Using structural topic modelling to predict users’ sentiment towards intelligent personal agents. An application for Amazon’s echo and Google Home

Abstract

Despite growing levels of usage of Intelligent Personal Assistants (hereinafter, IPA), their in-home usage has not been studied in depth by scholars. To increase our understanding of user interactions with IPA, our research created a theoretical framework rooted in technology acceptance models and Uses and Gratification Theory. Our empirical method designs an ambitious analysis of natural and non-structured narratives (user-generated con- tent) on Amazon’s Echo and Google Home. And to identify key aspects that differentially influence the evaluation of IPA our method employs machine-learning algorithms based on text summarisation, structural topic modelling and cluster analysis, sentiment analysis, and XGBoost regression, among other approaches. Our results reveal that (hedonic and utilitarian) benefits gratification, social influence and facilitating conditions have a direct impact on the users’ sentiment for IPA. To sum up, designers and managers should recognise the challenge of increasing the customer satisfaction of current and potential users by adjusting doubtful users’ technical skills and the (hedonic, cognitive, and social) benefits and functionalities of IPA to avoid boredom after a short lapse of time. Finally, the discussion section outlines future lines of research and theoretical and managerial implications.