Online platforms have a significant influence on consumer purchase patterns, making consumer decision-making in the digital marketplace more complicated. Although there has been research on impulse buying, little is known about the ways in which various platform-related and behavioral elements combine to influence such behavior. The study addressed gaps in understanding the combined effects of these factors and provided a structured framework for analyzing online impulse buying for digital commerce stakeholders. Using Interpretive Structural Modeling (ISM), the study sought to identify important components, such as behavioral biases and platform design elements, and investigate how these interacted. The most important characteristics were identified by expert consensus using the Nominal Group Technique (NGT). These factors were then examined to create a structured framework that captured causal and hierarchical linkages. MICMAC analysis improved the ISM model by highlighting the elements that can cause more extensive behavioral reactions by classifying factors according to their influencing (Ip) and dependent power (Dp). The results showed that specific platform features and design elements produced a feeling of urgency and information overload, which in turn fueled impulsive purchasing behavior by amplifying behavioral biases through mechanisms like financial incentives and social proof. By illustrating how these elements interacted at various levels in the model, the study also demonstrates the organization of the elements to illustrate the process. From a practical viewpoint, the findings offer insights for marketers to identify the behavioral cues of the consumers that influence their purchases and offer implications for policymakers to implement rules to balance the designing of online platforms with user welfare.