Online Reviews as Antecedents of Purchase Intention in Omnichannel Environments: A Canonical Correlation and Information-Systems Perspective

Abstract

Online reviews constitute a pivotal component of the contemporary consumer decision-making process, particularly within omnichannel retail ecosystems that increasingly rely on interconnected computer information systems, big data analytics, and Internet of Things (IoT) infrastructure to capture, transmit, and act upon user-generated content (UGC). As consumers increasingly navigate purchases across multiple touchpoints – physical stores, e-commerce platforms, and mobile applications UGC functions as a critical information intermediary that shapes attitudes, moderates perceived risk, and stimulates purchase intention. The present study investigates how online reviews influence consumer behaviour in an omnichannel environment, drawing on the Stimulus–Organism–Response (SOR) theoretical framework and the Cognition–Affect–Conation (CAC) model. Employing a descriptive cross-sectional survey design with a valid sample of 506 respondents, canonical correlation analysis was used to examine the relationship between review-related attitudinal–perceptual dimensions and two dependent variables: purchase intention and customer shopping experience. Findings confirm that omnichannel marketing strategy (showrooming/webrooming) and perceived risk significantly influence purchase intention, while online reviews alone do not independently enhance the overall customer experience, a construct mediated by numerous post-purchase and operational factors. These results extend the theoretical understanding of eWOM dynamics in multi-touchpoint retail settings, connect this behavioural evidence to the growing computer-information-systems literature on big-data-driven review analytics and IoT-enabled omnichannel data integration, and carry practical implications for retail managers and information-systems architects seeking to optimise review integration strategies.