Predictive Model Establishment for The Online Shopping Experience Factors Affecting Repurchase Intention Using Stepwise Linear Regression

The COVID-19 pandemic has brought drastic changes in the way people purchase goods/products. The behavior of people shifted to online shopping and an increase in electronic transactions. In the present, the e-commerce industry in the Philippines continues to grow; however, it is still not as established as developed countries. For online businesses to succeed in the highly competitive online environment, a better understanding of the different factors affecting consumer behavior is vital. This study investigated the e-commerce sector wherein the focus is the online shopping factors that affect the repurchase intention of consumers from multiple generations, namely Baby Boomers, Generation X, Generation Y, and Generation Z. Seven online shopping experience factors were determined in the study affecting consumers' repurchase intention. The study used stepwise linear regression and Cronbach's alpha for data analysis, and the predictive models were validated. The results show that Customer Satisfaction and Perceived Usefulness affect the online repurchase intention for Baby Boomers. At the same time, Perceived Security, Transactionality, and Website Quality are the factors that affect Generation X's repurchase intention. Furthermore, Availability, Perceived Usefulness, and Website Quality affect repurchase intention for Generation Y. Lastly, Perceived Ease of Use, Perceived Security and Customer Satisfaction affects the repurchase intention for Generation Z. Aside from repurchase intention, these factors also significantly affect online store revisit. These results will be helpful for e-commerce company owners in improving the features of their platforms based on consumers' preferences. Recommendations include for e-commerce sites to improve cybersecurity, and user-friendly interface, convenient payment methods, clear refund or return policies, and consistent monitoring and updating of product stocks.