Online Impulsive Buying Behavior Using Partial Least Squares Algorithm

Abstract

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.