This study examines the limitations of a one-size-fits-all approach to human capital management in Islamic banks, exploring how non-linear dynamics and institutional factors influence financial performance. Utilizing the Random Forest algorithm, a machine learning approach adept at capturing non-linearities and interaction effects, the analysis draws on data from 135 Islamic banks across 32 countries from 2017 to 2023, comprising 886 bank-year observations. The results reveal a non-linear, three-phase relationship between human capital and financial performance: (1) initial investments result in stable performance, (2) a decline follows due to resource-intensive workforce development, especially in smaller and younger banks, and (3) eventual stabilization highlights inefficiencies and diminishing returns. Larger and older banks are better equipped to absorb human capital investment costs, whereas smaller and younger banks face greater challenges, highlighting the limitations of a one-size-fits-all approach to managing human capital. From a managerial perspective, the three-phase pattern suggests that human capital investments should be strategically timed and scaled according to bank size and maturity to avoid short-term performance deterioration and enhance long-term value creation.
Muhammad Bilal Zafar (Tue,) studied this question.