In this study, we delve into the multifaceted landscape of online purchase intention and behavior by investigating the underlying factors that shape consumer decision-making. Employing a comprehensive research approach, we explore the diverse antecedents that influence individuals' propensity to engage in online purchases. Through empirical analysis and advanced methodologies, we uncover hidden variations and nuances within the online consumer base, shedding light on previously unexplored dimensions of purchasing behavior. Our findings contribute to a deeper understanding of the complexities inherent in online commerce and offer valuable insights for businesses aiming to optimize their strategies in the digital marketplace.

Benchmarking analysis often encounters aggregation bias, obscuring the true efficiency levels of individual sub-units within a larger system. To mitigate this bias, we propose a methodological framework for dissecting sub-unit efficiency, enabling a more granular assessment of performance metrics. By employing advanced statistical techniques, including data envelopment analysis (DEA) and hierarchical modeling, we explore the efficiency landscape of individual sub-units while accounting for contextual factors and interdependencies. Through empirical validation and case studies, we demonstrate the efficacy of our approach in uncovering hidden inefficiencies and facilitating targeted interventions for performance improvement. Our research contributes to enhancing the accuracy and reliability of benchmarking analysis by providing a nuanced understanding of sub-unit efficiency dynamics and addressing aggregation bias in comparative assessments.