Adoption of internet of things in residential smart homes
A structural equation modeling approach
Bibliographic Data
| ID | 6454904 |
|---|---|
| Authors | Amir Mohammad Norouzzadeh, Seyed Pendar Toufighi (0000-0002-9561-865X), Jan Vang, Abolfazl Edalatipour |
| Year | 2025 |
| Volume | 9 |
| Pages | 100665-100665 |
| Publication date | 2025-05-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Futures (JOURNAL) |
| Journal identifiers | ISSN: 2666-1888 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.sftr.2025.100665 |
| OpenAlex | W4410282412 |
| Language | EN |
| References cited | 64 |
The Internet of Things (IoT) is transforming modern living environments by enabling automation and connectivity in smart homes. This study explores the behavioral factors influencing IoT adoption in residential smart buildings. Using a descriptive-survey method, data were collected from 97 companies involved in the smart home sector through structured questionnaires. The research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the relationships among adoption variables. Findings indicate that performance expectancy (perceived usefulness) is the most significant factor driving IoT adoption. Security and privacy risks have a notable negative impact, while trust, hedonic motivation, effort expectancy, and social influence significantly affect user attitudes. Facilitating conditions and perceived value also contribute to behavioral intention. The results highlight the need for IoT developers to prioritize user involvement and robust security designs to foster trust. The study offers practical implications for smart home manufacturers, policymakers, and urban planners by identifying key adoption drivers in emerging markets. These insights are especially relevant for sustainable development and energy-efficient housing strategies
Architectural engineering · Business · Internet of Things · Internet privacy · Structural equation modeling · Computer Science · Consumer Retail Behavior Studies · Engineering · Impact of AI and Big Data on Business and Society · Technology Adoption and User Behaviour
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| Citation velocity | historical |
|---|---|
| Highly cited | No |