Jul 2026· Journal of Theoretical and Applied Electronic Commerce Research· Vol 21, pp. 227· 0 citations· 95 references
TL;DR
BNPL-specific financial literacy moderated the associations between algorithmic nudging, impulsive buying, and adverse financial outcomes, with the highest-literacy quartile exhibiting substantially attenuated debt trajectories.
Abstract
Artificial intelligence-integrated ‘buy now, pay later’ (BNPL) platforms are diffusing rapidly across the Middle East and North Africa (MENA), raising concerns about consumer financial vulnerability. Drawing on choice architecture, payment decoupling, and financial literacy literatures, this study examines how three platform-level features—algorithmic nudging, AI personalization intensity, and perceived ease of credit—are associated with impulsive buying tendency and downstream financial outcomes, and whether BNPL-specific financial literacy attenuates these associations. A multi-method design combined cross-sectional partial least squares structural equation modeling (N = 1247 active BNPL users in seven MENA countries) with a six-month longitudinal follow-up (N = 847, 68% retention). Algorithmic nudging was positively associated with impulsive buying tendency, which in turn was associated with elevated financial stress and longitudinal debt accumulation. The ‘loyalty trap’—a paradoxical state in which financially stressed consumers maintain high platform loyalty—is provisionally documented via piecewise longitudinal trajectories. We emphasize that this pattern is consistent with but not causally established by the present design, and we outline specific experimental and quasi-experimental research designs needed for causal identification. BNPL-specific financial literacy moderated the associations between algorithmic nudging, impulsive buying, and adverse financial outcomes, with the highest-literacy quartile exhibiting substantially attenuated debt trajectories. We discuss boundary conditions, alternative explanations, and the limits of causal inference in non-experimental panel data. Findings inform evolving BNPL regulatory frameworks in MENA, with particular relevance to nudge-transparency disclosures, contractual cooling-off periods, and credit-bureau reporting standards.
The growing adoption of algorithmic financial technologies, including robo-advisors, automated trading platforms and artificial-intelligence-based decision tools, raises a fundamental question regarding their actual contribution to investor rationality. This study investigates the extent to which algorithmic overconfidence, understood as an excessive reliance on automated systems, affects the quality of financial decisions. It also evaluates the direct and moderating role of financial literacy in this relationship. A comparative research design is employed to contrast Morocco, an emerging market characterized by lower literacy levels and a developing regulatory environment, with France, a mature financial ecosystem marked by stronger institutional structures and advanced digital integration. The empirical analysis relies on data collected through a questionnaire administered to 312 individual investors, split almost evenly between the two countries, and the relationships are tested using partial least squares structural equation modeling. The results show that algorithmic overconfidence significantly undermines financial decision quality, whereas financial literacy improves it and bmitigates the negative effect of overconfidence. Multi-group comparisons reveal that this harmful effect is more pronounced in Morocco, while the protective influence of literacy is stronger in France. Overall, the findings suggest that technology does not eliminate behavioral biases but instead reshapes their magnitude depending on the market context. The study contributes to behavioral finance by incorporating the technological dimension into the analysis of cognitive mechanisms and by highlighting financial literacy as a key protective factor. From a practical perspective, the evidence supports the need to reinforce financial education in emerging markets and to promote greater algorithmic transparency within advanced financial systems.
The rapid expansion of platform-mediated labor has reconfigured income generation for millions of workers, yet the structural determinants of their financial well-being remain insufficiently quantified, particularly in emerging-market contexts. This study empirically examines how algorithmic work strain and earnings volatility shape the financial well-being of platform gig workers engaged in ride-hailing and delivery services. Using a quantitative, cross-sectional survey design, primary data were collected from 100 active gig workers through a structured, digitally administered questionnaire. Data were analyzed in SPSS using descriptive statistics, Principal Component Analysis, linear regression, and Pearson correlation at α = 0.05. Five latent constructs, were extracted which were productivity and daily output, financial well-being and satisfaction, operational and workplace challenges, work-related financial attitudes, and platform support and engagement. Regression analysis was also conducted to confirm the work-related financial attitudes and predict their daily productivity, indicating that financial pressure drives compensatory labor supply. Operational and workplace challenges were studied on the basis of mean score among the five constructs, closely followed by financial well-being and satisfaction, while platform sector type (ride-hailing versus delivery) relationship with financial satisfaction suggested that precarity is systemic rather than sector-specific. These findings extend the algorithmic-management literature by empirically linking financial strain to labor-supply behavior and carry direct implications for platform governance, minimum-earnings policy, and portable social-security design.
M. Jain, Sheetal Vijayvargiya· International Journal of Adv...· 0 citations
AI-powered investment platforms have fundamentally altered how retail investors in India access and act on financial information. Despite rapid platform adoption the psychological mechanisms through which AI recommendations, platform embedded behavioural nudges and perceived regulatory oversight shape investor decision-making remain poorly understood especially in emerging market contexts. This study addresses that gap by examining the direct and mediated pathways through which these three antecedents influence investment behaviour, operating through trust in AI and cognitive bias as dual mediating mechanisms. Drawing on the Technology Acceptance Model, Nudge Theory, Prospect Theory, and Trust-in-Automation literature, this study develops and tests an integrated structural model. Primary data were collected via a structured survey of 276 retail investors actively using digital investment platforms in India. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to assess the measurement model and test eight hypotheses, including two mediation relationships, using bias-corrected bootstrapping with 5,000 subsamples. Five of six direct hypotheses were supported, with behavioural nudging emerging as the most powerful predictor of cognitive bias activation (β = 0.492, p < 0.001, f² = 0.324) — a medium effect that substantially exceeds the influence of AI recommendation quality. Both mediation hypotheses were confirmed: trust partially mediates the path from AI recommendation quality to investment behaviour (indirect β = 0.041, p = 0.036), while cognitive bias partially mediates the path from behavioural nudging to investment behaviour (indirect β = 0.070, p = 0.034). The study's most significant finding is the non-support of H4: perceived investor oversight enhanced trust in AI (β = 0.180, p = 0.002) but did not reduce bias susceptibility (β = 0.103, p = 0.096). This dissociation indicates that oversight functions as a legitimacy signal rather than a debiasing mechanism — investors feel more confident using AI-assisted platforms under visible regulation, but are not more rational in their decision-making. This is among the first studies to simultaneously operationalise AI recommendation quality, behavioural nudging, and perceived investor oversight as antecedents within a unified structural model, validated on Indian retail investor data. The oversight-bias dissociation also has direct implications for regulators: disclosure-oriented frameworks may build investor confidence without improving decision quality, suggesting the need for behavioural design standards alongside transparency mandates.
Anushree Adithya Balike· EPRA International Journal o...· 0 citations
Generation Z has come of age alongside a rapid expansion of artificial-intelligence (AI)-enabled financial technologies, including robo-advisors, chatbot-based budgeting assistants, and algorithmic investment platforms. While digital financial literacy and social-media exposure are established predictors of young adults' financial self-efficacy and saving behaviour, the specific contribution of AI-tool usage remains comparatively under-examined. This study proposes and tests a structural model in which AI usage/reliance (AIU) predicts financial behaviour (FB) both directly and indirectly through financial self-efficacy (FSE), controlling for financial literacy (FL). Data are collected from a stratified random sample of 350+ Gen Z respondents (ages 18–27), stratified by gender, region, and income band. Measurement and structural parameters are estimated via structural equation modelling (SEM; WLSMV estimator for ordinal indicators), following confirmatory factor analysis of four latent constructs. This manuscript presents the full analytical framework variable definitions, sampling design, descriptive and inferential statistics, SEM specification, model-fit criteria, and visualization plan together with a reproducible R/Python pipeline.
A. Saravanan, G. John· International journal of com...· 0 citations
Payroll-anchored retail borrowers—individuals whose monthly remuneration is routed into an account at the lending institution through a salary-project arrangement—constitute the volume backbone of unsecured consumer lending in Kazakhstan, generating the largest origination flow, the lowest realized default rate, and the majority of the systemic regulatory and capital sensitivities of second-tier banks. Payroll anchoring also changes the lender’s information set, which motivates a study of how that advantage translates into model performance and borrower outcomes. We design and internally validate an explainable hybrid artificial-intelligence framework stratified by client tenure into two production models: a Weight-of-Evidence (WOE) logistic-regression scorecard for new salary-project applicants, and a hybrid scorecard for repeat applicants, in which a stacked ensemble of LightGBM, CatBoost and a multi-head self-attention neural network contributes a single WOE-encoded predictor to a second-stage L2-regularized logistic regression. The hybrid recovers a substantial share of the ensemble’s discriminatory lift while preserving an auditable, monotone scorecard at the point of decision, and isotonic recalibration restores the predicted probabilities of default to the empirical bad-rate scale required for IFRS 9 expected-credit-loss accrual and risk-based pricing. We report discrimination, calibration and stability evidence under a strict anti-leakage protocol and set out the structural preconditions under which the architecture transfers to other emerging-market payroll-anchored portfolios. We are explicit about scope: a true out-of-time validation and a full group-conditional fairness audit are identified as required next steps rather than claimed here. The contribution is a reproducible, interpretable scoring design that exploits payroll visibility while retaining full coefficient interpretability inside the production decision engine.
Gulnaz Zakariya, A. Moldagulova, Nor’ashikin Ali· Applied Informatics· 0 citations
The 'AI Valuation Paradox' refers to the persistent gap between how much companies invest in artificial intelligence and how those investments are reflected in market valuations for S&P 500 firms. This study helps explain this paradox by showing that AI is not valuable in isolation; rather, value emerges when AI is combined with firm-specific complementary assets. When you invest in AI, you get negative marginal gains that are not important. The main new idea is to formalize and evaluate an integrated valuation method that divides firm value into three parts: basic value, real-option premium, and behavior-happiness adjustment. This model can explain 53% of the changes in Tobin's q. A study of dynamic events talks about a predicted "integration dip," which shows that the initial mood, which is lifted by story-driven hype, sharply corrects itself during operational implementation, which causes systemic instability. The results show that this difference exists because we are using tools from the Industrial Age to value assets from the Information Age, and the market constantly undervalues organizational change that is difficult and depends on the path. The results can be used and are important for two things: planning plans and looking at money.
Gabriel Silva Atencio· Computing&AI Connect· 0 citations