Factors influencing the acceptance of IoT devices in a smart home environment

Abstract

The Internet of Things (IoT) has emerged as a promising subsegment within the global Information and Communication Technology (ICT) market, as a result of the quick advancements in communication technologies and infrastructure.

A number of smart IoT devices in a smart home setting have been introduced in the market for the convenience of consumers. The amount of IoT devices being released to the market which possess characteristics of the Artificial Intelligence (AI) and IoT technology has increased rapidly with data suggesting the growth of sales of such devices in the UK market (Government Office for Science, 2021). While the sales of smart IoT devices are on the rise, consumer acceptance rates of smart devices such as smart lighting, plugs, bathroom scales, smoke alarms and smart baby monitors indicate a very low rate of ownership in the UK households. (Government Office for Science, 2021). This discrepancy can be attributed to various factors, with privacy, security concerns and trust in IoT providers emerging as significant obstacles. Understanding the specific nature of these concerns along with a multitude of other relevant factors and their impact on consumer behaviour is crucial for industry stakeholders to effectively address the challenges and promote wider acceptance of smart home devices.

While the previous versions of technology acceptance models were constructed for various technological innovations with focus on technology of those times, their focus was not on the IoT technology. The user behaviour of IoT technology in a home environment is receiving significant attention from studies applying technology acceptance models recently however these studies aim to assess the applicability of historical models to this newly emerged technology whereas the current research aims to develop a technology acceptance model of IoT devices (TAM-IOT) within the home environment including the key factors from historical models along with most recent contemporary factors such as perceived risks and trust in IoT providers that are significant from a consumer’s perspective.

This thesis aimed to study the factors influencing the acceptance of IoT devices using Technology Acceptance Model (TAM) (Davis, 1989) as a guiding theory along with its various extensions such as TAM2 (Venkatesh and Davis, 2000), TAM3 (Venkatesh and Bala, 2008), Unified Theory of Acceptance and Use of Technology model (UTAUT) (Venkatesh et al., 2003) and UTAUT2 (Venkatesh, Thong and Xu, 2012). The main aim of the study was to identify and reinstate the impact of attitude on behavioural intention of consumers and study the diverse relationships between factors influencing the consumer behaviour towards IoT devices.

The study achieved its aims by pragmatist methodology of exploratory study to identify the choice of factors to develop a hypothetical deductive approach tested using questionnaires as a quantitative research method. The acquired data was analysed using techniques such as structural equation modelling, least square regression etc. to derive the ranking of factors determining the hierarchy of the factors and finally leading to the development of a Technology Acceptance Model of Internet Of Things (TAM-IOT). The results of the studies indicated that socio-demographic variable of education had a significant moderating impact on the relation between perceived ease of use and intention to buy/use as well as between perceived usefulness and intention to buy/use. However, other moderators such as age, gender and income level showed less statistical significance with p values >0.05 not supported by the sample data for this study and hence were rejected disapproving their significant moderation impact in previous studies.

Several individual path coefficients proved to be statistically reliable specially the perceived usefulness and attitude influencing the intention to buy/use IoT devices, it was noteworthy to find that the combined effect of all the factors on overall model of technology acceptance affected the fit statistics making it advisable for further research for accurate future estimations. The study enabled the ranking of factors significant to the overall model with trust in IoT providers along with perceived usefulness and attitude as one of the most significant factors and social influence as the least significant factor affecting the intention to buy/use smart IoT devices.