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Modeling household adoption of IoT-based home security in Dhaka: a PLS-machine learning framework
Arif Mahmud1, Ashikur Rahman2, Fahmid Al Farid3
1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Frontiers in Big Data
|February 20, 2026
Summary
This study reveals key factors influencing Internet of Things (IoT) adoption for home security in Dhaka. Vulnerability and response cost significantly predict user intention, guiding future strategies for enhanced residential security.
Area of Science:
- Technology Adoption
- Residential Security
- Emerging Markets
Background:
- Bangladesh faces challenges in Internet of Things (IoT) deployment.
- Investigating factors for IoT adoption in residential security is crucial for Dhaka.
Purpose of the Study:
- To explore factors influencing IoT adoption for residential security in Dhaka.
- To analyze the contributions of these factors to adoption intention.
Main Methods:
- A hybrid approach combining Protection Motivation Theory (PMT) and Attitude-Social Influence-Self-Efficacy (ASE) models.
- Utilized partial least squares (PLS) and machine learning (ML) techniques.
- Survey data from 348 household heads in Dhaka.
Main Results:
- Identified key predictors of IoT adoption intention: vulnerability, response cost, attitude, response efficacy, self-efficacy, social influence, and severity.
- Vulnerability emerged as the most significant predictor.
- Achieved 34.9% variance and 74.28% accuracy in predicting intention.
Conclusions:
- The integrated PMT and ASE models offer novel insights into technology adoption in emerging markets.
- Findings can enhance public awareness of home security, improving public order in Dhaka.
- Insights can guide businesses in developing effective marketing strategies for consumer adoption.
