Unlocking the smart home: exploring key factors affecting the smart lock adoption intention

This study examines the factorial structure of salient user beliefs associated with smart locks. We also examine the predictive value of the identified constructs on the smart lock adoption intention and we evaluate gender differences in the predictive value of the identified constructs.

The study assumes pragmatic epistemological stance and it leverages mixed-methods research design. The research progresses through three stages: belief elicitation, exploratory factor analysis and confirmatory factor analysis within a nomological network. New groups of participants were recruited for each stage of the study.

We find that while potential adopters express a broad range of perceived benefits and concerns associated with smart locks, only the perceived relative advantage of smart locks vis-à-vis conventional locks in providing safety and security is significantly correlated with adoption intention for both genders. We also find that perceived novel benefits are a significant predictor of the smart lock adoption intention for women, but not for men.

Our results indicate that perceived relative advantage can be the singular critical consideration in the adoption of smart home technologies that replace incumbent solutions. The results also demonstrate that gender-specific models can better capture gender effects that influence technology adoption and use.

Smart home technology vendors would need to convince prospective users that new technology is better than the incumbent solutions on the core affordances of the incumbent technology. Men and women differ in the consideration of novel benefits afforded by novel technologies.

This is among the first studies to examine salient beliefs that affect smart home technology adoption. The findings suggest that the traditional models (TAM, UTAUT) do not capture the key salient beliefs that can influence innovative smart home technology adoption. The study also suggests that gendered models are needed to understand technology adoption in contexts where technology adoption intersects with gender roles.