Online Impulsive Buying Behavior Using Partial Least Squares Algorithm




Online impulsive buying behavior, visual appeal, price attributes, perceived enjoyment, perceived usefulness, SOR framework, PLS algorithm


Impulsive purchasing is one of many fascinating subjects to investigate in e-commerce research. Online buying facilitates purchases and provides impulsive buyers with a venue to meet their demands. Impulse buying occurs when a client feels a sudden, overpowering need to make a purchase, which is a common practice among online shoppers. Therefore, the study’s purpose is to apply the partial least square (PLS) algorithm to investigate the factors driving online impulsive buying behavior (OIBB). Besides, in this research, the stimulus organism response (SOR) model was used as the research’s guiding theory; with the stimulus such as visual appeal and price attributes, the organism comprising perceived enjoyment and perceived usefulness, and response as OIBB. In addition, a non-probability sampling method was employed to collect data from 313 customers who shared their online impulsive purchase experiences through a Google Forms online survey. The collected data was analyzed using the PLS technique to assess the reliability, convergent validity, and discriminant validity of the variables, as well as test the proposed hypotheses. The findings reveal that visual appeal and price attributes were positive connections to perceived enjoyment and perceived usefulness. Likewise, perceived enjoyment and perceived usefulness also impacted positively OIBB. Additionally, the findings disclosed that visual appeal and price attributes also were associated positively with OIBB.


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Author Biography

Dam Tri Cuong, Industrial University of Ho Chi Minh City, Vietnam

Dam Tri Cuong received his Ph.D. degree in management from University of Economics Ho Chi Minh City in 2017. He is currently working as a head of strategic management division at Industrial University of Ho Chi Minh City. His research areas include data analysis, optimal model analysis, consumer behavior, and brand management analysis.


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How to Cite

Cuong, D. T. . (2023). Online Impulsive Buying Behavior Using Partial Least Squares Algorithm. Journal of ICT Standardization, 11(03), 217–236.