This booklet synthesises findings from:

Peer-Reviewed Academic Studies

The articles draw heavily on behavioural economics and psychology journals, such as the Journal of Marketing, Journal of Consumer Research, Marketing Science, and Journal of Retailing. These studies often utilise:

Causal Inference & Transaction Logs: Researchers use difference-in-differences (DiD) models to analyse multi-year retailer transaction logs, proving a direct link between BNPL adoption and increased purchase frequency.
Neuroscience Research: Findings are supported by fMRI brain imaging studies that measure activation in the parietal cortex and right insula to track the physical “pain of paying”.
Cognitive Load & Eye-Tracking: Controlled laboratory experiments use eye-tracking to observe how instalment pricing acts as a visual anchor and reduces decision fatigue.
Working Papers: Economic insights are sourced from institutions like the National Bureau of Economic Research (NBER) and Harvard Business School.

Industry Research and Platform Data

Aggregated data from major global payment and e-commerce platforms provides high-volume statistical significance:

Major Payment Gateways: Analysis of over 150,000 checkout sessions from Stripe and data from platforms like Shopify and BigCommerce.
Market Analysis Firms: Reports from McKinsey, Accenture, and RBC Capital Markets track global adoption trends and merchant performance.
UX Research Institutes: Usability benchmarks from the Baymard Institute and Nielsen Norman Group regarding cart abandonment and checkout friction.

Controlled Experiments and A/B Testing

The findings include data from randomised controlled experiments designed to isolate the impact of BNPL:

Timing Experiments: Studies testing the difference between showing BNPL pricing early (on Product Detail Pages) versus only at checkout.
Multi-Variant Tests: Three-arm experiments (control vs. badge-only vs. full integration) measuring immediate metrics like Add-to-Cart (ATC) rates and long-term Customer Lifetime Value (LTV).

Government and Regulatory Reports

Authoritative data on consumer risk and financial health is pulled from:

The Federal Reserve: Research from the Richmond, Kansas City, and Boston Feds, including the Survey of Household Economics and Decisionmaking (SHED).
Regulatory Audits: Policy briefs and design audits from the Consumer Financial Protection Bureau (CFPB) and the Financial Conduct Authority (FCA).

Real-World Case Studies

The articles reference specific performance data from diverse retail sectors:

Fashion & Beauty: Metrics from brands like ASOS, Zara, Sephora, and MAC Cosmetics.
High-Ticket Items: Implementations by Amazon, Apple, Best Buy, and Expedia to evaluate BNPL’s impact on expensive categories.
Niche Markets: Performance results from smaller specialised retailers like Ninepine and Ditur.

Systematic Literature Reviews

Comprehensive reviews of online consumer behaviour (e.g., Kanwal et al., 2021) synthesise patterns from over 60 academic articles covering a decade of research into gender differences, trust, and mental accounting

Every claim is cited. Every statistic is sourced. Every recommendation is testable.

BNPL

Buy-Now-Pay-Later Optimisation