- Original authors / creators
- M Vijayananth; N Saravanabhavan
- Original publication
- Frontiers in Artificial Intelligence · Frontiers Media SA · 2026-06-01
- License & retained notices
- CC BY 4.0
Copyright © 2026 Vijayananth and Saravanabhavan.
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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Article-specific license URI: https://creativecommons.org/licenses/by/4.0/ - Edition changes
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- Evidence limits
- Adjacent research, not a directly sampled family-office study. Applicability to a family office requires separate assessment. The research has a few limitations that can be addressed in future research. The study is cross-sectional in nature and does not permit causal inferences; longitudinal or experimental studies may provide deeper insights into the evolution of investors’ behavior. The self-reported measures used in this study might suffer from response bias; the use of actual investor behavioral or trading data would improve the validity of the results. In addition, although the model demonstrated satisfactory explanatory and predictive capability, the global fit indices (SRMR and NFI) did not fully meet conventional covariance-based SEM thresholds. Therefore, the findings should be interpreted with appropriate caution. Future studies may refine the measurement model and explore alternative model specifications to improve overall model fit. The focus of the study on middle-income retail investors in India may limit the generalizability. Cross-validating the model with different investor categories and regions would strengthen the model’s validity. The model examines human–AI interaction competency as a boundary condition, but other factors such as the transparency of algorithms, interface design and investor expertise can also be investigated. Furthermore, human–AI interaction competency was assessed using self-reported measures, which may not fully capture objective technological competency. Future research may incorporate objective indicators such as digital literacy, investment expertise, or behavioral trading measures to further validate the moderating mechanism. Another important direction for future research is the evolution of trust in AI-driven environments, particularly across different market conditions and technological environments.
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- Rights and full text checked 2026-11-05. Included in this volume on 2026-11-05. Original source dates remain unchanged.
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