Understanding User Perception and Intention to Use Smart Homes for Energy Efficiency: A Survey

TL;DR

This study investigates how user perceptions, norms, and habits influence the intention to use smart home technology for energy efficiency, combining behavioral theories and advanced modeling to identify key predictors and design recommendations.

Contribution

It integrates TPB and NAM with a novel PLS-SEM and Random Forest approach to analyze user behavior in smart homes, providing new insights into behavioral drivers for energy efficiency.

Findings

Personal norms strongly predict energy-efficient smart home use.

Social norms and attitudes significantly influence usage intentions.

Habit enhances the link between personal norms and behavioral intention.

Abstract

Smart Home technology is increasingly seen as a solution for improving household energy efficiency. However, its energy-saving potential depends largely on how consumers use the system. To explore how user perception and intention to use Smart Home can influence energy efficiency, we develop a research model combining the theory of planned behavior (TPB) and the norm activation model (NAM), based on a comprehensive literature review. We collect data by surveying users of Smart Home systems (N = 363) and apply a partial least squares structural equation model (PLS-SEM) extended by a Random Forest algorithm to capture both linear and non-linear causal relationships. Results show that personal norms, shaped by a sense of responsibility and awareness of environmental consequences, are the strongest predictors of energy-efficient smart home use. Social norms and attitudes also significantly contribute to the intention to use these systems efficiently. Moreover, past behavior strengthens the link between personal norms and behavioral intention, highlighting the role of habit in shaping energy-related actions. To maximize the energy-saving potential of Smart Homes, system design should focus on reinforcing personal moral norms, supporting long-term engagement through habit-forming features, delivering personalized feedback on environmental and financial outcomes, and embedding green automation defaults. Implementing policy mechanisms that financially reward household energy savings presents a powerful lever for reducing emissions through improved energy efficiency in residential buildings.