Aug 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 697-705· 0 citations
TL;DR
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).
Abstract
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.
It is indicated that AI-powered personal finance applications are consistently associated with improved financial confidence and more frequent, though not necessarily more diversified, investment activity among young users, with effects moderated by financial literacy, trust in automation, and platform design quality.
Shashank Adagond, D. R. G KARGAL· International Scientific Jou...· 0 citations
This study analyzes the influence of Artificial Intelligence (AI) in altering the investment behaviour and financial confidence of women entrepreneurs. Specifically, it analyzes how AI adoption, perceived utility, and financial literacy influence investment decisions and, in turn, affect financial confidence. A quantitative and descriptive study design was utilized, with data collected from 165 women entrepreneurs through an online structured questionnaire. The questionnaire includes 20 items spanning five constructs: AI Adoption, Financial Literacy, Perceived Usefulness, Investment Behaviour, and Financial Confidence, assessed using a 5-point Likert scale. Structural Equation Modelling (SEM) was utilized to examine the hypothesized associations, with model adequacy validated using fit indices. The findings demonstrate that AI adoption, perceived utility, and financial literacy greatly influence investment activity, which positively improves financial confidence. These results underscore the revolutionary potential of AI in allowing women entrepreneurs to make educated investment decisions and boost their financial self-efficacy. The study provides useful insights for policymakers, financial institutions, and technology developers trying to boost AI-driven financial empowerment among women entrepreneurs.
Sarika Verma, Avinash Gupta· International journal of inn...· 0 citations
This study examines the influence of financial literacy and financial behaviour on investment decision making among Generation Z, with financial attitude as a mediating variable. A quantitative survey design was employed, involving 125 Generation Z respondents (aged 18–27) currently investing in mutual funds, equities, or digital/crypto assets. Data were analysed using a regression-based path analysis on standardized composite construct scores, with mediation tested through bootstrapping (5,000 resamples). The measurement model showed adequate convergent validity, discriminant validity, and reliability for all constructs. Financial behaviour significantly affected financial attitude (β = 0.434, p < .001), while the effect of financial literacy on attitude was not significant (β = 0.150, p = .069). Financial attitude significantly affected investment decision making (β = 0.424, p < .001), and both financial literacy (β = 0.301, p < .001) and financial behaviour (β = 0.158, p = .043) significantly affected it directly. Financial attitude partially mediated the effect of financial behaviour on investment decisions (VAF = 53.3%), but did not significantly mediate the effect of financial literacy. These findings suggest that financial behaviour shapes investment decisions partly through attitude formation, while financial literacy operates mainly through a direct pathway.
With Artificial Intelligence (AI) and automated choice architectures remodelling consumer finance, traditional measures of financial literacy are ineffective in measuring how individuals network with, assess and supersede algorithmic stimuli. For bridging this theoretical and empirical gap, this paper operationalises a new construct - Contextual Financial Judgment (CFJ) – defined as a unique cognitive-attitudinal competence to appraise programmed financial recommendations vis-a-vis non-quantifiable, qualitative life realities.
Building upon Dual-Process Theory, Human Agency Theory and the Industry 5.0 human-centric AI paradigm, this study introduces a comprehensive psychometric scale architecture and Structural Equation Modeling (SEM) framework to measure human agency in personal finance. Firstly, the study establishes a theoretically refined 12-item scale consisting of three reflective first-order dimensions: Contextual Financial Awareness (CFA), In-Depth Systemic Knowledge (ISK) and Evaluative and Overriding Attitude (EOA). Secondly, Contextual Financial Judgment (CFJ) is integrated as a second-order latent construct into a structural model that links exogenous antecedents like Algorithmic Exposure and Perceived Financial Importance, to vital downstream outcomes like Decision Autonomy, Mitigation of Impulse Financial Behaviour and Long-Term Value Alignment, while Explainable AI (XAI) is modelled as a moderating mechanism strengthening the translation of systemic knowledge into overriding attitude. This is further supported by six testable propositions (P1 to P6) to guide future empirical testing.
Finally, the study creates a conceptual structural architecture of AI-mediated financial governance and presents a comprehensive, five-step methodological roadmap for psychometric validation (EFA, CFA and PLS-SEM/CB-SEM), thereby offering a rigorous foundation for evaluating consumer agency in AI-driven financial ecosystems.
Meenakshi Wagh· International Journal of Lat...· 0 citations
In recent years, the real-world implementation of artificial intelligence (AI) technology in the financial sector has continued to deepen. With more accurate prediction models, higher operational efficiency, and personalized services, this technology has reshaped the generation model of investment decision-making. However, the large-scale deployment of AI has also given rise to two core categories of risks. First, AI systems inherently face the problems of algorithmic opacity and implicit systemic bias. Second, preexisting cognitive biases among retail investors—such as overconfidence and the anchoring effect—are further amplified by AI-powered digital financial platforms. This study draws on two core theoretical frameworks, behavioral finance and AI ethics, to focus on the coexistence logic of human behavioral biases and algorithmic biases, and develops an analytical tool that integrates both types of bias. At the same time, the study identifies conceptual gaps in the existing traditional definition of financial literacy, puts forward the concept of algorithmic literacy as a component of digital literacy, and clarifies the core competencies investors must have in AI-driven investment scenarios: the ability to critically evaluate AI-generated investment advice, and to offset their own behavioral biases through active human engagement. This study argues that AI development must implement robust ethical principles encompassing transparency, accountability, and digital trust, to optimize governance systems that promote the responsible use of AI. By moving beyond the narrow framework of traditional financial literacy, this research provides support for aligning AI innovation with consumers’ financial well-being. The study’s findings are usable for financial education practitioners, policymakers, and financial institutions, to advance investor protection, and ensure that technological research and development aligns with ethically sound financial decision-making.
K. Jalaja, G. Ashoka· Journal of Commerce, Economi...· 0 citations
This study explores how financial socialization (FS), attitude toward risk (ATR), access to financial services (AFS), and behavioral control (BC) affect responsible financial consumption (RFC), with mediating effects of financial literacy (FL) and financial attitude (FA). Among the variables, FS represents the social dimension, ATR, BC, and FA represent the psychological dimension, AFS represents the structural dimension, and FL represents the cognitive dimension.
By adopting a purposive sampling method, 418 valid responses were collected from respondents who can make financial decisions independently and reside in Bangladesh, a developing economy in Asia. In terms of age, the respondents were at least 18 years old, indicating their maturity as adults. Furthermore, their occupational status ranged from being students to service holders, entrepreneurs, homemakers and retired personnel. Using partial least squares–structural equation modeling, the direct effects of the predictor variables are assessed, alongside the indirect effects of FL and FA.
The results suggest that ATR, AFS and BC, along with FL and FA, are significant determinants of RFC. FS is not a significant predictor in the relationship with RFC, until mediated by FA (full mediation). The mediating effect of FL reveals partial mediation among the significant predictors of RFC. The same result is also observed in the indirect relationship of ATR, AFS and BC, with the mediating effect of FA.
Built on the theories of planned behavior, social learning and human capital, it contributes to the field of behavioral finance to drive economic well-being. As it incorporates social, psychological, structural and cognitive aspects through which an individual’s responsible consumption behavior can be affected, the results add significant value to the existing literature and provide a lead to further analysis on this crucial matter of behavioral finance.
Md. Sagar Islam Khan, Anindita Rai, Ankon Paul et al.· Asia-Pacific Journal of Busi...· 0 citations
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