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ASSESSING THE ADOPTION OF AI TECHNOLOGIES IN LAW ENFORCEMENT IN ANGELES CITY: A TECHNOLOGY ACCEPTANCE MODEL APPROACH

📘 Volume 11 📄 Issue 12 📅 December 2025

👤 Authors

Dr. Rhem Rick N Corpuz, Kate Bynum, Josh Timothy Torres, Angel Villanueva 1
1. Angeles University Foundation, College of Criminal Justice Education, College of Criminal Justice Education, Angeles City, Pampanga, Philippines

📄 Abstract

This study would examine the acceptance of artificial intelligence (AI) applications among law enforcement personnel using the Technology Acceptance Model (TAM) as its theoretical framework. It would aim to determine the extent to which perceived usefulness, perceived ease of use, attitude toward usage, and behavioral intention would influence actual usage behavior of AI technologies in policing. Adopting a quantitative-correlational research design, the study would utilize a structured survey instrument based on validated TAM constructs to collect data from selected law enforcement officers. Descriptive statistics would be used to present respondents’ perceptions, while inferential analyses including correlation and regression methods would be employed to assess the relationships among key variables. The findings would provide empirical insights into the determinants of technology adoption behavior within law enforcement contexts. Moreover, the results would inform policy recommendations, capacity-building initiatives, and digital transformation strategies aimed at promoting responsible and effective use of AI in crime prevention, investigation, and operational decision-making.

🏷️ Keywords

Artificial Intelligence Law Enforcement Technology Acceptance Model Policing Innovation

🔗 DOI

View DOI - (https://doi.org/10.36713/epra25477)

📚 How to Cite:

Dr. Rhem Rick N Corpuz, Kate Bynum, Josh Timothy Torres, Angel Villanueva , ASSESSING THE ADOPTION OF AI TECHNOLOGIES IN LAW ENFORCEMENT IN ANGELES CITY: A TECHNOLOGY ACCEPTANCE MODEL APPROACH , Volume 11 , Issue 12, December 2025, EPRA International Journal of Multidisciplinary Research (IJMR) , DOI: https://doi.org/10.36713/epra25477

🔗 PDF URL

https://cdn.eprapublishing.org/article/202512-01-025477.pdf

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