Online impulse buying: a dual analytical approach using partial least square structural equation modelling and fuzzy set qualitative comparative analysis

Purpose Driven by the convenience and ease of shopping provided by digital platforms, impulse buying has emerged as the most prominent outcome of online retailing. Therefore, the present study aims to examine customer impulse buying in the online shopping environment using the underpinnings of latent state-trait theory. Design/methodology/approach Data were collected from 550 respondents through a structured questionnaire. Partial least square structural equation modelling (PLS-SEM) was employed to examine the hypothesised relationships, while fuzzy-set qualitative comparative analysis (fsQCA) was used to identify distinct configurations of conditions associated with online impulse buying. Findings The results obtained through PLS-SEM reveal that digital platform cues, namely visual appeal and online reviews and customer traits, namely materialism and shopping enjoyment tendency, significantly influence online impulse buying. Complementing these findings, the fsQCA results identify multiple configurations of platform cues, hedonic browsing and customer traits associated with higher levels of online impulse buying. Originality/value This study integrates PLS-SEM with the fsQCA approach to examine impulse buying in the digital shopping environment. Integrating these two analytical approaches not only examines linear relationships among factors but also identifies the optimal combination of variables that explains impulse buying in a digital shopping environment. Thus, the findings can theoretically strengthen the concept of impulse buying and offer implications for marketers and policymakers.