Modeling Innovation Adoption through Reinforcement Learning: The Influence of Risk Perception and Change Readiness

Authors

    Henrik Solberg Department of Strategy and Management, Norwegian School of Economics, Bergen, Norway
    Lerato Nkosi * Department of Business Management, University of Pretoria, Pretoria, South Africa lerato.nkosi@up.ac.za
    Laura Sánchez Romero Department of Innovation and Entrepreneurship, Autonomous University of Madrid, Madrid, Spain
https://doi.org/10.61838/

Keywords:

Innovation Adoption, Reinforcement Learning, Change Readiness, Risk Perception

Abstract

Objective: 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.

Methods and Materials: 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 ( ).

Findings: 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 ( ).

Conclusion: The findings provides a highly accurate, dynamic computational model that predicts the temporal evolution of human behavioral adaptation during organizational technological transitions.

 

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Published

2026-05-01

Submitted

2025-11-09

Revised

2026-02-16

Accepted

2026-02-25

Issue

Section

Articles

How to Cite

Solberg, H., Nkosi, L., & Sánchez Romero, L. (2026). Modeling Innovation Adoption through Reinforcement Learning: The Influence of Risk Perception and Change Readiness. International Journal of Innovation Management and Organizational Behavior (IJIMOB), 1-11. https://doi.org/10.61838/