Adoption of Explainable Artificial Intelligence in Retail Investors’ Decision-Making : Evidence from India

This research examined the extent to which retail investors in India trusted and expressed confidence, as well as their intention to adopt an automated trading platform utilizing explainable artificial intelligence (XAI). Methodology : The study used primary data based on a survey-based design, obtained through a structured questionnaire administered to retail investors located in Tier-I and Tier-II cities in India. A total of 378 respondents were considered for the study after due scrutiny. The questionnaire included the measures of “perceived explainability,” “trust,” “perceived risk,” “information quality,” “confidence,” and “intention to adopt” explainable investment platforms. In addition to reliability assessment, construct validity was measured, and the hypotheses were tested by examining the relationships between the variables using PLS-SEM path analysis. Multi-group analysis was also used to compare the investor responses from Tier-I and Tier-II cities. R

Perceived explainability had a statistically significant effect on both investors’ trust and confidence, according to the results of the research. Both trust and confidence, in turn, played a major role in determining the likelihood that an investor intended to use/adopt an explainable investment platform. However, perceived risk was found to have a negative effect on investor confidence. Furthermore, observed differences between investors in Tier I and Tier II cities highlighted the influence of contextual factors on adoption behaviour towards AI-based investment platforms. Practical Implications : The results of this research indicated that investment platforms can improve investors’ trust and confidence when they provide clear, reliable, and transparent explanations along with automated recommendations. Originality/Value : Unlike previous studies, the current study is unique as it uncovered that explainability by building trust and confidence drives the adoption, which provides a clearer understanding of investor behaviour beyond traditional adoption models.