Browsing engagement as a mediator of task complexity and decision-making dependence: a Bayesian mediation analysis in online shopping

Purpose This study investigates how task complexity influences users’ decision-making dependence on e-commerce platforms through the mediating role of browsing engagement. By analyzing habitual and exploratory interactions, the study aims to provide insights into how complex shopping tasks shape users’ reliance on platform features. Design/methodology/approach The study employs a Bayesian analytical framework and surveys 600 South Korean users of Coupang and AliExpress. Using a Bayesian extension of ANOVA (BANOVA), it models the mediation effect of browsing engagement between task complexity and decision-making dependence. The approach addresses a key methodological gap in handling ordinal dependent variables in mediation analysis. Findings The findings reveal that increased task complexity leads to higher browsing engagement, which in turn enhances decision-making dependence on platform functionalities. Exploratory browsing behaviors, driven by the cognitive demands of complex tasks, help reduce users’ mental strain while fostering habitual engagement with platform features, ultimately influencing purchase decisions. Originality/value This study offers a novel contribution to the literature by utilizing a mediation framework based on the Bayesian extension of ANOVA, suitable for ordinal-scale dependent variables. Additionally, it establishes decision-making dependence as a structured framework for measuring users’ engagement depth with digital platforms or mobile applications. By categorizing users into distinct reliance levels, this concept offers a flexible approach to assessing platform dependency across digital contexts.