KMAN Publication Inc. (KMANPUB) International Journal of Innovation Management and Organizational Behavior (IJIMOB) 3041-8992 6 Serial Number 26 2026 05 01 Modeling Innovation Adoption through Reinforcement Learning: The Influence of Risk Perception and Change Readiness Modeling Innovation Adoption through Reinforcement Learning: The Influence of Risk Perception and Change Readiness 1 11 10.61838/kman.ijimob.5272 EN Journal Article 2025 11 09 <table> <tbody> <tr> <td> <p><strong>Objective:</strong> The objective of this study was to elucidate the temporal dynamics of organizational innovation adoption by integrating empirical psychological metrics of risk perception and change readiness with a computational reinforcement learning framework.</p> <p><strong>Methods and Materials: </strong>This study utilized a cross-sectional design, sampling mid-to-senior level managers from the corporate sector in South Africa. Primary data regarding individual risk perception, baseline change readiness, and behavioral adoption intent were collected utilizing a structured, validated online questionnaire. The data analysis phase employed a novel hybrid methodology: inferential statistics via Structural Equation Modeling (SEM) were integrated with a mathematical Reinforcement Learning (RL) architecture. Specifically, a -learning algorithm was developed where the empirical psychometric scores directly parameterized the internal variables of synthetic agents, mapping readiness to the learning rate ( ) and risk perception to the environmental penalty weight ( ) and temporal discount factor ( ).</p> <p><strong>Findings: </strong>The empirical baseline analysis revealed a strong positive correlation between change readiness and the final innovation adoption rate ( ), while risk perception exerted a severe suppressive effect on the ultimate probability of adoption ( ). When mapped to the computational simulation, the fully optimized -learning model significantly outperformed static predictive models, successfully accounting for of the variance in actual innovation adoption trajectories ( ). The algorithmic simulation accurately demonstrated that agents with high change readiness exhibited an accelerated , quickly escalating their expected utility for adoption, whereas high risk perception effectively delayed transition by mathematically magnifying the operational penalty ( ) and minimizing the perceived future value of the new technology ( ).</p> <p><strong>Conclusion: </strong>The findings provides a highly accurate, dynamic computational model that predicts the temporal evolution of human behavioral adaptation during organizational technological transitions.</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> https://www.journals.kmanpub.com/index.php/ijimob/article/download/5272/9582