Does Fintech and Digitalization Adoption Impact Employee Performance Efficiency? Evidence from a Machine Learning Approach

We demonstrate that fintech and digitalization adoption is positively associated with employee-based efficiency, proxied by the natural logarithm of revenue per employee. Additionally, we utilize two machine learning-based feature selection approaches, [Formula: see text]-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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Publication Details

Journal
Quarterly Journal of Finance
Published
2026-01-30
DOI
https://doi.org/10.1142/s2010139226500023
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
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article

Does Fintech and Digitalization Adoption Impact Employee Performance Efficiency? Evidence from a Machine Learning Approach

He Ren, Yi Zheng
Quarterly Journal of Finance
FinTech, Crowdfunding, Digital Finance
article

Does Fintech and Digitalization Adoption Impact Employee Performance Efficiency? Evidence from a Machine Learning Approach

He Ren, Yi Zheng
article en

Abstract

We demonstrate that fintech and digitalization adoption is positively associated with employee-based efficiency, proxied by the natural logarithm of revenue per employee. Additionally, we utilize two machine learning-based feature selection approaches, [Formula: see text]-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.

Quarterly Journal of Finance
Texas Woman's University (US), SUNY New Paltz (US)
Decent work and economic growth
Openalex Percentile: Top 77%
FinTech, Crowdfunding, Digital Finance
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