Purpose: The growing adoption of artificial intelligence (AI) in the financial services sector has led to the emergence of robo-advisory platforms that offer automated investment advice. This study examined the factors affecting users’ intention to invest in AI-based platforms. Design/

Methodology: The study was based on the technology acceptance model (TAM), as well as behavioral finance and consumer psychology concepts, making it concentrate on five variables, namely: Perceived Algorithmic Accuracy (PAA), Cognitive Absorption (CA), Emotional Trust (ET), Perceived Value Co-Creation (PVC), and Artificial Intelligence Usage (AIU). Emotional trust was used as a mediator, and AI Usage as a moderator. The survey was conducted through online questionnaires on 445 active financial investors. PLS-SEM was used to analyze the responses.

Findings: The results indicated that PAA, CA, ET, and PVC significantly influenced investment intention. It was established that ET partially mediated the relationship between PAA and investment intention. Furthermore, AIU exerted a strong mediating effect on CA on investment intention, but this mediation was not notable in ET and PVC. These findings indicated that emotional and cognitive involvement, as well as trust in the use of algorithms, were significant factors in user behavior. Practical

Implications: The research provided first-hand insights into how financial technology providers could improve trust, user engagement, and platform engineering. Originality/

Value: This study integrated technical, psychological, and participatory elements into a single model of robo-advisor adoption, with emotional trust and cognitive absorption as key determinants of investment intention.

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