EXPLORING DETERMINANTS OF SAVING AND FINANCING ASPECTS IN ISLAMIC BANKS: AN INSIGHT FROM INDONESIA

Armiadi MUSA¹ --- Bismi KHALIDIN² --- Hafas FURQANI³ --- Azharsyah IBRAHIM⁴⁺ --- Nurrahmah IS⁵

^1,3,5Department of Islamic Economics, Universitas Islam Negeri Ar-Raniry, Banda Aceh, Indonesia.
²Department of Islamic Economics Law, Universitas Islam Negeri Ar-Raniry, Banda Aceh, Indonesia.
⁴Department of Islamic Banking and Finance, Universitas Islam Negeri Ar-Raniry, Banda Aceh, Indonesia.

Keywords:Islamic bank, Saving, Financing, Commercial bank, Savings banks, Macroeconomics factors.

ARTICLE HISTORY: Received:26 March 2022, Revised:7 July 2022, Accepted:18 July 2022, Published:5 August 2022

JEL Classification:G21; G210.

Contribution/Originality: This is one of the very few studies that provide new evidence demonstrating the impact of interest rates and macroeconomic variables on savings and financing products of Islamic banking in Indonesia. The findings emphasize the importance of considering these variables when formulating relevant policy.

1. INTRODUCTION

As financial institutions, banks perform a crucial function by collecting money from the public in the form of savings or deposits and then redistributing it as credit. Numerous economic activities necessitate the involvement of a bank in order to improve the standard of living of the populace (Sawitri & Febrian, 2018). Banks are required to collect public revenues in order to fulfill their obligations. The Indonesian Banking Act No. 10/1998 defines a bank as a commercial enterprise that collects public savings and distributes them in the form of credit and/or other means to improve the standard of living of the public. Banking is one of the factors contributing to a nation's development. This is because the fundamental function of banking is to collect deposits from the public and distribute them back to the public in the form of credit or financing (Pimada, Mawardi, & Herianingrum, 2017). This function is frequently called the intermediary financial function (Ansari, 2008). Indonesia's financial sector consists of both conventional and Islamic institutions. Islamic banking plays a substantial role in the economy as a component of the national banking system (Setyowati, 2019) but differs fundamentally in financial/operational activities (Hanafi, 2021). One of the guiding principles of Islamic banking operations (Rosyadah, Arifin, Muhtadi, & Safik, 2020) is profit and loss sharing, which does not apply to conventional banks that use the interest system (Choudhury, 2011). According to Act No. 21/2008, Islamic banking encompasses all aspects of Islamic banks and Islamic business units, including institutions, commercial activities, and methods and processes for conducting their business activities. Islamic banks serve as investment managers, investors, and service providers as commercial entities (tamwil) and social entities (maal) (Fathurrahman & Sari, 2020; Rachmatulloh & Solekah, 2021). Islamic banks as investment managers receive cash from investors/customers based on wadiah yad dhamanah (deposit), mudharabah (profit sharing), and ijarah (lease) principles (Umam, Salam, & Rizal, 2021). Indonesians have welcomed Islamic-infused economic expansion with open arms. This is reflected in the growing number of Islamic banking institutions, which include newly converted conventional provincial state banks such as Bank Aceh, Bank NTB, and Bank Nagari, as well as others in the process of becoming fully fledged Islamic banks. As a result, Islamic banking in Indonesia is expected to expand (Khoirunissa, 2003; Rosyadah et al., 2020). Islamic Commercial Banks (Bank Umum Syariah - BUS) and Islamic Rural Banks (Bank Pembiayaan Rakyat Syariah – BPRS) have dramatically increased the number of their branches and expanded their geographical reach during the past decade. Not only are banks a source of funding for small, medium, and substantial investment loans, but they can also impact the cycle of the entire economy (Cham, 2018).

The primary job of banks as financial intermediaries is to collect public monies to meet financing demands. Islamic banks participate in investing and finance activities while distributing funds (Nugraha & Darmansyah, 2019). The term investment is employed because the fundamental principle is one of participation or investment. Profits are based on the performance of the enterprise for which the participants have agreed to a profit-sharing ratio (Kasri & Kassim, 2009). Financing is utilized because Islamic banks supply funds to meet market demand.

Savings and financing, which are at the center of Islamic banking's operating activities, are important components that cannot be separated (Setijawan, Sulistyoningsih, Arundina, Prasetyo, & Yuniarti, 2021). This differentiation will benefit Islamic banking, and it is through these banking activities that the intermediary functions of banking and economic growth will be accomplished (Nisak & Ibrahim, 2014). Several studies have shown that the ups and downs of the savings and financing of Islamic banking in Indonesia were influenced by several factors, including non-performing financing (Apriyanthi, Purbayati, & Setiawan, 2020; Primadhita, Primatami, & Budiningsih, 2021; Rusmiati, 2021; Sirait, 2021) , inflation (Juliyanti, 2016; Primadhita et al., 2021; Rahmany, 2020; Rahmawati, 2020; Rusmiati, 2021) , profit-sharing ratios (Sirait, 2021), depositors' funds (Apriyanthi et al., 2020; Hafizh, Hidayah, & Silalahi, 2020; Juliyanti, 2016; Primadhita et al., 2021; Rosyadah et al., 2020; Rusmiati, 2021; Sirait, 2021) , exchange rate (Apriyanthi et al., 2020; Rahmany, 2020), central bank rate (Hafizh et al., 2020; Juliyanti, 2016; Rahmany, 2020; Rahmawati, 2020) , stock price index (Hafizh et al., 2020; Juliyanti, 2016), debt to equity ratio (Rusmiati, 2021), economic growth, liquidity ratio, financing risk, and stock ownership ratio (Fadila & Pangestuti, 2022; Hafizh et al., 2020). In a broader context, other relevant investigations have also been conducted. Using the panel smooth transition model,  Mensi, Hammoudeh, Tiwari, & Al-Yahyaee (2020) investigated the nonlinear relationship between Islamic banking development, crucial macroeconomic parameters, and economic expansion across Islamic nations. In addition, Ab Rahman, Ahmad, & Arshad (2021) determined the elements that could assist Islamic banks in obtaining additional deposits. The study demonstrated that Return on Assets, bank concentration, and Business Enterprise Depositor have a positive and significant effect on Islamic Bank Deposits, whereas Capital Adequacy Ratio has a negative and significant effect. Moreover, Tohidinia, Oryoie, & Mohseni-Cheraghlou (2021) examined the influence of savings on macroeconomic variables, focusing on the effect of benevolent savings in Iran. The results indicate that charitable savings have a beneficial influence on total consumption and total investment in the short term.

However, these researchers drew their conclusions from insufficient studies on finance sources, locations, or characteristics. Sirait (2021), for example, sought to investigate the factors that influenced murabahah financing in the Bank Tabungan Negara Medan chapter. Meanwhile, Apriyanthi et al. (2020) only investigated the elements that determined the level of construction sector finance. In addition, Rahmany (2020) single-handedly investigated the policies that influenced mudharabah savings in Indonesian Islamic banking. A similar study was also conducted by Nafis & Sudarsono (2021) that analyzed the factors that affected the mudharabah financing in Islamic commercial banks in Indonesia. Next, Wijaya (1994) conducted research on savings and financing and confirmed the relationship between fundraising and the amount of credit distributed in East Java. Nurhasniya (2004) studied the relationship only between savings and the development of the number of credits. Meanwhile, Zulkhibri (2018) only examined the association between time deposits and the amount of credit at Islamic banks in Malaysia.

Therefore, the purpose of this study is to conduct an exhaustive analysis of the factors that influence the savings and financing of Islamic banking in Indonesia. This study will investigate the influence of profit-sharing ratio, central bank rate, inflation, exchange rate, consumer price index, and IDX composite index on Islamic bank deposits and financing in Indonesia. The results of this study will inform planning, policy, and decision-making on the future growth of finance and savings products in Islamic banking in Indonesia and the rest of the world.

The article is set out in the following format: the following section provides a thorough explanation of the research strategy, data collection methods, research samples, and data analysis. The study will next present the findings and critically analyze them by comparing them to prior research and literature. This report concludes with a summary of the study's findings and recommendations that can serve as a foundation for future research.

2. LITERATURE REVIEW

2.1. Profit-Sharing Rate

In Islamic banking, profit sharing is a one-of-a-kind provision available for public transactions where the sharing ratio must be determined in advance at the outset of the contract (Mirakhor & Iqbal, 2013). The profit-sharing rate is the method by which an Islamic financial institution determines the profit ratio as the basis for depositor distributions. A higher rate can encourage Islamic financing-related investments, such as musharakah and mudharabah. Profit sharing and revenue sharing comprise the profit-sharing calculation mechanism utilized by Islamic banks (Wahab & Rahman, 2011). Some studies have shown that profit-sharing rates have a positive and significant effect on savings and financing (Irwanto & Hendrawati, 2020). The level of profit sharing has a substantial relationship to the public interest in placing their savings in Islamic banks (Ab Rahman et al., 2021). In addition, profit sharing has been shown to have a positive and significant influence on customers' decisions in choosing to save in Islamic banks (Mubarok, Hamid, & Arif, 2020).

2.2. Bank of Indonesia Rate

The Bank Indonesia rate (BI rate) is a policy interest rate that reflects Bank Indonesia's monetary policy and is announced to the public. Bank Indonesia is strengthening its monetary operation framework by instituting a new policy rate, the BI 7-Day (Reverse) Repo Rate, which will serve as the new base interest rate. The BI 7-day (Reverse) Repo Rate instrument is used as the new policy rate because it may have an immediate effect on the money market, banking, and real sectors (BI, 2022a). Interest rate fluctuations on the Interbank Money Market reflect the operational objectives of monetary policy. The Interbank Money Market is designed to track fluctuations in bank deposit and lending rates. If Bank Indonesia predicts that future inflation will exceed its target, the BI rate will be increased along with other economic variables (BI, 2022). The monetary policy stance is determined each month through the monthly Board of Governors' meeting with monthly material coverage.

2.3. Inflation

Inflation is defined as an increase in the cost of goods and services caused by a significant increase in demand relative to market supply (Huda, 2018). In other words, too much money is spent on too few products (Effendi & Yuniarti, 2018). According to Almarzoqi, Mansour, & Krichene (2018), inflation has a significant impact on the growth in capital of Islamic banks, such that the change in these assets can increase Islamic banks' profitability, and where profitability increases, so does the rate of return on assets. High inflation will disrupt Islamic banks' efforts to increase income, and as a result, Islamic banks' efforts to expand their asset base will be redirected (Havidz, Jianmu, Aima, & Ali, 2017). High inflation will cause a decrease in the number of assets owned by Islamic banks, resulting in a strong push to increase the number of assets, resulting in the least amount of profit produced by Islamic banks (Sitompul, Ichsan, & Nasution, 2021).

2.4. Consumer Price Index

The CPI is conceptually based on the cost-of-living index hypothesis. The cost of living is a personal concept determined by an individual's preferences for various types of products and services as well as the prices at which they can be obtained (Chen & Hu, 2018). Due to the impossibility of determining the price of every item and service that consumers value, the theory of the cost-of-living index applied to the measurement of consumer prices typically focuses on a narrower set of goods and services, namely those purchased during a given period (Shapiro & Wilcox, 1996). The CPI is utilized to determine the rate of change in the cost of living for urban consumers. This is accomplished by estimating the average change in prices paid by urban consumers for a predetermined market basket of constant-quality goods and services (Haqqoni & Pramana, 2021). The CPI is calculated using a sample of prices because it is impossible to track the prices paid by every urban consumer for every purchase they make (Wynne & Sigalla, 1994).

2.5. Exchange Rate

The exchange rate, also referred to as the currency exchange rate, is the quotation of the market price of a foreign currency in the relevant local currency (Narayan, 2022). The exchange rate represents the rate at which one foreign currency is exchanged for another and is used in a variety of transactions, including international trade, tourism, international investment, and short-term money flow between nations that transcend geographical or legal boundaries (Feng, Yang, Gong, & Chang, 2021). The exchange rate is an external factor affecting the number of third-party funds (Lilley, Maggiori, Neiman, & Schreger, 2022). Thus, in the case of Indonesia, the rupiah's depreciation against the US dollar reflects the country's uncertain economic outlook, which will result in a negative response from the business community. The exchange rate is believed affect the expansion of Islamic bank third-party funds. Islamic bank-managed third-party funds are susceptible to fluctuations in the rupiah exchange rate and tend to rise in tandem with the currency's strength (Sitompul et al., 2021).

Currency fluctuations and the likelihood of a substantial rupiah depreciation motivate community funds to relocate or rush to high-quality banks and international banks in both foreign and domestic institutions. In addition to experiencing commercial difficulties as a result of the turbulence, bank debtors will also be unable to pay principal and interest on their loans (Feng et al., 2021). As a result, banks experience liquidity issues, which raises the cost of financing and causes them to be unable to meet their obligations to third-party funds (Lilley et al., 2022). With the withdrawal of funds from various Indonesian companies, banks face a liquidity crisis, a decline in the value of assets in the form of credit and securities purchased by banks, a reduction in the capital adequacy ratio (CAR) due to losses originating from reserves for a decrease in the quality of productive assets, and loan interest default.

2.6. Composite Stock Price Index

In Indonesia, the composite stock price index is known as the IHSG (Indeks Harga Saham Gabungan), and it is one of the stock market indices used by the Indonesia Stock Exchange as an indicator of stock market trend movements, describing market conditions at the time, whether active or dormant (Fuad & Yuliadi, 2021). This index tracks the price movements of all common and preferred stocks traded on the Indonesia Stock Exchange. When the IHSG shows a significant increase in the Indonesian economy's conducive condition, the Composite Stock Price Index reflects the Indonesian economy, and vice versa (Robiyanto, 2018). Several factors, such as the SBI interest rate, inflation, rupiah exchange rate, and global exchange, such as the Dow Jones index, should be considered to determine what factors support the movement of the IHSG (Rosalyn, 2018).

3. METHOD

This study relied on quantitative data, which are already available at three Indonesian state institutions in charge of economic and financial issues: the Central Bank of Indonesia (Bank Indonesia – BI), the Financial Services Authority (Otoritas Jasa Keuangan – OJK), and Statistics Indonesia (Badan Pusat Statistik – BPS). The data were generated in time series over a five-year period, from January 2015 to December 2019, resulting in 60 observations. The statistical tools are limited to two tests, Pearson’s correlation test and Spearman’s rank correlation coefficient test utilizing the significance level (α) and the correlation coefficient (r). The significance level indicates the validity of the relationship between variables, while the correlation coefficient shows the significance of the relationship. We also use a combination or addition system. In addition, a balanced average method using profit-sharing rate (PSR) data for the total, and profit loss sharing (PLS) financing was also used because the primary data source does not contain all PSR data.

4. RESULTS AND DISCUSSIONS

4.1. Overview of Islamic Banking Performance in Indonesia

The discussion in this section begins with an overview of the growth of savings accounts and profit-sharing rate during the observation period in order to comprehensively analyze the relationship between savings and the profit-sharing rate. Figure 1 shows the growth of Islamic banks’ assets in Indonesia over the last five years, indicating a significant increase during that time. From 2015 to 2019, the assets increased one-fold, or more than 100%, at a rate of 20% per year.

In 2015, the total assets of Islamic banks were IDR 263.5 trillion and rose to IDR 524.6 trillion in 2019. However, it should be noted that, while the number of assets increases annually, the increase varies from quarter to quarter, as illustrated in Figure 1. One factor influencing asset growth was depositors' or third-party funds represented by savings accounts and financing revenues.

Figure 2 depicts the concurrent growth of two key variables, savings and financing, over the observation period. It indicates that the amount of savings exceeds the amount of financing, although the difference is insignificant. This result suggests that the financing to deposit ratio (FDR) in the Islamic banking industry increased significantly during the analyzed time period, reaching nearly 100%. Additionally, the performance of the capital adequacy ratio (CAR) and non-performing financing (NPF) also influenced the growth of Islamic banking during the same period. Similar to the term “non-performing loan” (NPL) in conventional banking literature, NPF refers to financing issues in Islamic banking. Similarly, CAR is the banking industry term for the ratio between a bank's capital and its risk-weighted assets and current liabilities (Quan, Ramasamy, Rasiah, Yen, & Pillay, 2019; Solihatun, 2014). These variables are usually utilized to measure liquidity and profitability variables.

Figure 3 displays the relatively stable liquidity and profitability of Islamic commercial banks in Indonesia during the observation period. For example, the annual average NPF level was 4.6%, which was lower than the limit set by the OJK (5%). Similarly, the CAR level was shown to be a positive indicator within the OJK range.

Return on assets (ROA) is another metric used to assess Islamic banking performance. In the financial industry, it refers to a financial ratio that shows how profitable a company is in comparison to its total assets. Figure 4 shows how the ROA value of Islamic banks fluctuated dynamically during the observation period. The average value was less than 2.0, and there were even months when the ROA value was less than 0.5%.

This data shows that Islamic commercial banks in Indonesia were not particularly profitable during the observation period. Their focus was not on utilizing the assets, but on developing an ecosystem that could last for a longer period of time.

4.2. Correlation Between Savings and Profit-Sharing Rate

Profit and loss sharing (PLS) refers to Sharia-compliant equity financing structures within the Islamic finance sector. It essentially complies with the prohibition on interest, suggesting an equitable distribution of risks and profits among the parties involved in a financial transaction based on their agreed ratios, i.e., profit-sharing rate (PSR). The amount of money remaining after expenses and other obligations have been deducted from earnings represents the amount of money saved. The savings are held in cash or cash equivalents (e.g., bank deposits), which are risk-free and offer correspondingly low returns (Kagan & Howard, 2021). Investing allows for the growth of savings but necessitates risking capital. The return on investment contracts is uncertain or not fixed in the Islamic banking system (Kurniawansyah, 2016). The relationship between the two variables is known by using two correlation approaches, namely the Pearson correlation or product-moment correlation and Spearman’s rank correlation.

Savings or third-party funds (TPF) from various types of savings at national Islamic banks totaled IDR 210.761 trillion in 2015. During that time, savings conditions improved year after year, with fluctuations at certain points. The total amount of savings held by Islamic commercial banks as of December 2019 was IDR 416.558 trillion. In comparison to January 2014, the five-year growth rate of total savings was 97.64%, or nearly 20% per year.

In Indonesia, Islamic banks provide two popular savings plans. Wadiah savings are based on a Wadiah contract in which the money is treated as a deposit and the customer receives all profits as bonuses. Mudharabah savings are profit shared based on the Mudharabah contract with the customers. This type of saving includes both savings and deposit products.

Figure 6 demonstrates that the increase in Wadiah savings during the observation period was substantial. In 2015, the Wadiah savings of Islamic banks in Indonesia totaled 15 trillion Indonesian Rupiah. In 2019, this amount nearly tripled, or increased by 300%, to reach IDR 57,652 trillion. This indicates that Wadiah annual savings growth is approximately 60% on average. The growth of Mudharabah savings from 2015 to 2019 is depicted in Figure 7. Mudharabah savings are nearly identical to Wadiah savings because they are not for profit sharing but rather for bonuses given to Mudharabah customers by the bank. The rate of growth of Mudharabah savings is comparable to that of Wadiah savings. In 2015, Mudharabah saved approximately IDR 61.44 trillion, or approximately four times as much as Wadiah (Wadiah demand deposits). In 2019, the growth of these products reached IDR 133.25 trillion, representing a total increase of 116% or a monthly increase of 23%.

Based on Figure 8, Mudharabah deposit savings grew considerably during the observed years (2015–2019). In five years, Mudharabah deposits increased by 88%, from IDR 119.72 trillion in 2015 to IDR 225.65 trillion in 2019, totaling IDR 225.65 trillion. The annual growth rate of Mudharabah deposits is approximately 17.69% on average. Mudharabah deposits accounted for approximately 54.18% of the total savings of Islamic banks in Indonesia, the largest proportion of all types of deposits (see Figure 5). This indicates that the public's interest in Mudharabah deposits is relatively greater than that of the other two forms of saving.

This section describes the current state of the average profit-sharing rate (PSR) for Islamic banks in Indonesia. Table 1 demonstrates that the PSR fluctuated in Islamic banks during the observation period (2015–2019). Under the assumption of a weighted average, the PSR level decreased according to BI and OJK. The result of the Pearson correlation between total savings and the PSR indicates that the growth and development of the number of savings during the observation period have no relationship with the PSR offered by Islamic banks. However, the Pearson correlation test between Wadiah savings (WadSav) and its PSR (PSRwadSav) reveals a relatively strong relationship between the two variables (0.760).

		IBDep Tot	Wad Sav	Mudh Sav	Mudh Dep	PSR dep	PSR wadsav	PSR mudhsav	PSR MudhDep
IBDep Tot	Pearson Correlation Sig. (2-tailed) N	1	0.983**	0.993**	0.989**	-0.750**	0.785**	-0.885**	-0.817**
			0.000	0.000	0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60	60
Wad Sav	Pearson Correlation Sig. (2-tailed) N	0.983**	1	0.972**	0.971**	-0.769**	0.760**	-0.897**	-0.820**
		0.000		0.000	0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60	60
Mudh Sav	Pearson Correlation Sig. (2-tailed) N	0.993**	0.972**	1	0.976**	-0.725**	0.786**	-0.873**	-0.809**
		0.000	0.000		0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60	60
Mudh Dep	Pearson Correlation Sig. (2-tailed) N	0.989**	0.971**	0.976**	1	-0.771**	0.771**	-0.919**	-0.865**
		0.000	0.000	0.000		0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60	60
PSR dep	Pearson Correlation Sig. (2-tailed) N	-0.750**	-0.769**	-0.725**	-0.771**	1	-0.600**	0.876**	0.838**
		0.000	0.000	0.000	0.000		0.000	0.000	0.000
		60	60	60	60	60	60	60	60
PSR wadsav	Pearson Correlation Sig. (2-tailed) N	0.785**	0.760**	0.786**	0.771**	-0.600**	1	-0.668**	-0.719**
		0.000	0.000	0.000	0.000	0.000		0.000	0.000
		60	60	60	60	60	60	60	60
PSR mudh sav	Pearson Correlation Sig. (2-tailed) N	-0.885**	-0.897**	-0.873**	-0.919**	0.876**	-0.668**	1	0.923**
		0.000	0.000	0.000	0.000	0.000	0.000		0.000
		60	60	60	60	60	60	60	60
PSR Mudh Dep	Pearson Correlation Sig. (2-tailed) N	-0.817**	-0.820**	-0.809**	-0.865**	0.838**	-0.719**	0.923**	1
		0.000	0.000	0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed).

Table 2 displays the Spearman’s rank correlation between savings and the PSR that was nearly identical to Table 1 with a value of 0.798, or 79%. Thus, it is possible to conclude that there is a relationship between Wadiah savings (Spearman’s rank) and the PSR. Table 1 also reveals no correlation between Mudharabah savings and deposits and the PSR. Therefore, it can be concluded that in both calculations, the Pearson correlation and Spearman’s rank correlation, the growth of Mudharabah savings and deposits was unaffected by the profit-sharing rate.

		IBDep Tot	Wad Sav	Mudh Sav	Mudh Dep	PSR dep	PSR wadsav	PSR mudhsav	PSR MudhDep
IB Dep Tot	Correlation coefficient	1.000	0.982**	0.994**	0.992**	-0.739**	0.806**	-0.965**	-0.844**
	Sig. (2-tailed)		0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
Wad Sav	Correlation coefficient	0.982**	1.000	0.970**	0.963**	-0.719**	0.798**	-0.941**	-0.828**
	Sig. (2-tailed)	0.000		0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
Mudh Sav	Correlation coefficient	0.994**	0.970**	1.000	0.986**	-0.733**	0.818**	-0.962**	-0.843**
	Sig. (2-tailed)	0.000	0.000		0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
Mudh Dep	Correlation coefficient	0.992**	0.963**	0.986**	1.000	-0.727**	0.819**	-0.960**	-0.849**
	Sig. (2-tailed)	0.000	0.000	0.000		0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
PSR dep	Correlation coefficient	-0.739**	-0.719**	-0.733**	-.727**	1.000	-0.674**	0.770**	0.813**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000		0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
PSR wadsav	Correlation coefficient	0.806**	0.798**	0.818**	0.819**	-0.674**	1.000	-0.815**	-0.838**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000		0.000	0.000
	N	60	60	60	60	60	60	60	60
PSR mudhsav	Correlation coefficient	-0.965**	-0.941**	-0.962**	-0.960**	0.770**	-0.815**	1.000	0.872**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000		0.000
	N	60	60	60	60	60	60	60	60
PSR Mudh Dep	Correlation coefficient	-0.844**	-0.828**	-0.843**	-0.849**	0.813**	-0.838**	0.872**	1.000
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed).

4.3. The Correlation Between Financing and the PSR

This section discusses financing products in their entirety as well as in terms of their various types. The discussion also examines the variables or factors that influence the financing itself, but from the perspective of the Islamic banking instrument. The graph below depicts the Islamic banking industry's total financing during the observation period (2015–2019).

Figure 9 depicts a 44.46% increase in financing over the five years in the observation period. This indicates that the average annual increase in financing growth is 8.89%, which is substantial. In 2015, Islamic banks financed a total of IDR 197.28 trillion, which rose to IDR 351.8 trillion in 2019. The percentage level is relatively less than 10%, but its stability is deemed satisfactory.

Figure 10 illustrates the significant and consistent growth of PLS-based financing in Islamic banks in Indonesia from 2015 to 2019. PLS-based financing totaled IDR 63.58 trillion in 2015, rising to IDR 108.92 trillion in 2019. During the observation period, the increase nearly doubled, reaching 171%, and averaged 34.26% per year. In comparison to its Mudharabah counterparts, Musharakah financing is the most dominant of the two PLS-based financings.

Figure 11 explains the development of Murabahah financing in Islamic banking in Indonesia from 2015 to 2019. In 2015, the Murabahah financing amounted to IDR 115.98 trillion, increasing to IDR 160.65 trillion in 2019 by 38.52%, with an average growth of 7.7% per year. The percentage increase in financing is not too significant, but the nominal of Murabahah financing is the most dominant among other financing in the Islamic banking industry.

		IBFin Tot	PLS Fin	Mudh Fin	Musy Fin	Mura Fin	PSR fin	PSR pls	PSR mudh	PSR musy	PSR mura
IBFin Tot	Pearson Correlation	1	0.996**	0.373**	0.995**	0.969**	-0.982**	-0.957**	-0.914**	-0.927**	-0.966**
	Sig. (2-tailed)		0.000	0.003	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PLS Fin	Pearson Correlation	0.996**	1	0.339**	1.000	0.946**	-0.982**	-0.968**	-0.935**	-0.943**	-0.954**
	Sig. (2-tailed)	0.000		0.008	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
Mudh Fin	Pearson Correlation	0.373**	0.339**	1	0.317*	0.448**	-0.306*	-0.320*	-0.337**	-0.289*	-0.323*
	Sig. (2-tailed)	0.003	0.008		0.014	0.000	0.018	0.013	0.008	0.025	0.012
	N	60	60	60	60	60	60	60	60	60	60
Musy Fin	Pearson Correlation	0.995**	1.000**	0.317*	1	0.943**	-0.983**	-0.968**	-0.935**	-0.944**	-0.954**
	Sig. (2-tailed)	0.000	0.000	0.014		0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
Mura Fin	Pearson Correlation	0.969**	0.946**	0.448**	0.943**	1	-0.943**	-0.889**	-0.807**	-0.836**	-0.967**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000		0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR fin	Pearson Correlation	-0.982**	-0.982**	-0.306*	-0.983**	-0.943**	1	0.978**	0.900**	0.952**	0.983**
	Sig. (2-tailed)	0.000	0.000	0.018	0.000	0.000		0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR pls	Pearson Correlation	-0.957**	-0.968**	-0.320*	-0.968**	-0.889**	0.978**	1	0.943**	0.990**	0.933**
	Sig. (2-tailed)	0.000	0.000	0.013	0.000	0.000	0.000		0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR mudh	Pearson Correlation	-0.914**	-0.935**	-0.337**	-0.935**	-0.807**	0.900**	0.943**	1	0.943**	0.829**
	Sig. (2-tailed)	0.000	0.000	0.008	0.000	0.000	0.000	0.000		0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR musy	Pearson Correlation	-0.927**	-0.943**	-0.289*	-0.944**	-0.836**	0.952**	0.990**	0.943**	1	0.889**
	Sig. (2-tailed)	0.000	0.000	0.025	0.000	0.000	0.000	0.000	0.000		0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR mura	Pearson Correlation	-0.966**	-0.954	-0.323*	-0.954**	-0.967**	0.983**	0.933**	0.829**	0.889**	1
	Sig. (2-tailed)	0.000	0.000	0.012	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed).

Table 3 depicts the relationship between total financing and the PSR, which is found to be significant at 98.2%. It implies that the relationship between these two variables is statistically significant (Aini & Masih, 2018). However, the direction of the relationship between the two is negative, or inversely proportional. This is indicated by the negative symbol (-) in the numbers above. The table also shows the Pearson correlation test results between Murabahah financing and the PSR, which indicates a significant relationship.

Table 3 also shows that the Pearson correlation test results between Murabahah financing and its PSR are -0.967 in the sixth row and 0.000 in the eleventh column. The figure demonstrates Murabahah financing's dominance in Indonesia's Islamic banking industry. This financing is not based on a profit-sharing system, but rather on buying and selling with a profit margin. The correlation test result in Table 3 shows a strong relationship between murabahah financing and the PSR with a value of -0.967. It demonstrates a strong but negative relationship between these two variables, reaching 96.7%, implying that the higher the profit-sharing rate, the lower the amount of financing, and vice versa.

Table 4 displays the correlation test results between financing and its PSR level using Spearman’s rank correlation. This is indicated by the correlation coefficient values that reached above 90% for all tested variables except for the correlation between Mudharabah financing and its PSR level (PSRmudh), which has a value of 85.4%. This means that, on average, the relationship between financing and the PSR is robust with a value above 90%.

		IBFin Tot	PLS Fin	Mudh Fin	Musy Fin	Mura Fin	PSR fin	PSR pls	PSR mudh	PSR musy	PSR mura
IBFin Tot	Correlation coefficient	1.000	1.000**	0.411**	1.000**	0.997**	-0.984**	-954**	-0.854**	-0.900**	-0.979**
	Sig. (2-tailed)		0.000	0.001	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PLS Fin	Correlation coefficient	1.000**	1.000	0.411**	1.000**	0.997**	-0.985**	-955**	-0.854**	-0.902**	-0.980**
	Sig. (2-tailed)	0.000		0.001	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
Mudh Fin	Correlation coefficient	0.411**	0.411**	1.000	0.407**	0.419**	-0.372**	-00.453**	-0.518**	-0.455**	-0.334**
	Sig. (2-tailed)	0.001	0.001		0.001	0.001	0.003	0.000	0.000	0.000	0.009
	N	60	60	60	60	60	60	60	60	60	60
Musy Fin	Correlation coefficient	1.000**	1.000**	0.407**	1.000	0.996**	-0.984**	-0.954**	-0.853**	-0.899**	-0.981**
	Sig. (2-tailed)	0.000	0.000	0.001		0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
Mura Fin	Correlation coefficient	0.997**	0.997**	0.419**	0.996**	1.000	-0.979**	-0.949**	-0.849**	-0.895**	-0.974**
	Sig. (2-tailed)	0.000	0.000	0.001	0.000		0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR fin	Correlation coefficient	-0.984**	-0.985**	-0.372**	-0.984**	-0.979**	1.000	0.967**	0.826**	0.925**	0.992**
	Sig. (2-tailed)	0.000	0.000	0.003	0.000	0.000		0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR pls	Correlation coefficient	-0.954**	-0.955**	-0.453**	-0.954**	-0.949**	0.967**	1.000	0.865**	0.979**	0.944**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000		0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR mudh	Correlation coefficient	-0.854**	-0.854**	-0.518**	-0.853**	-0.849**	0.826**	0.865**	1.000	0.872**	0.803**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR musy	Correlation coefficient	-0.900**	-0.902**	-0.455**	-0.899**	-0.895**	0.925**	0.979**	0.872**	1.000	0.892**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.000
	N	60	60	60	60	60	60	60	60	60	60
PSR mura	Correlation coefficient	-0.979**	-0.980**	-0.334**	-0.981**	-0.974**	0.992**	0.944**	0.803**	0.892**	1.000
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed).

4.4. The Relationship Between Savings/Financing and Interest Rates

With reference to current interest rates, this section discusses the relationship between savings and financing. Table 5 displays the Pearson correlation test results for total savings (IBDepTot) and interest rates (BIRate), which are highly significant at p = 0.000. The correlation coefficient of 0.901, or 90.1%, is inversely proportional to the relationship. The relationship between these two variables, however, is inverse, implying that the higher the conventional bank deposit interest rate, the lower the number of mudharabah deposits, or vice versa.

		IBDep Tot	Wad Sav	Mudh Sav	Mudh Dep	CBR sd	CBR td	BI Rate
IBDep Tot	Pearson Correlation Sig. (2-tailed) N	1	0.983**	0.993**	0.989**	-0.990**	-0.964**	-0.604**
			0.000	0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60
Wad Sav	Pearson Correlation Sig. (2-tailed) N	0.983**	1	0.972**	0.971**	-0.976**	-0.877**	-0.639**
		0.000		0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60
Mudh Sav	Pearson Correlation Sig. (2-tailed) N	0.993**	0.972**	1	0.976**	-0.990**	-0.830**	-0.563**
		0.000	0.000		0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60
Mudh Dep	Pearson Correlation Sig. (2-tailed) N	0.989**	0.971**	0.976**	1	-0.983**	-0.901**	-0.668**
		0.000	0.000	0.000		0.000	0.000	0.000
		60	60	60	60	60	60	60
CBR sd	Pearson Correlation Sig. (2-tailed) N	-0.990**	-0.976**	-0.990**	-0.983**	1	0.867**	0.601**
		0.000	0.000	0.000	0.000		0.000	0.000
		60	60	60	60	60	60	60
CBR td	Pearson Correlation Sig. (2-tailed) N	-0.864**	-0.887**	-0.830**	-0.901**	0.867**	1	0.829**
		0.000	0.000	0.000	0.000	0.000		0.000
		60	60	60	60	60	60	60
BI Rate	Pearson Correlation Sig. (2-tailed) N	-0.604**	-0.639**	-0.563**	-0.668**	0.601**	0.829**	1
		0.000	0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed).

These results support the study by Rusmita & Cahyono (2016), who sought to find the determinants of Indonesian conventional and Islamic bank depositors' trust during the Covid-19 pandemic. It also supports the study by Zulkhibri (2018), who aimed to examine the impact of monetary policy on Islamic bank financing in Malaysia.

The Pearson correlation demonstrates that the reality and phenomenon of the relationship between savings and interest rates are nearly identical to those described in Table 6 calculated by the Spearman’s rank correlation.

		IBDep Tot	Wad Sav	Mudh Sav	Mudh Dep	CBR sd	CBR td	BI Rate
IBDep Tot	Correlation coefficient Sig. (2-tailed) N	1.000	0.982**	0.994**	0.992**	-0.992**	-0.808**	-0.505**
			0.000	0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60
Wad Sav	Correlation coefficient Sig. (2-tailed) N	0.982**	1.000	0.970**	0.963**	-0.972**	-0.795**	-0.497**
		0.000		0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60
Mudh Sav	Correlation coefficient Sig. (2-tailed) N	0.994**	0.970**	1.000	0.986**	-0.992**	-0.805**	-0.495**
		0.000	0.000		0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60
Mudh Dep	Correlation coefficient Sig. (2-tailed) N	0.992**	0.963**	0.986**	1.000	-0.986**	-0.812**	-0.511**
		0.000	0.000	0.000		0.000	0.000	0.000
		60	60	60	60	60	60	60
CBR sd	Correlation coefficient Sig. (2-tailed) N	-0.992**	-0.972**	-0.992**	-0.986**	1.000	0.809**	0.491**
		0.000	0.000	0.000	0.000		0.000	0.000
		60	60	60	60	60	60	60
CBR td	Correlation coefficient Sig. (2-tailed) N	-0.808**	-0.795**	-0.805**	-0.812**	0.809**	1.000	0.670**
		0.000	0.000	0.000	0.000	0.000		0.000
		60	60	60	60	60	60	60
BI Rate	Correlation coefficient Sig. (2-tailed) N	-0.505**	-0.497**	-0.495**	-0.511**	0.491**	0.670**	1.000
		0.000	0.000	0.000	0.000	0.000	0.000
		60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed).

As shown in Table 7, the total and the type of savings have a significant relationship with the interest rate variable. The significance level is high, but the correlation coefficient is low, indicating that the relationship is not overly strong, with the exception of Murabahah financing, which exceeds 70%. This result validates the findings of Apriyanthi et al. (2020), who examined the factors that influence customers' selection of the Islamic banking system in East Java, Indonesia, for savings.

		IBFinTot	PLS Fin	MudhFin	Musy Fin	MuraFin	Finwc	Finl	Finc	BI Rate
IBFinTot	Correlation coefficient	1	0.996**	0.373**	0.995**	0.969**	0.987**	0.989**	0.999**	-0.546**
	Sig. (2-tailed)		0.000	0.003	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
PLS Fin	Correlation coefficient	0.996**	1	0.339**	1.000**	0.946**	0.983**	0.987**	0.994**	-0.486**
	Sig. (2-tailed)	0.000		0.008	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
MudhFin	Correlation coefficient	0.373**	0.339**	1	0.317*	0.448**	0.458**	0.326**	0.355**	-0.421**
	Sig. (2-tailed)	0.003	0.008		0.014	0.000	0.000	0.011	0.005	0.001
	N	60	60	60	60	60	60	60	60	60
Musy Fin	Correlation coefficient	0.995**	1.000**	0.317*	1	0.943**	0.980**	0.987**	0.994**	-0.480**
	Sig. (2-tailed)	0.000	0.000	0.014		0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Mura Fin	Correlation coefficient	0.969**	0.946**	0.448**	0.943**	1	0.951**	0.963**	0.967**	-0.716**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000		0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Finwc	Correlation coefficient	0.987**	0.983**	0.458**	0.980**	0.951**	1	0.959**	0.982**	-0.514**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000		0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Finl	Correlation coefficient	0.989**	0.987**	0.326**	0.987**	0.963**	0.959**	1	0.985**	-0.569**
	Sig. (2-tailed)	0.000	0.000	0.011	0.000	0.000	0.000		0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Finc	Correlation coefficient	0.999**	0.994**	0.355**	0.994**	0.967**	0.982**	0.985**	1	-0.541**
	Sig. (2-tailed)	0.000	0.000	0.005	0.000	0.000	0.000	0.000		0.000
	N	60	60	60	60	60	60	60	60	60
BI Rate	Correlation coefficient	-0.546**	-0.486**	-0.421**	-0.480**	-0.716**	-0.514**	-0.569**	-0.541**	1
	Sig. (2-tailed)	0.000	0.000	0.001	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed)

To obtain more reliable results, the correlation between financing and the BI Rate is also generated using the Spearman’s rank correlation, as shown in Table 8. In general, the correlation test results are consistent with Table 7, which were also obtained using Pearson’s correlation.

The results in Table 8 show that the BI rate has a significant correlation with the financing of Islamic banks. Likewise, the results of Spearman’s rank correlation with the level of significance also shows a significant relationship. The sign or direction of the relationship between the variables is negative. Fatihin & Hadi (2018) supported these findings, which found a strong but negative correlation between these variables. Another study, conducted by Hilman (2016), also shows similar results to our research, specifically on the factor affecting Mudharabah deposits of Islamic banking in Indonesia.

		IBFinTot	PLS Fin	MudhFin	Musy Fin	MuraFin	Finwc	Finl	Finc	BI Rate
IBFinTot	Correlation coefficient	1.000	1.000**	0.411**	1.000**	0.997**	0.972**	0.998**	0.998**	-0.496**
	Sig. (2-tailed)		0.000	0.001	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
PLS Fin	Correlation coefficient	1.000**	1.000	0.411**	1.000**	0.997**	0.971**	0.998**	0.999**	-0.496**
	Sig. (2-tailed)	0.000		0.001	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
MudhFin	Correlation coefficient	0.411**	0.411**	1.000	0.407**	0.419**	0.488**	0.407**	0.403**	-0.396**
	Sig. (2-tailed)	0.001	0.001		0.001	0.001		0.001	0.001	0.002
	N	60	60	60	60	60	60	60	60	60
Musy Fin	Correlation coefficient	1.000**	1.000**	0.407**	1.000	0.996**	0.970**	0.997**	0.999**	-0.497**
	Sig. (2-tailed)	0.000	0.000	0.001		0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Mura Fin	Correlation coefficient	0.997**	0.997**	0.419**	0.996**	1.000	0.967**	0.995**	0.996**	-0.501**
	Sig. (2-tailed)	0.000	0.000	0.001	0.000		0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Finwc	Correlation coefficient	0.972**	0.971**	0.488**	0.970**	0.967**	1.000	0.967**	0.967**	-0.461**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000		0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Finl	Correlation coefficient	0.998**	0.998**	0.407**	0.997**	0.995**	0.967**	1.000	0.997**	-0.492**
	Sig. (2-tailed)	0.000	0.000	0.001	0.000	0.000	0.000		0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Finc	Correlation coefficient	0.998**	0.999**	0.403**	0.999**	0.996	0.967**	0.997**	1.000	-0.497**
	Sig. (2-tailed)	0.000	0.000	0.001	0.000	0.000	0.000	0.000		0.000
	N	60	60	60	60	60	60	60	60	60
BI Rate	Correlation coefficient	-0.496**	-0.496**	-0.396**	-0.497**	-0.501**	-0.461**	-0.492**	-0.497**	1.000
	Sig. (2-tailed)	0.000	0.000	0.002	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed).

4.5. Correlation Between Savings/Financing and Macroeconomic Variables

Tables 9 and 10 describe the correlation between savings and macroeconomic variables. Table 9 shows a strong relationship between the growth of savings in the Islamic banking industry with macroeconomic conditions with a significance level of 99% (α = 0.000). The table also shows that the relationship between savings and inflation is negative. Observing the tables closely revealed that total savings (IBDepTot) in Islamic banks have a significant and robust effect on inflation (INF = -0.711), consumer price indexes (CPI = 0.975), exchange rates (KURS = 0.623), and stock price index (IHSG = 0.886). The relationships between these variables, except for inflation, are positive.

These findings suggest that as savings increase, so do consumer price indexes, exchange rates, and stock price indexes, while inflation decreases. The relationship between the two types of savings, Wadiah and Mudharabah, resembles the results of Mudharabah deposit accounts, either in terms of coefficient correlation value or significance level. Except for inflation, the relationships between the variables tested are positive. Similar results have been obtained by Rozikin & Sholekhah (2020) and Rosyadah et al. (2020) in their studies to find the factors affecting saving intention in Indonesian Islamic banking.

		IBDepTot	Wad Sav	MudhSav	MudhDep	INF	CPI	KURS	IHSG
IBDep Tot	Correlation coefficient	1	0.983**	0.993**	0.989**	-0.711**	0.975**	0.623**	0.886**
	Sig. (2-tailed)		0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
Wad Sav	Correlation coefficient	0.983**	1	0.972**	0.971**	-0.716**	0.964**	0.579**	0.881**
	Sig. (2-tailed)	0.000		0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
Mudh Sav	Correlation coefficient	0.993**	0.972**	1	0.976**	-0.707**	0.974**	0.628**	0.862**
	Sig. (2-tailed)	0.000	0.000		0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
Mudh Dep	Correlation coefficient	0.989**	0.971**	0.976**	1	-0.774**	0.983**	0.591**	0.886**
	Sig. (2-tailed)	0.000	0.000	0.000		0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
INF	Correlation coefficient	-0.711**	-0.716**	-0.707**	-0.774**	1	-0.829**	-0.370**	-0.606**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000		0.000	0.004	0.000
	N	60	60	60	60	60	60	60	60
CPI	Correlation coefficient	0.975**	0.964**	0.974**	0.983**	-0.829**	1	0.621**	0.835**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000		0.000	0.000
	N	60	60	60	60	60	60	60	60
KURS	Correlation coefficient	0.623**	0.579**	0.628**	0.591**	-0.370**	0.621**	1	0.347**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.004	0.000		0.007
	N	60	60	60	60	60	60	60	60
IHSG	Correlation coefficient	0.886**	0.881**	0.862**	0.886**	-0.606**	0.835**	0.347**	1
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.007
	N	60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed).

		IBDepTot	Wad Sav	MudhSav	MudhDep	INF	CPI	KURS	IHSG
IBDep Tot	Correlation coefficient	1.000	0.982**	0.994**	0.992**	-0.738**	0.995**	0.674**	0.863**
	Sig. (2-tailed)		0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
Wad Sav	Correlation coefficient	0.982**	1.000	0.970**	0.963**	-0.719**	0.974**	0.638**	0.859**
	Sig. (2-tailed)	0.000		0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
Mudh Sav	Correlation coefficient	0.994**	0.970**	1.000	0.986**	-0.741**	0.995**	0.680**	0.864**
	Sig. (2-tailed)	0.000	0.000		0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
Mudh Dep	Correlation coefficient	0.992**	0.963**	0.986**	1.000	-0.749**	0.986**	0.657**	0.880**
	Sig. (2-tailed)	0.000	0.000	0.000		0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
INF	Correlation coefficient	-0.738**	-0.719**	-0.741**	-0.749**	1.000	-0.747**	-0.489**	-0.625**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000		0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60
CPI	Correlation coefficient	0.995**	0.974**	0.995**	0.986**	-0.747**	1.000	0.690**	0.862**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000		0.000	0.000
	N	60	60	60	60	60	60	60	60
KURS	Correlation coefficient	0.674**	0.638**	0.680**	0.657**	-0.489**	0.690**	1.000	0.453**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000		0.000
	N	60	60	60	60	60	60	60	60
IHSG	Correlation coefficient	0.863**	0.859**	0.864**	0.880**	-0.625**	0.862**	0.453**	1.000
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed).

The variables in Table 11 exhibit a significant correlation between financing and macroeconomics. Financing is the amount of cash provided by one party to another in support of a planned investment, whether by the parties themselves or by institutions. In addition, the coefficient correlation between variables pertaining to finance and macroeconomics is deemed to be quite robust. The correlation value between exchange rates and stock prices exceeds 90% and 80%, respectively, whereas inflation and the consumer price index exceed 60%. These findings support the studies of Rusmita & Cahyono (2016) and Kebede (2020).

		INF	CPI	KURS	IHSG	IBFinTot	PLS Fin	Mudh Fin	Musy Fin	Mura Fin
INF	Correlation coefficient	1	-0.829**	-.0370**	-0.606**	-0.681**	-0.663**	-0.226**	-0.663**	-0.749**
	Sig. (2-tailed)		0.000	0.004	0.000	0.000	0.000	0.083	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
CPI	Correlation coefficient	-0.829**	1	0.621**	0.835**	0.967**	0.958**	0.358**	0.957**	0.971**
	Sig. (2-tailed)	0.000		0.000	0.000	0.000	0.000	0.005	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
KURS	Correlation coefficient	-0.370**	0.621**	1	0.347**	0.661**	0.674**	0.349**	0.671**	0.590**
	Sig. (2-tailed)	0.004	0.000		0.007	0.000	0.000	0.006	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
IHSG	Correlation coefficient	-0.606**	0.835**	0.347**	1	0.863**	0.840**	0.423**	0.837**	0.886**
	Sig. (2-tailed)	0.000	0.000	0.007		0.000	0.000	0.001	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
IBFin Tot	Correlation coefficient	-0.681**	0.967**	0.661**	0.863**	1	0.996**	0.373**	0.995**	0.969**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000		0.000	0.003	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
PLS Fin	Correlation coefficient	-0.663**	0.958**	0.674**	0.840**	0.996**	1	0.339**	1.000**	0.946**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000		0.008	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Mudh Fin	Correlation coefficient	-0.226	0.358**	0.349**	0.423**	0.373**	0.339**	1	0.317*	0.448**
	Sig. (2-tailed)	0.083	0.005	0.006	0.001	0.003	0.008		0.014	.000
	N	60	60	60	60	60	60	60	60	60
Musy Fin	Correlation coefficient	-0.663**	0.957**	0.671**	0.837**	0.995**	1.000**	0.317*	1	0.943**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.014		0.000
	N	60	60	60	60	60	60	60	60	60
Mura Fin	Correlation coefficient	-0.749**	0.971**	0.590**	0.886**	0.969**	0.946**	0.448**	0.943**	1
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	60	60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed).

Table 12 reveals the Spearman’s rank correlation test results, which are not much different from the results of the Pearson correlation test. In this test, the correlations between financing and the macroeconomic variables (inflation, consumer price index, exchange rate, and stock price index) are positively significant, except for inflation.

Regardless of the results, Islamic banking institutions in Indonesia have not had a significant impact on the Indonesian economy. In the export business, for example, the number of financings to real sectors is relatively lower than the provision of financing to the consumer sectors. These findings have been highlighted by Havidz et al. (2017), Effendi & Yuniarti (2018) and Hafizh et al. (2020) in their studies on the factors influencing the financial sustainability of banking in Indonesia.

		INF	CPI	KURS	IHSG	IBFinTot	PLS Fin	Mudh Fin	Musy Fin	Mura Fin
INF	Correlation coefficient	1.000	-0.747**	-0.489**	-0.625**	-0.736**	-0.737**	-0.268*	-0.737**	-0.726**
	Sig. (2-tailed)		0.000	0.000	0.000	0.000	0.000	0.038	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
CPI	Correlation coefficient	-0.747**	1.000	0.690**	0.862**	0.997**	0.997**	0.407**	0.997**	0.994**
	Sig. (2-tailed)	0.000		0.000	0.000	0.000	0.000	0.001	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
KURS	Correlation coefficient	-0.489**	0.690**	1.000	0.453**	0.685**	0.685**	0.432**	0.684**	0.686**
	Sig. (2-tailed)	0.000	0.000		0.000	0.000	0.000	0.001	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
IHSG	Correlation coefficient	-0.625**	0.862**	0.453**	1.000	0.861**	0.861**	0.432**	0.861**	0.855**
	Sig. (2-tailed)	0.000	0.000	0.000		0.000	0.000	0.001	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
IBFin Tot	Correlation coefficient	-0.736**	0.997**	0.685**	0.861**	1.000	1.000**	0.411**	1.000**	0.997**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000		0.000	0.001	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
PLS Fin	Correlation coefficient	-0.737**	0.997**	0.685**	0.861**	1.000**	1.000	0.411**	1.000**	0.997**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000		0.001	0.000	0.000
	N	60	60	60	60	60	60	60	60	60
Mudh Fin	Correlation coefficient	-0.268*	0.407**	0.432**	0.432**	0.411**	0.411**	1.000	0.407**	0.419**
	Sig. (2-tailed)	0.038	0.001	0.001	0.001	0.001	0.001		0.001	0.001
	N	60	60	60	60	60	60	60	60	60
Musy Fin	Correlation coefficient	-0.737**	0.997**	0.684**	0.861**	1.000**	1.000**	0.407**	1.000	0.996**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.001		0.000
	N	60	60	60	60	60	60	60	60	60
Mura Fin	Correlation coefficient	-0.726**	0.994**	0.686**	0.855**	0.997**	0.997**	0.419**	0.996**	1.000
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.001	0.000
	N	60	60	60	60	60	60	60	60	60

Note: ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed).

5. CONCLUSION

The aforementioned findings indicate that savings and financing products in Islamic banks are affected by a variety of interest rates, including conventional bank interest rates and reference interest rates. They are also affected by macroeconomic variables such as inflation, the consumer price index, the exchange rate, and the stock price index. With the exception of inflation, all influences are positive, which means that when the consumer price index, exchange rate, and stock price index increase, so do savings and financing, whereas a rise in inflation could cause savings and financing to decrease. Although statistically significant, the correlation between savings and the PLS is deemed to be non-existent due to the negative direction of savings. Overall, the findings indicate that Islamic banking savings and financing products in Indonesia are highly sensitive to changes in macroeconomic conditions.

These findings suggest that Islamic banking policies will continue to evolve in Indonesia. The financial authorities must consider a comprehensive formula to accommodate and adapt to changing macroeconomic conditions. The findings are unique to Islamic banks in that they suggest important policy implications for management (practitioners) to increase their focus on business enterprise customers and improve the bank's market share and profitability in order to increase deposits while taking advantage of periods of high inflation to attract more depositors. The results of this study provide investors and other stakeholders with additional empirical evidence, bolstered by previous studies, and provide an alternative reference for investors, particularly Muslim investors, when making investment decisions in Islamic banking financing.

However, this study has limitations in terms of sample size and research design. The sampling was restricted to five years and sixty observations, and the model was restricted to using the Pearson and Spearman correlation tests. Therefore, future research could include additional sampling periods and employ alternative models, such as regression or partial least squares (PLS), to obtain more reliable results.

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

Ab Rahman, S., Ahmad, N. H., & Arshad, N. C. (2021). Bank specific and macroeconomics Factors influencing islamic banks deposits. International Journal of Islamic Business, 6(2), 35-57.Available at: https://doi.org/10.32890/ijib2021.6.2.4.

Aini, S., & Masih, M. (2018). Investigating the major determinants of islamic bank savings: Malaysian evidence. Germany: University Library of Munich.

Almarzoqi, R. M., Mansour, W., & Krichene, N. (2018). Islamic macroeconomics: A model for efficient government, stability and full employment. London: Routledge.

Ansari, A. G. (2008). Capita select of islamic banking in Indonesia. Yogyakarta: UII Press.

Apriyanthi, R., Purbayati, R., & Setiawan, S. (2020). Factors affecting construction sector financing in islamic banking in Indonesia. ProBank, 5(1), 25-35.

BI. (2022). BI 7-day reverse repo rate. Retrieved from: https://www.bi.go.id/en/fungsi-utama/moneter/bi-7day-rr/default.aspx.

BI. (2022a). BI 7-day reverse repo rate. Retrieved from https://www.bi.go.id/en/fungsi-utama/moneter/bi-7day-rr/default.aspx.

Cham, T. (2018). Determinants of Islamic banking growth: An empirical analysis. International Journal of Islamic and Middle Eastern Finance and Management, 11(1), 18-39.Available at: https://doi.org/10.1108/imefm-01-2017-0023.

Chen, M., & Hu, X. (2018). Linkage between consumer price index and purchasing power parity: Theoretic and empirical study. The Journal of International Trade & Economic Development, 27(7), 729-760.Available at: https://doi.org/10.1080/09638199.2018.1430164.

Choudhury. (2011). Islamic economics and finance: An epistemological inquiry (Contributions to Economic Analysis). United Kingdom: Emerald Group Publishing Limited.

Effendi, K. A., & Yuniarti, R. D. (2018). Credit risk and macroeconomics of Islamic banking in Indonesia. Journal of Smart Economic Growth, 3(1), 45-56.

Fadila, A., & Pangestuti, D. C. (2022). Examining the effect of economic collison: Case on credit performance in islamic banking. International Journal of Finance & Banking Studies (2147-4486), 11(1), 132-145.Available at: https://doi.org/10.20525/ijfbs.v11i1.1578.

Fathurrahman, A., & Sari, M. K. (2020). Determinant of total assets of Sharia banks in Indonesia (An Error Correction Model Approach). Amwaluna: Journal of Islamic Economics and Finance, 4(2), 300-310.

Fatihin, M. K., & Hadi, N. H. (2018). Determinants of Sharia Banking market share growth in Indonesia. Airlangga International Journal of Islamic Economics and Finance, 1(2), 87-92.Available at: https://doi.org/10.20473/aijief.v1i2.20805.

Feng, G.-F., Yang, H.-C., Gong, Q., & Chang, C.-P. (2021). What is the exchange rate volatility response to COVID-19 and government interventions? Economic Analysis and Policy, 69, 705-719.Available at: https://doi.org/10.1016/j.eap.2021.01.018.

Fuad, F., & Yuliadi, I. (2021). Determinants of the composite stock price index (IHSG) on the Indonesia stock exchange. Journal of Economics Research and Social Sciences, 5(1), 27-41.Available at: https://doi.org/10.18196/jerss.v5i1.11002.

Hafizh, M., Hidayah, N., & Silalahi, P. R. (2020). Macroeconomics and profit sharing financing in Islamic banking in Indonesia: The third parties fund as intervening. Journal of Islamic Accounting and Finance, 8(2), 131-147.Available at: https://doi.org/10.35836/jakis.v8i2.183.

Hanafi, S. M. (2021). Determinant of market share in the Indonesian islamic banking industry. Muqtasid: Journal of Islamic Banking and Economics, 12(1), 1-16.Available at: https://doi.org/10.18326/muqtasid.v12i1.1-16.

Haqqoni, M. G. A., & Pramana, S. (2021). Implementation of marketplace data in the production of consumer price index in Indonesia. Data Science, Preprint(Preprint), 1-17.Available at: https://doi.org/10.3233/DS-210037.

Havidz, S., Jianmu, Y., Aima, M., & Ali, H. (2017). Do bank specifics and macroeconomics determine banks’ efficiency? Evidence from selected Asian Islamic Banks. International Journal of Economic Research, 14(9), 425-439.

Hilman, I. (2016). The factors affecting Mudharabah deposits of Sharia banking in Indonesia. International Journal of Business and Management Invention, 5(9), 56-66.Available at: http://repository.ekuitas.ac.id/handle/123456789/182.

Huda, N. (2018). Islamic macroeconomics: Theoretical approach. Jakarta: Prenada Media.

Irwanto, G., & Hendrawati. (2020). The influence of profit sharing rate, interest, inflation, dollar exchange rate on murabahah deposits. Undergraduate Thesis. Jakarta: STIE Indonesia.  

Juliyanti, R. (2016). Effect of BI rate, inflation, and JCI on third party funds (TPF) and their implications for financing at Islamic banks (January 2010 - June 2016 Period). Jakarta: Faculty of Economics and Business Syarif Hidayatullah State Islamic University Jakarta.

Kagan, J., & Howard, E. (2021). Savings. Retrieved from https://www.investopedia.com/terms/s/savings.asp.

Kasri, R., & Kassim, S. H. (2009). Empirical determinants of saving in the Islamic banks: Evidence from Indonesia. Journal of King Abdulaziz University: Islamic Economics, 22(2), 181-201.Available at: https://doi.org/10.4197/islec.22-2.7.

Kebede, D. A. (2020). Determinants of interest free financial products in Ethiopian banking industry. Journal of Regional Development and Financing Perspectives, 8(3), 219-230.Available at: https://doi.org/10.22437/ppd.v8i3.9241.

Khoirunissa, D. (2003). Consumers' preference toward Islamic banking (Case Study in Bank Muamalat Indonesia and Bank BNI Syariah). Iqtisad Journal, 4(2), 145-163.Available at: https://doi.org/10.20885/iqtisad.vol4.iss2.art3.

Kurniawansyah, D. (2016). The effect of profit loss sharing funding ratio and profit loss sharing financing ratio on profitability of islamic commercial banks in Indonesia with efficiency and risk as intervening variables. Journal of Accounting and Finance, 18(1), 44-58.

Lilley, A., Maggiori, M., Neiman, B., & Schreger, J. (2022). Exchange rate reconnect. The Review of Economics and Statistics, May(9), 1-11.Available at: https://doi.org/10.1162/rest_a_00978.

Mensi, W., Hammoudeh, S., Tiwari, A. K., & Al-Yahyaee, K. H. (2020). Impact of Islamic banking development and major macroeconomic variables on economic growth for Islamic countries: Evidence from panel smooth transition models. Economic Systems, 44(1), 100739.Available at: https://doi.org/10.1016/j.ecosys.2019.100739.

Mirakhor, A., & Iqbal, Z. (2013). An introduction to Islamic finance: Theory and practice. Singapore: John Wiley & Sons (Asia).

Mubarok, F., Hamid, A., & Arif, M. (2020). Macroeconomics fluctuations and its impact on musharaka financing. Journal of Finance and Banking, 24(2), 164-174.Available at: https://doi.org/10.26905/jkdp.v24i2.4061.

Nafis, R. K., & Sudarsono, H. (2021). Factors affecting Mudharabah financing at islamic commercial banks in Indonesia. Scientific Journal of Islamic Economics, 7(1), 164-173.

Narayan, P. K. (2022). Understanding exchange rate shocks during COVID-19. Finance Research Letters, 45, 102181.Available at: https://doi.org/10.1016/j.frl.2021.102181.

Nisak, B., & Ibrahim, A. (2014). Analysis of Musyarakah financing risk management at Baitul Qiradh Bina Insan Mandiri Banda Aceh. Share: Journal of Islamic Economics and Finance, 3(1), 1-15.Available at: https://doi.org/10.22373/share.v3i1.1054.

Nugraha, D., & Darmansyah, A. (2019). The effect of Sharia funding and financing product toward profitability of islamic commercial banks in Indonesia. ICORE, 5(1), 622-632.

Nurhasniya, S. (2004). Analysis of the role of development in the number of demand deposits, savings and time deposits on the development of total credit and development of the number of bank Indonesia certificates (Case Study on 10 National Foreign Exchange Commercial Banks). Thesis.  

Pimada, L. M., Mawardi, I., & Herianingrum, S. (2017). Desterminants of Islamic bank deposit in Indonesia. Academic Research International, 8(2), 152-160.

Primadhita, Y., Primatami, A., & Budiningsih, S. (2021). Determinants of profit sharing financing in islamic banking. EKOMABIS: Journal of Business Management Economics, 2(1), 1-10.

Quan, L. J., Ramasamy, S., Rasiah, D., Yen, Y. Y., & Pillay, S. D. (2019). Determinants of Islamic banking performance: An empirical study in Malaysia (2007 to 2016). Humanities & Social Sciences Reviews, 7(6), 380-401.Available at: https://doi.org/10.18510/hssr.2019.7664.

Rachmatulloh, D. P., & Solekah, N. A. (2021). Antecedents of saving decision at sharia banks: Islamic financial literacy, religiosity and service quality (studies on millennial generation). Al-Tijary: Journal of Islamic Economics and Business, 6(2), 97-111.

Rahmany, S. (2020). Policy factors affecting Mudharabah savings in islamic banking in Indonesia. Journal of Shariah Banking, 1(2), 122-137.

Rahmawati, I. (2020). Analysis of financing determinants in Islamic banking in Indonesia: Autoregressive distributed lag (ARDL) approach. Undergraduate Thesis, Airlangga University, Surabaya.  

Robiyanto, R. (2018). Performance evaluation of stock price indexes in the Indonesia stock exchange. International Research Journal of Business Studies, 10(3), 173-182.Available at: https://doi.org/10.21632/irjbs.10.3.173-182.

Rosalyn, A. M. (2018). The effect of rupiah exchange rate and inflation rate towards composite stock price index in Indonesia stock exchange. Russian Journal of Agricultural and Socio-Economic Sciences, 78(6), 53-58.Available at: https://doi.org/10.18551/rjoas.2018-06.05.

Rosyadah, P. C., Arifin, N. R., Muhtadi, R., & Safik, M. (2020). Factors that affect savings in islamic banking. AL-ARBAH: Journal of Islamic Finance and Banking, 2(1), 33-46.

Rozikin, A. Z., & Sholekhah, I. (2020). Islamic financial literacy, promotion, and brand image towards saving intention in Sharia Bank. Iqtishadia, 13(1), 95-106.Available at: https://doi.org/10.21043/iqtishadia.v13i1.6489.

Rusmiati, R. (2021). Factors affecting Murabahah financing in Pt. Mandiri Sharia bank in Indonesia for the 2012-2020 period. Riau: Sultan Syarif Kasim State Islamic University.

Rusmita, S. A., & Cahyono, E. F. (2016). The effect of macroeconomic variables, financing from Islamic commercial banks and Islamic IKNB on Indonesian exports in November 2013-April 2016. RATIO: Journal of Shariah Banking, 2(2), 235-242.

Sawitri, N. N., & Febrian, E. (2018). Determinants of demand for Islamic banking services: A survey on moslem public servants in Indonesia. Malaysian Journal of Consumer and Family Economics, 21(S2), 65-73.

Setijawan, E., Sulistyoningsih, M. A., Arundina, T., Prasetyo, M. B., & Yuniarti, F. R. (2021). Does social performance enhance financial inclusion? Evidence from Indonesia’s islamic banking industry. OJK Working Paper, 21(7), 1-35.

Setyowati, N. (2019). Macroeconomic determinants of Islamic banking products in Indonesia. Economies, 7(2), 53.Available at: https://doi.org/10.3390/economies7020053.

Shapiro, M. D., & Wilcox, D. W. (1996). Mismeasurement in the consumer price index: An evaluation. NBER Macroeconomics Annual, 11(1), 93-142.

Sirait, W. A. R. (2021). Factors that affect murabaha financing at the State savings bank Sharia Branch Office Medan. Medan: North Sumatra State Islamic University.

Sitompul, S., Ichsan, R. N., & Nasution, L. (2021). The influence of exchange rate, inflation, for the results of the development assets of islamic banks. Journal of Economics, Finance and Management Studies, 4(3), 138-148.Available at: https://doi.org/10.47191/jefms/v4-i3-05.

Solihatun, S. (2014). Analysis of non performing financing (NPF) of islamic commercial banks in Indonesia 2007–2012. JEPE: Journal of Development Economics, 12(1), 58-68.

Tohidinia, A., Oryoie, A. R., & Mohseni-Cheraghlou, A. (2021). Benevolent savings and macroeconomic variables: Some empirical evidence from Iran. Macroeconomics and Finance in Emerging Market Economies, 14(3), 278-290.Available at: https://doi.org/10.1080/17520843.2020.1854810.

Umam, F. N., Salam, A. N., & Rizal, A. (2021). Determinants of Mudharabah term deposit: A case of Indonesia islamic banks. Journal of Economics Research and Social Sciences, 5(2), 167-180.Available at: https://doi.org/10.18196/jerss.v5i2.12445.

Wahab, N. A., & Rahman, A. R. A. (2011). A framework to analyse the efficiency and governance of zakat institutions. Journal of Islamic Accounting and Business Research, 2(1), 43-62.Available at: https://doi.org/10.1108/17590811111129508.

Wijaya, W. R. (1994). Efforts to raise public funds as a source of funds for the release of credit at national private commercial banks in level I Regions of East Java. Undergraduate Thesis. Surabaya: University of Surabaya.  

Wynne, M. A., & Sigalla, F. D. (1994). The consumer price index. Federal Reserve Bank of Dallas Economic and Financial Policy Review, 1994(Q2), 1-22.

Zulkhibri, M. (2018). The impact of monetary policy on Islamic bank financing: Bank-level evidence from Malaysia. Journal of Economics, Finance and Administrative Science, 23(46), 306-322.Available at: https://doi.org/10.1108/jefas-01-2018-0011.

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