Application of the TAM model for assesing the acceptance of IoT technology in a residential security application
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Abstract
Residential crime continues to be a significant concern, and traditional security systems relying oHousing is an area vulnerable to crime, especially if it is not supported by an adequate security system. Many housing complexes still rely on conventional security systems that only involve officers without technological support. Therefore, the application of technology, especially the Internet of Things (IoT), is needed to improve housing security systems. The success of a system is largely determined by the level of user acceptance, which can be measured using the Technology Acceptance Model (TAM). This study aims to measure user acceptance of an IoT-based housing security system using the TAM model. Data were obtained from 100 respondents and analyzed using the PLS-SEM method to test the research hypotheses. The results showed that four hypotheses had a significant relationship, namely the relationship between Subjective Norm (SN) and Perceived Usefulness (POU), Perceived Ease of Use (PEU) and POU, PEU and Attitude Toward Use (ATU), and POU and Behavioral Intention (BEI). Meanwhile, the other four hypotheses did not show a significant relationship.n manual monitoring are often insufficient in addressing modern security challenges. With the rapid development of Internet of Things (IoT) technology, digital security solutions offer new opportunities for improving surveillance and access control within housing environments. This study aims to assess user acceptance of an IoT-based residential security application by applying the Technology Acceptance Model (TAM). A quantitative survey method was used, involving 100 respondents who evaluated the prototype after testing it directly. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Perceived Ease of Use significantly affects both Perceived Usefulness and Attitude Toward Using, while Perceived Usefulness strongly influences Behavioral Intention. However, Attitude Toward Using shows a marginal impact on Behavioral Intention, and Behavioral Intention does not significantly predict Actual Use. These findings reveal the dominant factors influencing acceptance and highlight areas for improvement in IoT-based security applications.
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