







































 

 

 
20 

© 2022 Conscientia Beam. All Rights Reserved. 

DOES THE BUSINESS MODEL INCREASE BANK STABILITY?   

 

 

 Loan Thi Nguyet 
Nguyen1 

 Phuong Thi Lan 
Tran2+ 

 Nguyet Thi Minh 
Trinh3 

 Anh Quynh Le4 

 

1,2,3,4Applied School of Finance and Banking, Dai Nam University, Vietnam. 
1Email: loanntn@dainam.edu.vn Tel: +84.977186893 
2Email: phuongtl@dainam.edu.vn Tel: +84.389123898 
3Email: nguyetttm@dainam.edu.vn Tel: +84.397966340 
4Email: anhlq@dainam.edu.vn Tel: +84.838382185 

 
 

(+ Corresponding author) 

 ABSTRACT 
 
Article History 
Received: 27 April 2022 
Revised: 17 June 2022 
Accepted: 30 April 2022 
Published: 14 July 2022 
 

Keywords 
Business model 
Bank risk 
Bank stability 
Non-performance loans. 
 

 

 
Research on risk or sustainability in the banking system does play an important role in 
the banking industry, where competitiveness increases unceasingly. Simultaneously, the 
trend of diversifying banks’ business models is becoming more popular. Thus, this paper 
attempts to investigate the impact of business model diversification on bank risk and 
stability. The proxy of business models includes (1)Non-net interest income; (2) trading 
income. The paper applied Generalized Least Squares (GLS) to conduct empirical 
research on 18 joint-stock commercial banks listed on the Vietnam Stock Exchange from 
2010 to 2019 (The GLS with panel data). The results indicated the negative impact of 
non-net-interest income on bank stability. Trading in foreign exchange, gold has no 
meaning for bank risk. The study offers some theoretical and practical implications for 
banks to control better risks based on new empirical findings. Especially, the 
diversification of business models is ineffective, and banks need more suitable solutions. 
 

Contribution/Originality: This study has an important contribution as it shows that business model 

diversification is ineffective for banks in developing countries. Therefore, this result has great significance in adjusting 

for profitable activities other than lending. 

 

1. INTRODUCTION 

The two-level banking system is organized in Vietnam. The State Bank of Vietnam is responsible for 

implementing monetary policy, performing the function of inspection and state management of currency, banking 

activities, and foreign exchange; perform the functions of the Central Bank in terms of money issuance, banking of 

credit institutions, and provision of monetary services to the Government. The second level includes commercial 

banks, cooperative banks, development banks, people’s credit funds, microfinance institutions, finance companies, 

finance leasing companies, and insurance companies.  

In 2021, the stock market witnessed a strong growth rate of 27 commercial banking stocks. To be considered the 

lifeblood of the economy, the bank has proven its performance when profits are still growing, despite the impact of 

the COVID-19 epidemic. The restructuring, especially the digital transformation of the commercial banking system, 

is a matter of concern to policymakers, administrators, and investors, especially the ownership, size, and type of 

commercial bank. 

Financial Risk and Management Reviews 
2022 Vol. 8, No. 1, pp. 20-26. 
ISSN(e): 2411-6408 
ISSN(p): 2412-3404 
DOI: 10.18488/89.v8i1.3062 
© 2022 Conscientia Beam. All Rights Reserved. 

 
 
 

 
 
 

 

 
 
 
 

https://orcid.org/0000-0002-5539-4786
https://orcid.org/0000-0001-7427-5662
https://orcid.org/0000-0002-8386-7456
https://orcid.org/0000-0002-6058-2538
mailto:loanntn@dainam.edu.vn
mailto:phuongtl@dainam.edu.vn
mailto:nguyetttm@dainam.edu.vn
mailto:anhlq@dainam.edu.vn
https://www.doi.org/10.18488/89.v8i1.3062


Financial Risk and Management Reviews, 2022, 8(1): 20-26 

 

 
21 

© 2022 Conscientia Beam. All Rights Reserved. 

So how do the above factors affect the bank risk level of Vietnamese commercial banks? Through data retrieved 

from 18 joint-stock commercial banks listed on the Vietnam Stock Exchange in the period 2010 - 2019, this paper 

analyzes the impact of business models (different varieties of income), thereby offering some suggestions to contribute 

as a basis for the implementation of restructuring and digital transformation at Vietnamese commercial banks in the 

coming time. 

 

2. LITERATURE REVIEW 

A business model is defined as the way an organization does business (Magretta, 2002) and aims to advance the 

company’s overall goals (this goal can be financial or non-financial (Yip & Bocken, 2018). Business models can analyze 

and evaluate productivity (Osterwalder, Pigneur, & Tucci, 2005). The growing interest in analyzing business models 

in enterprise innovation showed its usefulness in sustainable innovation (Bocken, Short, Rana, & Evans, 2014; Boons 

& Lüdeke-Freund, 2013; Lüdeke-Freund, 2010; Stubbs & Cocklin, 2008). Recently, it has been quite diverse. In 

addition to the business type of borrowing and lending, other business models such as money transfer, money 

withdrawal, and bill payment services have also become popular. For banks, business model diversification is other 

activities within the bank to increase the bank’s goals and mainly financial goals. Implementing business model 

diversification in enterprises in general and banks, in particular, has certain risks. 

Bank risk is the opposite of bank stability, meanwhile, sustainability represents a sustainable business with little 

volatility but high profits (Köhler, 2015). Therefore, sustainability will focus heavily on business results, capital 

adequacy ratios, and volatility of business results (Köhler, 2015). As a result, the bank’s sustainability is inversely 

proportional to the risk in the bank. Therefore, some studies research bank stability (Abuzayed, Al-Fayoumi, & 

Molyneux, 2018; Köhler, 2015; Nguyen, Skully, & Perera, 2012), and others do evaluate research on risk (Nguyen, 

Ho, Van Nguyen, Pham, & Nguyen, 2021). In addition, several market studies focus on bank performance (Mergaerts 

& Vander Vennet, 2016). One factor considered important in influencing banks’ risk is the diversification of business 

models (Mahdaleta, Muda, & Nasir, 2016). Accordingly, the diversification of business models will aim to create more 

sources of income for the bank (Köhler, 2015; Nguyen et al., 2021). Diversifying business models in the bank make 

different sources of income besides the main source - interest income. Business models describe how banks generate 

profits, what customers they serve, and which distribution channels they use (Köhler, 2015). 

Researches evaluating the impact of business model diversification on the sustainability of banks is still 

controversial. Studies show a positive effect of the business model on bank stability (Froot, Scharfstein, & Stein, 1993; 

Köhler, 2015; Trivedi, 2015b). Banks implemented diversification of business models at that time, and business results 

grew well from this diversification (Froot et al., 1993; Froot & Stein, 1998). This will show that the bank’s operating 

system for non-interest businesses is highly effective. The increase in non-interest income has helped banks develop 

more stably and reduce bank risk due to the increased role of banks as financial intermediaries (Köhler, 2015). The 

diversification of business models will help reduce the level of risk concentration in the bank. People perform better 

based on management skills or existing management systems (Trivedi, 2015b). Besides the positive impact of business 

model diversification on bank sustainability, there is still a negative impact on bank sustainability (Abuzayed et al., 

2018; DeYoung & Torna, 2013; Nguyen et al., 2021; Williams & Prather, 2010). Banks will face more risks when 

diversifying their business models when the management system is limited (Nguyen et al., 2021). Concurrently, 

increased participation in the derivatives market and larger loan portfolios have increased the bank’s exposure to 

capital ratios (DeYoung & Torna, 2013). Besides, some studies show that diversifying business models helps reduce 

risk but does not make banks more sustainable (Abuzayed et al., 2018). 

 

3. METHODOLOGY 

3.1. Empirical Research Model 

This paper follows the previous research of Köhler (2015), presenting as follows: 



Financial Risk and Management Reviews, 2022, 8(1): 20-26 

 

 
22 

© 2022 Conscientia Beam. All Rights Reserved. 

𝐵𝑎𝑛𝑘 𝑟𝑖𝑠𝑘𝑖𝑡 = 𝛼𝑖 + 𝛽1 ∗ 𝐵𝑎𝑛𝑘 𝑏𝑢𝑠𝑖𝑛𝑒𝑠 𝑚𝑜𝑑𝑒𝑙𝑖𝑡 + 𝛽2 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖𝑡 + 𝜀𝑖𝑡 

  

Table 1 presents the description of the variable. 

 

Table 1. Variables description. 

Variables Description Expected sign 

Dependent variables 
Z-score Bank risk, calculated by the sum of ROA and CAR, divided 

by SDROA 
 

RACAR  Risk-adjusted CAR of banks= CAR/SDCAR  
Main independent variables 
NNII  The fraction of net non-interest income divided by net 

operating income 
(+) 

Trade The fraction of trading income divided by net operating 
income 

(+) 

Control variables 
SIZE Banks’ total assets (+) 
Loans The ratio of total loans to total assets (-) 
CAR The ratio of equity on total assets. (+) 

 

In the model, the bank risk index is used in contrast to bank stability. Particularly, the higher the Zscore and 

RACAR, the more sustainable the bank or reduce the bank risk. 

 

3.2. Data  

Data is collected through the financial statements of commercial joint-stock banks listed in Vietnam from 2010 

to 2019. The collected data will be cleaned when excluding banks with missing values in 3 years and a missing rate 

of more than 15% of the general data. The final data included in the analysis of 18 banks were retained. The results 

describe the data collected in Table 2. The mean Zscore is 25.19, the largest is 57.43, and the smallest is 0.49. The 

mean RACAR was 23.02, the maximum was 53.31, and the smallest was 0. The mean NNII was 0.20, the maximum 

was 0.57, and the smallest was -0.05. The mean TRADE is 0.559, the largest is 8.54, and the smallest is -8.89. The 

mean  LOANS is 0.55, the maximum is 0.74, and the smallest is 0.14. The mean CAR is 0.1, the maximum is 0.4, and 

the minimum is 0. 

 

Table 2. Descriptive variables. 

Variables Mean Std. Dev. Min. Max. 

Zscore 25.197 16.722 0.495 57.440 
RACAR 23.028 15.896 0 53.314 
NNII 0.207 0.163 -0.055 0.576 
TRADE 0.560 3.473 -8.893 8.657 
SIZE 32.596 1.083 30.162 34.937 
LOANS 0.556 0.120 0.145 0.743 
CAR 0.108 0.064 0 0.401 
Observations=180  

 

4. METHODOLOGY 

The study uses panel data analysis with basic models such as the fixed effect model (FEM) and random effect 

model (REM). Hausman test will be used to choose the FEM or REM model that is more suitable for the data. 

Additionally, autocorrelation and heteroskedasticity tests will be performed to check the model’s reliability. Finally, 

if these defects exist in the model, the authors will proceed to calibrate the model through the Generalized Least 

Squares (GLS) model. 



Financial Risk and Management Reviews, 2022, 8(1): 20-26 

 

 
23 

© 2022 Conscientia Beam. All Rights Reserved. 

The paper runs FEM and REM before the Hausman test, which compares the two models and shows that the 

FEM is more suitable than the REM in all models with the dependent variables Zscore and RACAR. However, the 

autocorrelation and heteroskedasticity of change test show that the model exists in these phenomena. Therefore, the 

GLS correction model will be used for the analysis. The empirical results are shown in Tables 3 and 4. 

Table 3 describes the results of the impact of business model diversification represented by NNII and TRADE 

on the sustainability of Zscore banks. The analysis results through the GLS model show that NNII has a negative 

effect on Zscore (negative beta coefficient and satisfies statistical significance). TRADE does not affect Zscore (p-

value greater than 0.1). Simultaneously, the study also shows that CAR and SIZE positively affect Zscore (positive 

and statistically significant beta coefficient). LOANS does not affect Zscore (p-value greater than 0.1). 

 
Table 3. Regression with Zscore. 

Zscore (1) 
FEM 

(2) 
FEM 

(3) 
REM 

(4) 
REM 

(7) 
REM 

(8) 
GLS 

NNII -13.25*** 
(4.204) 

 -14.13*** 
(4.274) 

 -22.47*** 
(6.227) 

 

TRADE  0.0505 
(0.198) 

 0.00204 
(0.199) 

 -0.260 
(0.317) 

SIZE 0.261 
(1.418) 

0.464 
(1.819) 

1.604 
(1.297) 

1.957 
(1.579) 

6.284*** 
(1.068) 

5.495*** 
(1.143) 

LOANS 7.382 
(7.076) 

2.567 
(8.657) 

6.209 
(7.015) 

1.558 
(8.401) 

17.58* 
(9.394) 

17.02 
(10.90) 

CAR 179.4*** 
(10.47) 

183.3*** 
(12.83) 

181.0*** 
(10.50) 

185.5*** 
(12.48) 

157.5*** 
(15.33) 

162.5*** 
(16.98) 

Constant -4.020 
(44.54) 

-11.32 
(57.59) 

-47.17 
(40.70) 

-59.72 
(49.93) 

-202.0*** 
(32.87) 

-181.8*** 
(35.64) 

Observations 167 148 167 148 167 148 
Number of banks 18 18 18 18 18 18 
Hausman test    0.000    
Autocorrelation test   0.000    
Heteroskedasticity   0.000    

Note: Standard errors in parentheses. 
*** p<0.01, * p<0.1. 

Table 4 describes the results of the analysis of the impact of business model diversification represented by NNII 

and TRADE on the sustainability of RACAR banks. The analysis results through the GLS model show that NNII 

has the opposite effect on RACAR (negative beta coefficient and statistical significance).  

 
Table 4. Regression with RACAR. 

Racar (1) 
FEM 

(2) 
FEM 

(3) 
REM 

(4) 
REM 

(5) 
GLS 

(6) 
GLS 

NNII -12.71*** 
(4.126) 

 -13.63*** 
(4.182) 

 -21.29*** 
(5.851) 

 

TRADE  0.0527 
(0.194) 

 0.00573 
(0.194) 

 -0.216 
(0.299) 

SIZE 0.398 
(1.391) 

0.440 
(1.782) 

1.681 
(1.254) 

1.879 
(1.522) 

5.629*** 
(1.004) 

4.879*** 
(1.078) 

LOANS 6.443 
(6.944) 

1.587 
(8.481) 

5.562 
(6.847) 

0.846 
(8.191) 

17.39** 
(8.827) 

16.30 
(10.28) 

CAR 176.3*** 
(10.28) 

180.1*** 
(12.57) 

177.7*** 
(10.26) 

182.0*** 
(12.17) 

155.1*** 
(14.40) 

159.9*** 
(16.01) 

Constant -9.899 
(43.71) 

-11.76 
(56.43) 

-51.23 
(39.34) 

-58.51 
(48.12) 

-182.7*** 
(30.89) 

-163.1*** 
(33.62) 

Observations 167 148 167 148 167 148 
Number of banks 18 18 18 18 18 18 
Hausman test    0.000    
Autocorrelation test   0.000    
Heteroskedasticity   0.000    

Note: Standard errors in parentheses. 
*** p<0.01, ** p<0.05. 

 



Financial Risk and Management Reviews, 2022, 8(1): 20-26 

 

 
24 

© 2022 Conscientia Beam. All Rights Reserved. 

TRADE does not affect RACAR (p-value greater than 0.1). Concurrently, the study also shows that CAR and 

SIZE have a positive effect on RACAR (positive and statistically significant beta coefficient). LOANS have no impact 

on RACAR (p-value greater than 0.1). 

Zscore and RACAR show that diversifying business models negatively impact banks. It can be seen that in the 

developing financial market like Vietnam, banks are still gradually expanding other business forms. Therefore, banks 

still face difficulties in implementing expectations (Nguyen et al., 2021). In the case of Vietnam, the banking system 

has begun to build new business models beyond lending to generate interest income. And the use of resources 

available in the bank is still not effective. This has led to inconsistent or bad business results and reduced the 

sustainability of banks. In Vietnam, the business expansion based on fee activities (fees collected from lending, 

transaction, and payment activities) is consuming a lot of resources, and the income is not proportional (Abuzayed et 

al., 2018). In addition, the fee-based activities worsen the quality of loans due to the psychological burden of associated 

fees leading to restrictions or changes in customer behavior. 

Subsequently, TRADE does not affect the sustainability of the bank. This result shows that foreign exchange or 

gold trading activities reduce the bank’s sustainability. Although it is the only unit authorized to conduct foreign 

exchange business, this activity has not been effective for the bank. This shows that foreign exchange-related 

activities are not being effectively controlled. Moreover, the cause also comes from the policy of the State Bank by 

tightening foreign currency lending to pursue the goal of anti-dollarization of the economy by gradually shifting from 

lending transactions to buying and selling foreign currencies is one of the main reasons foreign exchange revenue 

declined. It can be seen that this is a common influence on the state. Additionally, the value of VND remained stable 

in recent years despite strong fluctuations of many domestic currencies worldwide. The difference in interest rates 

between VND and USD is no longer large, so there is not as much room to use financial leverage based on currency 

fluctuations as before. 

Bank size (SIZE) positively affects bank stability or reduces the bank’s risk. This result shows that expanding 

assets or scale helps banks approach customers better. This is a positive sign indicating the efficiency of the banks' 

size in recent times. Concurrently, CAR has a positive effect on sustainability, showing that the higher the capital 

adequacy ratio of the bank, the lower the risk. Although a high CAR may affect the effectiveness of lending activities, 

in terms of risk, the CAR ratio is helping banks better control risks. Finally, LOANS do not affect the bank risk. The 

risk of bad debt accompanies the increase or decrease in lending, so these two factors are currently in balance, leading 

to no effect of LOANS on bank risk. 

 

5. CONCLUSION AND IMPLICATIONS 

The paper systematizes the business model diversification theory and banks' risk and sustainability. Two non-

interest income activities from fees and foreign exchange and gold trading activities are typical for diversifying 

business models in banks. Furthermore, the study figured out the negative impact of NNII business model 

diversification on the bank’s sustainability through quantitative data analysis. Meanwhile, the TRADE business is 

not meaningful in the research period due to the influence of the government’s policy on the issue of monetary 

tightening. Finally, the paper suggests some following theoretical and practical implications based on new findings. 

 

5.1. Theoretical Implication 

The paper tested the relationship between business model diversification and bank risk. In the developing 

financial market, business activities other than lending are not bringing sustainability to the bank. Nevertheless, it 

can be said that lending activities are still an important factor in the maintenance and sustainable development of the 

bank. This will be an important factor in making a firmer addition to the relationship between business model 

diversification, bank risk, and bank stability. 

 



Financial Risk and Management Reviews, 2022, 8(1): 20-26 

 

 
25 

© 2022 Conscientia Beam. All Rights Reserved. 

5.2. Practical Implication 

The impact of business model diversification on bank risk will help managers in the bank have appropriate 

intervention policies to make non-interest income more effectively. As fee-based activity increases, processes and 

resources for fee-based operations are experiencing problems leading to increased bank risk. Simultaneously, income 

from foreign exchange or gold trading is not meaningful with bank risk, which shows that this activity needs to be 

maintained at a moderate level and wait for positive signals from the government about this foreign exchange business 

activity. 

 

6. LIMITATION AND FUTURE RESEARCH 

Although the paper found a positive effect of business model diversification on bank risk, however, this still exist 

some limitations. First, the article has not yet considered the mediating role of business results, non-performance 

loans, to bank risk. Diversifying business models can affect business results, and bank risks are different, so this nexus 

also has more in-depth judgments. Similar to non-performance loans, lending problems can affect non-performance 

loans, and bank risk may also need to be considered for a more thorough judgment. Second, the data source is 

currently only collected until 2019. Therefore, the effects of the COVID-19 pandemic and bank risk have not been 

considered. 

The study suggests that further research should consider adding more relationships with business results and 

non-performance loans to find more exciting relationships. The dataset can be collected closer to the present to 

include the impacts of a COVID-19 pandemic.  

 
Funding: This study received no specific financial support.    
Competing Interests: The authors declare that they have no competing interests.  
Authors’ Contributions: All authors contributed equally to the conception and design of the study. 

 

REFERENCES 

Abuzayed, B., Al-Fayoumi, N., & Molyneux, P. (2018). Diversification and bank stability in the GCC. Journal of International 

Financial Markets, Institutions and Money, 57, 17-43.Available at: https://doi.org/10.1016/j.intfin.2018.04.005. 

Bocken, N. M., Short, S. W., Rana, P., & Evans, S. (2014). A literature and practice review to develop sustainable business model 

archetypes. Journal of Cleaner Production, 65, 42-56.Available at: https://doi.org/10.1016/j.jclepro.2013.11.039. 

Boons, F., & Lüdeke-Freund, F. (2013). Business models for sustainable innovation: State-of-the-art and steps towards a research 

agenda. Journal of Cleaner Production, 45, 9-19.Available at: https://doi.org/10.1016/j.jclepro.2012.07.007. 

DeYoung, R., & Torna, G. (2013). Nontraditional banking activities and bank failures during the financial crisis. Journal of 

Financial Intermediation, 22(3), 397-421.Available at: https://doi.org/10.1016/j.jfi.2013.01.001. 

Froot, K. A., Scharfstein, D. S., & Stein, J. C. (1993). Risk management: Coordinating corporate investment and financing policies. 

the Journal of Finance, 48(5), 1629-1658.Available at: https://doi.org/10.2307/2329062. 

Froot, K. A., & Stein, J. C. (1998). Risk management, capital budgeting, and capital structure policy for financial institutions: An 

integrated approach. Journal of Financial Economics, 47(1), 55-82.Available at: https://doi.org/10.1016/s0304-

405x(97)00037-8. 

Köhler, M. (2015). Which banks are more risky? The impact of business models on bank stability. Journal of Financial Stability, 16, 

195–212.Available at: https://doi.org/10.1016/j.jfs.2014.02.005. 

Lüdeke-Freund, F. (2010). Towards a conceptual framework of'business models for sustainability'. Knowledge collaboration & 

learning for sustainable innovation, R. Wever, J. Quist, A. Tukker, J. Woudstra, F. Boons, N. Beute, eds., Delft, 25-29. 

Magretta, J. (2002). Why business models matter. Harvard Business Review, 80(5), 86-133. 

Mahdaleta, E., Muda, I., & Nasir, G. M. (2016). Effects of capital structure and profitability on corporate value with company size 

as the moderating variable of manufacturing companies listed on Indonesia Stock Exchange. Academic Journal of Economic 

Studies, 2(3), 30-43. 



Financial Risk and Management Reviews, 2022, 8(1): 20-26 

 

 
26 

© 2022 Conscientia Beam. All Rights Reserved. 

Mergaerts, F., & Vander Vennet, R. (2016). Business models and bank performance: A long-term perspective. Journal of Financial 

Stability, 22, 57-75.Available at: https://doi.org/10.1016/j.jfs.2015.12.002. 

Nguyen, M., Skully, M., & Perera, S. (2012). Market power, revenue diversification and bank stability: Evidence from selected 

South Asian countries. Journal of International Financial Markets, Institutions and Money, 22(4), 897-912.Available at: 

https://doi.org/10.1016/j.intfin.2012.05.008. 

Nguyen, T. T., Ho, H. H., Van Nguyen, D., Pham, A. C., & Nguyen, T. T. (2021). The effects of business model on Bank’s stability. 

International Journal of Financial Studies, 9(3), 1-12.Available at: https://doi.org/10.3390/ijfs9030046. 

Osterwalder, A., Pigneur, Y., & Tucci, C. L. (2005). Clarifying business models: Origins, present, and future of the concept. 

Communications of the Association for Information Systems, 16(1), 1.Available at: https://doi.org/10.17705/1cais.01601. 

Stubbs, W., & Cocklin, C. (2008). Conceptualizing a “sustainability business model”. Organization & Environment, 21(2), 103-

127.Available at: https://doi.org/10.1177/1086026608318042. 

Trivedi, S. R. (2015b). Banking innovations and new income streams: Impact on Banks’ performance. Vikalpa, 40(1), 28–

41.Available at: https://doi.org/10.1177/0256090915573616. 

Williams, B., & Prather, L. (2010). Bank risk and return: The impact of bank non-interest income. International Journal of Managerial 

Finance, 6(3), 220–244.Available at: https://doi.org/10.1108/17439131011056233. 

Yip, A. W., & Bocken, N. M. (2018). Sustainable business model archetypes for the banking industry. Journal of Cleaner Production, 

174, 150-169.Available at: https://doi.org/10.1016/j.jclepro.2017.10.190. 

 

 

  

 

 

 

 

 

 

 

 

 

 

Views and opinions expressed in this article are the views and opinions of the author(s), Financial Risk and Management Reviews shall not be responsible or 
answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. 

 


