What drives algo trading intentions? Insights from a stimulus organism response framework and importance performance...

The purpose of this research is to determine the factors that impact the decision of retail investors to use algo trading. Moreover, the current research aimed to evaluate these identified factors' relative importance and performance through Importance Performance Map Analysis (IPMA) to identify priority areas for enhancing adoption. The study adopted a quantitative approach and a single cross-sectional descriptive research methodology. The data was collected from 382 active investors and traders. The gathered data was analysed using Smart PLS 4. The bootstrapping function was utilized to test the hypothesis. The IPM Analysis function was employed to identify the most and least important factors with respect to importance and performance. It was found that social influence and technological infrastructure significantly shaped perceived usefulness, perceived ease of use, risk perception, and confidence in technology. Perceived usefulness, perceived ease of use, and confidence in technology positively influence the intention to use algorithmic trading, whereas risk perception negatively influences it. Additionally, these perceptual factors mediate the relationship between external stimuli (social influence and technological infrastructure) and adoption intention. IPMA results highlight technological infrastructure as the most critical driver of adoption, with risk perception emerging as the strongest deterrent. The study contributes to fintech adoption literature by integrating multiple theoretical perspectives within the Stimulus Organism Response (SOR) framework and provides practical insights for fintech platforms and policymakers seeking to enhance retail participation in algorithmic trading.