Investigating the Impact of Intellectual Capital on the Sustainable Financial Performance of Private Sector Banks in India

Abstract

The study aims to investigate the impact of intellectual capital (I.C) on the sustainable financial performance (F.P) of private sector banks (PSBs) in India. Data were gathered from 17 banks between 2010 and 2021 using Prowessiq (CMIE) and their annual financial reports. To evaluate the ways in which intellectual capital (I.C) affects sustainable financial performance (F.P), the modified value-added intellectual coefficient (MVAIC) methodology was applied. The human capital (HC), capital employed (CE), structural capital (SC), and relational capital (RC) were utilized as independent factors together with three control variables (leverage, size, and GDP), the return on capital employed (ROCE), and return on equity (ROE), which were used as dependent variables. The results show that RC and SC have a clear, statistically significant relationship with ROCE. Additionally, HC and CE have a direct positive and statistically significant effect on ROE. Overall, all of the I.C. components have significant impacts in increasing the efficiency and profitability of Indian private sector banks. Furthermore, the total intellectual capital (MVAIC) exhibits a statistically significant negative association with ROE but a substantial positive association with ROCE. It is advised that policymakers and managers focus more on the various I.C components because they are the key engines generating value for the banks in order to preserve a more sustainable F.P.

1. Introduction

Intellectual capital (I.C) has become an important issue in today’s fast-growing, technology-driven, and knowledge-based world. I.C is defined as a firm’s innovation, experience, knowledge, client contacts, and people expertise that provide a long-term competitive advantage and add value to the firm [1,2]. Work, workers, and workplaces are being restored faster than ever as the world recovers from the pandemic. Firms are now emphasizing intangible assets (particularly human capital) over physical and financial resources [3]. As a crucial intangible asset in this knowledge-driven era, employees are regarded as adept at transforming knowledge into the commodities and services that assist an organization in increasing its financial health and worth [4]. Intangible assets include more than just stakeholder relationships, patents, goodwill, staff knowledge investment, and copyrights [5]. The organization defines I.C as intangible assets, specifically with respect to firms that can utilize them to generate value by transforming them into products, services, and new processes.

Four key elements frame I.C: human capital (HC), capital employed (CE), structural capital (SC), and relational capital (RC) [6,7,8,9,10,11,12]. HC comprises employees’ expertise, capabilities, commitment, experience, expertise, professionalism, and intellectual abilities [13,14]. All of these can be used to achieve and improve the organization’s value [3,15,16,17]. CE (comprising financial and physical capital) is essential for generating wealth for the organization. SC is related to intellectual property and databases, such as trademarks, copyrights, innovations, and patents. It also includes infrastructure resources associated with the organizational structure, culture, management system, policies, techniques, methodology, and the firms’ operations that encourage the human resource to create and use their knowledge [14,17,18]. Metaphorically, HC acts as the backbone of the firm. As a result, banks must use their workforce efficiently and effectively to build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation (SDGs 9). The relationships that an organization has with its stakeholders, which include the suppliers, marketing channels, customers, creditors, shareholders, and the government, are referred to as RC. The success of an organization is dependent on the quality of its internal and external relationships [4,6,17,19].

Several studies have been conducted in developed countries to determine the effects of intellectual capital (I.C) on financial performance (F.P). The findings showed that I.C is a tactical resource that enhances an organization’s value performance and competitiveness [20]. The supremacy of I.C encapsulates the conversion of the industrial economy into a knowledge-based economy in the twenty-first century. Indeed, in developing countries such as India, less substantial literature on the link between firm performance and intellectual capital efficiency has been produced. As a developing country, India needs to tap into and expand this intellectual capital in order to restructure its economy. Since gaining its independence in 1947, India’s economy has passed through a number of phases of growth. The government has undertaken numerous initiatives at various levels to promote economic growth and stimulate domestic and foreign investment.

By acting as a financial mediator between diverse parties, the banking industry serves as the backbone for a country’s sustainable economic development [21]. A financial system’s contribution to sustainable economic growth increases when resources are effectively generated and allocated. A well-functioning financial intermediation system also assists in the economic risk mitigation process. When productivity is increased in a way that, for instance, strengthens the capital buffers that absorb risk, it can lead to more and better innovations, investment returns, and the increased safety and stability of the banking industry. Likewise, profitability or efficiency metrics may serve as early warning signs of changing financial system strengths or weaknesses. Therefore, researchers have always been interested in investigating and measuring productivity and profitability in the banking sector. This study attempts to assess I.C’s effectiveness in regard to private sector banks, taking into account the importance of I.C as well as its components (HC, CE, SC, RC) in order to promote sustained, inclusive, and sustainable economic growth, as well as full and productive employment for all (SDG’s 8). India is in the process of transforming from a developing to a developed economy; thus, it is essential that the banking sector has a strong and sustainable foundation. Currently, there are 22 private sector banks, 12 public sector banks, 44 foreign banks, and 43 and 1484 regional and urban cooperative banks, respectively. In the financial year of 2021–2022, the total assets of the public sector banks were USD 1594.51 billion and those of private sector banks were USD 925.05 billion.

In the current situation, the COVID-19 pandemic has had a negative effect on a number of sectors in India and throughout the world. This pandemic has halted years of progress in every country. Because of this paradigm shift, it is now critical to understand why countries should invest in I.C (specifically HC). HC is at the forefront of innovation. At the HC Experience Summit 2022, industry experts recently stated that employee experience is more important than customer experience because the former contributes to enhanced workforce productivity and adds value to the firm. To mitigate the economic impact of COVID-19, the Indian banking sector has shifted to a knowledge-based orientation and diversification approach, demonstrating the essence of I.C in the country. It is critical to invest in human capital to enable workers to be productive and innovative in order to build long-term value, competitiveness, and sustainable development. To succeed in the corporate sector, every economy relies on knowledge and intangible assets. Every economy aspires to be a “knowledge economy”. In the current period, a pool of intangible assets can provide a competitive advantage by improving corporate performance by providing innovative products to global customers at reasonable prices. Every entity seeks to profit in order to survive in the business world, since intangible assets are essential inputs required to excel and grow in today’s corporate world. Innovation can be provided through products, processes, and positions in the banking industry. This innovation is undoubtedly dependent on investments in various sources or assets. In the current study, we took human capital, capital employed, structural capital, and relational capital as the proxies for I.C. These intellectual capital proxies can further drive innovation in the banking sector. As I.C is a major element of the banking sector that is essential to survival in the business world, financial performance is a much more important aspect.

This is the primary reason for conducting this research, which aims to investigate intellectual capital (I.C) and its effects on the sustainable financial performance (F.P) of private sector banks (PSBs) and to analyze their relevance for attaining SDGs 8 and 9. The study takes into consideration 17 PSBs, including data obtained from financial statements that were collected from annual reports and the Prowessiq database from 2010 to 2021. The technique employed is panel data estimation, and it is carried out in three different ways (with the fixed effect and with the cross-section random effect and without fixed and random effects). The productivity and profitability of the bank are the two dimensions used to assess its sustainable F.P. For the following reasons, in our research, we selected sustainable F.P measures. Firstly, there is a strong correlation between long-term organizational success and sustainable F.P. Secondly, I.C, which has relevance for both financial and non-financial achievements over a long period of time, is a component of long-term productivity and growth. However, less extensive literature is available that can allow us to identify the link between I.C and banks’ sustainable F.P [22,23], especially in SAARC nations, where literature is scarce. Therefore, this research offers extensive knowledge of the influence of I.C on the sustainable F.P of the Indian private sector banks (PSBs).

The current study adds to the existing literature in several ways. First, less literature has concentrated on I.C and its application during the COVID-19 economic downturn. In order to determine its impact on private banks in India, our analysis took the COVID-19 era into consideration. Accordingly, these studies are more prevalent in the west, while they are less prevalent in Asia, in general, and India, in particular. Our research revealed that I.C had a significant favorable impact on sustainable F.P. The study’s findings allow us to offer several recommendations for private sector banks in terms of the management and application of intellectual capital (I.C). I.C is one of the essential resources that offer a business a long-term competitive advantage and enhance the performance of the business in a modern knowledge-driven environment. In contrast to earlier research, this study used the MVAIC technique to assess how I.C. affects the sustainable F.P of Indian PSBs.

This article’s remaining sections are as follows. Section 2 contains a review of the relevant literature. Section 3 discusses the research design and methodology of the study. Section 4 concerns the empirical findings and discussions, while Section 5 summarizes the study’s conclusions and provides a discussion, followed by the limitations and scope for future research.

2. Literature Review

2.1. Valuation of Intellectual Capital

Intellectual capital (I.C) has been defined by several scholars from various perspectives. Some define it as intangible assets, and some describe it as knowledge-based resources that offer a business a competitive advantage [24]. I.C refers to an organization’s capacity to generate value, compete effectively, and perform well by utilizing its intangible resources [25]. Intellectual property, information, education, and experience that can be leveraged to increase the value of the company are all included in a recent definition of intellectual capital [26]. I.C is defined by the authors of [27] as the knowledge-related challenges experienced by enterprises. In addition, [16] defines I.C as the entire intangible assets that are not included in the company’s balance sheet. In fact, despite the increasing importance of I.C. to scholars and business management, the existing literature lacks a well-recognized definition.

A broad consensus classifies the I.C into three components, referred to as the VAIC model by [7,8] and including HC, CE, and SC [11,25,28]. The education, experience, abilities, expertise, and competencies of personnel are regarded as the most valuable component of I.C, since they assist a business in achieving its goals and add value to the organization [13,14,15]. HC also considers the education and training of staff members, their loyalty, satisfaction, and creativity [29,30]. SC is associated with the organizational structure and the organizational culture that provide support to human capital and produce value through the effective application of technology [16]. The ability of a business to produce value depends on its physical and financial capital, or CE [31]. However, there are drawbacks to the VAIC model. To overcome these drawbacks, the authors of [10,11] created the MVAIC model, which includes an additional element of intellectual capital, namely RC. RC includes a firm’s relationships with its customer, suppliers, shareholders, creditors, and marketing channels [4,32]. Additionally, the term “RC” can refer to a company’s networks with outside organizations, as well as its internal relationships [33]. Through the use of the MVAIC, this study applies HC, SC, CE, and RC to a comprehensive degree.

2.2. Intellectual Capital and Firm Performance

Numerous studies have demonstrated that I.C improves bank financial performance and has the potential to provide a business with a competitive advantage [21,31,34,35,36,37]. Various approaches, such the MVAIC [20,38,39,40] and the VAIC, have been used by various researchers to measure the degree of I.C [21,31,37]. Additionally, earlier research used a variety of econometric methodologies, including GMM techniques [20] and OLS techniques [34]. Previous research indicated a positive association [22,41,42], while some studies supported a conflicting relationship between the firm F.P and I.C [43,44].

I.C was found to be inversely correlated with SC but positively correlated with CE and HC, according to an analysis of the Tanzanian banking sector in [45]. A substantial positive association between F.P and I.C was found in a [34] study of Indian banks, and [46] an investigation of the Malaysian financial industry revealed a similar relationship between the VAIC and ROA. The research performed by the authors of [47] was conducted in Uganda and examined whether I.C was a significant factor in small and medium audit practices, but not in simple cases when professional acting was used. Additionally, I.C works with professionalism to improve how small and medium audit practices operate. HC, an element of I.C, has a considerable influence on an organization’s performance according to a [48] study conducted in Luxembourg and Belgium, utilizing data from 200 banking institutions.

According to [42], there was a considerable positive correlation between the SCE and ROA, the growth of sales, ATR, and Tobin’s Q in Tanzanian service and manufacturing enterprises between 2010 and 2019. Using a panel of 37 Indian banks, the authors of [49] found that the aggregate I.C had positive but minor impacts on cost efficiency, revenue efficiency, and profit efficiency. In the study reported in [5] on Ghanaian insurance firms, it was found that there is a correlation between insurers’ profitability and I.C. In contrast to capital employed (CE), structural capital (SC) and human capital (HC) have less impact on performance, according to a study reported in [36]. As per [3], a study on Malaysian banks, I.C has a substantial association with F.P in terms of ROA, ROE, and leverage. Human capital is a crucial element in Ghana for cost and pure technical efficiencies, according to [50]. Additionally, CEE and HCE have favorable impacts on profit efficiency.

Vietnamese commercial banks were examined by the authors of [51], who came to the conclusion that there was a positive association between I.C and the efficiency of the banks’ total cost, purely technical, and allocative functions. In their investigation of Bangladeshi banks, the authors of [52] discovered that while SC and RC exhibit a positive relationship, HC is negatively related to banks’ performance and risk-taking behavior. Another study was conducted by the authors of [53] on the South Korean manufacturing industry, and they discovered that CE was the important factor affecting firm performance, followed by HC and SC. They also discovered that RC had a negative impact on firm profitability. According to [1], both HC and CE determine a firm’s profitability. Recently, the authors of [54] proposed an extended VAIC model for the Turkish manufacturing sector that included innovation and customer capital, and they discovered that SCE had a favorable effect on the firms’ profit growth and that innovation capital efficiency directly affected the firms’ productivity.

In a study on the Malaysian public sector, the authors of [55] discovered a strong theoretical association between intellectual capital and long-term economic growth. According to the study, there are specific forms of I.C that are crucial for promoting the public sector’s sustainable economic performance. Using linear regression analysis, the authors of [56] discovered that 8 of the 12 variables used to quantify I.C had a significant effect on the assessment of innovation output. According to [57], the efficiency of bank branches in Taiwan is negatively associated with RC rather than SC or HC. The productivity of insurance firms is positively impacted by I.C, CE, and HC according to the authors of [58], who used panel data analysis and the GMM estimation approach to evaluate Ghanaian insurance companies. Another study by the authors of [59] found that HC is a key component of I.C with respect to technical efficiency among non-listed banks and overseas banks. However, numerous studies have supported an inverse association between organizational effectiveness and I.C [20,34,39,41,44,60].

India’s economy is among the fastest-growing in the world, and it will rank among the top three global economic powers in the next 10 to 15 years [61]. A few additional empirical studies have also been carried out to assess the relationship between the F.P and I.C of the banking sector [34,35,49,62,63], but this association needs to be confirmed for many other sectors in various nations. Little research, however, has been conducted in India, and that which exists demonstrates inconsistent and diverse results. In two ways, the current study differs from earlier studies. Firstly, in this research, we applied panel data analysis, which was performed using three different methods (with the fixed effect and with the cross-section random effect and without fixed and random effects). Secondly, we used the MVAIC technique, which takes into account the HC, CE, SC, and RC parts of I.C.

3. Research Design and Methodology

3.1. Research Framework

The main motivation for conducting the current study was to precisely comprehend how various elements of intellectual capital (I.C) affect the sustainable financial performance (F.P) of PSBs banks and to analyze their relevance for attaining SDGs 8 and 9. Productivity and profitability, which are gauged by ROE and ROCE, are the dimensions or factors used to assess sustainable F.P. According to our hypothesis, the elements of intellectual capital are favorably correlated with sustainable F.P, which ultimately contributes to the achievement of Sustainable Development Goals 8 and 9. Figure 1 displays the four components of I.C, and these four components demonstrate their impacts on sustainable F.P. The United Nations’ Sustainable Development Goals 8 and 9 were taken into consideration when selecting the sustainable F.P parameters.

3.2. Data Collection

The current study applies a modified value-added coefficient methodology (MVAIC) and investigates the impact of I.C on the sustainable financial performance (F.P) of private sector banks (PSBs) in India. The data of the PSBs were collected from financial annual reports and the Prowessiq database of the Center for Monitoring the Indian Economy (CMIE). Out of 22 PSBs, data for 17 banks were gathered from 2010 to 2021. Five banks were not included, as they only began their operations after 2014.

3.3. Methodology

The study employs panel data analysis to examine the impact of I.C on sustainable financial performance (F.P). A simple panel least squares analysis is conducted with the fixed effect and cross-section random effect and without fixed and random effects. This study uses the MVAIC to assess the impact of I.C on sustainable F.P. Many researchers from various nations, such as the authors of [22,31,40,54,60,64], have applied the MVAIC and panel regression technique. The different proxies are used to represent the human capital efficiency (HCE), capital employed efficiency (CEE), structural capital efficiency (SCE), and relational capital efficiency (RCE). The data for the different variables of each private sector bank are organized in panel form. In the first stage, descriptive statistics and a correlation matrix of variables are generated to understand the nature and relationships between variables. In the second stage, before running the panel estimation [65,66,67,68], the stationarity of the variables is checked. The various unit root tests of Levin, Lin and Chu (LLC), Im, and Pesaran and two sets of Fisher-type tests using ADF and Phillips–Perron (PP) tests, as defined by [69,70,71,72], were used to examine the stationarity of the data series of various variables. We discovered that the data series of these various variables were not at the stationary level. Then, we observed the first difference, since all of the variables were discovered to be stationary at the first difference. The panel estimation was performed later. The firm performance was assessed using two dependent variables: the return on equity (ROE) and return on capital employed (ROCE) [22,39,40,54,73]. The current study attempted to fill a gap by including the ROCE as a dependent variable, which has been overlooked in previous research. The models were developed for each dependent variable. At the first stage of estimation, the impacts of independent elements of intellectual capital (HC, CE, RC, and SC) on each dependent variable were examined. In addition, the combined effect was studied using the above-mentioned variables (measured through the MVAIC). The study used three control variables, including the firm size [22,53,64], leverage [1,22,40], and GDP, employed to minimize outside influences.

3.3.1. Statistical Model Briefing

Using panel data, we can control for variables that are difficult to measure or compute, such as those that change over time but not consistently across nations or businesses. The individual heterogeneity of a firm, organization, or country is taken into account in panel data analysis. Variables suitable for multilevel or hierarchical modeling at various levels of analysis can be included in panel data analysis [66].

3.3.2. Panel Data Analysis with Fixed Effects (FE)

When we are primarily interested in examining the influences of variables that diverge over time, panel data analysis is typically conducted with fixed effects (FE). FE aids us in identifying the relationships between exogenous and endogenous factors within an entity (company, country, etc.) [66]. Each organization or country has a different identity that may or may not have an impact on the exogenous or explanatory variables. When using FE, we make the assumption that the internal factors of a particular country or entity may affect or prejudice the exogenous or endogenous variables, and we must take control of these factors. This serves as a support for the hypothesis that the residual and explanatory factors of an entity are related. With FE, the influences of such time-invariant descriptions are eliminated, allowing us to evaluate the overall impact of the exogenous variable on the endogenous variable [74,75,76].

3.3.3. Panel Data Analysis with Cross-Section Random Effects (CSRE)

The use of a random effects model in panel data analysis is justified by the assumption that, in contrast to a fixed effects model, differences between companies or countries are unintended and unrelated to the explanatory or exogenous variables considered in the model [66]. The crucial distinction between fixed and random effects is not whether or not they are stochastic but rather whether the neglected individual effect illustrates components that are associated with the model’s repressors [77]. Time-invariant variables can serve as explanatory variables, since random effects (RE) assume that the organization error term is not associated with the exogenous variable.

3.4. Measurement of Variables

The entire information about the variables is provided below (

Table 1. The MVAIC is calculated mathematically as shown below.

S. No	Variables	Sign	Description of Variables	Citations
Independent Variables
1.	Capital Employed Efficiency	CEE	CEE = The ratio of value added divided by CE (VA/CE) CE = Equity + long-term borrowings Value added = operating profit + employee cost + depreciation	[11,62,78,79,80]
2.	Human Capital Efficiency	HCE	HCE = The ratio of value added divided by HC (VA/HC) HC = Total employee expenditure
3.	Structural Capital Efficiency	SCE	SCE = The ratio of structural capital divided by VA [(VA–HC)]/VA SC = Value added–human capital
4.	Relational Capital Efficiency	RCE	RCE = The ratio of value added divided by the relational capital (VA/RC) RC = The amount invested in marketing, selling, and advertising expenses
5.	Modified Value-Added I.C	MVAIC	MVAIC = The overall intellectual capital (CEE + HCE + SCE + RCE)
Dependent Variables
6.	Return on Equity	(ROE)	Profit available to equity shareholders/shareholders’ equity	[40,78]
7.	Return on Capital Employed	(ROCE)	EBIT/capital employed EBIT = Earnings before interest and tax
Control Variables
8.	Financial Leverage	LEV	Debt/shareholder’s equity	[34,49,81,82]
9.	Total Assets	Size	The natural logarithm of total assets
10.	Gross Domestic Product	GDP	The total government spending and consumer investment plus (exports–imports)

Source: developed by the authors.

).

3.5. Models

Four regression models were developed to assess the impact of intellectual capital (I.C) on the banks’ sustainable financial performance. Models 1 and 2 examine how I.C elements (HC, CE, SC, and RC) relate to the return on equity (ROE) and return on capital employed (ROCE), respectively. Similar to model 2, models 3 and 4 investigate how, combined, I.C (MVAIC) is related to ROE and ROCE, respectively.

• Model (1)

ROE = α + β0 + β1 (HCE) _it + β2 (CEE) _it + β3 (SCE) _it + β4 (RCE) _it β5 (SIZE) _it + β6 (LEVTA) _it + β7 (GDP) _it + e _it

• Model (2)

ROCE = α + β0 + β1 (HCE) _it + β2 (CEE) _it + β3 (SCE) _it + β4 (RCE) _it β5 (SIZE) _it + β6 (LEVTA) _it + β7 (GDP) _it + e _it

• Model (3)

ROE = α + β0 + β1 (MVAIC) _it + β2 (SIZE) _it + β3 (LEV) _it + β4 (GDP) _it + e _it

• Model (4)

ROCE = α + β0 + β1 (MVAIC) _it + β2 (SIZE) _it + β3 (LEV) _it+ β4 (GDP) _it + e _it

4. Results and Discussion

4.1. Descriptive Statistics and Correlation Analysis

The descriptive statistics for each dependent and independent variable are shown in

Table 2. Descriptive statistics of independent variables and dependent variables.

	N	Mean	S. D	Skewness	Kurtosis	Jarque–Bera	Probability	CV%
HC	187	8.046	3.094	0.388	4.011	12.667	0.001	38.453
CE	187	0.509	0.295	0.869	3.392	24.738	0.000	57.956
SC	187	0.851	0.150	−8.010	100.439	75,977.28	0.000	17.626
RC	187	0.652	3.589	11.654	155.552	185,564.2	0.000	550.460
MVAIC	187	10.061	4.265	2.902	35.143	8313.125	0.000	42.391
ROE	187	7.310	13.984	−2.941	13.492	1127.503	0.000	191.299
ROCE	187	2.119	0.870	−0.423	3.349	6.547	0.037	41.057
LEV	187	11.161	3.593	0.461	3.060	6.661	0.035	32.192
GDP	187	16.042	0.428	−0.467	1.389	27.023	0.000	2.667
SIZE	187	11.409	1.343	0.109	2.244	4.817	0.089	11.771

Source: developed by the authors with E-views 12.

. The value produced by HC, one of the four components of I.C, is comparatively high, because HC demonstrates the highest mean value. Similar results have also been found in other investigations [1,22,40,53,83,84,85]. According to Pulic’s 2000 study, HC produces 25–40% of the total value added. The average of SC, HC, and RC, taken together, is 10.588, which is significantly higher than the average of CE (0.509). In comparison to HC, the mean values of the other intellectual capital components are fairly low. According to earlier studies, businesses generate more value through I.C than they do through physical assets [22,39,53,54,64,83,84,85]. The leverage, GDP, and size have mean values of 11.161, 16.042, and 11.409, respectively. The high values of the standard deviation and CV of the various independent and dependent variables signify the high volatility of various I.C components and various profitability indicators. The Jarque–Bera statistics indicate that the data series of different variables are not normally distributed, except for the size of the control variable.

According to the multiple correlation analysis in

Table 3. Correlation Analysis.

	ROE	ROCE	HC	CE	SC	RC	MVAIC	LEV	GDP	SIZE
ROE	1.000	0.354	0.576	0.061	0.494	−0.424	0.082	−0.250	−0.292	0.081
ROCE	0.354	1.000	0.397	−0.492	−0.014	0.257	0.470	−0.649	0.169	0.664
HC	0.576	0.397	1.000	−0.159	0.413	−0.144	0.607	−0.088	−0.292	0.380
CE	0.061	−0.492	−0.159	1.000	−0.022	−0.124	−0.152	0.647	−0.124	−0.583
SC	0.494	−0.014	0.413	−0.022	1.000	−0.918	−0.439	−0.089	−0.129	0.143
RC	−0.424	0.257	−0.144	−0.124	−0.918	1.000	0.695	0.006	0.107	0.076
MVAIC	0.082	0.470	0.607	−0.152	−0.439	0.695	1.000	−0.017	−0.135	0.304
LEV	−0.250	−0.649	−0.088	0.647	−0.089	0.006	−0.017	1.000	−0.186	−0.416
GDP	−0.292	0.169	−0.292	−0.124	−0.129	0.107	−0.135	−0.186	1.000	0.286
SIZE	0.081	0.664	0.380	−0.583	0.143	0.076	0.304	−0.416	0.286	1.000

Source: developed by the authors with E-views 12.

, the independent variables HC, SC, CE, and MVAIC have positive correlations with the dependent variable ROE. In contrast, RC is negatively correlated with ROE. HC, RC, and MVAIC are positively correlated with ROCE, while CE and SC are negatively correlated with ROCE. The CE shows negative associations with the other components, namely HC (−0.159), RC (−0.124) and SC (−0.022). Additionally, HC also shows negative correlations with CE (−0.159) and RC (−0.144) and a positive correlation with SC (0.413). RC shows a negative association with the other three components of I.C, viz., CE (−0.124), HC (−0.144), and SC (−0.918). However, SC is positively correlated with HC (0.413), followed by CE (−0.022) and RC (−0.918).

4.2. Unit Root Test

The unit root tests were carried out to investigate whether the data series of the different independent, dependent, and control variables are stationary or not. In the present study, the tests of Levin, Lin and Chu (LLC), Breitung test statistics, and two sets of Fisher-type tests using ADF and Phillips–Perron (PP) tests, as proposed by Maddala and Wu and Choi, were used.

Hypothesis 0 (H0).

There is a unit root, and the data series of different variables are not stationary.

Hypothesis 1 (H1).

There is no unit root, and the data series of different variables are stationary.

In the initial stage, unit root tests were run at a certain level. However, none of the data series of the various variables (independent, dependent, and control) were discovered to be stationary at this level. Unit root tests were then performed at the first difference.

Table 4. Panel unit root at the first difference.

	ROE	ROCE	HC	CE	SC	RC	MVAIC	LEV	SIZE	GDP
LLC
None	−10.008 (0.000)	−10.420 (0.000)	−11.490 (0.000)	−9.725 (0.000)	−12.711 (0.000)	−6.300 (0.000)	−12.341 (0.000)	−12.213 (0.000)	−4.574 (0.000)	−7.474 (0.000)
With C and T	−3.819 (0.000)	−8.919 (0.000)	−5.830 (0.000)	−2.926 (0.000)	−1.645 (0.049)	0.236 (0.593)	−6.865 (0.000)	−8.385 (0.000)	−33.855 (1.000)	−5.537 (0.000)
With C	−3.841 (0.000)	−7.834 (0.000)	−7.906 (0.000)	−3.597 (0.000)	−4.662 (0.000)	−0.891 (0.186)	−9.666 (0.000)	−6.519 (0.000)	−27.672 (1.000)	−0.308 (0.378)
Breitung t-stat
None	−4.596 (0.000)	−6.477 (0.000)	−5.997 (0.000)	−5.174 (0.000)	−6.244 (0.000)	−5.556 (0.000)	−6.759 (0.000)	−6.947 (0.000)	−0.800 (0.211)	−5.272 (0.000)
With C and T	−2.285 (0.011)	−4.084 (0.000)	−1.553 (0.060)	−1.558 (0.059)	1.850 (0.967)	−2.044 (0.020)	−1.189 (0.117)	−3.087 (0.001)	−2.025 (0.021)	−2.120 (0.017)
With C	−2.658 (0.003)	−4.100 (0.000)	−2.102 (0.017)	−2.973 (0.001)	−2.718 (0.003)	−2.688 (0.003)	−2.033 (0.021)	−3.752 (0.000)	−3.888 (0.000)	−3.148 (0.000)
ADF
None	123.658 (0.000)	143.038 (0.000)	142.930 (0.000)	115.837 (0.000)	146.966 (0.000)	108.553 (0.000)	144.098 (0.000)	148.635 (0.000)	82.568 (0.000)	79.980 (0.000)
With C and T	42.021 (0.162)	63.966 (0.001)	47.091 (0.067)	39.511 (0.237)	54.658 (0.013)	41.864 (0.166)	58.415 (0.000)	64.211 (0.001)	63.115 (0.001)	24.296 (0.890)
With C	68.460 (0.000)	85.582 (0.000)	83.479 (0.000)	64.028 (0.001)	80.280 (0.000)	72.178 (0.000)	92.608 (0.000)	81.809 (0.000)	90.713 (0.000)	56.035 (0.010)
PP
None	215.787 (0.000)	212.237 (0.000)	195.689 (0.000)	243.648 (0.000)	249.607 (0.000)	230.047 (0.000)	199.422 (0.000)	240.238 (0.000)	255.926 (0.000)	264.791 (0.000)
With C and T	138.195 (0.000)	152.903 (0.000)	115.148 (0.000)	180.423 (0.000)	164.142 (0.000)	192.369 (0.000)	146.950 (0.000)	197.930 (0.000)	267.203 (0.000)	299.427 (0.000)
With C	182.098 (0.000)	176.005 (0.000)	160.810 (0.000)	203.247 (0.000)	204.480 (0.000)	206.439 (0.000)	169.060 (0.000)	191.511 (0.000)	290.389 (0.000)	299.692 (0.000)

Source: developed by the authors with E-views 12.

reports the outcomes of test investigating the panel unit root of three dependent, four independent, and three control variables at the first difference. According to the LLC test, the data series of all the independent and three control variables at first difference are stationary. According to the LLC test, the data series of all the independent and dependent variables are stationary except for the size. SC is stationary at the 5% significance level. The results of the maximum tests reveal that the data series of all the variables are stationary at the first difference. Although some of the tests show non-stationarity regarding the data series of some variables, the maximum tests show that the data series are stationarity at the three different levels (without C and T, with C, and with C and T). We reject the null hypothesis that the data series of the different variables are not stationary and accept the alternate hypothesis that the data series of all the independent, dependent, and control variables are stationary at the first difference.

4.3. Panel Least Squares Estimation Results

The findings of the panel least squares estimation with the cross-section random effect and fixed effect and without fixed and random effects are presented in

Table 5. Panel least squared estimation using the cross-section random and fixed effects ROE.

	Without Fixed and Random Effects		Fixed Effect		With Cross-Section Random Effect
	Coefficient	T Statistics	Coefficient	T Statistics	Coefficient	T Statistics
HC	2.010 *	5.902 (0.000)	1.993 *	5.554 (0.000)	2.010 *	5.671 (0.000)
CE	12.431 *	2.903 (0.004)	12.221 *	2.729 (0.007)	12.431 *	2.789 (0.005)
SC	−2.340	−0.172 (0.863)	−3.769	−0.264 (0.791)	−2.340	−0.165 (0.868)
RC	−1.328 **	−2.367 (0.019)	−1.389 **	−2.339 (0.020)	−1.328 **	−2.275 (0.024)
GDP	−0.448	−0.184 (0.854)	−0.771	−0.299 (0.764)	−0.448	−0.176 (0.859)
LEV	−0.643 **	−2.284 (0.023)	−0.654 **	−2.199 (0.029)	−0.643 **	−2.194 (0.029)
SIZE	−1.238	−0.832 (0.406)	−0.994	−0.625 (0.532)	−1.238	−0.799 (0.424)
C	−0.587	−1.020 (0.309)	−0.575	−0.959 (0.338)	−0.587	−0.980 (0.328)
R-squared	0.522		0.534		0.522
Adjusted R-squared	0.501		0.460		0.501
Durbin–Watson stat	2.668		2.737		2.668
F-statistic	25.180 (0.000)		7.235 (0.000)		25.180 (0.000)

* (p ≤ 0.01), ** (p ≤ 0.05). Source: developed by the authors with E-views 12.

. The ROE is the dependent variable in this situation, and there are four independent variables (CE, HC, RC, and SC), as well as three control variables for the GDP, leverage, and size. The results demonstrate that I.C significantly contributes to the enhancement of the bank’s operational efficiency and the wealth creation and value of Indian banks. These outcomes support the results of [22,34,58,73].

The findings of the simple panel least squares analysis demonstrate that the HC and CE elements of I.C having significant and positive impacts on the ROE (p-value < 0.01). RC also exhibits a negative significant impact on the ROE (p-value < 0.05). Additionally, the leverage and size control variables also show significant negative impacts on the ROE. The R-squared and adjusted R-squared values are 0.522 and 0.501, respectively. This means that the different explanatory variables properly explain 52.20 percent of the variance in the dependent variable (ROE). Durbin–Watson was used to verify the autocorrelation or serial correlation of the model. Since the value of Durbin–Watson is 2.668, and this value is greater than 2 but less than 3, this means there is no serious problem of autocorrelation or serial correlation in the model. The F-statistic or ANOVA exhibits the overall good fitness of the model. For the present model, the F-statistic value is 29.039, and the associated p-value is less than 0.01, which means that the overall model is a good fit. The following is the equation of the model used to predict the ROE:

ROE = −0.587 + 12.431(CE) + 2.010(HC) − 1.328(RC) − 0.643(LEV)

The panel least squares analysis with the fixed effect shows that CE, HC, and RC are the significant variables for the prediction of the ROE. CE and HC are found to be significant at the 1% level and RC is found to be significant at the 5 percent level, respectively. Leverage, as a control variable, also significantly impacts on the ROE. CE and HC show a positive association with the ROE, while RC and leverage show an inverse relationship with the ROE. Since the results of the fixed effect are similar to the simple panel least squares, this means that the heterogeneity of the banks does not affect the results. The following is the equation of the fixed effect mode:

ROE = −0.575 + 12.221(CE) + 1.993(HC) − 1.389(RC) − 0.654(LEV)

The results of the cross-section random effect model are similar to those of the fixed effect model. Again, CE and HC show significant positive impacts on the ROE. In contrast, RC shows an inverse association with the ROE. In cross-section random effect analysis, the deviations between organizations are assumed to be accidental and uncorrelated with the explanatory or exogenous variables. A cross-section random effect model is also used to check the time-variant effect or random disturbances. In the present study, the findings of all three models were found to be similar. This means that the heterogeneity of the banks and the time-variant effect do not influence the results. The following is the equation of the cross-section random effect model:

ROE = −0.587 + 12.431(CE) + 2.010(HC) −1.328(RC) − 0.643(LEV)

Two out of the four elements of I.C, namely HC and CE, are significantly and positively associated with the ROE. On the other hand, RCE has a negative association with the ROE. An increase in relational capital leads to lower profitability. The findings are consistent with the results of [1,22,54,64]. Indian private sector banks can improve their profitability by increasing their investment in the knowledge and skills of the workforce (HCE) and balancing this with greater investment in tangible assets (CEE). However, these results are similar to those of previous studies, such as [5,22,54,64,73,86,87].

In this model (

Table 6. Panel least squares estimation using the cross-section random and fixed effect ROCE.

	Without Fixed and Random Effects		Fixed Effect		Cross-Section Random Effect
	Coefficient	T Statistics	Coefficient	T Statistics	Coefficient	T Statistics
HC	0.021	1.379 (0.169)	0.020	1.236 (0.218)	0.021	1.326 (0.186)
CE	0.198	0.998 (0.319)	0.194	0.935 (0.350)	0.198	0.959 (0.338)
SC	4.735 *	7.505 (0.000)	4.801 *	7.241 (0.000)	4.735 *	7.215 (0.000)
RC	0.253 *	9.718 (0.000)	0.258 *	9.344 (0.000)	0.253 *	9.341 (0.000)
GDP	0.396 *	3.496 (0.000)	0.405 *	3.383 (0.000)	0.396 *	3.360 (0.001)
LEV	−0.049 *	−3.778 (0.000)	−0.047 *	−3.453 (0.000)	−0.049 *	−3.63 (0.000)
SIZE	−0.012	−0.183 (0.854)	−0.018	−0.252 (0.800)	−0.012	−0.176 (0.860)
C	−0.027	−1.023 (0.307)	−0.028	−1.014 (0.311)	−0.027	−0.983 (0.326)
R-squared	0.555		0.566		0.555
Adjusted R-squared	0.536		0.497		0.536
Durbin–Watson stat	2.077		2.109		2.077
F-statistic	28.732 (0.000)		8.243 (0.000)		28.732 (0.000)

* (p ≤ 0.01). Source: developed by the authors with E-views 12.

), the findings of the panel least squares analysis with the cross-section random effect and fixed random effect are reported. Here, the dependent variable is the ROCE. At the 1% level of significance, the components of I.C, namely RC and SC, are discovered to be significant. The link between the SCE and ROCE is positive. It indicates that private sector banks are effective in utilizing their internal intangible assets to increase the experience and abilities of their staff, therefore raising the productivity and profitability of PSBs [22,51,54,73], whereas few studies, such as [42,63], discovered a negative impact of SC on profitability. RCE also has a positive relationship with the ROCE. This demonstrates that increasing the capital invested in marketing and selling expenses and having a good relationship with stakeholders improve firm performance and facilitate the creation of a competitive edge. These results are in line with previous findings [1,20,86,87]. The two control variables, the GDP and leverage, are also found to be significant at the 1 percent level of significance. The control variable of GDP positively influences the ROCE, leverage, and size, showing a negative association with the dependent variable. The FR-squared and adjusted R-squared of the cross-section random effect value are 0.555 and 0.536. This indicates that all of the independent variables, taken together, may adequately explain the 55.54 and 53.60 variance in the ROCE. The p-value is less than 0.01, and the F-statistic is 28.73. Durbin–Watson’s ratio is 2.077, which means there is no serious problem of autocorrelation or serial correlation. The following is the equation of panel least squares with the cross-section random effect and without fixed and random effects:

ROCE = −0.027 + 0.253(RC) + 4.735(SC) + 0.396(GDP) − 0.049(LEVERAGE)

The panel least squares analysis with the fixed effect shows that the RC, SC, GDP, and leverage are found to be significant at the 1 percent level of significance. In this model, the R-squared and adjusted R-squared values are 0.566 and 0.497, respectively. The value of the F-statistic or ANOVA value is 8.243. It is significant at 1 percent. The following is the equation:

ROCE = −0.028 + 0.258(RC) + 4.801(SC) + 0.405(GDP) − 0.047(LEVERAGE)

Table 7. Panel least squares estimation using the cross-section random and fixed effect ROE.

	Without Fixed and Random Effects		Fixed Effect		Cross-Section Random Effect
Variables	Coefficient	T Statistics	Coefficient	T Statistics	Coefficient	T Statistics
MVAIC	−0.858 *	−4.422 (0.000)	−0.810 *	−3.969 (0.000)	−0.858 *	−4.301 (0.000)
GDP	−8.708 *	−3.020 (0.002)	−8.917 *	−2.968 (0.003)	−8.708 *	−2.937 (0.003)
LEV	0.194	0.579 (0.563)	0.192	0.546 (0.585)	0.194	0.563 (0.574)
SIZE	2.886 ***	1.691 (0.092)	3.065 ***	1.700 (0.091)	2.886 ***	1.644 (0.102)
C	−0.549	−0.719 (0.472)	−0.558	−0.711 (0.477)	−0.549	−0.700 (0.484)
R-squared	0.138		0.177		0.138
Adjusted R-squared	0.117		0.066		0.117
Durbin–Watson stat	2.101		2.196		2.101
F-statistic	6.581 (0.000)		1.600 (0.059)		6.581 (0.000)

* (p ≤ 0.01), *** (p ≤ 0.1). Source: developed by the authors with E-views 12.

and

Table 8. Panel least squares estimation using the cross-section random and fixed effect ROCE.

	Without Fixed and Random Effects		Fixed Effect		Cross-Section Random Effect
Variables	Coefficient	T Statistics	Coefficient	T Statistics	Coefficient	T Statistics
MVAIC	0.066 *	8.538 (0.000)	0.066 *	8.025 (0.000)	0.066 *	8.175 (0.000)
GDP	0.302 *	2.606 (0.010)	0.312 *	2.548 (0.011)	0.302 *	2.495 (0.013)
LEV	−0.047 *	−3.542 (0.000)	−0.046 *	−3.212 (0.001)	−0.047 *	−3.392 (0.000)
SIZE	0.105 ***	1.541 (0.125)	0.101	1.375 (0.171)	0.105 ***	1.476 (0.141)
C	−0.015	−0.511 (0.610)	−0.016	−0.500 (0.617)	−0.015	−0.489 (0.625)
R-squared	0.400		0.410		0.400
Adjusted R-squared	0.386		0.330		0.386
Durbin–Watson stat	2.103		2.131		2.103
F-statistic	27.428 (0.000)		5.148 (0.000)		27.428 (0.000)

* (p ≤ 0.01), *** (p ≤ 0.1). Source: developed by the authors with E-views 12.

represent the relationship between the overall intellectual capital (measured by the MVAIC model) and firm performance using the ROE and ROCE as dependent variables. There are three control variables, namely the GDP, leverage, and size. The model uses panel least squares estimation with the cross-section random and fixed effects and without the fixed and random effects. In Table 7, the overall I.C (measured by the MVAIC) is found to be significant at the 1 percent level of significance. The relationship between the MVAIC and ROE is negative. The control variables of the GDP and size are found to be significant at the 5 percent and 10 percent levels of significance. The GDP and leverage are negatively related to the ROE. The p-value is less than 0.001, and the F-statistic is 6.581. Durbin–Watson’s ratio is 2.101, which means there is no serious problem of autocorrelation.

Table 8 shows that the total I.C (as determined by the MVAIC) is significant at the 1% level of significance. The ROCE is favorably impacted by the MVAIC in this model. At the 5% level of significance, the control variables GDP and leverage are determined to be significant. At the 10% level, the significance of size is discovered. ROCE has a positive relationship with the GDP and size. ROCE and leverage appear to be inversely related. The associated and adjusted R-square values are 0.400 and 0.386. This means that the one significant independent variable and control variable contribute 40 percent of the variance in the dependent variable ROCE. The p-value is less than 0.01, and the F-statistic is 27.428. There are no significant issues with serial correlation or autocorrelation according to Durbin–Watson’s ratio, which is 2.03.

4.4. Hausman Test

The Hausman test results are shown in

Table 9. Hausman Test.

Test Summary	Variables	Chi-Sq. Statistic	Chi-Sq d.f	Prob
Hausman Test	ROE	19.123	6	0.004
	ROCE	29.766	6	0.000
	ROE (MVAIC)	0.844	3	0.838
	ROCE (MVAIC)	9.839	3	0.020

Source: developed by the authors with E-views 12.

. The Hausman test is sometimes referred to as the model misspecification test. This test aids researchers in deciding whether or not to use a fixed effect model or a cross-section random model when analyzing panel data [22,42,54]. If the null hypothesis is accepted, the random model is the most appropriate one for the given data series. The acceptance of the alternative hypothesis reveals that the fixed effect model best fits the provided data series.

In the method used in the current investigation, the Hausman test is performed four times. First, the ROE is used as the dependent variable. In this instance, the related chi-square statistic value (19.123) is relatively high, and the p-value is less than 0.05, indicating that the null hypothesis is rejected and the alternative hypothesis is accepted. This indicates that the fixed effect model is a good fit model when using the ROE as the dependent variable and all the others as the independent variables (HC, SC, RC, and CE), together with two control variables. Secondly, the Hausman test is carried out using the return on capital employed (ROCE) as a dependent variable. Again, in this case, the associated chi-square value of the model is quite high, and the p-value is <0.05. This means that, again, in the case of the ROCE, the fixed effect model is the best-suited model as compared to the random model.

Thirdly, the Hausman test is carried out using the modified value added of the intellectual capital (MVAIC) as the independent variable, two control variables (leverage and size), and ROE as a dependent variable. Since the p-value of the associated chi-square test is greater than 0.05, this indicates the acceptance of the null hypothesis. It demonstrates that a random model is preferred over a fixed model in this case. Lastly, this test is conducted using the MVAIC as an independent variable, two control variables, and ROCE as a dependent variable. In this case, the p-value of the Hausman test is less than 0.05, and the associated chi-square test statistic is 9.839. This demonstrates that the null hypothesis is rejected and the alternate hypothesis is accepted, which further means that the fixed effect model is a good fit in the present case.

In the first, second, and fourth models, the heterogeneity of the banks is a major concern, as shown by the preference for the fixed effect model over the random model. The time-variant effect is the main issue with the third model, as shown by the preference of the random model over the fixed effect model.

5. Conclusions and Implications

The current study aimed to investigate intellectual capital (I.C) and the impact of its components on the sustainable financial performance (F.P) of private sector banks (PSBs) operating in India over an eleven-year period (2010–2021). For the period of 2010 to 2021, data from 17 PSBs currently operating in India were gathered. The modified value-added coefficient technique (MVAIC) was used to calculate the I.C of the banks. The two financial ratios, namely the return on equity (ROE) and return on capital employed (ROCE), were used as dependent variables in depicting the sustainable financial performance of Indian PSBs. The I.C dimensions of human capital (HC), capital employed (CE), structural capital (SC), and relational capital (RC) were used as independent variables, and the GDP, size, and financial leverage were used as control variables. The data were arranged in balanced panel data form by retrieving the data for each dependent and independent variable over 11 years, including 17 PSBs. By using several unit root techniques, the stationarity of the dataset for the various variables was examined in the first stage. At a given level and the first difference, unit root tests were performed. All of the variable data series were discovered to be stationary at the first difference. As a result, all of the dependent and independent variable data series were changed into the first difference. Then, using two dependent variables (ROE and ROCE), a simple panel least squares analysis was performed using three alternative procedures (simple panel least squares, the fixed effect, and the cross-section random effect). The four models were developed to examine the impacts of the independent elements of intellectual capital (HC, CE, SC, and RC) on each dependent variable. In addition, the combined effect was studied using the above-mentioned variables (measured through the MVAIC). The study also used three control variables, namely the size, GDP, and leverage.

The empirical findings contribute to the body of research demonstrating that I.C plays a crucial role in providing private sector banks with a competitive edge and fostering value creation and sustainable financial performance (F.P). Our empirical findings revealed that the elements of I.C, the capital employed efficiency (CEE) and human capital efficiency (HCE), are positively and significantly related to the return on equity (ROE) [5,22,31,54,73,86,87], whereas the other two elements of I.C, the relational capital efficiency (RCE) and structural capital efficiency (SCE), also positively influence the return on capital employed (ROCE) [22,51,54,73,86,88]. However, the relational capital efficiency (RCE) shows a negative relationship with the ROE. The overall intellectual capital (I.C), as measured by the MVAIC, exhibits a positive and significant relationship with the ROCE and is negatively associated with the ROE. According to our findings, I.C plays a significant role in adding value to the banking industry. The elements of I.C all contribute significantly to the organization’s increased productivity and profitability. These findings suggest that financial institutions that wish to boost their productivity and profitability should intensify their commitment to their HC, CE, SC, and RC. Through strategic planning, training, and development programs, HC may increase the staff’s knowledge, talents, and skills. Since HC is the prime contributor to invention, creativity, and inspiration, it provides the company with a competitive advantage. By effectively and optimally using their physical assets and SC, which have a positive correlation with the ROCE, Indian private sector banks can also increase their profitability. This implies that the banks’ profitability depends significantly on their investments in tangible and intangible assets, such as systems, databases, client relationships, and software.

Even though the empirical findings of this study show a favorable correlation between the sustainable financial performance of private sector banks and their I.C, thus far, the banks have invested a small amount of funds in the development of their I.C compared to other developing nations. Therefore, it is imperative that PSBs improve their investments by strengthening their IC. Examples of this include investments in technology, employee training, and development sessions and the improvement of client relationships by offering a better service. Therefore, this study may serve as a reference for executives and regulators in order to assess the effectiveness of the banks’ I.C and to prioritize investing in its growth.

5.1. Implications of Findings

The results of this study have several practical and managerial implications for private sector banks. Indian banks can improve their productivity and profitability levels through the optimal use of their human capital (HC) and capital employed (CE), as these two factors have positive and significant relationships with the return on equity (ROE), and the relational and structural capital (RC, SC) have a considerable positive link with the return on capital employed (ROCE). This implies that HC is a vital source of creativity and innovation, which aids in the achievement of inclusive and sustainable growth for both the bank and the country’s economy. However, spending money on tangible and intangible resources such as software, systems, processes, procedures, and customer relationships are crucial for achieving profitability. Therefore, banks must have a robust infrastructure that promotes inclusive and sustainable economic growth.

Our study suggested that I.C can play an important role in enhancing the firm’s performance. It is one of the valuable resources that provide a competitive edge to the firm in this knowledge-based world. Managers need to focus on the role of I.C and invest in various I.C components. The three elements of intellectual capital, the HC, CE, and SC, positively and significantly contribute to the bank’s financial performance, and Indian private sector banks can enhance their profitability through the proper utilization and strengthening of the knowledge and skills of employees and by investing in physical assets and non-tangible assets. The least important component of intellectual capital is RC. As a result, we believe that private sector banks should place greater emphasis on relational capital in order to maintain regular contact with their stakeholders and create value. Even though the study’s findings show a positive correlation between the I.C elements and financial indicators, thus far, Indian banks have invested less in I.C than banks in other emerging nations. As a result, it is important that private sector banks increase their investment by enhancing their I.C. This includes the building of strong customer relationships by providing better services, spending more on research, development, and technology, and the effective administration of training and development programs to improve the skills and knowledge of the workforce. In order to maintain a competitive edge in this information- and knowledge-based era, the policymakers of the private sector banks can use the information provided by this research to continuously improve these components of intellectual capital.

5.2. Limitations and Future Scope

Although every effort was made in this research to conduct a thorough investigation, it is impossible to take into account all the contributing aspects. In order to offer suggestions for future studies, it is necessary to acknowledge the current research paper’s shortcomings. This study’s primary goal was to determine how intellectual capital affects private sector banks’ sustainable financial performance. To further understand how I.C affects the other pillars of the banks, a study could be conducted on the public sector, foreign sector, regional rural banks, small finance banks, and payments banks. Additionally, this study took into account solely Indian private sector banks. Additionally, the current work only examined issues pertaining to private banks in India; however, a comparative study should be performed to investigate how I.C affects the financial performance of banks in both the public and private sectors. Furthermore, future studies may take into account a sample of banks in various SAARC and ASEAN nations. Future studies could also consider other sectors in order to verify the influence of intellectual capital on the firm’s performance. Another drawback is that this study did not take into account the Tobin-Q or other productivity and performance metrics that can have an impact on intellectual capital. Future studies could be undertaken using alternate methods, such as the national IC index and balance scorecard, to analyze the efficacy of I.C, since this study used the MVAIC approach. This study was entirely based on secondary data, but it could be further developed by asking upper-level bank personnel about their perceptions of the institution’s financial performance. This would enable the investigator to pinpoint the variables that affect an institution’s financial performance in different ways. Last but not least, the data for this study covered the years of 2010 to 2021, while future studies may potentially take the impact of COVID-19 on the banking sector into account (for pre-COVID and post-COVID analyses). As a result, as this study focused on the broader perspective of the private bank sector, it may serve as a foundation for subsequent research.