The efficient utilization of both tangible and intangible assets is crucial for organizational success, especially in knowledge-intensive industries. Intellectual capital (IC), encompassing intangible assets like knowledge and expertise, is considered a vital strategic resource driving competitive advantage, particularly in the service sector. The banking industry, a significant service sector, relies heavily on IC for sustainable growth. This study focuses on the Indian banking sector, which has experienced remarkable economic growth in recent years but needs to consider the crucial role of IC in furthering this progress. Understanding the relationship between IC and financial performance (FP) in this context is vital. Existing literature has produced mixed results on the connection between IC and FP. This study aims to address these inconsistencies and provide a comprehensive analysis of the impact of IC, measured using the modified value-added intellectual coefficient (MVAIC), on the FP of Indian public sector banks, considering factors like endogeneity and the volatile nature of banking revenues by employing the generalized method of moments (GMM) model. The study's findings will be significant for policymakers, stakeholders and the banking industry in developing strategies for sustainable financial performance and value generation.

Intellectual capital (IC) is defined in various ways, often referring to intangible resources that contribute to firm value but are not reflected on balance sheets. Commonly, IC is categorized into human capital (HC), capital employed (CE), structural capital (SC), and relational capital (RC). HC represents employee knowledge and skills; SC encompasses organizational systems and infrastructure; RC includes relationships with stakeholders; and CE reflects the efficiency of resources used. Several models exist to measure IC, with the value-added intellectual coefficient (VAIC) and its modified version (MVAIC) being widely used. MVAIC addresses some of the limitations of VAIC, such as ignoring RC. Prior research on the relationship between IC and firm performance shows mixed results, with some studies finding a positive association and others not. Studies have highlighted HC and SC as significant components of IC influencing firm performance. While some research has focused on combined IC measures like MVAIC and their effect on FP, especially in developed economies, there's a gap in research examining this specifically for Indian public sector banks, particularly employing robust econometric methods like GMM.

This study employs unbalanced panel data from 23 Indian public sector banks (IPSBs) between 2010 and 2021, sourced from annual reports and the ProwessIQ database. The MVAIC model is used to measure IC, incorporating HC, CE, SC, and RC. The study uses five financial performance (FP) indicators as dependent variables: return on assets (ROA), return on equity (ROE), return on capital employed (ROCE), market performance (Tobin's Q), and earnings per share (EPS). Two control variables, leverage and firm size, are included. Descriptive statistics and correlation analysis are performed to understand the data's nature and check for multicollinearity. Panel unit root tests (LLC, ADF, PP) are conducted to assess data stationarity. The generalized method of moments (GMM) is used to address endogeneity and model dynamic characteristics, offering more reliable results than ordinary least squares (OLS). Five models assess the individual effects of IC components on each FP indicator, while five additional models examine the combined effect of MVAIC. The study considers several diagnostic tests to validate the GMM estimations, including tests for serial correlation and the Sargan test for over-identifying restrictions.

Descriptive statistics show that HCE has the highest mean value among IC elements. Correlation analysis reveals positive correlations between HCE, CEE, RCE, and MVAIC, with ROA, ROE, and TQ. SCE shows a negative correlation with ROA, ROE, TQ, and ROCE. Panel unit root tests indicate that all variables are stationary at the first difference. GMM estimations show that the lag of the dependent variable significantly affects the current value. Individual IC component analysis reveals that HCE has a significantly positive effect on ROA, ROE, and EPS, while CEE and SCE exhibit significantly negative effects on ROA, ROE, and EPS. RCE shows a significant negative effect on EPS. Analysis of the combined impact of MVAIC (Models 6-10) shows a significantly positive effect on ROA, ROE, EPS, and ROCE, suggesting that increased IC improves bank profitability. However, MVAIC exhibits a negative impact on Tobin's Q. Leverage consistently shows a significant negative effect on all FP indicators.

The study's findings confirm a significant positive relationship between intellectual capital (IC) and the financial performance (FP) of Indian public sector banks. The GMM estimations, addressing endogeneity concerns, demonstrate the robustness of this relationship. The prominent role of human capital efficiency (HCE) underscores the importance of investing in employee skill development and knowledge management. The negative impact of capital employed efficiency (CEE) and structural capital efficiency (SCE) highlights the need for efficient resource allocation and infrastructure optimization within these banks. The negative relationship between leverage and FP emphasizes the importance of prudent financial management and minimizing excessive debt. The positive impact of MVAIC on most FP indicators reinforces the overall beneficial effects of investing in and effectively utilizing IC resources. The results highlight the significant contribution of IC to bank profitability and emphasize the need for a holistic approach to IC management.

This study provides valuable insights into the relationship between intellectual capital (IC) and financial performance (FP) in Indian public sector banks. The findings demonstrate a significant positive influence of IC, particularly human capital, on several key FP indicators. The study emphasizes the importance of efficient resource allocation and the strategic management of both tangible and intangible assets. Future research could expand this analysis to include other types of banks (private, cooperative, etc.), incorporate additional financial performance metrics, and explore different IC measurement methodologies.

This study is limited to Indian public sector banks, potentially limiting the generalizability of the findings to other banking sectors or regions. The use of specific financial performance indicators may also influence the results. Furthermore, while the study utilizes a robust econometric model (GMM), unobserved factors could still influence the results, although the GMM method reduces this risk. The study also relies on data obtained from annual reports and a specific database, introducing potential data limitations. Future research can improve the study by incorporating other data sources and different measures of intellectual capital.