Modeling the use of voice based assistant devices (VBADs): A machine learning base an exploratory study using cluster analysis and correspondence analysis

•The research probes into the diversified describing words for VBADs.•Explored the usage intentions behind VBADs using machine learning based cluster analysis.•Perceptual brand mapping of four VBADs brands using correspondence analysis.•Perceptual brand map compares brands across points of parity vis-à-vis distinction. There is a growing prevalence of Voice-Based Assistant Devices (VBADs) in our urban lives. The unique and lively user and interaction experience offered by this smart and Artificial Intelligence (AI) enabled device, not only helps to serve a variety of functions for its users but also provides them companionship to relieve the boredom. The present study attempts to explore the various describing words or phrases that the users of VBADs often use describing them, by making use of the projective word association technique. Post this using the cluster analysis the study has aimed to probe into the usage intentions of the users regarding these devices, examining if these are used for purely functional purposes or are also capable of meeting some kind of friendly relationship bond with their users. The cluster analysis was performed using machine learning and mathematical modeling. Further, the study explores a perceptual brand mapping for a few popular brands of VBADs using correspondence analysis, to understand if these brands have more points of parity in common or are they distinct across each other. The findings indicate that besides meeting the regular functional goals for the people, these devices also bond with the people by becoming their partners, friends, or members of the family. There exists a distinction across brands studied, where though all the brands satisfied their users by serving different functional purposes, however only Google Assistant and Alexa were capable of forming relational ties with their users. The brands engineering these devices can essentially base their product development and user interaction model taking the findings of this study into consideration for improving the future user experience.