Potential technology of virtual reality (VR) with Ai-powered object recognition and analysis towards technology users in entertainment

Nizam, Nurul Zarirah and Kamarudin, Nurhayati and Bakri, Mohammed Hariri (2025) Potential technology of virtual reality (VR) with Ai-powered object recognition and analysis towards technology users in entertainment. Journal of Information Systems Engineering and Management, 10. pp. 949-956. ISSN 2468-4376

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Abstract

Introduction: Virtual Reality (VR) has swiftly transformed into a ground-breaking technology, augmenting experiences across multiple domains, especially in entertainment. Objectives: This study examines the amalgamation of Artificial Intelligence (AI)-driven object detection with virtual reality (VR) to enhance interactivity and engagement in entertainment experiences. Integrating AI algorithms enables VR systems to actively assess and interact with virtual worlds, enhancing user engagement and informational precision. Methods: The study investigates technological elements affecting the efficacy of AI-driven VR systems, their prospective uses in entertainment, and user attitudes towards usability, precision, and overall experience. Employing quantitative methodology and statistical instruments such as SPSS. Results: The results demonstrate a robust correlation between simplicity of use, perceived utility, and the adoption of AI-driven VR technology. These observations offer essential direction for developers seeking to enhance VR systems for wider acceptance. The coefficient of determination is 0.708, which indicates that Perceived Usefulness, Perceived Ease of Use, Performance Expectancy, Effort Expectancy explain 70.8 % of the variability in acceptance of VR technology. If p is less than 0.05 in this study, one may conclude that the coefficients are statistically substantially different from 0 if they are significantly different. There is a statistically significant effect of each variable on the acceptance of VR technology Conclusions: The quality of a model's ability to predict the value of a dependent variable, such as acceptance of VR technology, is measured by the "R" statistic, which requires the description of the multiple correlation coefficient. The degree of prediction that is excellent is shown by the value of 0.715, which suggests that. The "R Square" statistic measures the amount of variation in the acceptance of VR technology variable that can be explained by Perceived Usefulness, Perceived Ease of Use, Performance Expectancy, Effort Expectancy.

Item Type:	Article
Uncontrolled Keywords:	AI recognition, Acceptance of VR technology, Perceived ease of use, Perceived usefulness, Performance expectancy, Effort expectancy, Entertainment
Divisions:	Faculty of Technology Management and Technopreneurship
Depositing User:	Sabariah Ismail
Date Deposited:	13 Aug 2026 03:03
Last Modified:	13 Aug 2026 03:03
URI:	http://eprints.utem.edu.my/id/eprint/30236
Statistic Details:	View Download Statistic

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