A model for predicting user intention to use wearable IoT devices at the workplace

Abstract

The internet of things refers to devices that are connected to the Internet and communicate with each other providing many benefits to users, but they could also violate their privacy. The main objective of this study is to analyse the factors that influence employees’ intention to use wearable devices at the workplace. In this study, a review of the literature regarding acceptance of technologies and influencing factors such as risk and trust is used to develop a conceptual model. The proposed conceptual model was tested using a survey conducted among employees of an IT consulting firm, with a total of 76 participants. Partial least square path and Adaptive Neuro-Fuzzy Inference modelling were used to validate and predict these factors influence on users’ intention to use these devices. The findings indicate that the perceived usefulness of a wearable IoT device provides the strongest motivation for individuals to use it at the workplace. Further results show that applying the ANFIS approach helps improve the predictability of user intention to use IoT devices. Keywords: Behaviour intention, Privacy, Trust, Wearable devices, Internet of Things (IoT), Adaptive Neuro-Fuzzy Inference systems (ANFIS), Partial Least Square Modelling (PLS)