Jul 2026· EPRA International Journal of Economic and Business Review· pp. 79· 0 citations
TL;DR
The study's most significant finding is the non-support of H4: perceived investor oversight enhanced trust in AI but did not reduce bias susceptibility, indicating that oversight functions as a legitimacy signal rather than a debiasing mechanism.
Abstract
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.
Robo-advisory services offer automated, algorithm-based investment advice and portfolio management, enabling cost efficiency and wider access to financial services. Despite these advantages, adoption in emerging economies remains limited due to psychological, cognitive, and social barriers. Trust is particularly critical in financial decision-making, as it reduces perceived risk and uncertainty associated with algorithm-driven systems. Trust is an important component, since adoption depends on people's faith in technological advances and the safety of their investments. The study purposes to contribute to the larger in the field of financial technology analysing the factors which impact on the adoption of Robo-advisory services and provide new insights into the psychological and social dynamics. Using a quantitative research design, primary data were collected from 285 investors through a structured questionnaire. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was employed to test the proposed relationships among financial awareness, behavioral biases, innovativeness, subjective norms, trust, and intention to use robo-advisory services. The results indicate that trust is the strongest predictor of adoption intention and significantly mediates the effects of key antecedent variables. The findings could help policy-makers and financial institutions to enhance the features of robo-advisory services with targeted interventions and marketing strategies.
Neenu Bala, Saloni Raheja· Journal of Intelligent Decis...· 0 citations
Background: Behavioural finance challenges the assumption of fully rational investment decisions by showing that cognitive biases can shape how retail investors evaluate risk, information and market opportunities. In this context, integrating behavioural biases with the Theory of Planned Behaviour provides a useful framework for examining the investment intentions of retail investors.
Aims: This study examines the influence of behavioral biases and TPB components such as attitude, subjective norms, and perceived behavioral control on investment intentions of retail investors based in Delhi-NCR region, where behavioral biases are treated as second order construct, consisting of anchoring, availability, loss aversion, mental accounting, overconfidence, representativeness, and regret aversion.
Method: A structured questionnaire was administered to 379 retail investors using purposive and snowball sampling techniques. The data were analysed using PLS-SEM (SmartPLS 4.0) with bootstrapping based on 10,000 resamples.
Findings: Behavioral biases emerged as the strongest predictor of intention to invest (β = 0.441, p < 0.001) followed by perceived behavioral control (β = 0.199, p < 0.001), attitude (β = 0.159, p = 0.001), while subjective norms were insignificant (β = 0.088, p = 0.113). It was further noted that attitude mediated the link between behavioral biases and the intention to invest (β = 0.149, p < 0.001). The model accounted for 58.6% of variance (R² = 0.586).
Conclusion: This study provides a validated second-order model of behavioral biases and demonstrates that biases outperform TPB constructs, thus broadening TPB to behavioral finance theory. This mediation by attitude also suggests that there are two parallel decision routes for investment, one involves evaluation of attitudes, while the other route goes directly to making the decisions based on affects. This study advises on design of intervention strategies to mitigate bias and boost self-efficacy among advisors, fintech, regulators and educators.
U. Gutt, Fiza Bhateja· Asian Journal of Economics B...· 0 citations
Indian retail investors increasingly deviate from rational decision-making during periods of stock-market volatility, yet the mechanisms underlying this deviation remain incompletely modelled in the Indian context. Drawing on Prospect Theory, this study examines how five behavioural biases, Prudent and Precautionary Behaviour, Financial Heuristics, Self-Regulation Bias, Anchoring Bias, and Information Heuristics, shape retail investors' buy, hold, and sell decisions across positive and negative volatility phases. Primary survey data were collected from 329 individual investors across Tier I, II, and III Indian cities using a structured, validated questionnaire. Confirmatory Factor Analysis and Structural Equation Modelling, conducted in SPSS and AMOS, validated a hierarchical five-factor bias structure (Cronbach's α = .693–.910; 59.37% variance explained) and tested ten hypothesised bias–decision paths. Seven paths were statistically significant, and behavioural biases consistently exerted stronger effects during negative volatility than positive volatility, with anchoring bias and self-regulation bias emerging as the strongest predictors of irrational decisions. These findings extend Prospect Theory's loss-aversion principle to India's retail-investor context and carry direct implications for investor education, behavioural nudges on digital trading platforms, and advisor-level behavioural profiling.
Kantesha Sanningammanavara, Ashoka S· International Journal For Mu...· 0 citations
This study explores how retail investors in Nepal build trust in Financial Robo-Advisory (FRA) services and how that trust affects their willingness to use such platforms. Using data collected from 520 retail investors and analyzed through PLS-SEM, the study found that trust is strongly shaped by factors such as personal trust tendency, perceived reliability and competence of robo-advisors, social influence, enjoyment in using digital platforms, supporting infrastructure, price value, and ease of access. However, effort expectancy showed a negative effect, suggesting that many investors in Nepal still perceive FinTech systems as difficult to understand due to limited digital literacy and low exposure to advanced financial technologies. The findings further reveal that younger investors are more confident and willing to adopt robo-advisory services than older investors, who generally prefer traditional financial consultation methods. By extending the UTAUT framework with trust-related dimensions, the study offers useful insight into digital financial adoption in emerging economies. The results highlight the need for financial institutions and policymakers to improve digital awareness, simplify platform design, strengthen internet accessibility, and ensure transparent and secure financial services to encourage wider adoption of robo-advisory systems.
This study investigates how behavioral biases influence the investment decisions of retail investors in Kolkata, India, with particular emphasis on the moderating roles of financial literacy and financial awareness. Despite the rapid expansion of India’s financial markets and increased retail participation, investors often exhibit irrational behavior driven by psychological biases. This study seeks to answer the following research question: To what extent do behavioral biases affect the investment decisions of retail investors in Kolkata, India, and how effectively do financial literacy and financial awareness mitigate these effects? Using primary data collected from 444 retail investors in Kolkata, this study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the conceptual framework. The findings reveal that behavioral biases—namely overconfidence, anchoring, herd behavior, and loss aversion—significantly and negatively affect investment decisions. However, financial literacy and financial awareness not only positively influence decision-making but also significantly moderate the relationship between behavioral biases and investment outcomes. This study contributes to behavioral finance literature by distinguishing between financial literacy and financial awareness as separate constructs and demonstrating their dual role as both direct and moderating factors. The findings have important implications for policymakers, financial educators, and investment advisors in designing targeted interventions to improve investor decision-making.
Ujjal Sanyal, Furquan Uddin, Mohammad Razi-ur-Rahim et al.· Analytics· 1 citation
This study examines the impact of behavioural finance on investment decision-making among retail investors associated with selected investment banks in India. Moving beyond traditional rational finance frameworks, the research focuses on two primary psychological dimensions: heuristics and prospect theory. Primary data were gathered through a structured questionnaire from a sample of 184 active investors using a convenience sampling technique. The data were analyzed using Pearson correlation and multiple linear regression analysis. The empirical results demonstrate that both heuristics and prospect theory have a statistically significant negative relationship with effective investment decision-making. Specifically, a heavy reliance on cognitive mental shortcuts and prospect-related illusions (such as loss aversion and anchoring) significantly reduces the overall rationality and quality of investment choices. Comparatively, heuristics exert a substantially stronger negative influence on decision-making performance than prospect theory. The findings suggest that financial institutions and policymakers must design targeted advisor frameworks and behavioral financial education initiatives to minimize systematic biases and enhance market efficiency.
Nikhil Dommeti· International journal of bus...· 0 citations
