoption of e-government by building trust. Electronic markets, 12(3), 157-162.
Yigitcanlar, T., Corchado, J. M., Mehmood, R., Li, R. Y. M., Mossberger, K., & Desouza, K. (2021). Responsible urban innovation with local government artificial intelligence (AI): A conceptual framework and research agenda. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 71.
Yu, S., & Carroll, F. (2022). Implications of AI in National Security: Understanding the Security issues and Ethical challenges. In Artificial Intelligence in Cyber Security: Impact and Implications: Security Challenges, Technical and Ethical Issues, Forensic Investigative Challenges (pp. 157-175). Cham: Springer International Publishing.
Vasileiou, K., Sachini, E., & Stergioulas, L. K. (2020). The impact of trust and attitude on the intention to use AI in the public sector. Government Information Quarterly, 37(3), 101453.
Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 1-12
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Zhang, J., Liu, Y., & Xue, L. (2020). Exploring the factors influencing government adoption of artificial intelligence in China. Government Information Quarterly, 37(1), 101395.
Zhang, W., & Gutierrez, O. (2007). Information technology acceptance in the social services sector context: An exploration. Social Work, 52(3), 221-231.
Zhang, X., & Maruping, L. M. (2008). Household technology adoption in a global marketplace: Incorporating the role of espoused cultural values. Information systems frontiers, 10, 403-413.
Published
2023-06-01
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: <https://myjms.mohe.gov.my/index.php/ijbtm/article/view/22357>. Date accessed: 15 sep. 2024.
Section
Articles
