A Parameterized Decision Tree Classifier to Identify Customers' Online Purchase Intention: An Indian Case

ABSTRACT Nuclear families with almost all working members have resulted in increased purchasing power as well as increased need for varied utilities. Due to paucity of time as well as COVID‐19, online shopping platforms have flourished and expanded in India notably. The growing statistics of users of varied online social media platforms have made Electronic Word of Mouth, popularly called eWOM, a popular influencer in customer decision‐making during an online purchase. However, customer purchase intention is strongly influenced by electronic word of mouth (eWOM) communication generated by other customers on social networking sites. However, eWOM adoption is further influenced by other parameters too. This study investigates the underlying parameters influencing eWOM adoption and its influence on the Indian customer's purchase intention. Taking the decision tree classifier as the basis, the study implemented a cross‐sectional self‐administered investigation to gather data from a convenience sample of university students of Bharati Vidyapeeth's Educational Campus, New Delhi. The results were used to create a parameterized decision tree classifier to predict potential Indian customers. This model is the first of its kind in the above domain and can help achieve the financial objective of organizations and businesses to attract new customers. Study results revealed that all predictors of eWOM adoption were notable enough to be considered. It can also yield significant markers for digital content creators as well as business managers to develop viral marketing campaigns and gauge consumer preferences and behavior. These findings and framework shall contribute to the knowledge base of both digital marketers and online customers through a detailed understanding of social media as well as information.