Understanding the Factors Affecting Robo Advisory Adoption Intention Under the Lens of Big Five Personality and FOMO Using the SOR Model

Advancements in the financial industry are transforming as algorithmic investment programs are introduced to meet the growing needs of technology-linked investors in the financial decision-making process. There are several investors interested in algorithm-based programs, such as robo-advisory services, but many psychological factors affecting users’ adoption of these platforms have not been studied. By integrating theories such as the stimulus-organism-response theory, the Big Five Personality Traits and the emotional framework of fear of missing out (FOMO), this study proposes a comprehensive model of robo-advisor behavioural intention. Data from 400 active investors in major cities were analysed using structural equation modelling. Results show that the personality traits of openness and conscientiousness have a noteworthy impact on trust levels in robo-advisory systems, while neuroticism shows an unexpected positive correlation, suggesting that emotionally vulnerable people might prefer emotionally neutral, rule-based systems. Alongside this, FOMO was directly influenced by trust in and attitudes towards robo-advisors, suggesting its dual nature as both an affective and a cognitive driver. Results show the role of personality-based and emotion-focused design approaches in fintech adoption. Theoretical implications unite behavioural finance with technology acceptance theory, while practical implications facilitate user-oriented platform design in emerging economies.