Abstract:
This study investigates the influence of intellectual capital on Vietnamese bank performance from 2011 to 2023. To calculate intellectual capital, we used the value of the intellectual coefficient. We employed various panel methods and eventually chose the system GMM estimator to evaluate our baseline results. According to the findings, the value of the intellectual coefficient positively influences bank performance. Furthermore, when observing the effect of intellectual capital’s components (i.e., capital employed, human capital employed, and structural capital employed efficiency), they considerably enhance bank performance. As a result, the research has substantial implications for management and academics. For instance, it implies that bank managers should focus on intellectual capital and its components to strengthen bank performance further.
Keywords: intellectual capital, Value intellectual coefficient, capital employed efficiency, human capital employed efficiency, and structural capital efficiency.
1. Introduction
For economic growth to occur, a sound financial system is required. The banking industry is often recognized as the most powerful sector within a financial system (Haris et al., 2019). The Vietnamese banking sector has faced numerous hurdles and political changes since the country's independence in 1945. Vietnamese banks suffer from poor public policy and the lack of international audits. Since 1992, the Vietnamese banking system has comprised a mix of state-owned, joint-stock, joint-venture, and international banks (Ho et al., 2021). In September 2005, the Vietnamese central bank decided to equitize all five state-owned banks to maximize the transparency of the financial industry (Le & Ngo, 2020). Since 2007, the State Bank of Vietnam has permitted foreign banks to offer a complete range of financial services as Vietnamese banks (c). As a result, it leads to fierce competition in the banking market, thereby affecting Vietnamese banks’ income. Therefore, domestic banks need to improve intellectual capital (IC) efficiency.
Most bank studies (Al-Musali and Ismail, 2016) concluded that bank- and country-specific variables influence FB significantly, but these studies ignored the role of IC (Haris et al., 2019). This study is the first attempt to use the Value Intellectual Coefficient (VAIC) model to evaluate the influence of IC on FB of 26 Vietnamese banks from 2011 to 2023. Most recent research that has used the VAIC model created by Joshi et al. (2013) has concluded that VAIC is a simple-to-use and effective tool for assessing the influence of IC on FB.
The study aims to contribute to the field of bank management as follows. First, this study builds on earlier research by revealing the importance of VAIC components in delivering outstanding banking system performance in a dynamic setting. Second, it assesses and measures the influence of bank-specific and macro variables on performance. Finally, because Pool Ordinary Least Square (OLS), Fixed Effects (FE), and Random Effects (RE) do not address heteroskedasticity and autocorrelation, the study introduces GMM, which is used to pass difficulties in data analysis. In summary, this study develops and verifies models of the interactions between VAIC dimensions, bank-specific variables (BSVs), and macro variables (MVs).
2. Literature Reviews
2.1. Intellectual capital
In 1969, John Kenneth Galbraith, an economist, developed the initial definition of IC. Galbraith defines IC as an intangible asset. Others say that IC encompasses all knowledge, information, intellectual property, experiences, social networks, abilities, and competencies contributing to corporate performance (Subramaniam & Youndt, 2005).
IC comprises three components: human, structural, and relational capital. Human capital is defined as the sum of an employee's abilities, skills, experience, and educational background. Structural capital refers to the institutionalized knowledge and codified experiences preserved in an organization's image, culture, routines, processes, information systems, and patents (Gilbert et al., 2017). Coleman (1988) describes relational capital as the resources in social networks where individuals or organizations interact.
2.2. VAIC and Bank Performance
The VAIC model is widely used for calculating IC performance. There are the following three components used to determine the value of VAIC: (1) capital employed efficiency (CEE), (2) human capital employed efficiency (HCE), and (3) structural capital efficiency (SCE) (Haris et al., 2019). Human capital and structural capital efficiency are measured by HCE and SCE (Rehman et al., 2021).
The study will examine the linkage between IC efficiency and FB by using CEE, SCE, and HCE as indices of IC efficiency. On the other hand, return on assets (ROA) and return on equity (ROE) are utilized as indicators of FB. Numerous studies have been undertaken using the VAIC model to examine the relationship between IC efficiency and FB; however, their results are mixed. Rehman et al. (2021) discovered a good association between the VAIC and ROA of Islamic banks in 23 nations. The outcome of the study by Haris et al. (2019) in Pakistan and the United States indicated a positive connection between VAIC and ROE. Although CE and HCE also have positive influences on FB (Al-Musalli & Ku Ismail, 2016; Joshi et al., 2013), HCE has the most significant impact on FB. Therefore, the hypothesis is suggested:
H1: Intellectual capital has a positive influence on FB.
H2: Intellectual capital components (CEE, HCE, and SCE) have positive influences on FB.
3. Methodology
3.1. Data Source
Financial reports of Vietnamese commercial banks from 2011 to 2023 were used to compile bank-level data. Banks with 100 % foreign ownership are excluded to ensure that the results are comparable. Despite the author's desire to collect data from all 31 commercial banks, only 26 banks have sufficient data for at least thirteen years. In addition, the paper also employs country-level data from the General Statistics Office.
3.2. Variable Selections
3.2.1. Dependent variables
Le & Ngo (2020) and Tan et al. (2017) conducted research using two earnings metrics: ROA and ROE. The ROA is computed by dividing net income by average assets, and it represents how capable the bank is of generating revenue by using assets. ROE = net earnings / average equity.
3.2.2. Independent variables
Current literature suggests different approaches to measuring intellectual capital. The VAIC, the conventional methodology created, is a suitable and trustworthy proxy for assessing IC's impact. The author uses VAIC as an independent variable, as in previous investigations (Xu and Wang, 2018;). The following formula is used to compute it:
VAICit = CEEit + HCEit + SCEit (1)
where VAICit denotes the VAIC of ith banks at time t. CEEit represents the CEE (capital employed efficiency) of bank i at time t, HCEit refers to the HCE (human capital efficiency) of bank i at time t and SCEit refers to the SCE (structural capital efficiency) of bank i at time t, respectively. However, to calculate the variables above, it is necessary to compute the value-added (VA) amount to measure the VAIC decompositions (Ozkan et al. 2017, Xu and Wang, 2018), which is:
VAit = OPit + PCit + Dit + Ait (2)
where VAit refers to the value made by ith bank at time t; OPit refers to the operating profit of a bank i at time t; PCit represents the total cost of salaries and other benefits of the ith bank at time t. Dit refers to the depreciation expense of ith bank at time t; Ait means the amortization and depreciation expenses of ith bank at time t.
Once VAit is calculated, the VAICit components are measured as follows:
CEEit = VAit / CEit (3)
where CEi represents capital held by a bank and is calculated by the difference of total assets and total liability).
HCEit = VAit / HCit (4)
where HCit represents human capital calculated by the sum of salary and all other expenses incurred on employees.
SCEit = SCit / VAit (5)
where SCit representing structural capital is computed by the difference of VAit and HCit (SCit = VAit - HCit).
3.2.3. Control and Dummy Variables
This article investigates the influence of BSVs (bank-specific variables) and MV (macroeconomic variable) on profitability (ROA and ROE). In this study, bank size (BSIZE) is computed by the natural logarithm of total assets (Tan et al, 2017) and total debt / total assets is LEV to assess the influence of BSVs (Ozkan et al., 2017).
The dummy variable bank type (BTYPE) is also used in the study to examine ownership effects (Haris et al., 2019). If the bank is privately owned, BTYPE is 1; otherwise, it is 0. The growth rate of the gross domestic product (GDP) is used by MV to control the influence of economic growth (Haris et al., 2019).
3.3. Regression Models
The authors propose four models as follows.
ROAit = β0 + β1 VAICit + β2 BSIZEit + β3LEVit + β4 BTYPE + β5 GDP + εit
ROEit = β0 + β1 VAICit + β2 BSIZEit + β3LEVit + β4 BTYPE + β5 GDP + εit
ROAit = β0 + β1HCEit + β2 SCEit + β3CEEit + β4 BSIZEit + β5LEVit + β6 BTYPEit + β7 GDPt + εit
ROEit = β0 + + β1 HCEit + β2 SCEit + β3CEEit + β4 BSIZEit + β5LEVit + β6 BTYPE it + β7 GDPt + εit
4. Results
4.1. Descriptive statistics
The paper used data of 26 Vietnamese banks from 2011 to 2023, resulting in 37 observations.
For ease of interpretation, we pay attention to our main variables. According to Table 3, the average ROA and ROE percentages are 0.1812 (18.12%) and 0.1345 (13.45%), respectively. The results indicate that the banks generate 0.1812 VND and 0.1345 VND for every Vietnamese currency of assets and equity used. It implies that Vietnamese banks have not utilized their assets or equity to make profits (less than 20%). The average value of VAIC is 5.1452, implying that Vietnamese banks generate 2.8452 VND for every Vietnamese currency of IC used. HCE is the most important component among CEE, HCE, and SCE, with an average value of 4.9151 compared to 0.3512 and 0.5173, respectively, for CEE and SCE. This is comparable with those of Ozkan et al. (2017) in Turkey and Tran & Vo (2020) in Thailand. Other variables are presented in Table 2.
4.2. Correlation analysis
Table 1 demonstrates that VAIC has positive correlations with ROA and ROE. In addition, CEE, HCE, and SCE also have strong links to VAIC.
Table 1. Correlation matrix
Source: Stata 15.0
In the next step, the Hausman (1978) test is used to determine which model, the FE or RE model, is appropriate for regression. The outcomes in Table 2 show that the null hypothesis (H0) is not accepted for all, so the FE model is inconsistent. Therefore, the FE model is appropriate for Vietnamese banks.
Table 2. Hausman test
Source: Stata 15.0
4.3. Fixed effects
The connection between VAIC, CEE, HCE, SCE, and ROA, as well as ROE, is shown in Table 4. CEE, HCE, and SCE are more effective at explaining FB than VAIC alone, according to the findings. Table 3 shows that when VAIC is broken down into CEE, HCE, and SCE, CEE has the greatest effect on Vietnamese bank performance, which is in line with Xu, Haris, and Yao's (2019) argument. The performance of banks is also influenced by HCE and SCE.
Table 3. Fixed effects regression
Source: Stata 15.0
However, in addition to the FE model, the authors use the Wooldridge and Modified Wald tests to see if autocorrelation and heteroskedasticity are present in the four models. The results show that in four models, both autocorrelation and heteroskedasticity are present. As a result, in four research models, using the FE model does not fix autocorrelation and heteroskedasticity.
4.4. Generalized method of moments (GMM)
Xu et al. (2019) argued that GMM (Table 4) may deal with issues about heteroskedasticity, autocorrelation, and endogeneity. Because GMM allows for the use of instrumental variables and lagged variables, valid instruments must be used to ensure that the GMM results are consistent. The following are extended regression models based on GMM shown in Table 5.
Table 4: Econometric models based on GMM
Source: Stata 15.0
The test results of models e, f, g, and h are shown in Table 6 below. The outcome confirms that the instrumental variables are not endogenous in all models in this study. Furthermore, the results also show that second-order autocorrelation is insignificant, so it makes GMM estimation valid. The results also show that FB (ROA and ROE) is affected significantly by past year performance (ROAt-1 and ROEt-1). The result also illustrates that a one-year lagged VAIC (VAICt-1) has a significant influence on ROA and ROE, respectively. However, it has a smaller influence than the current year VAIC. CEEt-1, HCEt-1, and SCEt-1 have a less significant influence on ROA and ROE than CEE, HCE, and SCE. After all things considered, we confirm that the results shown in Table 5 are the best optimal.
Table 5: GMM estimation
Source: Stata 15.0
5. Discussion
CEE, HCE, and SCE also have positive influences on bank profitability (ROA and ROE), respectively, which is consistent with a study by Tran and Vo (2020). However, this argument contradicts the research of Xu et al. (2019) on the Chinese and Pakistan banking sectors. HCE has positive influences on profits (ROA and ROE) and this result is not in line with the research of Vo (2018) about Thailand’s banking system. The results also show that FB (ROA and ROE) is affected significantly by past year performance (ROAt-1 and ROEt-1). We also obtained that VAICt-1, CEEt-1, HCEt-1, and SCEt-1 have positive impacts on ROA and ROE, but their impacts are less significant than current CEE, HCE, and SCE. These are consistent with the research of Tran and Vo (2020) and indicate that banks must continuously improve IC if they would like to maintain superior FB in the long term.
Regarding control variables, the significant and positive coefficients of BSIZE demonstrate that larger banks perform better than smaller counterparts. This is contrary to the findings of Le & Ngo (2020). Also, banks with higher leverage earn higher profitability regardless of the measures, consistent with the study of Ozkan et al. (2017). Further, the coefficient of BTYPE is positive and significant, implying that privately owned banks are more profitable than state-owned banks. It can be explained that state-owned banks usually apply lower interests in both loans and deposits than the others, and have to subsidies other private businesses, driving them to have lower net incomes. Finally, GDP is positive and significantly associated with bank profitability, stating that economic growth enhances profitability as generally perceived. This is in line with the existing literature (Ho et al., 2020).
6. Conclusions
Nowadays, IC is widely recognized as a source of competitive advantage and future value creation in today’s knowledge-based economy. By using the VAIC approach, first, this study examines the influence of IC on Vietnamese bank performance (ROA, ROE) from 2011 to 2023. Second, this paper focuses on how IC and its components influence ROA and ROE using the GMM method. Lastly, the effect of IC at these institutions is then investigated in this paper. The baseline result reveals the following: (1) IC has a positive effect on FB, (2) CEE is the key driver of profit in Vietnamese banks, and (3) the lagged CEE, HCE, and SCE have a significant influence on FB.
Several practical contributions are suggested in this work. To begin, the current analysis indicates that the bank should invest in IC resources to increase profitability. Because banks provide financial services that necessitate sophisticated information systems and good procedures. As a result, bank executives must be cognizant of the relevance of IC and manage it efficiently to generate additional value. Second, the empirical data demonstrate that Vietnamese banks rely heavily on physical and financial resources (CEE) to generate profits, thus managers need to make effective use of these resources to improve performance. Third, because the banking industry is a knowledge-intensive industry, it necessitates highly skilled and well-trained individuals (HCE) to handle financial intricacies and establish strong relationships with stakeholders to resolve agency issues. As a result, investing in human capital has a lot of potential to boost bank profits. Finally, because SCE has a big influence on FB, Vietnamese banks place a premium on value creation through maintaining positive customer connections and enhancing company reputation.
There are certain drawbacks that need to be address. Firstly, international banks are not mentioned in the bank list. Second, both relational and inventive capital are ignored. Finally, the analysis ignores the link between intellectual capital and other bank characteristics. Therefore, these will be completed in the future.
Acknowledgments:
This research is funded by Van Lang University.
Reference:
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KHÁM PHÁ MỐI QUAN HỆ GIỮA VỐN TRÍ TUỆ
VÀ HIỆU QUẢ KINH DOANH CỦA NGÂN HÀNG VIỆT NAM
TS. Hoàng Thành Nhơn
ThS. Trương Công Bắc
Giảng viên Khoa Thương mại, Trường Đại học Văn Lang
Tóm tắt:
Nghiên cứu tác động của vốn trí tuệ đối với hiệu quả hoạt động của ngân hàng Việt Nam từ năm 2010 đến năm 2023. Để tính toán vốn trí tuệ, nhóm tác giả sử dụng giá trị hệ số trí tuệ. Nhóm tác giả đã sử dụng các phương pháp dữ liệu bảng khác nhau và cuối cùng chọn phương pháp ước lượng GMM hệ thống để đánh giá kết quả. Theo kết quả nghiên cứu, giá trị hệ số trí tuệ có tác động tích cực đến hiệu quả hoạt động của ngân hàng. Hơn nữa, khi quan sát tác động của các thành phần vốn trí tuệ (tức là vốn sử dụng, vốn nhân lực và hiệu quả sử dụng vốn cơ cấu), chúng có tác động đáng kể trong cải thiện hiệu quả hoạt động của ngân hàng. Do đó, nghiên cứu này có ý nghĩa quan trọng đối với quản lý và giảng viên. Điều này có nghĩa là các nhà quản lý ngân hàng nên tập trung vào vốn trí tuệ và các thành phần của nó, để tăng cường hiệu quả hoạt động của ngân hàng hơn nữa.
Từ khóa: vốn trí tuệ, hệ số giá trị trí tuệ, hiệu suất sử dụng vốn, hiệu suất vốn nhân sự sử dụng, hiệu suất vốn cấu trúc.
[Tạp chí Công Thương - Các kết quả nghiên cứu khoa học và ứng dụng công nghệ, Số 2 tháng 2 năm 2024]
