Predicting wearable IoT Adoption: Identifying core consumers through Machine learning algorithms

Abstract

Internet of Things (IoT) technology has been integrated into a diverse array of products, including watches, glasses, lighting systems, and home services, and has garnered widespread consumer acceptance. While the overall IoT market has reached a stage of maturity, the adoption of wearable devices, a key subset of IoT technologies, lags behind. Recognizing the need to identify potential demand for these wearable devices, this study leverages data from the 2019 MCR survey (N=3,922) and employs five machine learning algorithms for analysis. Among these, the random forest model demonstrates the highest accuracy in predicting consumer adoption of wearable devices. Based on this model, 17 major predictors influencing adoption have been identified. The study's findings suggest that women in their 10 s and 20 s are the most likely potential core consumers for wearable devices. These individuals are characterized by a high expenditure-to-income ratio and stringent consumption standards that take into account product quality, price, design, shopping efficiency, and brand reputation. This research contributes to the expansion of the advertising marketing literature by being among the first to employ machine learning techniques for consumer targeting strategies in the wearable device sector. © 2024 Elsevier Ltd