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Machine learning analysis reveals fintech adoption significantly boosts employee efficiency, suggesting important implications for firms.
We demonstrate that fintech and digitalization adoption has a significant positive impact on employee-based efficiency, proxied by the natural logarithm of revenue per employee. Additionally, we utilize two machine learning-based feature selection approaches, F-regression, and random forest, to identify the most significant variables within the fintech categories as potential candidates for the prediction process. Furthermore, we employ a machine learning model selection process based on the root mean squared error (RMSE) standard. Our findings indicate that the Extra Trees Regressor yields the lowest RMSE in the tested sample, making it the most effective model for predicting employee-based performance efficiency in our sample. Overall, our findings suggest that the use of fintech and digitalization significantly boosts firm efficiency from the perspective of employee productivity.
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Zheng et al. (2026) studied this question.
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