KMAN Publication Inc. (KMANPUB) International Journal of Innovation Management and Organizational Behavior (IJIMOB) 3041-8992 6 Serial Number 24 2026 01 01 Predicting Employee Creativity Trajectories Through Longitudinal Deep Learning Models of Motivation and Job Design Predicting Employee Creativity Trajectories Through Longitudinal Deep Learning Models of Motivation and Job Design 1 10 10.61838/kman.ijimob.5277 EN Journal Article 2025 07 27 <table> <tbody> <tr> <td> <p><strong>Objective:</strong> The objective of this study was to predict long-term employee creativity trajectories by developing a longitudinal deep learning model integrating evolving motivational states and job design characteristics.</p> <p><strong>Methods and Materials: </strong>This longitudinal study followed 611 full-time employees from knowledge-intensive organizations in Spain across four measurement waves over 18 months. Repeated measures of intrinsic and extrinsic motivation, psychological empowerment, and job design characteristics (including autonomy, task significance, skill variety, and feedback quality) were collected using validated instruments. Employee creativity was assessed using a multi-source composite index incorporating self-ratings, supervisor evaluations, and peer nominations. Long Short-Term Memory (LSTM) neural networks were employed to model non-linear temporal relationships and predict individual creativity trajectories. Model performance was evaluated using root mean square error, mean absolute error, explained variance, and trajectory similarity indices. Feature importance was examined using attention mechanisms and SHAP values, and unsupervised clustering was applied to identify distinct creativity development profiles.</p> <p><strong>Findings: </strong>The deep learning model explained 76.1% of variance in unseen creativity trajectories. Intrinsic motivation (β_importance = 0.31, p &lt; .001) and job autonomy (β_importance = 0.24, p &lt; .001) emerged as the strongest predictors of sustained creative growth. Four statistically distinct creativity trajectories were identified: accelerated growth (34.5%), stable growth (41.2%), stagnant (16.7%), and declining (7.6%). Employees in the accelerated growth cluster demonstrated significantly higher increases in intrinsic motivation and job autonomy over time (p &lt; .001), whereas the stagnant and declining clusters exhibited significant decreases in psychological empowerment and increases in role overload (p &lt; .01).</p> <p><strong>Conclusion: </strong>Employee creativity is a dynamic developmental capability shaped by continuous interactions between motivation and job design, and longitudinal deep learning provides a powerful framework for predicting creative growth and identifying intervention targets.</p> </td> </tr> </tbody> </table> https://journals.kmanpub.com/index.php/ijimob/article/download/5277/9591