Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques

Abstract

The Internet of Things (IoTs) market for consumers includes a huge variety of smart home devices. This research is conducted to investigate what factors influence customer purchase of smart home security systems. A new model is developed and the data collection is performed from the customers' reviews on the smart home security systems on Amazon.com. Totally, 12,678 reviews were collected to investigate customers' preferences in purchasing smart home security systems. To analyze the collected data, we develop a new method using text mining, unsupervised clustering, and a neuro-fuzzy system. The factors are discovered from textual reviews using the Latent Dirichlet Allocation (LDA) technique. To segment the customers' preferences the Expectation-Maximization (EM) technique was used. ANFIS technique was then used to find the relationship between the factors and customers' purchase intention. It was demonstrated that the use of text mining can be effective in the analysis of customers’ preferences from online customer reviews. The results of the data analysis are provided and discussed.