Algorithmic Influence and Consumer Psychology in Buy-Now-Pay-Later (BNPL) Adoption: Evidence from Generation Z and Millennials in Southeast Asia

Ah Huai Ah Chan; Prashanth Beleya; Kaladevi Murugesu; Diana Airawaty; Sadegh Salehi

doi:10.48161/qaj.v6n2a2248

Authors

Ah Huai Ah Chan Department of Management, Faculty of Business and Communications, INTI International University, 71800 Nilai, Malaysia; https://orcid.org/0009-0006-9470-8377
Prashanth Beleya Department of Management, Faculty of Business and Communications, INTI International University, 71800 Nilai, Malaysia; https://orcid.org/0000-0001-6815-1460
Kaladevi Murugesu Department of Management, Faculty of Business and Communications, INTI International University, 71800 Nilai, Malaysia; https://orcid.org/0009-0009-9314-1534
Diana Airawaty Department of Management, Faculty of Economics, Universitas Mercu Buana Yogyakarta 55753, Indonesia; https://orcid.org/0000-0002-2587-0438
Sadegh Salehi Department of Management, Faculty of Humanities and Social Sciences, University of Mazandaran, Babolsar 47416-13534 Iran. https://orcid.org/0000-0003-2108-2132

DOI:

https://doi.org/10.48161/qaj.v6n2a2248

Keywords:

Buy-Now-Pay-Later (BNPL), Social Media Algorithms, Digital Nudging, Technology Acceptance Model (TAM), Generation Z, Southeast Asia, Consumer Finance Adoption.

Abstract

The rapid diffusion of Buy-Now-Pay-Later (BNPL) services across Southeast Asia has coincided with the unprecedented expansion of social media ecosystems underpinned by data-driven algorithms. While BNPL has been framed as a tool to democratize credit access and stimulate consumption, the influence of algorithmic curation on its adoption remains underexplored in scholarly literature. This study addresses how social media algorithms influence BNPL adoption among Generation Z and Millennial in Malaysia, Singapore and Indonesia by integrating psychological mechanisms such as Fear Of Missing Out (FOMO) and impulsive buying with platform level algorithmic influence. Anchored in the Technology Acceptance Model (TAM), digital nudging theory, and the Stimulus–Organism–Response (S-O-R) framework, the study conceptualizes algorithms personalization as stimulus that indirectly shapes financial decision-making through cognitive and emotional pathways. A quantitative research design was employed, drawing survey data from 300 BNPL-exposed respondents across the three countries. Regression was used to test direct, mediating, and moderating relationships, supplemented by multiple regression analysis for robustness. Findings reveal that buying behaviors, FOMO and behavioral targeting exert strong direct effects on BNPL adoption, while algorithmic personalization operates These insights extend existing digital finance adoption frameworks by incorporating algorithmic influence as a key antecedent and highlight the dual impact of social media facilitating financial inclusion while simultaneously amplifying overspending risks. The study advances theoretical understanding of technology-mediated financial behaviors and provides actionable implications for regulators and digital lenders in Southeast Asia. It calls for enhanced algorithmic transparency, risk-based consumer education, and adaptive credit scoring models to promote responsible BNPL usage without stifling fintech innovation.

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