13th Annual International Conference on Industrial Engineering and Operations Management

A Predictive Model on a Consumer’s Impulsive Buying Intention Towards Facebook Live Online Selling Using Binary Logistic Regression

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Track: Modeling and Simulation

Abstract of the Study 

During the COVID-19 crisis, the digital economy expanded. Due to COVID-19 restrictions, people have become more reliant on online buying. Many businesses shifted their approach to online platforms, thus engaging more customers. The live-stream shopping campaign in the Philippines is continuously growing due to more social media interaction with consumers. Small independent merchants have shifted to Facebook Live Selling, which shows real-time video, as a direct selling technique. This study examined the factors influencing impulse buying behavior amongst consumers in Facebook Live Selling. Three hundred eighty-four (386) respondents, ages 18 to 55 participated in the online survey that consisted of their demographics and questions based on the internal and external factors affecting the impulsiveness of a consumer towards buying in Facebook live-selling. The study used Binary Logistic Regression to predict whether an individual will purchase an item on impulse in Facebook live-selling. The dependent variable was categorized by the individual’s intention towards the product, it was denoted as (0 = will not buy; 1 = will buy). The model revealed that among the factors, only sex (ρ = 0.049), frequency of purchase (ρ = 0.00), impulse buying tendency (ρ = 0.00), and trust propensity (ρ = 0.035) are significant predictor variables in the model, which were only internal factors. Overall, the model helps broaden our understanding of how the latest marketing strategy (live selling) affects the impulse buying behavior of consumers. Limitations and implications are further discussed in the study.

Published in: 13th Annual International Conference on Industrial Engineering and Operations Management, Manila, Philipines

Publisher: IEOM Society International
Date of Conference: March 7-9, 2023

ISBN: 979-8-3507-0543-0
ISSN/E-ISSN: 2169-8767