The Influence of The Business Ecosystem and Customer Base On Increasing Third-Party Funds In The Region III Office

ABSTRACT


Introduction
The main activity of banking operations is to collect funds from the public.The collection of third-party funds in banking is a source of funding obtained by banks from the public consisting of savings, current accounts and deposits deposited by individuals, companies, or other institutions in banks.Banking third-party funds are used to provide helpful in providing trust to the public or customers.Third Party Funds (DPK) must also be allocated properly as they can help in encouraging the growth of the country's real sector.Finding cheap funds is the key to banks to be sustainable and provide competitive loan interest rates.Cheap funds in banking are called low cost funding (Forrest, Liu, Forrest, & Liu, 2022).
Low cost funding is a third-party bank deposit product with low interest such as savings and current account products.To obtain cheap funds from the public, the deposit of funds must be a transactional account that digital-based banking.Every bank in Indonesia must innovate in providing services to customers.Digital-based innovation is highly highlighted today to respond to competition in line with the rapid growth of financial technology (fintech) supported by digital developments.In the era of the industrial revolution 4.0 is an opportunity for conventional banks to innovate into digital banking, which plays a very important role in the banking industry so that banks remain sustainable amid global challenges.To be sustainable, Bank BTN must increase its innovation in digital banking (Ikhwanudin, Wahyudi, & Andriyani, 2023).
Bank BTN is a leading State-Owned Commercial Bank (BUMN) in housing finance in Indonesia.With the largest asset number 5 based on bank info data in 2022.As a bank that focuses on mortgage disbursement, it has a vision to become The Best Mortgage Bank in South Asia by 2025 and one of the missions to be a financial partner for stakeholders in the housing ecosystem by providing comprehensive solutions and the best services through digital innovation.The market share of Bank BTN Subsidy KPR in 2022 is 66.7% and is expected to grow to 80% in 2023.Supported by Bank BTN's mission, which is to double low cost funding to 270 T (Mehdiabadi, Tabatabeinasab, Spulbar, Karbassi Yazdi, & Birau, 2020).
In making a low cost funding strategy, by analyzing the bank competency strategy 4.0, researchers use the analysis method The Balanced Scorecard Measures that Drive Performance from Robert S. Kaplan and David P. Norton 1992, namely to determine strategic decision making in banks using a balanced scorecard approach (Noreen, Shafique, Ahmed, & Ashfaq, 2023).Source : McKinsey Indonesia In the banking 4.0 era and the Covid-19 pandemic conditions, the market demands to facilitate customer transactions using Digital Banks.Digital Bank is an acquisition of a bank by fintech or a bank that turns into a digital bank.The fundamental difference between digital banks and conventional banks lies in their form (Purchase & Volery, 2020).
Conventional banks have a physical form in the form of branch offices while digital banks do not have branch offices.According to Juristech's sources, BCG.Of the total 250 digital banks worldwide, 20% who are the largest in Asia Pacific Accreditation Cooperation (APAC) are digital banks in East Asia that have been profitable in the last 3 years.The majority of profitable APAC Digital Banks are digital banks that have a corporate business ecosystem so that the business model is profitable (Chowdhury, Balaraman, & Liu, 2023).
Table 2: Top 10 Biggest Advantages of Digital Banks in APAC Source :Juristech, BCG The signal of continued national economic recovery contributes positively to the potential of the property sector going forward, especially in the projected growth of the mortgage business.In addition to economic recovery, the potential for increasing mortgage business in the future will also be encouraged by the realization of the Public Housing Savings Mortgage (TAPERA) program which has started in 2021 and the continued VAT and Loan to Value (LTV) stimulus which is an opportunity to develop mortgages in the upper middle segment.With the commitment to sustainability of the One Million House Government Program and the increasing middle income population dominated by the millennial generation and the emerging affluent segment, it is also a consideration for Bank BTN to expand its focus in also developing the housing business in the future, especially in increasing market share in the millennial and emerging affluent segments (Royan, 2014).
One of the implementations that will be implemented in the banking sector with the preparation of the Himbara Cluster is that each Association of State-Owned Banks will specialize in serving the community.Bank BTN itself in the Himbara Cluster is focused on the residential sector both in the commercial and consumer businesses.To support this, the tactical direction of the Ministry of SOEs for Bank BTN in 2022 is to implement capital strengthening as a foundation for housing loan market expansion.
The demographic bonus and the growth of the middle class population dominated by the millennial generation are also driving the housing finance business in the future where based on data from the Central Statistics Agency (BPS), currently the number of generation Z and millennials dominates the productive age and will continue to grow for the next 10 years which makes an opportunity to develop the housing business in the first home buyer segment which is the captive market of Bank BTN.

Graph 1 Indonesia's population projection (million) Source : Bappenas
To support the realization of the vision and mission of Bank BTN in 2025 as the largest housing distribution bank in Indonesia, three important things must be done by Bank BTN, including: 1. CASA growth of 114 T 2. Decrease in CoL to 2.04% 3. Decrease in BOPO 5% per year The three things above can increase Bank BTN's profit which is the company's main goal.Bank BTN must have a sustainable funding source called CASA (Current Account Savings Account).CASA consists of savings and current accounts is one of the sources of cheap funds for banks because the interest paid to their customers is lower than the type of loan provided by the bank.In the banking world, third-party funds are a very important source of funds for banks.Third-party funds are used by banks to channel credit and increase liquidity.In addition, Bank BTN became a transactional bank by digitizing Banking to increase Fee Based Income.
Bank BTN's CoL (Cost of Liabilities) in 2022 of 2.93% tends to be higher than the average CoL of the banking industry in Indonesia of 2.04%.Bank BTN's CoL decreased until September 2022 but increased in December 2022 due to the increase in BI's benchmark interest rate.The strategy to reduce CoL is by increasing customer base through low cost funding and distributing thick-margin mortgages such as non-subsidized home mortgages.
BOPO stands for Operating Cost to Operating Income which is a financial ratio used to measure the efficiency of a bank's operational costs, in carrying out its operational activities.Decrease in BOPO with efficiency of operational costs and interest expenses as well as increase operating income and interest income of banks.
Supported based on the transformation roadmap (stages) of the Ministry of SOEs, there are 5 priority formulations of the Ministry of SOEs which are the basis for the next policy direction as shown in figure 1 as follows: One of the implementation strategies that will be implemented in the banking sector with the preparation of the Himbara Cluster is that each Association of State-Owned Banks will specialize in serving the community.Bank BTN itself in the Himbara Cluster is focused on the residential sector both in the commercial and consumer businesses.To support this, the tactical direction of the Ministry of SOEs for Bank BTN in 2022 is to implement capital strengthening as a foundation for housing loan market expansion with one of the business model innovation strategies, including the development of a housing business business ecosystem.
A business ecosystem is a network of organizations including suppliers, distributors, customers, competitors, government agencies, and so on that are involved in the delivery of certain products or services through competition and cooperation.The idea is that each entity in an ecosystem influences and is influenced by the others, creating an ever-evolving relationship in which each entity must be as flexible and adaptable to survive as in a biological ecosystem.
The initial perspective of the ecosystem, that the company offers a way to expand strategic thinking based on the logic of orientation towards profit, a broad perspective and anticipation allows the company to focus not only on current customer needs and the value of current exploitation (Fader, 2020).According to (Mu, 2015)   The Kakao Bank ecosystem, which is the largest bank in South Korea, was founded in 2017 and is owned by Kakao Corp. Main products include loans, deposits, credit cards and remittances.Recorded profit in 2019, after two (2) years of operation the bank benefited from the entry of customers from Kakao Corp which was dominated by 32% of shareholders were from the Bank.The total number of bank account holders at the bank reached 12.5 million online bank applications, almost 44.3% of the population in South Korea, the majority of whom are > 15 years old.

Figure 3 Cocoa Bank Ecosystem
Source: techcrunch,Korea times, Tokyo Fintech, Reuters, Company Website Based on these global conditions and the competition trend of Digital Banks against Conventional banks, to win the market, Conventional Banks must have a company business ecosystem which is the main factor to create a sustainable and profitable bank.The case study that will be analyzed by the researcher is PT.Bank Tabungan Negara (persero) Tbk.
There are 5 (five) ecosystem business models that are the focus of Bank BTN banks, namely (1).Property related: cement, building shops, (2).Industrial Estate: Ngoro Industri Mojokerto (3).Trade: wholesaler, supplier, (4).Healthcare: Hospitals, Clinics, (5).Education: Universities, schools.Of the five business models, the author will conduct research on the Property related model which is Bank BTN's core business is KPR with the number of private distributors and active customers owned consisting of developer partners, notaries, material suppliers (cement, building shops), sub-contractors (heavy equipment trucks), workers, and other derivative businesses.
Based on the background of the above problems, the author will examine the Strategy to Increase Third Party Funds through a Business Ecosystem strategy and increase Customer Base consisting of 16 branch offices, 82 sub-branch offices and a total The Influence of The Business Ecosystem and Customer Base On Increasing Third-Party Funds In The Region III Office of 1152 developer partners both individuals and companies at the Regional Office III of PT.Bank Tabungan Negara (Persero), Tbk.This research is expected to be an input for the management of Bank BTN regional office III for the selection of marketing marketing strategies and can be applied in all BTN branch offices in Indonesia.As well as providing many benefits for customers, such as easy access to various products and services, improved service quality, and transaction security.

Research Methods
Judging at the existing problem, the researcher applies a quantitative approach.The quantitative approach to business is research in the field of business with the aim of research through empirical assessment involving measurement and analysis of numbers.Quantitative research stands alone in the sense that it does not meet the needs of analytical interpretation.(Zikmund, Babin, Carr, & Griffin, 2013) This research was chosen because it has the purpose of proving, refuting, or giving credence to existing theories.Quantitative research is characterized by a deductive approach that tests objective theories by examining the relationships between variables.These variables can be measured through questionnaires so that numerical data can be described by applying statistical procedures.This type of research uses variable measurement and correlation testing between variables to describe patterns, correlations, or causal relationships (Creswell, 2014;Leavy, 2017).Quantitative researchers center on testing theories and hypotheses using quantitative data to observe whether confirmed or not (Johnson & Christensen, 2019).This study uses a survey research design that describes quantitatively about trends, attitudes, and opinions of a population, or tests for associations between variables of a population, by studying the population sample (Creswell & Creswell, 2017).

Descriptive Statistical Test
Descriptive statistical analysis is a form of data presentation that informs a group of data in the form of an overview, collection, and summary of data so that the information can be more easily understood.The data used in this study include business ecosystem data, customer base data, and Bank BTN deposits at the Regional Office III for the 2022 Quarter III period.The tool used in this study is Eviews 10.
The following are the results of the descriptive statistical test:  1 shows that the number of observational data obtained in this study was 64.The mean of the variable value of Third Party Funds (DPK) is 51891.28with a standard deviation (SD) of DPK of 7324,774.The mean value is greater than the standard deviation, which is 51891.28> 7324.774 which means that the distribution of deposit values is good.
The data is homogeneous, meaning that it does not have a large gap between the lowest and highest values of DPK variables during the study period.The highest value (maximum) of DPK is 65109 while the lowest value (minimum) of DPK is 42316 and the median of DPK is 48713.
The average value (mean) of the business ecosystem is 2.717656 with a standard deviation (SD) of the business ecosystem of 0.878259.The mean is greater than the standard deviation, which is 2.717656 > 0.878259 which means that the distribution of business ecosystem value is good.The data is homogeneous, meaning that it does not have a large gap between the lowest and highest values of business ecosystem variables during the study period.The highest value (maximum) of the business ecosystem is 4.37, while the lowest value (minimum) of the business ecosystem is 1.32 and the median inflation is 2.97.
The average value (mean) of customer base is 3.67656 with a standard deviation (SD) of Customer Base of 0.730231.The mean value is greater than the standard deviation, which is 3.67656 > 0.730231 which means that the distribution of customer base values is good.The data is homogeneous, meaning that it does not have a large gap between the lowest and highest values of Customer Base variables during the study period.The highest value (maximum) Customer Base is 5.27, while the lowest value (minimum) Customer Base is 2.58 and the median Customer Base is 3.42.

Classical Assumption Test
In regression analysis one very important step not to miss is the classical assumption test.Where the classical assumption test serves to guarantee certainty that the data generated in the regression equation is valid, consistent and unbiased.Here are the results of testing classical assumptions:

Normality Test
The normality test is the initial stage of the classical assumption test carried out and one of the processes that greatly influences the regression model.A good regression model is one that has normally distributed data.To be able to find out whether the data used is normally distributed or not, this can be known through a normality test.As for this study, the variables tested for normality are Bank BTN DPK (Y), Business Ecosystem (X1) and Customer Base (X2).If the probability value of the Jarque Bera test > 0.05 then H0 is received or the data is normally distributed.However, if the probability value of the Jarque Bera test < 0.05, then H0 is rejected or the data is not normally distributed.

Figure 1 Normality Test Source: Eviews 10 (Data processed)
Based on the results of the normality test above, where the calculation of the normality test using the Eviews 10 application shows that the value of Jarque Bera is at 121.8500 with a p-value of 0.000000.Where H0 is rejected with a P-value of < 0.05 or 0.000000 < 0.05.Then it can be concluded that the data is not normally distributed.
In this case, some statistical analysts say that if the amount of data is more than 30 (n>30), then the data has been assumed to be normally distributed data (Widana, 2020).However, the author still strives to improve the data so that the data is ensured to be distributed normally.The authors improved the normality test data with the Kolmogorov-Smirnov one-sample statistical test through a monte carlo approach.Here are the results of the data processing:

Figure 2 Kolmogorov-Smirnov Normality Test with Monte Carlo Approach
Based on the results of the normality test using One-Sample Kolmogorov-Smirnov with the Monte Carlo approach above, the Monte Carlo Sig.(2-tailed) value is 0.094.This shows that H0 is accepted, with a P-Value > 0.05 or 0.094 > 0.05.So it can be concluded that the data used in this study are normally distributed.

Multicollinearity Test
After the normality test, the next stage is the multicollinearity test.This test aims to determine the presence or absence of linear relationships between the variables studied.If the value of Variance Inflation Factor (VIF) < 10.00, it can be concluded that multicollinearity does not occur.However, if the value of Business Ecosystem Variance (VEB) > 10.00, it is stated that there are symptoms of multicollinearity.2, the results of the multicollinearity test using Eviews 10 show that the value of Business Ecosystem Variance (VEB) of all independent variables from this study < 10.00 where the VIF value of the business ecosystem level ratio (X1) is 2.583116, the customer base value (X2) is 3.33251 .From these data, it can be concluded that in this study there were no symptoms of multicollinearity.

Heteroscedasticity Test
The next test is the heteroscedasticity test.This test aims to test a regression model whether there is an inequality of variance from the residual one observation to another.The regression model is said to be good if homoscedasticity or no heteroscedasticity occurs.To test heteroscedasticity can be done using the Breusch-Pagan-Godfrey method.The basis for the decision in this case is that if the probability value > 0.05 then the model is declared not to contain heterokedasticity (homokedasticity), while vice versa, if the probability value is < 0.05 then the model is stated to contain heterokedasticity.Based on Table 3 heteroscedasticity test results using Eviews 10 above, the P-Value value is indicated by the Prob value.Chi-Square (3) at obs*R-square of 0.3538 > 0.05.So it can be concluded that the data in this study contains homoscedasticity or heteroscedasticity does not occur.

Autocorrelation Test
In this study, the data used to estimate the linear regression model is time series data, so the assumption of autocorrelation-free is needed.The autocorrelation test aims to test whether in a multiple linear regression model there is a correlation between confounding error in period t and confounding error in period t-1 (previous period).Source: Eviews 10 (Data processed) Based on graph 3, it can be seen that the dl value in this study is 1.4990 and du is 1.6946 and 4-du is 2.3054 and 4-dl is 2.5010 and Durbin Watson value is 0.584179.So it can be concluded that the DW value is in the area of positive autocorrelation and in the study there are symptoms of autocorrelation.

Test the hypothesis
To determine the significance and influence of the variables used, a hypothesis test was carried out as follows: T Test (Partial Test) The T test (partial test) is used to test how each independent variable (X) affects the dependent variable (Y).The results of hypothesis testing based on data processing using Eviews 10 are as follows: Source: Eviews 10 (Data processed) Based on the simultaneous test or f test in table 4.5 using Eviews 10 shows that the significance value is 0.000000.The expected significance level is 0.05 (5%) which means a probability value of 0.000000 < 0.05 so that it can be concluded that the business ecosystem and customer base simultaneously (together) affect DPK.

Test Coefficient of Determination (R2)
The coefficient of determination serves to indicate the degree of relationship between the dependent variable (Y) and the independent variable (X) or the extent to which the contribution of the independent variable affects the dependent variable.Here are the results of testing the coefficient of determination using Eviews 10: Source: Eviews 10 (Data processed) From table 6 above, it can be seen that the result of the coefficient of determination (R2) is 0.713113, which means that the contribution of all independent variables, namely the business ecosystem and customer base, affects the dependent variable, namely DPK by 71%.The remaining 29% is influenced by other variables.Other variables that can affect DPK include the BI Rate as conducted by Mohammad Wahiddudin (2018).

Multiple linear regression analysis
Usually in performing multiple linear regression tests, all stages in the classical assumption test must pass and be fulfilled.However, in this study there was 1 stage of the calcic assumption test that was not met, namely the autocorrelation test.Therefore, this study did not use ordinary multiple linear regression analysis (Least Squares).The multiple linear regression analysis carried out is using Robust Least Squares.The following is the result of processing Robust Least Squares multiple linear regression analysis data using Eviews 10:

Conclusion
This research was conducted with the aim to see the influence of business ecosystem and customer base on Bank BTN's deposits.In addition, this study also aims to encourage an increase in deposits as well as to become the main target of Bank BTN.This study used multiple regression analysis method which was analyzed using the Eviews 10 application platform.As described in chapter IV, the conclusions of this study can be drawn as follows: Partially, the business ecosystem has a positive but significant effect on increasing Bank BTN's deposits.This means that when the business ecosystem rises, deposits will increase and vice versa.Meanwhile, the customer base has a positive and significant influence on the increase in deposits of Bank BTN.That is, when the customer base rises, DPK will also increase, and vice versa.
Simultaneously, the business ecosystem and customer base together have a significant influence on Bank BTN's deposits.

Figure
Figure 1.1 Priority Formulation of the Ministry of SOEs 2020-2024 identify future customer needs and future value for exploration In the old retail model, customers used banking products & services directly.In the digital age, customers are offered the option to consume services from e-Commerce, ridehailing, travel sites and banking products into basic services, no longer being the first line of customer contact.Banks need to embrace the model of future banking (open banking).

Figure 2
Figure 2 Creating a New Distribution Channel For Bank End User Source : Boston Consulting Group

Table 3
The autocorrelation test research model used in this study is the Durbin Watson test.This can be known by comparing the DW value with the d value from Durbin Watson's table.The following are the results of the autocorrelation test using the Durbin Watson model processed using Eviews 10.

Table 4 Partial T Test Test F (Simultaneous Test)
Simultaneous Test or F Test is a static test that aims to determine whether the independent variables together affect or not affect the dependent variable.Here are the results of simultaneous tests using eviews 10: