Examining the Acceptance of WhatsApp Stickers Through Machine Learning Algorithms
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WhatsApp stickers are gaining popularity among university students due to their pervasiveness, specifically in educational WhatsApp groups. However, the acceptance of stickers by university students is still in short supply. Thus, this research aims to empirically examine the determinants affecting the acceptance of WhatsApp stickers through a proposed theoretical model by integrating the technology acceptance model (TAM) with the uses and gratifications theory (U&G). A questionnaire survey was circulated to collect data from 372 university students who have been engaged in a “Group Talk” in WhatsApp. A novel approach was employed to analyze the hypothesized relationships among the constructs in the research model through the use of machine learning algorithms. The results pointed out that IBk and RandomForest classifiers have performed better than the other classifiers in predicting the actual use of stickers with an accuracy of 78.57%. The research findings are believed to provide future directions for stickers developers to better promote stickers in educational activities. © 2021, Springer Nature Switzerland AG.