The Impact of Age and Cognitive Style on E-Commerce Decision-Making: A Multi-Method Approach

Abstract

This dissertation explores how age and cognitive style influence decision-making in e-commerce, with a focus on visual information processing. Cognitive style is an individual-difference decision factor that describes an individual’s general tendency to either make quick gut-feel decisions (Satisficer) on one extreme or be very meticulous in gathering evidence before making a well-informed decision (Maximizer) on the other extreme. A novel eye-tracking-based construct, Visual Perceptual Comprehensiveness (VPC), was developed and validated to measure the breadth and deliberation of visual attention of participants who completed a series of online shopping tasks under different bias conditions (i.e., vividness, order, control). VPC was developed to investigate individual decision-making processes in an attempt to understand how and why individuals may fall prey to cognitive biases, which are systematic errors in judgement and decision-making. The study draws on the Attention Drift Diffusion Model (aDDM), Dual Process Theory, and Cognitive Bias Theory. A pilot study of 17 participants validated the study design, followed by a main study of 54 participants to test the hypotheses. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and multigroup analysis (PLS-MGA), using two bootstrapped data augmentation approaches. Findings show that older adults exhibited significantly lower VPC as hypothesized, while maximizers demonstrated lower VPC than satisficers, contrary to expectations. Cognitive style moderated the age–VPC relationship, mitigating age-related declines in visual processing. VPC strongly predicted decision effort, suggesting that broader and more deliberate visual attention is associated with longer decision times. However, VPC showed weak or inconsistent relationships with decision quality and perceived outcomes, implying that increased visual attention does not necessarily translate into better or more satisfying decisions. Task type significantly moderated several effects, revealing that the presence and nature of cognitive bias (e.g., vividness or order) influences how individual differences affect decision-making. This research introduces a new construct to the NeuroIS literature, emphasizes age and cognitive style as critical individual differences, and offers practical implications for designing more inclusive and bias-resilient digital decision environments.