From Digital Visibility to Completed Purchase in Online Retail

Abstract

Online enterprises can buy digital visibility before they have earned confidence in the transaction that follows. This study develops and tests a dual pathway model in which digital promotion shapes purchase intention through attitude, while perceived transaction security operates through trust and post-purchase satisfaction. The model also separates purchase intention from completed-shopping behaviour, allowing the analysis to identify where favourable evaluation fails to become action. The response export contained 53 records collected between 18 July and 7 August 2026. Seven records failed the purchase or consent screens, leaving 46 eligible recent online shoppers. Reflective measurement and structural relationships were estimated with partial least squares structural equation modelling and 5,000 bootstrap resamples, supplemented by rank correlations, heteroskedasticity-consistent regression, channel comparisons, and an importance-performance map. Digital promotion strongly predicted attitude (beta = 0.652), and security predicted trust (beta = 0.588). Attitude and satisfaction predicted intention, whereas the unique direct trust coefficient became non-significant when these evaluations entered together. The promotion-attitude-intention indirect effect was 0.369, and the security-trust-satisfaction-intention effect was 0.210; both bootstrap intervals excluded zero. Purchase intention predicted completed-shopping behaviour (beta = 0.534), although the behavioural scale had limited reliability. Social media and search exposure exceeded email exposure. The paper contributes an assurance-to-action model that treats visibility, transaction assurance, customer evaluation, and behavioural conversion as separate enterprise capabilities. The small, male-dominated convenience sample makes the findings exploratory, but the results show why enterprise-support programmes should connect promotion with payment assurance, fulfilment, and verified conversion metrics.