Consumer Perspectives for Purchase Intentions of Online Pharmacy Products Using Deep Learning

In recent years, online business models have been developed due to the availability of low-cost, high-speed internet. Online pharmacies using e-commerce sites now can reach to their customers easily and sell medications online. It is easy for customers to evaluate prices and product reviews before purchasing medicines. To understand the factors that affect consumers' behavioral and purchase intention from online pharmacies, a modified UTAUT model was used to investigate five determinants related to adoption intention namely perceived trust, perceived risk, perceived ease of use, performance expectancy, and social influence. This research delves into the viewpoints of online pharmacy customers, specifically examining the effects of deep learning algorithms. With their unparalleled accessibility and ease of use, internet pharmacies have revolutionized the healthcare industry. If these digital platforms are to be optimized for user happiness and optimal operation, it is crucial to understand how people perceive and interact with them. Customers now have more alternatives than ever before for getting their medications, Most prediction models' inputs were determined to be criteria pertaining to online activities and behaviors. Various deep learning techniques are used in purchase intention prediction because of their superior capacity to manage complex and massive datasets with little to no human oversight. The predictions using CNN and LSTM models achieve a high level of accuracy and are studied in our model with a modified framework for purchase prediction using these algorithms.