Influence of spin-off decision on financing risk: Empirical insight from Indonesian Islamic banks

Zulfikar Bagus Pambuko; Jaka Sriyana; Akhsyim Affandi; Abdul Hakim

doi:10.12688/f1000research.157435.1

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Research Article

Influence of spin-off decision on financing risk: Empirical insight from Indonesian Islamic banks

[version 1; peer review: 2 approved with reservations]

Zulfikar Bagus Pambuko ^1,2, Jaka Sriyana², Akhsyim Affandi², Abdul Hakim²

PUBLISHED 18 Oct 2024

Author details Author details

¹ Universitas Muhammadiyah Magelang, Magelang, 56172, Indonesia
² Universitas Islam Indonesia, Yogyakarta, 55598, Indonesia

Zulfikar Bagus Pambuko
Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Jaka Sriyana
Roles: Conceptualization, Investigation, Software, Supervision, Validation, Writing – Review & Editing

Akhsyim Affandi
Roles: Conceptualization, Investigation, Supervision, Validation, Writing – Review & Editing

Abdul Hakim
Roles: Conceptualization, Investigation, Supervision, Validation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background

Spin-offs play a significant role in organizational development strategies, particularly in Islamic banking, by fostering entrepreneurship, innovation, and Shariah-compliant management practices. Indonesia stands as a pioneer in implementing the dual banking system and has established a spin-off policy to foster the growth of Islamic banking. This study investigates whether the spin-off decision has a significant impact on financing risk in Indonesian Islamic banks.

Methods

Financing risk is measured by the non-performing financing ratio, while the spin-off decision is represented by a dummy variable equal to 1 for the post-spin-off period and 0 for the pre-spin-off period. This study utilizes data from semi-annual reports of 35 Indonesian Islamic banks and analyzes it using a dynamic panel model with the Generalized Method of Moments (GMM).

Results

The findings reveal that spin-offs significantly reduce financing risk, thereby enhancing the financial resilience and boosting investor appeal. Notably, this implies that Islamic banks operating as Islamic windows exhibit a higher level of financing risk compared to fully-fledged Islamic banks. Furthermore, a noteworthy pattern emerges that spin-off Islamic banks with substantial assets demonstrate greater risk in comparison to their counterparts with more modest assets. System GMM also confirmed the result.

Conclusions

Islamic banks can significantly reduce their financing risks by establishing independent Islamic banks, or spin-offs. Unlike Islamic windows, which are typically integrated within conventional banks and face higher risk levels, standalone Islamic banks offer greater flexibility and control over their operations.

Keywords

Islamic Banks, Non-Performing Financing, Financial Resilience, Government Policy

Corresponding author: Zulfikar Bagus Pambuko

Competing interests: No competing interests were disclosed.

Grant information: Funding for this research was provided by Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi Number 0162/E5.4/DT.05.00/2023 through their Higher Education Excellence Basic Research program.  
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2024 Pambuko ZB et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Pambuko ZB, Sriyana J, Affandi A and Hakim A. Influence of spin-off decision on financing risk: Empirical insight from Indonesian Islamic banks [version 1; peer review: 2 approved with reservations]. F1000Research 2024, 13:1251 (https://doi.org/10.12688/f1000research.157435.1) First published: 18 Oct 2024, 13:1251 (https://doi.org/10.12688/f1000research.157435.1) Latest published: 28 Apr 2025, 13:1251 (https://doi.org/10.12688/f1000research.157435.3)

Introduction

Islamic banking has emerged as a vital component of the global financial landscape, offering a unique alternative based on principles grounded in ethical and Shariah-compliant practices (Al Ansari & Alanzarouti, 2020; Jaafar & Brightman, 2022). Islamic banks dominate 70% of the total assets in the global Islamic finance sector, signifying a growth of 17% compared to the previous period. The primary driving force behind this accomplishment is the significant contribution of countries with Muslim-majority populations, particularly in the GCC, MENA, and Southeast Asia regions. This progress is primarily supported by effective governance and well-suited policies (ICD IsDB, 2022). In Indonesia, a prominent Muslim-majority nation, the Islamic banking sphere has showcased notable expansion and adaptability, establishing its role as a crucial element within the nation’s financial framework (Trinugroho et al., 2018). Within this period of growth, the spin-off policy has been contemplated as a strategy to navigate the complexities of contemporary banking while adhering Shariah principles (Pambuko & Sriyana, 2023; Trinugroho et al., 2021).

The phenomenon of spin-off becomes the one of the key dynamics influencing the financial performance of Indonesian Islamic banks (Pambuko & Sriyana, 2023; Trinugroho et al., 2021). As a strategy, this refers to the process of establishing separate entities from the parent institution, often with distinct business focuses (Chai et al., 2018). According to Article 68 of Law 21 of 2008, all Islamic windows are required to undergo a spin-off after 15 years or if their assets have exceeded 50% of their conventional parent bank’s assets (Nurkarim et al., 2021; Pambuko, 2019; Rokhmawati et al., 2022). These spin-offs were achieved through various methods, including pure spin-offs, conversions, mergers, and acquisitions (Al Arif, Masruroh, et al., 2020a). The rationale behind such spin-offs often involves streamlining operations, improving risk management, optimizing organizational structures, enhancing efficiency, fostering growth, and complying with sharia law (Al Arif, 2014; Al Arif et al., 2018; Hamid, 2015).

The Financial Services Authority of Indonesia (2023) reports a significant increase in Islamic banking institutions as of May 2023. There are now 13 Islamic commercial banks and 20 Islamic windows, compared to just 5 commercial banks and 27 windows before the spin-off policy’s implementation. This law, enacted in 2008, mandated all Islamic windows to become full-fledged Islamic banks by 2023 (Pambuko et al., 2024). However, many Islamic windows haven’t completed this process, highlighting the need for further study on how spin-offs impact Islamic banking performance and their adherence to Sharia principles. Furthermore, the issue of spin-offs has also become one of the key strategies to enhance the contribution of Islamic banking in other countries. As reported, Pakistan, Bahrain, Kuwait, and Afghanistan are among the countries earnestly implementing this issue in the development of Islamic banks (ICD IsDB, 2022). Therefore, Indonesia, as a pioneer of the implementation of spin-off strategy in the Islamic banking sector, deserves to be a benchmark in the implementation of this strategy, particularly as an effort to sustain the Islamic banking industry.

In the last decade, many researchers have directed their attention toward identifying the impact of spin-off policies on the performance of Islamic banks. Several indicators of financial performance have been focused on, including profitability (Hamid, 2015; Nurkarim et al., 2021), efficiency (Al Arif et al., 2018; Al Arif, Mufraini, et al., 2020b; Al Arif & Nabilah, 2022; Hadziq et al., 2022; Pambuko, 2019; Rusydiana et al., 2019), market share (Al Arif, 2017; Al Arif et al., 2023), asset growth (Al Arif, 2015a; Al Arif et al., 2017), financing growth (Al Arif, 2015b), deposit growth (Al Arif, 2014, 2018b, 2018a; Al Arif et al., 2024), and market power (Rayyani et al., 2022). While there’s been a lot of research on spin-offs, how they affect a bank’s risk of financing risk (bad loans) has not been well-studied. This is an important gap to address, especially considering the recent Basel III framework from the Basel Committee on Banking Supervision (BCBS). These new global regulations aim to make banks worldwide more stable by considering all their risks, both from loans they’ve made (on-balance sheet) and from their commitments (off-balance sheet exposures) (Abdurraheem et al., 2023).

Concurrently, the management of bad financing or non-performing financing (NPF) presents a critical challenge for Islamic banking institutions in Indonesia (Imaduddin, 2008; Nugroho et al., 2018; Soekapdjo et al., 2018). Non-performing financing, characterized by loans or financing contracts in default or at risk of default, can have far-reaching implications on the stability and credibility of financial institutions (Hernawati et al., 2021). In traditional banking, its known as non-performing loan (NPL). The existence of financing risk can hinder the stability of Islamic banking entities, potentially jeopardizing their capacity to facilitate economic growth and foster financial inclusivity (Banna et al., 2022). Considering the distinct characteristics of Islamic finance, rooted in risk-sharing and asset-backed principles, a comprehensive exploration of how spin-off decisions influence financing risk, specifically non-performing financing, requires further investigation.

Studies on NPF in Islamic banks or NPL in conventional banks are still hotly discussed. Research suggests that external factors play a bigger role in NPLs for conventional banks, while both internal and external factors impact NPFs in Islamic banks (Loang et al., 2023; Setiawan et al., 2017). Furthermore, despite some inconsistencies in results, from a bank-specific perspective, an increase in non-performing financing is influenced by bank size, operating efficiency ratio, liquid asset ratio, financing to deposit ratio, and foreign ownership (Havidz & Setiawan, 2015; Hosen & Muhari, 2019; Osunkoya et al., 2023; Rahim & Zakaria, 2013; Saw et al., 2022; Zheng et al., 2019). On the other hand, the decrease in non-performing financing is influenced by an increase in net operating profit, capital adequacy ratio, return on assets, cost to income ratio, deposit funds growth, return on equity, loan growth, and market concentration (Hartanto & Samputra, 2023; Hosen & Muhari, 2019; Muhammad et al., 2020; Rahim & Zakaria, 2013; Zheng et al., 2019). Similarly, macroeconomic conditions also impact changes in banking’s financing risk. Exchange rates, inflation, and economic policy uncertainty contribute to an increase in non-performing financing (Firmansyah, 2014; Hosen & Muhari, 2019; Karadima & Louri, 2021; Osunkoya et al., 2023; Zheng et al., 2019), while GDP and unemployment contribute to a decrease in non-performing financing (Ferhi, 2018; Hassan et al., 2019; Hernawati et al., 2021; Le & Diep, 2020; Zheng et al., 2019). A surplus in foreign exchange reserves can also trigger a financing boom, which in turn can affect financing quality (Kuncoro, 2024).

This scientific paper aims to bridge these knowledge gaps by offering an empirical analysis of the relationship between spin-off decision and the prevalence of financing risk in the Indonesian Islamic banking sector. While previous research suggests spin-off decisions can improve financial performance, the potential effect on financing risk has been overlooked. This study will address this gap by examining how spin-offs influence financing risk. By untangling these linked factors, we aim to provide valuable insights for both academics and Islamic bankers, fostering a deeper understanding of how strategic restructuring impacts the financial soundness of Islamic banks. Moreover, given the intricate relationships between spin-off strategies and financing risk, policy implications and strategic recommendations stemming from this study could serve as a catalyst for informed decision-making within the Islamic banking industry.

Methods

To examine the impact of spin-off decisions on financing risk, this study utilizes semi-annual bank-level data from 2006 to 2022 for Islamic banks in Indonesia. The bank-specific data, derived from the financial statements of 35 Islamic banks, were acquired from the Islamic Banking Statistics which is reported by Indonesian Financial Services Authority (FSA). The sample consists of 16 full-fledged Islamic banks and 19 Islamic windows. Macroeconomic data were sourced from Statistics Indonesia, Bank Indonesia, and OPEC basket price. We employed an unbalanced panel dataset encompassing 1032 observations.

Table 1 explains the data we used in this study. We looked at the ratio of non-performing financing (NPF) to total financing, which is a common measure of bad loans in Islamic banks (Saw et al., 2022). Spin-off is a proxy for spin-off decisions, represented by a dummy variable where 1 denotes post-spin-off time and 0 signifies pre-spin-off time. The bank-specific factors include bank size (log total assets of each Islamic bank) and market share, assessed by the individual Islamic bank’s asset over the total asset industry of Islamic banks. To understand the economic climate, we looked at the Consumer Price Index (CPI) for inflation and the Industrial Production Index (IPI) for economic growth. The variable OIL is based on the OPEC basket price, which represents the per-barrel oil price.

Table 1. Regression variables.

Variable	Description	Source
NPF	Non-performing financing	Islamic banking statistics
Spinoff	Dummy variable, it is one if the period after spin-off decision, zero otherwise	Banks’ profile
Bank size (SIZE)	Natural logarithm of total assets	Islamic banking statistics
Market share (MS)	The ratio of Islamic banks' assets to the industry's overall assets	Islamic banking statistics
CPI	Consumer price index	Bank of Indonesia
IPI	Industrial production index	Statistics Indonesia
Oil Price (OIL)	A weighted average price of crude oil blends produced by the OPEC member countries	OPEC basket price

This study utilizes a dynamic panel regression model with individual fixed effects to assess the impact of spin-off decisions on financing risk for Islamic banks in Indonesia. The dynamic panel approach addresses both unobserved bank-specific characteristics and potential endogeneity issues that might bias the results. To further account for potential simultaneity and correlation between variables, the study employs the Generalized Methods of Moment (GMM) estimation technique. Equation (1) details the empirical model used in this analysis, which incorporates the spin-off decision as the main independent variable. Additionally, control variables such as bank size, market share, and macroeconomic factors like inflation, economic growth, and oil price are included to account for their potential influence on financing risk.

(1)

Y_{it} = α + β_{1} Y_{it - 1} + β_{2} {Spinoff}_{t} + β_{3} {BankSpecific}_{i, t} + β_{4} {MacroEconomic}_{t} + ε_{i, t}

Where Y_it is the non performing financing (NPF) for bank i and year t; Spinoff_t is a dummy variable, it is one if the period after spin-off decision, zero otherwise; BankSpecific_i,t is control variable consisting of bank size dan market share; MacroEconomic_t is control variable consisting of inflation, economic growth, and oil price; and ε_i,t is the error term. The first lag of the dependent variable has been integrated into the model as an independent variable to address the possible persistence of NPF changes in time. Our analyses utilized a difference GMM Arellano-Bond estimator. To ensure robustness, we conducted a two-step System GMM estimator for further validation.

Then we analyze the impact of bank size on the interaction between spin-off decisions and non-performing financing. We created an interaction variable in the form of Spinoff_t*Size_it for this purpose. The equation (2) represents the estimation model.

(2)

Y_{it} = α + β_{1} Y_{it - 1} + β_{2} {Spinoff}_{t} + β_{3} {Spinoff}_{t}^{*} {Size}_{it} + β_{4} {BankSpecific}_{i, t} + β_{5} {MacroEconomic}_{t} + ε_{i, t}

Data analysis was conducted using EViews 12 by S&P Global. Prior to interpretation, diagnostic tests were performed. The Sargan test was employed to assess the validity of the instruments, while the Arellano-Bond test was used to detect the presence of autocorrelation. A well-specified model should exhibit valid instruments and no autocorrelation.

Results and Discussion

Descriptive analysis

Table 2 presents descriptive statistics for the variables spanning from 2006 to 2022. The average value of NPF as a financing risk proxy stands at 3.10%. This value remains below the designated upper limit of 5.00. The lowest NPF value, at 0.00%, indicates that certain Islamic banks have no risky financing, although the highest NPF value recorded is 53.27%. This relatively high variability is supported by a standard deviation of 4.17%. Regarding assets and market share, the lower mean values compared to the standard deviation suggest that Islamic banks in Indonesia are still dominated by a few larger banks.

Table 2. Descriptive analysis of variables.

	Obs.	Mean	Max	Min	Std. Dev.
SPINOFF	1032	0.27	1.00	0.00	0.45
NPF	1032	3.10	53.27	0.00	4.17
SIZE (IDR million)	1032	9579281.35	305727438.00	3176.00	22725240.62
MS	1032	3.23	40.13	0.01	6.39
CPI	1032	4.90	15.53	1.33	2.70
IPI	1032	122.54	151.76	95.71	18.33
OIL (IDR million/barrel)	1032	0.86	1.70	0.34	0.28

Empirical results

The analysis commenced by identifying the issue of multicollinearity among the independent variables. Table 3 displays the correlation scores among the independent variables. A correlation score of < 0.8 was indicative of the absence of multicollinearity (Mukhibad, Nurkhin et al., 2023b; Rizvi et al., 2020). The highest correlation between independent variables was 0.5678 (correlation between SIZE and IPI), followed by SIZE and MS (0.5592). These findings signify the absence of multicollinearity within the model.

Table 3. Correlation matrix.

	SIZE	MS	CPI	IPI	OIL
SIZE	1
MS	0.5592	1
CPI	-0.3750	0.0442	1
IPI	0.5678	-0.0440	-0.5417	1
OIL	0.1693	-0.0254	-0.0330	0.2726	1

The subsequent analysis examines the impact of spin-off decisions on the financing risk of Islamic banks. Table 4 presents the outcomes of the difference GMM and system GMM. Difference GMM estimator is depicted in regressions (1)-(4), while system GMM estimator in regressions (5)-(8). Regressions (1) and (5) only incorporate bank-specific variables, consisting of assets (SIZE) and market share (MS). In regressions (2) and (6), we include additional control variables such as inflation (CPI), economic growth (IPI), and global oil prices (OIL). In regressions (3), (4), (7), and (8), we introduce interaction variables, specifically spin-off*size. Towards the end of the table, it is indicated that no issues of instrumental validity and autocorrelation were found. To elaborate, the results of the Sargan test reject the null hypothesis, confirming the validity of the instruments. Furthermore, the results of the Arellano-Bond autocorrelation test, AR(2), also reveal the absence of autocorrelation in the model.

Table 4. Determinants of Islamic banks’ performance.

	Difference GMM				System GMM
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
NPF(-1)	0.3599* (1385.50)	0.3468* (349.62)	0.3555* (593.07)	0.3408* (134.95)	0.3768* (2221.58)	0.3737* (119.03)	0.3761* (172.18)	0.3687* (90.03)
spinoff	1.1110* (380.49)	0.6900* (63.40)	-31.4797* (-172.45)	-31.3797* (-40.51)	-1.5722* (-1614.53)	-1.6502* (-50.65)	-19.8628* (-95.16)	-23.4687* (-39.38)
spinoff*size			1.1062* (165.72)	1.0860* (37.81)			0.6136* (75.08)	0.7271* (36.60)
SIZE	0.1351* (58.24)	0.2951* (17.06)	-0.1487* (-34.82)	0.0808** (2.49)	0.2491* (1814.80)	0.2059* (6.34)	0.1836* (35.65)	0.2364* (7.82)
MS	0.1760* (239.99)	0.1574* (20.22)	0.1327* (32.35)	0.1042* (8.22)	0.0349* (57.10)	0.0366** (2.37)	0.0286* (7.65)	0.0327*** (1.66)
CPI		0.0038* (4.40)		0.0002 (0.10)		0.0035* (4.58)		0.0061* (3.01)
IPI		-0.0002 (-0.10)		-0.0065*** (-1.75)		0.0106* (4.66)		0.0047*** (1.72)
OIL		-1.1527* (-97.11)		-1.2491* (-49.54)		-1.2242* (-36.87)		-1.4065* (-34.90)
Sargan test	0.3890	0.3166	0.4097	0.3601	0.3991	0.3155	0.3998	0.3029
AR(1)	0.0000	0.0251	0.0297	0.0256
AR(2)	0.1660	0.1532	0.1452	0.1467
No. of Obs.	948	948	948	948	948	948	948	948

* p<0.01.

** p<0.05.

*** p<0.1.

The findings from the analysis provide compelling evidence regarding the influence of the lagged dependent variable, NPF(-1), which consistently exhibits a statistically significant positive effect across all models at the 1% significance level. This outcome substantiates the notion that the financing risk experienced in the preceding period contributes to an increase in the non-performing financing ratio during the present period. This result underscores a significant challenge in the Islamic banking sector, suggesting that the mechanisms in place to address the issue of bad financing have not yet achieved optimal effectiveness. This aligns with previous research conducted by Mukhibad, Jayanto et al. (2023a), who reached similar conclusions in their study. Notably, the finding underscores the persisting complexity of managing NPF in Islamic banking and emphasize the need for more refined strategies to mitigate such risks. Interestingly, the result contrasts with those of Hartanto & Samputra (2023), whose research indicated a potential improvement in the non-performing financing situation from period t-1 to period t. This variation emphasizes the intricate and many-sided aspects of how financing risk behaves in Islamic banking.

Regarding the main focus of this study, it becomes apparent that the spin-off decisions undertaken by Islamic banks wield a substantial and statistically significant impact, consistently evident at the 1% significance level across all models. This pervasive influence, however, diverges in models (1) and (2). Specifically, the majority of models reveal a noteworthy negative effect resulting from the strategic choice of Islamic banks to embark on spin-offs. This intriguing discovery underscores a pivotal insight – that the judicious adoption of spin-off strategies by Islamic banks yields a salutary outcome, effectively tempering the levels of financing risk. This outcome resonates with the emerging understanding that Islamic banks’ autonomy in resource management holds the potential to substantially shape their risk profile. This profound effect echoes the findings of a distinct study, where a compelling revelation was made that full-fledged Islamic banks exhibit a more adept ability to mitigate non-performing financing as compared to Islamic windows (Pambuko & Pramesti, 2020; Pernamasari, 2020). These findings collectively affirm the advantageous influence of spin-off decisions in fostering improved risk management practices and enhancing the overall stability of Islamic banking entities.

We also investigated the impact of size on financing risk. The analysis results reveal that size has a significant positive impact on non-performing financing, except in model (3) where it has a negative impact. The finding indicates that an increase in the asset size will increase financing risk. The result supports the finding of Hosen and Muhari (2019) and Saw et al. (2022). It is also validated the ‘bad management theory’ proposed by Berger and DeYoung (1997). Within this theoretical framework, the orchestration of poor management practices within banking institutions leads to lower loan quality and increases the level of non-performing loans. Furthermore, these results are corroborated by the estimations of the interaction variable (spinoff*size), which significantly influences non-performing financing. Thus, larger Islamic banks that undergo spin-offs carry a higher financing risk compared to smaller banks.

Further analysis concerning the impact of market share on the financing risk of Islamic banks was conducted. Across all models, a positive relationship between market share and non performing financing is revealed, except in models (6) and (8), where the relationship is significant at the 5% and 10% levels, respectively. The finding strengthens the previous estimations indicating that greater asset dominance increases the potential for non-performing financing. As for the control variables, we only found robust and negatively correlated estimates for NPF and OIL. This negative relationship explains that an increase in global oil prices will decrease financing risk for Islamic banks. With a certain level of oil price increase, related product prices become more competitive, stimulating business sectors and lowering financing risk. As Abimanyu et al. (2023) pointed out, Indonesia’s policy aims to ensure that global oil price movements contribute positively to the state’s finances and trade, while minimizing inflationary risks. The result also supports the findings of Ryandono et al. (2022) dan Al Jabri et al. (2022). On the other hand, economic growth and inflation exhibit inconsistent results across each model.

Conclusion

This study investigates the impact of spin-off decision – the separation of Islamic windows from their conventional parent banks to become full-fledged Islamic banks – on financing risk in Indonesian Islamic banks. We estimate over a substantial timeframe in the journey of Islamic banks in Indonesia, spanning from 2006 to 2022. Our findings reveal that Islamic windows carry a higher risk compared to full-fledged Islamic banks that undergo spin-offs. We also discover that spin-off Islamic banks with larger assets are more exposed to risk than those with smaller assets. This notice is supported by the regression that financing risk increases with the growth of assets and market share. In essence, our study not only contributes to the empirical understanding of Islamic banking dynamics but also advances the discourse surrounding prudent risk management strategies in financial systems governed by ethical and Shariah-compliant principles.

These findings yield several policy implications. We have strong evidence that the independence of Islamic banks makes them more sensitive to risk and adept at mitigating it. Therefore, the mandatory spin-off policy to be implemented for all Islamic windows by the end of 2023 is a viable approach. Moreover, the potential rise in market dominance by Islamic banks, which could amplify financing risk, calls for complementary policies to mitigate the potential negative outcomes.

Ethical considerations

This study does not involve human participants, animal subjects, or plant specimens.

Data availability

Underlying data

Zenodo: Influence of spin-off decision on financing risk: Empirical insight from Indonesian Islamic banks https://doi.org/10.5281/zenodo.13879461 (Pambuko, 2024).

The project contains the following underlying data:

− dataset financing risk.xlsx

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Extended data

The data used in this research is open-access and can be downloaded from the following website: Indonesian Banking Statistics on Financial Service Authority Website: https://www.ojk.go.id/en/kanal/perbankan/data-dan-statistik/statistik-perbankan-indonesia/Default.aspx; BPS-Statistics Indonesia: https://www.bps.go.id/en/statistics-table/2/MjA3NyMy/indeks-produksi-bulanan-industri-besar-dan-sedang-menurut-kbli-2-digit--kbli-2020---2010-100-.html; Bank Indonesia: https://www.bi.go.id/en/statistik/indikator/data-inflasi.aspx; and OPEC Basket Price: https://www.opec.org/opec_web/en/data_graphs/40.htm.

Software availability statement

EViews 12, developed by S&P Global and available at https://www.eviews.com/home.html, is a powerful econometric software accessible to researchers based on their license type. For those seeking a free version, a student version is available for download at https://www.eviews.com/EViews12/EViews12Univ/evuniv12.html#stud.

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Version 3

VERSION 3 PUBLISHED 18 Oct 2024

Author details Author details

¹ Universitas Muhammadiyah Magelang, Magelang, 56172, Indonesia
² Universitas Islam Indonesia, Yogyakarta, 55598, Indonesia

Zulfikar Bagus Pambuko
Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Jaka Sriyana
Roles: Conceptualization, Investigation, Software, Supervision, Validation, Writing – Review & Editing

Akhsyim Affandi
Roles: Conceptualization, Investigation, Supervision, Validation, Writing – Review & Editing

Abdul Hakim
Roles: Conceptualization, Investigation, Supervision, Validation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

Funding for this research was provided by Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi Number 0162/E5.4/DT.05.00/2023 through their Higher Education Excellence Basic Research program.  
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Pambuko ZB, Sriyana J, Affandi A and Hakim A. Influence of spin-off decision on financing risk: Empirical insight from Indonesian Islamic banks [version 1; peer review: 2 approved with reservations]. F1000Research 2024, 13:1251 (https://doi.org/10.12688/f1000research.157435.1)

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Reviewer Report 10 Jan 2025

Mutamimah Mutamimah, Universitas Islam Sultan Agung, Semarang, Central Java, Indonesia

Approved with Reservations

https://doi.org/10.5256/f1000research.172879.r353263

The article is well written and meets the rules for good articles. The problem formulation is based on research gaps from previous research results. Apart from that, the research method is also appropriate, including the indicators, and statistical analysis and results.
However, researchers need to attach the year in which the Islamic banking spin-off was carried out, because each Islamic banking spin-off is different.
In table 2 about Descriptive analysis of variables, the description of each variable should be explained well.
Furthermore, in conclusion, it must have limitations and future research agenda. It would be good to explain why the research did not analyze financing risks before the spin-off, so there will be a difference between before the spin-off and after the spin-off.
Good luck!

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Economics, Management, Financial Management

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Author Response 04 Feb 2025

Zulfikar Bagus Pambuko, Islamic Economics Law, Universitas Muhammadiyah Magelang, Magelang, Indonesia

04 Feb 2025

Author Response

We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the ... Continue reading We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1: The article is well written and meets the rules for good articles. The problem formulation is based on research gaps from previous research results. Apart from that, the research method is also appropriate, including the indicators, and statistical analysis and results. However, researchers need to attach the year in which the Islamic banking spin-off was carried out, because each Islamic banking spin-off is different.
Authors’ Response: Following your insightful suggestion, we have revised the Method section to provide a more comprehensive overview of the restructuring processes within the Indonesian Islamic banking industry. The newly added paragraph, which details historical data on spin-offs, conversions, and mergers, directly addresses your feedback regarding the need for more specific examples.
“The Indonesian Islamic banking landscape has witnessed various restructuring strategies. Pure spin-offs were employed by banks such as Bukopin Syariah (2008), BNI Syariah (2008), BRI Syariah (2010), BJB Syariah (2010), and Bank Nano Syariah (2024). Conversion, another common approach, was adopted by BTPN Syariah (2014), Bank Aceh Syariah (2016), BPD NTB Syariah (2018), and BPD Riau Kepri Syariah (2022). The formation of Bank Syariah Indonesia in 2021 marked a significant merger in the sector. Moreover, a combination of acquisition and conversion was utilized sequentially by banks including Bank Panin Dubai Syariah (2009), Bank Victoria Syariah (2010), BCA Syariah (2010), and Bank Aladin Syariah (2010).”

Reviewer’s Suggestion 2: In table 2 about Descriptive analysis of variables, the description of each variable should be explained well.
Authors’ Response: Thank you for your valuable feedback. We have addressed your concerns regarding variable descriptions in the descriptive analysis.
“Table 2 presents descriptive statistics for the variables spanning from 2006 to 2022. The average value of NPF, as a financing risk proxy, stands at 3.10%, remaining below the generally accepted upper limit of 5.00%. The lowest NPF value, at 0.00%, indicates that some Islamic banks exhibit no risky financing, while the highest recorded NPF value is 53.27%. This relatively high variability is supported by a standard deviation of 4.17%. SPINOFF, a dummy variable, has a mean of 0.27, indicating that spin-off events are relatively rare. Its standard deviation of 0.45 suggests moderate variability in occurrence. Regarding assets and market share, the lower mean values compared to the standard deviation suggest that Islamic banks in Indonesia are still dominated by a few larger banks, reflecting differences in institutional capacity and market reach.
Similarly, the Consumer Price Index (CPI) and Industrial Production Index (IPI) show moderate variability, with standard deviations of 2.70 and 18.33, respectively, reflecting macroeconomic conditions that influence Islamic banking performance. Oil prices (OIL), a critical variable for resource-dependent economies, have a mean of IDR 0.86 million per barrel, with relatively stable fluctuations (standard deviation of 0.28). This stability contrasts with the high variability observed in institutional size and non-performing financing.
Overall, the descriptive analysis underscores significant heterogeneity in institutional characteristics and economic factors, suggesting the need for tailored strategies in managing Islamic banks. Institutions must address wide disparities in size and market share, ensuring resilience to external economic conditions such as inflation, industrial output fluctuations, and oil price volatility.”

Reviewer’s Suggestion 3: Furthermore, in conclusion, it must have limitations and future research agenda. It would be good to explain why the research did not analyze financing risks before the spin-off, so there will be a difference between before the spin-off and after the spin-off. Good luck!
Authors’ Response: We appreciate your insightful comments. We have added the limitations and future research opportunities to the conclusion section.
“The present study has identified several limitations. The utilization of a dummy variable to assess the impact of spin-offs on financing risk, while effective in capturing the immediate effects of regulatory policies, may not fully account for the nuanced dynamics at play. Moreover, the study's scope is constrained by the use of semi-annual data up to 2022 and its focus on the Indonesian Islamic banking industry. To address these limitations, future research could benefit from employing a difference-in-differences analysis to more precisely estimate the causal impact of spin-offs. Furthermore, incorporating a broader range of internal and external variables, as well as expanding the analysis to include Islamic banks in Southeast Asia or OIC countries, would provide a more comprehensive understanding of the phenomenon.”
We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1: The article is well written and meets the rules for good articles. The problem formulation is based on research gaps from previous research results. Apart from that, the research method is also appropriate, including the indicators, and statistical analysis and results. However, researchers need to attach the year in which the Islamic banking spin-off was carried out, because each Islamic banking spin-off is different.
Authors’ Response: Following your insightful suggestion, we have revised the Method section to provide a more comprehensive overview of the restructuring processes within the Indonesian Islamic banking industry. The newly added paragraph, which details historical data on spin-offs, conversions, and mergers, directly addresses your feedback regarding the need for more specific examples.
“The Indonesian Islamic banking landscape has witnessed various restructuring strategies. Pure spin-offs were employed by banks such as Bukopin Syariah (2008), BNI Syariah (2008), BRI Syariah (2010), BJB Syariah (2010), and Bank Nano Syariah (2024). Conversion, another common approach, was adopted by BTPN Syariah (2014), Bank Aceh Syariah (2016), BPD NTB Syariah (2018), and BPD Riau Kepri Syariah (2022). The formation of Bank Syariah Indonesia in 2021 marked a significant merger in the sector. Moreover, a combination of acquisition and conversion was utilized sequentially by banks including Bank Panin Dubai Syariah (2009), Bank Victoria Syariah (2010), BCA Syariah (2010), and Bank Aladin Syariah (2010).”

Reviewer’s Suggestion 2: In table 2 about Descriptive analysis of variables, the description of each variable should be explained well.
Authors’ Response: Thank you for your valuable feedback. We have addressed your concerns regarding variable descriptions in the descriptive analysis.
“Table 2 presents descriptive statistics for the variables spanning from 2006 to 2022. The average value of NPF, as a financing risk proxy, stands at 3.10%, remaining below the generally accepted upper limit of 5.00%. The lowest NPF value, at 0.00%, indicates that some Islamic banks exhibit no risky financing, while the highest recorded NPF value is 53.27%. This relatively high variability is supported by a standard deviation of 4.17%. SPINOFF, a dummy variable, has a mean of 0.27, indicating that spin-off events are relatively rare. Its standard deviation of 0.45 suggests moderate variability in occurrence. Regarding assets and market share, the lower mean values compared to the standard deviation suggest that Islamic banks in Indonesia are still dominated by a few larger banks, reflecting differences in institutional capacity and market reach.
Similarly, the Consumer Price Index (CPI) and Industrial Production Index (IPI) show moderate variability, with standard deviations of 2.70 and 18.33, respectively, reflecting macroeconomic conditions that influence Islamic banking performance. Oil prices (OIL), a critical variable for resource-dependent economies, have a mean of IDR 0.86 million per barrel, with relatively stable fluctuations (standard deviation of 0.28). This stability contrasts with the high variability observed in institutional size and non-performing financing.
Overall, the descriptive analysis underscores significant heterogeneity in institutional characteristics and economic factors, suggesting the need for tailored strategies in managing Islamic banks. Institutions must address wide disparities in size and market share, ensuring resilience to external economic conditions such as inflation, industrial output fluctuations, and oil price volatility.”

Reviewer’s Suggestion 3: Furthermore, in conclusion, it must have limitations and future research agenda. It would be good to explain why the research did not analyze financing risks before the spin-off, so there will be a difference between before the spin-off and after the spin-off. Good luck!
Authors’ Response: We appreciate your insightful comments. We have added the limitations and future research opportunities to the conclusion section.
“The present study has identified several limitations. The utilization of a dummy variable to assess the impact of spin-offs on financing risk, while effective in capturing the immediate effects of regulatory policies, may not fully account for the nuanced dynamics at play. Moreover, the study's scope is constrained by the use of semi-annual data up to 2022 and its focus on the Indonesian Islamic banking industry. To address these limitations, future research could benefit from employing a difference-in-differences analysis to more precisely estimate the causal impact of spin-offs. Furthermore, incorporating a broader range of internal and external variables, as well as expanding the analysis to include Islamic banks in Southeast Asia or OIC countries, would provide a more comprehensive understanding of the phenomenon.”
Competing Interests: none Close
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COMMENTS ON THIS REPORT

Author Response 04 Feb 2025

Zulfikar Bagus Pambuko, Islamic Economics Law, Universitas Muhammadiyah Magelang, Magelang, Indonesia

04 Feb 2025

Author Response

We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the ... Continue reading We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1: The article is well written and meets the rules for good articles. The problem formulation is based on research gaps from previous research results. Apart from that, the research method is also appropriate, including the indicators, and statistical analysis and results. However, researchers need to attach the year in which the Islamic banking spin-off was carried out, because each Islamic banking spin-off is different.
Authors’ Response: Following your insightful suggestion, we have revised the Method section to provide a more comprehensive overview of the restructuring processes within the Indonesian Islamic banking industry. The newly added paragraph, which details historical data on spin-offs, conversions, and mergers, directly addresses your feedback regarding the need for more specific examples.
“The Indonesian Islamic banking landscape has witnessed various restructuring strategies. Pure spin-offs were employed by banks such as Bukopin Syariah (2008), BNI Syariah (2008), BRI Syariah (2010), BJB Syariah (2010), and Bank Nano Syariah (2024). Conversion, another common approach, was adopted by BTPN Syariah (2014), Bank Aceh Syariah (2016), BPD NTB Syariah (2018), and BPD Riau Kepri Syariah (2022). The formation of Bank Syariah Indonesia in 2021 marked a significant merger in the sector. Moreover, a combination of acquisition and conversion was utilized sequentially by banks including Bank Panin Dubai Syariah (2009), Bank Victoria Syariah (2010), BCA Syariah (2010), and Bank Aladin Syariah (2010).”

Reviewer’s Suggestion 2: In table 2 about Descriptive analysis of variables, the description of each variable should be explained well.
Authors’ Response: Thank you for your valuable feedback. We have addressed your concerns regarding variable descriptions in the descriptive analysis.
“Table 2 presents descriptive statistics for the variables spanning from 2006 to 2022. The average value of NPF, as a financing risk proxy, stands at 3.10%, remaining below the generally accepted upper limit of 5.00%. The lowest NPF value, at 0.00%, indicates that some Islamic banks exhibit no risky financing, while the highest recorded NPF value is 53.27%. This relatively high variability is supported by a standard deviation of 4.17%. SPINOFF, a dummy variable, has a mean of 0.27, indicating that spin-off events are relatively rare. Its standard deviation of 0.45 suggests moderate variability in occurrence. Regarding assets and market share, the lower mean values compared to the standard deviation suggest that Islamic banks in Indonesia are still dominated by a few larger banks, reflecting differences in institutional capacity and market reach.
Similarly, the Consumer Price Index (CPI) and Industrial Production Index (IPI) show moderate variability, with standard deviations of 2.70 and 18.33, respectively, reflecting macroeconomic conditions that influence Islamic banking performance. Oil prices (OIL), a critical variable for resource-dependent economies, have a mean of IDR 0.86 million per barrel, with relatively stable fluctuations (standard deviation of 0.28). This stability contrasts with the high variability observed in institutional size and non-performing financing.
Overall, the descriptive analysis underscores significant heterogeneity in institutional characteristics and economic factors, suggesting the need for tailored strategies in managing Islamic banks. Institutions must address wide disparities in size and market share, ensuring resilience to external economic conditions such as inflation, industrial output fluctuations, and oil price volatility.”

Reviewer’s Suggestion 3: Furthermore, in conclusion, it must have limitations and future research agenda. It would be good to explain why the research did not analyze financing risks before the spin-off, so there will be a difference between before the spin-off and after the spin-off. Good luck!
Authors’ Response: We appreciate your insightful comments. We have added the limitations and future research opportunities to the conclusion section.
“The present study has identified several limitations. The utilization of a dummy variable to assess the impact of spin-offs on financing risk, while effective in capturing the immediate effects of regulatory policies, may not fully account for the nuanced dynamics at play. Moreover, the study's scope is constrained by the use of semi-annual data up to 2022 and its focus on the Indonesian Islamic banking industry. To address these limitations, future research could benefit from employing a difference-in-differences analysis to more precisely estimate the causal impact of spin-offs. Furthermore, incorporating a broader range of internal and external variables, as well as expanding the analysis to include Islamic banks in Southeast Asia or OIC countries, would provide a more comprehensive understanding of the phenomenon.”
We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1: The article is well written and meets the rules for good articles. The problem formulation is based on research gaps from previous research results. Apart from that, the research method is also appropriate, including the indicators, and statistical analysis and results. However, researchers need to attach the year in which the Islamic banking spin-off was carried out, because each Islamic banking spin-off is different.
Authors’ Response: Following your insightful suggestion, we have revised the Method section to provide a more comprehensive overview of the restructuring processes within the Indonesian Islamic banking industry. The newly added paragraph, which details historical data on spin-offs, conversions, and mergers, directly addresses your feedback regarding the need for more specific examples.
“The Indonesian Islamic banking landscape has witnessed various restructuring strategies. Pure spin-offs were employed by banks such as Bukopin Syariah (2008), BNI Syariah (2008), BRI Syariah (2010), BJB Syariah (2010), and Bank Nano Syariah (2024). Conversion, another common approach, was adopted by BTPN Syariah (2014), Bank Aceh Syariah (2016), BPD NTB Syariah (2018), and BPD Riau Kepri Syariah (2022). The formation of Bank Syariah Indonesia in 2021 marked a significant merger in the sector. Moreover, a combination of acquisition and conversion was utilized sequentially by banks including Bank Panin Dubai Syariah (2009), Bank Victoria Syariah (2010), BCA Syariah (2010), and Bank Aladin Syariah (2010).”

Reviewer’s Suggestion 2: In table 2 about Descriptive analysis of variables, the description of each variable should be explained well.
Authors’ Response: Thank you for your valuable feedback. We have addressed your concerns regarding variable descriptions in the descriptive analysis.
“Table 2 presents descriptive statistics for the variables spanning from 2006 to 2022. The average value of NPF, as a financing risk proxy, stands at 3.10%, remaining below the generally accepted upper limit of 5.00%. The lowest NPF value, at 0.00%, indicates that some Islamic banks exhibit no risky financing, while the highest recorded NPF value is 53.27%. This relatively high variability is supported by a standard deviation of 4.17%. SPINOFF, a dummy variable, has a mean of 0.27, indicating that spin-off events are relatively rare. Its standard deviation of 0.45 suggests moderate variability in occurrence. Regarding assets and market share, the lower mean values compared to the standard deviation suggest that Islamic banks in Indonesia are still dominated by a few larger banks, reflecting differences in institutional capacity and market reach.
Similarly, the Consumer Price Index (CPI) and Industrial Production Index (IPI) show moderate variability, with standard deviations of 2.70 and 18.33, respectively, reflecting macroeconomic conditions that influence Islamic banking performance. Oil prices (OIL), a critical variable for resource-dependent economies, have a mean of IDR 0.86 million per barrel, with relatively stable fluctuations (standard deviation of 0.28). This stability contrasts with the high variability observed in institutional size and non-performing financing.
Overall, the descriptive analysis underscores significant heterogeneity in institutional characteristics and economic factors, suggesting the need for tailored strategies in managing Islamic banks. Institutions must address wide disparities in size and market share, ensuring resilience to external economic conditions such as inflation, industrial output fluctuations, and oil price volatility.”

Reviewer’s Suggestion 3: Furthermore, in conclusion, it must have limitations and future research agenda. It would be good to explain why the research did not analyze financing risks before the spin-off, so there will be a difference between before the spin-off and after the spin-off. Good luck!
Authors’ Response: We appreciate your insightful comments. We have added the limitations and future research opportunities to the conclusion section.
“The present study has identified several limitations. The utilization of a dummy variable to assess the impact of spin-offs on financing risk, while effective in capturing the immediate effects of regulatory policies, may not fully account for the nuanced dynamics at play. Moreover, the study's scope is constrained by the use of semi-annual data up to 2022 and its focus on the Indonesian Islamic banking industry. To address these limitations, future research could benefit from employing a difference-in-differences analysis to more precisely estimate the causal impact of spin-offs. Furthermore, incorporating a broader range of internal and external variables, as well as expanding the analysis to include Islamic banks in Southeast Asia or OIC countries, would provide a more comprehensive understanding of the phenomenon.”
Competing Interests: none Close
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Reviewer Report 07 Nov 2024

Azharsyah Ibrahim, Universitas Islam Negeri Ar-Raniry, Banda Aceh, Indonesia

Approved with Reservations

https://doi.org/10.5256/f1000research.172879.r336316

Summary
This paper investigates the impact of the spin-off decision (Islamic windows separating from conventional banks to become independent Islamic banks) on financing risk in Indonesian Islamic banks. Using semi-annual data from 2006-2022 for 35 Islamic banks, the study employs a dynamic panel model with Generalized Method of Moments (GMM) estimation. The key finding is that spin-offs significantly reduce financing risk, particularly for larger banks. The study also finds that larger Islamic banks and those with a higher market share generally face higher financing risk. The negative correlation between oil prices and non-performing financing suggests that increasing oil prices may indirectly benefit Islamic banks by stimulating the economy.
Evaluation

Is the work clearly and accurately presented and does it cite the current literature? Partly. While the presentation is generally clear, some areas could benefit from more precise language and further elaboration. The literature review, while extensive, could be structured more thematically to better highlight the existing research gaps and the study's contribution. Some cited works appear very recent (2023-2024) and I am unable to verify their accessibility/existence given my current knowledge cutoff. It's important to ensure all cited works are publicly accessible.
Is the study design appropriate and is the work technically sound? Partly. The use of a dynamic panel model with GMM is appropriate for addressing potential endogeneity. However, the justification for using the OPEC basket price as a sole indicator of global oil prices could be strengthened. A broader measure or a combination of indicators might be more representative. Additionally, more detail on the specific GMM implementation (e.g., lag selection, instrument validity tests beyond Sargan) would enhance the technical soundness.
Are sufficient details of methods and analysis provided to allow replication by others? Partly. While the data sources are mentioned, the specific variables used within the model (e.g., how "market share" is calculated) could be more explicitly defined. The provided dataset link on Zenodo was checked and is accessible. However, clear instructions on how to merge the bank-specific data with the macroeconomic data are necessary for full replication. More detail on the GMM implementation is also needed (as mentioned above).
If applicable, is the statistical analysis and its interpretation appropriate? Partly. The interpretation of the results generally aligns with the findings. However, the discussion of the inconsistent results for economic growth and inflation could be improved. Exploring potential reasons for these inconsistencies or considering alternative model specifications might provide more robust insights. Furthermore, while the Sargan test is reported, more comprehensive diagnostics related to the GMM estimation would strengthen the analysis.
Are all the source data underlying the results available to ensure full reproducibility? Yes. The data sources are listed and the Zenodo link provides the bank-level data. The macroeconomic data sources are also publicly accessible. However, as noted above, clearer instructions on data merging are needed.
Are the conclusions drawn adequately supported by the results? Partly. The main conclusion regarding the positive impact of spin-offs on reducing financing risk is supported by the findings. However, the claim that larger spin-off banks are more exposed to risk needs further clarification. While the results show that size increases risk in general, it's not entirely clear from the presented analysis whether this effect is amplified after the spin-off, or if larger banks simply start at a higher risk level pre-spin-off. More nuanced analysis and discussion are needed to fully support this specific conclusion.

Specific Recommendations for Improvement:

Literature Review: Organize the literature thematically and clearly articulate the research gap being addressed. Verify the accessibility of all cited works.
Methodology: Provide a stronger justification for the choice of oil price indicator. Elaborate on the specific GMM implementation details (lag selection, instrument details, and robustness checks).
Data and Replication: Provide clear definitions of all variables, including calculations. Include detailed instructions on how to merge the datasets used.
Statistical Analysis: Explore reasons for the inconsistent results of control variables. Provide more comprehensive GMM diagnostics.
Conclusion: Refine the discussion about the impact of size on risk for spin-off banks. Provide clearer evidence to support the claim of increased risk for larger spin-off banks compared to smaller ones. Consider separating the impact of size on risk in general from the specific impact on spin-off banks.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Islamic management, banking, and finance

CITE

Report a concern

Author Response 04 Feb 2025

Zulfikar Bagus Pambuko, Islamic Economics Law, Universitas Muhammadiyah Magelang, Magelang, Indonesia

04 Feb 2025

Author Response

We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the ... Continue reading We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1: Is the work clearly and accurately presented and does it cite the current literature? Partly. While the presentation is generally clear, some areas could benefit from more precise language and further elaboration. The literature review, while extensive, could be structured more thematically to better highlight the existing research gaps and the study's contribution. Some cited works appear very recent (2023-2024) and I am unable to verify their accessibility/existence given my current knowledge cutoff. It's important to ensure all cited works are publicly accessible.
Authors’ Response: We would like to express our gratitude for your valuable feedback in enhancing the quality of our article. In response to your suggestion, we have incorporated a dedicated section on literature review as follows:
“Literature Review
The management of bad financing or non-performing financing (NPF) presents a critical challenge for Islamic banking institutions in Indonesia (Imaduddin, 2008; Nugroho et al., 2018; Soekapdjo et al., 2018). Non-performing financing, characterized by loans or financing contracts in default or at risk of default, can have far-reaching implications on the stability and credibility of financial institutions (Hernawati et al., 2021). In traditional banking, its known as non-performing loan (NPL). The existence of financing risk can hinder the stability of Islamic banking entities, potentially jeopardizing their capacity to facilitate economic growth and foster financial inclusivity (Banna et al., 2022). Considering the distinct characteristics of Islamic finance, rooted in risk-sharing and asset-backed principles, a comprehensive exploration of how spin-off decisions influence financing risk, specifically non-performing financing, requires further investigation.
Bank-specific factors influencing financing risk
Research on NPF in Islamic banking and NPL in conventional banking reveals that various internal and external factors influence financing risk. For conventional banks, external factors such as macroeconomic conditions play a dominant role in shaping NPL levels, while in Islamic banks, both internal and external factors significantly affect NPF (Loang et al., 2023; Setiawan et al., 2017). Among bank-specific factors, an increase in NPF is associated with variables such as bank size, operating efficiency ratio, liquid asset ratio, financing-to-deposit ratio, and foreign ownership (Havidz & Setiawan, 2015; Hosen & Muhari, 2019; Osunkoya et al., 2023; Rahim & Zakaria, 2013; Saw et al., 2022; Zheng et al., 2019). Conversely, factors that contribute to a reduction in NPF include higher net operating profit, capital adequacy ratio, return on assets, cost-to-income ratio, deposit fund growth, return on equity, loan growth, and market concentration (Hartanto & Samputra, 2023; Hosen & Muhari, 2019; Muhammad et al., 2020; Rahim & Zakaria, 2013; Zheng et al., 2019).
Macroeconomic factors influencing financing risk
Macroeconomic conditions also significantly impact the level of financing risk in Islamic banks. Negative economic indicators such as exchange rate fluctuations, inflation, and economic policy uncertainty have been found to increase NPF levels (Firmansyah, 2014; Hosen & Muhari, 2019; Karadima & Louri, 2021; Osunkoya et al., 2023; Zheng et al., 2019). On the other hand, positive macroeconomic trends such as GDP growth and reduced unemployment contribute to a decrease in NPF (Ferhi, 2018; Hassan et al., 2019; Hernawati et al., 2021; Le & Diep, 2020; Zheng et al., 2019). Additionally, a surplus in foreign exchange reserves can trigger a financing boom, which may subsequently affect financing quality and contribute to increased NPF levels (Kuncoro, 2024).”

Reviewer’s Suggestion 2: Is the study design appropriate and is the work technically sound? Partly. The use of a dynamic panel model with GMM is appropriate for addressing potential endogeneity. However, the justification for using the OPEC basket price as a sole indicator of global oil prices could be strengthened. A broader measure or a combination of indicators might be more representative. Additionally, more detail on the specific GMM implementation (e.g., lag selection, instrument validity tests beyond Sargan) would enhance the technical soundness.
Authors’ Response:
We have added an explanation regarding the use of the OPEC basket price in the “Method” section.
“This price serves as a crucial benchmark in determining crude oil prices in the global market, including Indonesia, although it is merely one of many indicators influenced by various complex factors.”
Additionally, we have provided a more detailed explanation of lag selection in GMM estimation.
“A lag order of one (lag 1) was used for estimation”

Reviewer’s Suggestion 3: Are sufficient details of methods and analysis provided to allow replication by others? Partly. While the data sources are mentioned, the specific variables used within the model (e.g., how "market share" is calculated) could be more explicitly defined. The provided dataset link on Zenodo was checked and is accessible. However, clear instructions on how to merge the bank-specific data with the macroeconomic data are necessary for full replication. More detail on the GMM implementation is also needed (as mentioned above).
Authors’ Response: Thank you for the suggestion. Supplementary information regarding the variables has been provided in Table 1.
“Non-performing financing is the ratio of bad financing by total financing”
“Inflation is measured using the Consumer Price Index (CPI), which compares the total cost of a fixed basket of goods and services in the current period to the cost of the same basket in a base period”
“Economic growth is measured using the Industrial Production Index (IPI), which is calculated by dividing the value of production in a specific period by the value of production in the base year 2010.”
The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 4: If applicable, is the statistical analysis and its interpretation appropriate? Partly. The interpretation of the results generally aligns with the findings. However, the discussion of the inconsistent results for economic growth and inflation could be improved. Exploring potential reasons for these inconsistencies or considering alternative model specifications might provide more robust insights. Furthermore, while the Sargan test is reported, more comprehensive diagnostics related to the GMM estimation would strengthen the analysis.
Authors’ Response: Following the reviewer’s suggestion, we have enhanced the discussion of economic growth and inflation in the “empirical results” section. Furthermore, we have provided a more detailed statistical analysis to elucidate the differences between the difference GMM and system GMM estimators.
“On the other hand, economic growth and inflation exhibit inconsistent results across different model specifications. Inflation generally exerts a positive influence on financing risk, as observed in models 2, 6, and 8. This finding aligns with previous studies, which suggests that inflation can reduce purchasing power, leading to increased loan defaults (Awdeh et al., 2024; Patiu & Eleazar, 2024). However, the impact of economic growth on financing risk is more nuanced, with negative effects observed in the difference GMM estimation and positive effects in the system GMM estimation. This discrepancy might be attributed to the non-linear relationship between economic growth and NPF, where difference GMM captures short-term effects while system GMM captures long-term effects. The positive relationship in the system GMM, which is considered to provide more robust estimates, may reflect the potential for credit booms during periods of economic expansion, leading Islamic banks to loosen lending standards (Castro, 2013; Huizinga & Laeven, 2019; Mongid et al., 2023). Furthermore, this divergence in results may be explained by the finite sample bias in difference GMM, which relies solely on differences across time periods and excludes the level data.”

Reviewer’s Suggestion 5: Are all the source data underlying the results available to ensure full reproducibility? Yes. The data sources are listed and the Zenodo link provides the bank-level data. The macroeconomic data sources are also publicly accessible. However, as noted above, clearer instructions on data merging are needed.
Authors’ Response: Thank you for the suggestion. The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 6: Are the conclusions drawn adequately supported by the results? Partly. The main conclusion regarding the positive impact of spin-offs on reducing financing risk is supported by the findings. However, the claim that larger spin-off banks are more exposed to risk needs further clarification. While the results show that size increases risk in general, it's not entirely clear from the presented analysis whether this effect is amplified after the spin-off, or if larger banks simply start at a higher risk level pre-spin-off. More nuanced analysis and discussion are needed to fully support this specific conclusion.
Authors’ Response: The conclusion that larger spin-off banks are more exposed to risk is derived from the interaction variables between the spin-off and size variables (spinoff*size), as detailed in the fifth paragraph of the "Empirical Results" section.
“Furthermore, these results are corroborated by the estimations of the interaction variable (spinoff*size), which significantly influences non-performing financing. Thus, larger Islamic banks that undergo spin-offs carry a higher financing risk compared to smaller banks. As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”

Specific Recommendations for Improvement:
Reviewer’s Suggestion 7: Literature Review: Organize the literature thematically and clearly articulate the research gap being addressed. Verify the accessibility of all cited works.
Authors’ Response: Please refer to response number 1.

Reviewer’s Suggestion 8: Methodology: Provide a stronger justification for the choice of oil price indicator. Elaborate on the specific GMM implementation details (lag selection, instrument details, and robustness checks).
Authors’ Response: Please refer to response number 2.

Reviewer’s Suggestion 9: Data and Replication: Provide clear definitions of all variables, including calculations. Include detailed instructions on how to merge the datasets used.
Authors’ Response: Please refer to response number 4.

Reviewer’s Suggestion 10: Statistical Analysis: Explore reasons for the inconsistent results of control variables. Provide more comprehensive GMM diagnostics.
Authors’ Response: Based on the literature review, our implementation of the GMM model adheres to the established guidelines. While inconsistencies in the estimation results between difference GMM and system GMM for some variables are common, the inconsistencies in the spin-off, size, and IPI variables might be attributed to endogeneity issues better addressed by system GMM or measurement errors in difference GMM. This can be explained by the inherent differences between the two estimators. Difference GMM uses first differences to eliminate fixed effects, whereas system GMM combines instruments from both first differences and levels, allowing it to capture more of the variability in the data (The preceding explanation offers a more in-depth analysis of response number 4).

Reviewer’s Suggestion 11: Conclusion: Refine the discussion about the impact of size on risk for spin-off banks. Provide clearer evidence to support the claim of increased risk for larger spin-off banks compared to smaller ones. Consider separating the impact of size on risk in general from the specific impact on spin-off banks.
Authors’ Response: we have explained separately in the last 2 paragraphs in the Results and Discussion section related to the general impact of size on NPF, followed by a specific examination of the interaction between spin-off and size on NPF. The positive interaction term between spin-off and size (spinoff*size) on Table 4 suggests an increased risk of NPF for larger spin-off banks. we have also added a supporting statement, “As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”
We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1: Is the work clearly and accurately presented and does it cite the current literature? Partly. While the presentation is generally clear, some areas could benefit from more precise language and further elaboration. The literature review, while extensive, could be structured more thematically to better highlight the existing research gaps and the study's contribution. Some cited works appear very recent (2023-2024) and I am unable to verify their accessibility/existence given my current knowledge cutoff. It's important to ensure all cited works are publicly accessible.
Authors’ Response: We would like to express our gratitude for your valuable feedback in enhancing the quality of our article. In response to your suggestion, we have incorporated a dedicated section on literature review as follows:
“Literature Review
The management of bad financing or non-performing financing (NPF) presents a critical challenge for Islamic banking institutions in Indonesia (Imaduddin, 2008; Nugroho et al., 2018; Soekapdjo et al., 2018). Non-performing financing, characterized by loans or financing contracts in default or at risk of default, can have far-reaching implications on the stability and credibility of financial institutions (Hernawati et al., 2021). In traditional banking, its known as non-performing loan (NPL). The existence of financing risk can hinder the stability of Islamic banking entities, potentially jeopardizing their capacity to facilitate economic growth and foster financial inclusivity (Banna et al., 2022). Considering the distinct characteristics of Islamic finance, rooted in risk-sharing and asset-backed principles, a comprehensive exploration of how spin-off decisions influence financing risk, specifically non-performing financing, requires further investigation.
Bank-specific factors influencing financing risk
Research on NPF in Islamic banking and NPL in conventional banking reveals that various internal and external factors influence financing risk. For conventional banks, external factors such as macroeconomic conditions play a dominant role in shaping NPL levels, while in Islamic banks, both internal and external factors significantly affect NPF (Loang et al., 2023; Setiawan et al., 2017). Among bank-specific factors, an increase in NPF is associated with variables such as bank size, operating efficiency ratio, liquid asset ratio, financing-to-deposit ratio, and foreign ownership (Havidz & Setiawan, 2015; Hosen & Muhari, 2019; Osunkoya et al., 2023; Rahim & Zakaria, 2013; Saw et al., 2022; Zheng et al., 2019). Conversely, factors that contribute to a reduction in NPF include higher net operating profit, capital adequacy ratio, return on assets, cost-to-income ratio, deposit fund growth, return on equity, loan growth, and market concentration (Hartanto & Samputra, 2023; Hosen & Muhari, 2019; Muhammad et al., 2020; Rahim & Zakaria, 2013; Zheng et al., 2019).
Macroeconomic factors influencing financing risk
Macroeconomic conditions also significantly impact the level of financing risk in Islamic banks. Negative economic indicators such as exchange rate fluctuations, inflation, and economic policy uncertainty have been found to increase NPF levels (Firmansyah, 2014; Hosen & Muhari, 2019; Karadima & Louri, 2021; Osunkoya et al., 2023; Zheng et al., 2019). On the other hand, positive macroeconomic trends such as GDP growth and reduced unemployment contribute to a decrease in NPF (Ferhi, 2018; Hassan et al., 2019; Hernawati et al., 2021; Le & Diep, 2020; Zheng et al., 2019). Additionally, a surplus in foreign exchange reserves can trigger a financing boom, which may subsequently affect financing quality and contribute to increased NPF levels (Kuncoro, 2024).”

Reviewer’s Suggestion 2: Is the study design appropriate and is the work technically sound? Partly. The use of a dynamic panel model with GMM is appropriate for addressing potential endogeneity. However, the justification for using the OPEC basket price as a sole indicator of global oil prices could be strengthened. A broader measure or a combination of indicators might be more representative. Additionally, more detail on the specific GMM implementation (e.g., lag selection, instrument validity tests beyond Sargan) would enhance the technical soundness.
Authors’ Response:
We have added an explanation regarding the use of the OPEC basket price in the “Method” section.
“This price serves as a crucial benchmark in determining crude oil prices in the global market, including Indonesia, although it is merely one of many indicators influenced by various complex factors.”
Additionally, we have provided a more detailed explanation of lag selection in GMM estimation.
“A lag order of one (lag 1) was used for estimation”

Reviewer’s Suggestion 3: Are sufficient details of methods and analysis provided to allow replication by others? Partly. While the data sources are mentioned, the specific variables used within the model (e.g., how "market share" is calculated) could be more explicitly defined. The provided dataset link on Zenodo was checked and is accessible. However, clear instructions on how to merge the bank-specific data with the macroeconomic data are necessary for full replication. More detail on the GMM implementation is also needed (as mentioned above).
Authors’ Response: Thank you for the suggestion. Supplementary information regarding the variables has been provided in Table 1.
“Non-performing financing is the ratio of bad financing by total financing”
“Inflation is measured using the Consumer Price Index (CPI), which compares the total cost of a fixed basket of goods and services in the current period to the cost of the same basket in a base period”
“Economic growth is measured using the Industrial Production Index (IPI), which is calculated by dividing the value of production in a specific period by the value of production in the base year 2010.”
The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 4: If applicable, is the statistical analysis and its interpretation appropriate? Partly. The interpretation of the results generally aligns with the findings. However, the discussion of the inconsistent results for economic growth and inflation could be improved. Exploring potential reasons for these inconsistencies or considering alternative model specifications might provide more robust insights. Furthermore, while the Sargan test is reported, more comprehensive diagnostics related to the GMM estimation would strengthen the analysis.
Authors’ Response: Following the reviewer’s suggestion, we have enhanced the discussion of economic growth and inflation in the “empirical results” section. Furthermore, we have provided a more detailed statistical analysis to elucidate the differences between the difference GMM and system GMM estimators.
“On the other hand, economic growth and inflation exhibit inconsistent results across different model specifications. Inflation generally exerts a positive influence on financing risk, as observed in models 2, 6, and 8. This finding aligns with previous studies, which suggests that inflation can reduce purchasing power, leading to increased loan defaults (Awdeh et al., 2024; Patiu & Eleazar, 2024). However, the impact of economic growth on financing risk is more nuanced, with negative effects observed in the difference GMM estimation and positive effects in the system GMM estimation. This discrepancy might be attributed to the non-linear relationship between economic growth and NPF, where difference GMM captures short-term effects while system GMM captures long-term effects. The positive relationship in the system GMM, which is considered to provide more robust estimates, may reflect the potential for credit booms during periods of economic expansion, leading Islamic banks to loosen lending standards (Castro, 2013; Huizinga & Laeven, 2019; Mongid et al., 2023). Furthermore, this divergence in results may be explained by the finite sample bias in difference GMM, which relies solely on differences across time periods and excludes the level data.”

Reviewer’s Suggestion 5: Are all the source data underlying the results available to ensure full reproducibility? Yes. The data sources are listed and the Zenodo link provides the bank-level data. The macroeconomic data sources are also publicly accessible. However, as noted above, clearer instructions on data merging are needed.
Authors’ Response: Thank you for the suggestion. The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 6: Are the conclusions drawn adequately supported by the results? Partly. The main conclusion regarding the positive impact of spin-offs on reducing financing risk is supported by the findings. However, the claim that larger spin-off banks are more exposed to risk needs further clarification. While the results show that size increases risk in general, it's not entirely clear from the presented analysis whether this effect is amplified after the spin-off, or if larger banks simply start at a higher risk level pre-spin-off. More nuanced analysis and discussion are needed to fully support this specific conclusion.
Authors’ Response: The conclusion that larger spin-off banks are more exposed to risk is derived from the interaction variables between the spin-off and size variables (spinoff*size), as detailed in the fifth paragraph of the "Empirical Results" section.
“Furthermore, these results are corroborated by the estimations of the interaction variable (spinoff*size), which significantly influences non-performing financing. Thus, larger Islamic banks that undergo spin-offs carry a higher financing risk compared to smaller banks. As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”

Specific Recommendations for Improvement:
Reviewer’s Suggestion 7: Literature Review: Organize the literature thematically and clearly articulate the research gap being addressed. Verify the accessibility of all cited works.
Authors’ Response: Please refer to response number 1.

Reviewer’s Suggestion 8: Methodology: Provide a stronger justification for the choice of oil price indicator. Elaborate on the specific GMM implementation details (lag selection, instrument details, and robustness checks).
Authors’ Response: Please refer to response number 2.

Reviewer’s Suggestion 9: Data and Replication: Provide clear definitions of all variables, including calculations. Include detailed instructions on how to merge the datasets used.
Authors’ Response: Please refer to response number 4.

Reviewer’s Suggestion 10: Statistical Analysis: Explore reasons for the inconsistent results of control variables. Provide more comprehensive GMM diagnostics.
Authors’ Response: Based on the literature review, our implementation of the GMM model adheres to the established guidelines. While inconsistencies in the estimation results between difference GMM and system GMM for some variables are common, the inconsistencies in the spin-off, size, and IPI variables might be attributed to endogeneity issues better addressed by system GMM or measurement errors in difference GMM. This can be explained by the inherent differences between the two estimators. Difference GMM uses first differences to eliminate fixed effects, whereas system GMM combines instruments from both first differences and levels, allowing it to capture more of the variability in the data (The preceding explanation offers a more in-depth analysis of response number 4).

Reviewer’s Suggestion 11: Conclusion: Refine the discussion about the impact of size on risk for spin-off banks. Provide clearer evidence to support the claim of increased risk for larger spin-off banks compared to smaller ones. Consider separating the impact of size on risk in general from the specific impact on spin-off banks.
Authors’ Response: we have explained separately in the last 2 paragraphs in the Results and Discussion section related to the general impact of size on NPF, followed by a specific examination of the interaction between spin-off and size on NPF. The positive interaction term between spin-off and size (spinoff*size) on Table 4 suggests an increased risk of NPF for larger spin-off banks. we have also added a supporting statement, “As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”
Competing Interests: none Close
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COMMENTS ON THIS REPORT

Author Response 04 Feb 2025

Zulfikar Bagus Pambuko, Islamic Economics Law, Universitas Muhammadiyah Magelang, Magelang, Indonesia

04 Feb 2025

Author Response

We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the ... Continue reading We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1: Is the work clearly and accurately presented and does it cite the current literature? Partly. While the presentation is generally clear, some areas could benefit from more precise language and further elaboration. The literature review, while extensive, could be structured more thematically to better highlight the existing research gaps and the study's contribution. Some cited works appear very recent (2023-2024) and I am unable to verify their accessibility/existence given my current knowledge cutoff. It's important to ensure all cited works are publicly accessible.
Authors’ Response: We would like to express our gratitude for your valuable feedback in enhancing the quality of our article. In response to your suggestion, we have incorporated a dedicated section on literature review as follows:
“Literature Review
The management of bad financing or non-performing financing (NPF) presents a critical challenge for Islamic banking institutions in Indonesia (Imaduddin, 2008; Nugroho et al., 2018; Soekapdjo et al., 2018). Non-performing financing, characterized by loans or financing contracts in default or at risk of default, can have far-reaching implications on the stability and credibility of financial institutions (Hernawati et al., 2021). In traditional banking, its known as non-performing loan (NPL). The existence of financing risk can hinder the stability of Islamic banking entities, potentially jeopardizing their capacity to facilitate economic growth and foster financial inclusivity (Banna et al., 2022). Considering the distinct characteristics of Islamic finance, rooted in risk-sharing and asset-backed principles, a comprehensive exploration of how spin-off decisions influence financing risk, specifically non-performing financing, requires further investigation.
Bank-specific factors influencing financing risk
Research on NPF in Islamic banking and NPL in conventional banking reveals that various internal and external factors influence financing risk. For conventional banks, external factors such as macroeconomic conditions play a dominant role in shaping NPL levels, while in Islamic banks, both internal and external factors significantly affect NPF (Loang et al., 2023; Setiawan et al., 2017). Among bank-specific factors, an increase in NPF is associated with variables such as bank size, operating efficiency ratio, liquid asset ratio, financing-to-deposit ratio, and foreign ownership (Havidz & Setiawan, 2015; Hosen & Muhari, 2019; Osunkoya et al., 2023; Rahim & Zakaria, 2013; Saw et al., 2022; Zheng et al., 2019). Conversely, factors that contribute to a reduction in NPF include higher net operating profit, capital adequacy ratio, return on assets, cost-to-income ratio, deposit fund growth, return on equity, loan growth, and market concentration (Hartanto & Samputra, 2023; Hosen & Muhari, 2019; Muhammad et al., 2020; Rahim & Zakaria, 2013; Zheng et al., 2019).
Macroeconomic factors influencing financing risk
Macroeconomic conditions also significantly impact the level of financing risk in Islamic banks. Negative economic indicators such as exchange rate fluctuations, inflation, and economic policy uncertainty have been found to increase NPF levels (Firmansyah, 2014; Hosen & Muhari, 2019; Karadima & Louri, 2021; Osunkoya et al., 2023; Zheng et al., 2019). On the other hand, positive macroeconomic trends such as GDP growth and reduced unemployment contribute to a decrease in NPF (Ferhi, 2018; Hassan et al., 2019; Hernawati et al., 2021; Le & Diep, 2020; Zheng et al., 2019). Additionally, a surplus in foreign exchange reserves can trigger a financing boom, which may subsequently affect financing quality and contribute to increased NPF levels (Kuncoro, 2024).”

Reviewer’s Suggestion 2: Is the study design appropriate and is the work technically sound? Partly. The use of a dynamic panel model with GMM is appropriate for addressing potential endogeneity. However, the justification for using the OPEC basket price as a sole indicator of global oil prices could be strengthened. A broader measure or a combination of indicators might be more representative. Additionally, more detail on the specific GMM implementation (e.g., lag selection, instrument validity tests beyond Sargan) would enhance the technical soundness.
Authors’ Response:
We have added an explanation regarding the use of the OPEC basket price in the “Method” section.
“This price serves as a crucial benchmark in determining crude oil prices in the global market, including Indonesia, although it is merely one of many indicators influenced by various complex factors.”
Additionally, we have provided a more detailed explanation of lag selection in GMM estimation.
“A lag order of one (lag 1) was used for estimation”

Reviewer’s Suggestion 3: Are sufficient details of methods and analysis provided to allow replication by others? Partly. While the data sources are mentioned, the specific variables used within the model (e.g., how "market share" is calculated) could be more explicitly defined. The provided dataset link on Zenodo was checked and is accessible. However, clear instructions on how to merge the bank-specific data with the macroeconomic data are necessary for full replication. More detail on the GMM implementation is also needed (as mentioned above).
Authors’ Response: Thank you for the suggestion. Supplementary information regarding the variables has been provided in Table 1.
“Non-performing financing is the ratio of bad financing by total financing”
“Inflation is measured using the Consumer Price Index (CPI), which compares the total cost of a fixed basket of goods and services in the current period to the cost of the same basket in a base period”
“Economic growth is measured using the Industrial Production Index (IPI), which is calculated by dividing the value of production in a specific period by the value of production in the base year 2010.”
The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 4: If applicable, is the statistical analysis and its interpretation appropriate? Partly. The interpretation of the results generally aligns with the findings. However, the discussion of the inconsistent results for economic growth and inflation could be improved. Exploring potential reasons for these inconsistencies or considering alternative model specifications might provide more robust insights. Furthermore, while the Sargan test is reported, more comprehensive diagnostics related to the GMM estimation would strengthen the analysis.
Authors’ Response: Following the reviewer’s suggestion, we have enhanced the discussion of economic growth and inflation in the “empirical results” section. Furthermore, we have provided a more detailed statistical analysis to elucidate the differences between the difference GMM and system GMM estimators.
“On the other hand, economic growth and inflation exhibit inconsistent results across different model specifications. Inflation generally exerts a positive influence on financing risk, as observed in models 2, 6, and 8. This finding aligns with previous studies, which suggests that inflation can reduce purchasing power, leading to increased loan defaults (Awdeh et al., 2024; Patiu & Eleazar, 2024). However, the impact of economic growth on financing risk is more nuanced, with negative effects observed in the difference GMM estimation and positive effects in the system GMM estimation. This discrepancy might be attributed to the non-linear relationship between economic growth and NPF, where difference GMM captures short-term effects while system GMM captures long-term effects. The positive relationship in the system GMM, which is considered to provide more robust estimates, may reflect the potential for credit booms during periods of economic expansion, leading Islamic banks to loosen lending standards (Castro, 2013; Huizinga & Laeven, 2019; Mongid et al., 2023). Furthermore, this divergence in results may be explained by the finite sample bias in difference GMM, which relies solely on differences across time periods and excludes the level data.”

Reviewer’s Suggestion 5: Are all the source data underlying the results available to ensure full reproducibility? Yes. The data sources are listed and the Zenodo link provides the bank-level data. The macroeconomic data sources are also publicly accessible. However, as noted above, clearer instructions on data merging are needed.
Authors’ Response: Thank you for the suggestion. The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 6: Are the conclusions drawn adequately supported by the results? Partly. The main conclusion regarding the positive impact of spin-offs on reducing financing risk is supported by the findings. However, the claim that larger spin-off banks are more exposed to risk needs further clarification. While the results show that size increases risk in general, it's not entirely clear from the presented analysis whether this effect is amplified after the spin-off, or if larger banks simply start at a higher risk level pre-spin-off. More nuanced analysis and discussion are needed to fully support this specific conclusion.
Authors’ Response: The conclusion that larger spin-off banks are more exposed to risk is derived from the interaction variables between the spin-off and size variables (spinoff*size), as detailed in the fifth paragraph of the "Empirical Results" section.
“Furthermore, these results are corroborated by the estimations of the interaction variable (spinoff*size), which significantly influences non-performing financing. Thus, larger Islamic banks that undergo spin-offs carry a higher financing risk compared to smaller banks. As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”

Specific Recommendations for Improvement:
Reviewer’s Suggestion 7: Literature Review: Organize the literature thematically and clearly articulate the research gap being addressed. Verify the accessibility of all cited works.
Authors’ Response: Please refer to response number 1.

Reviewer’s Suggestion 8: Methodology: Provide a stronger justification for the choice of oil price indicator. Elaborate on the specific GMM implementation details (lag selection, instrument details, and robustness checks).
Authors’ Response: Please refer to response number 2.

Reviewer’s Suggestion 9: Data and Replication: Provide clear definitions of all variables, including calculations. Include detailed instructions on how to merge the datasets used.
Authors’ Response: Please refer to response number 4.

Reviewer’s Suggestion 10: Statistical Analysis: Explore reasons for the inconsistent results of control variables. Provide more comprehensive GMM diagnostics.
Authors’ Response: Based on the literature review, our implementation of the GMM model adheres to the established guidelines. While inconsistencies in the estimation results between difference GMM and system GMM for some variables are common, the inconsistencies in the spin-off, size, and IPI variables might be attributed to endogeneity issues better addressed by system GMM or measurement errors in difference GMM. This can be explained by the inherent differences between the two estimators. Difference GMM uses first differences to eliminate fixed effects, whereas system GMM combines instruments from both first differences and levels, allowing it to capture more of the variability in the data (The preceding explanation offers a more in-depth analysis of response number 4).

Reviewer’s Suggestion 11: Conclusion: Refine the discussion about the impact of size on risk for spin-off banks. Provide clearer evidence to support the claim of increased risk for larger spin-off banks compared to smaller ones. Consider separating the impact of size on risk in general from the specific impact on spin-off banks.
Authors’ Response: we have explained separately in the last 2 paragraphs in the Results and Discussion section related to the general impact of size on NPF, followed by a specific examination of the interaction between spin-off and size on NPF. The positive interaction term between spin-off and size (spinoff*size) on Table 4 suggests an increased risk of NPF for larger spin-off banks. we have also added a supporting statement, “As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”
We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1: Is the work clearly and accurately presented and does it cite the current literature? Partly. While the presentation is generally clear, some areas could benefit from more precise language and further elaboration. The literature review, while extensive, could be structured more thematically to better highlight the existing research gaps and the study's contribution. Some cited works appear very recent (2023-2024) and I am unable to verify their accessibility/existence given my current knowledge cutoff. It's important to ensure all cited works are publicly accessible.
Authors’ Response: We would like to express our gratitude for your valuable feedback in enhancing the quality of our article. In response to your suggestion, we have incorporated a dedicated section on literature review as follows:
“Literature Review
The management of bad financing or non-performing financing (NPF) presents a critical challenge for Islamic banking institutions in Indonesia (Imaduddin, 2008; Nugroho et al., 2018; Soekapdjo et al., 2018). Non-performing financing, characterized by loans or financing contracts in default or at risk of default, can have far-reaching implications on the stability and credibility of financial institutions (Hernawati et al., 2021). In traditional banking, its known as non-performing loan (NPL). The existence of financing risk can hinder the stability of Islamic banking entities, potentially jeopardizing their capacity to facilitate economic growth and foster financial inclusivity (Banna et al., 2022). Considering the distinct characteristics of Islamic finance, rooted in risk-sharing and asset-backed principles, a comprehensive exploration of how spin-off decisions influence financing risk, specifically non-performing financing, requires further investigation.
Bank-specific factors influencing financing risk
Research on NPF in Islamic banking and NPL in conventional banking reveals that various internal and external factors influence financing risk. For conventional banks, external factors such as macroeconomic conditions play a dominant role in shaping NPL levels, while in Islamic banks, both internal and external factors significantly affect NPF (Loang et al., 2023; Setiawan et al., 2017). Among bank-specific factors, an increase in NPF is associated with variables such as bank size, operating efficiency ratio, liquid asset ratio, financing-to-deposit ratio, and foreign ownership (Havidz & Setiawan, 2015; Hosen & Muhari, 2019; Osunkoya et al., 2023; Rahim & Zakaria, 2013; Saw et al., 2022; Zheng et al., 2019). Conversely, factors that contribute to a reduction in NPF include higher net operating profit, capital adequacy ratio, return on assets, cost-to-income ratio, deposit fund growth, return on equity, loan growth, and market concentration (Hartanto & Samputra, 2023; Hosen & Muhari, 2019; Muhammad et al., 2020; Rahim & Zakaria, 2013; Zheng et al., 2019).
Macroeconomic factors influencing financing risk
Macroeconomic conditions also significantly impact the level of financing risk in Islamic banks. Negative economic indicators such as exchange rate fluctuations, inflation, and economic policy uncertainty have been found to increase NPF levels (Firmansyah, 2014; Hosen & Muhari, 2019; Karadima & Louri, 2021; Osunkoya et al., 2023; Zheng et al., 2019). On the other hand, positive macroeconomic trends such as GDP growth and reduced unemployment contribute to a decrease in NPF (Ferhi, 2018; Hassan et al., 2019; Hernawati et al., 2021; Le & Diep, 2020; Zheng et al., 2019). Additionally, a surplus in foreign exchange reserves can trigger a financing boom, which may subsequently affect financing quality and contribute to increased NPF levels (Kuncoro, 2024).”

Reviewer’s Suggestion 2: Is the study design appropriate and is the work technically sound? Partly. The use of a dynamic panel model with GMM is appropriate for addressing potential endogeneity. However, the justification for using the OPEC basket price as a sole indicator of global oil prices could be strengthened. A broader measure or a combination of indicators might be more representative. Additionally, more detail on the specific GMM implementation (e.g., lag selection, instrument validity tests beyond Sargan) would enhance the technical soundness.
Authors’ Response:
We have added an explanation regarding the use of the OPEC basket price in the “Method” section.
“This price serves as a crucial benchmark in determining crude oil prices in the global market, including Indonesia, although it is merely one of many indicators influenced by various complex factors.”
Additionally, we have provided a more detailed explanation of lag selection in GMM estimation.
“A lag order of one (lag 1) was used for estimation”

Reviewer’s Suggestion 3: Are sufficient details of methods and analysis provided to allow replication by others? Partly. While the data sources are mentioned, the specific variables used within the model (e.g., how "market share" is calculated) could be more explicitly defined. The provided dataset link on Zenodo was checked and is accessible. However, clear instructions on how to merge the bank-specific data with the macroeconomic data are necessary for full replication. More detail on the GMM implementation is also needed (as mentioned above).
Authors’ Response: Thank you for the suggestion. Supplementary information regarding the variables has been provided in Table 1.
“Non-performing financing is the ratio of bad financing by total financing”
“Inflation is measured using the Consumer Price Index (CPI), which compares the total cost of a fixed basket of goods and services in the current period to the cost of the same basket in a base period”
“Economic growth is measured using the Industrial Production Index (IPI), which is calculated by dividing the value of production in a specific period by the value of production in the base year 2010.”
The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 4: If applicable, is the statistical analysis and its interpretation appropriate? Partly. The interpretation of the results generally aligns with the findings. However, the discussion of the inconsistent results for economic growth and inflation could be improved. Exploring potential reasons for these inconsistencies or considering alternative model specifications might provide more robust insights. Furthermore, while the Sargan test is reported, more comprehensive diagnostics related to the GMM estimation would strengthen the analysis.
Authors’ Response: Following the reviewer’s suggestion, we have enhanced the discussion of economic growth and inflation in the “empirical results” section. Furthermore, we have provided a more detailed statistical analysis to elucidate the differences between the difference GMM and system GMM estimators.
“On the other hand, economic growth and inflation exhibit inconsistent results across different model specifications. Inflation generally exerts a positive influence on financing risk, as observed in models 2, 6, and 8. This finding aligns with previous studies, which suggests that inflation can reduce purchasing power, leading to increased loan defaults (Awdeh et al., 2024; Patiu & Eleazar, 2024). However, the impact of economic growth on financing risk is more nuanced, with negative effects observed in the difference GMM estimation and positive effects in the system GMM estimation. This discrepancy might be attributed to the non-linear relationship between economic growth and NPF, where difference GMM captures short-term effects while system GMM captures long-term effects. The positive relationship in the system GMM, which is considered to provide more robust estimates, may reflect the potential for credit booms during periods of economic expansion, leading Islamic banks to loosen lending standards (Castro, 2013; Huizinga & Laeven, 2019; Mongid et al., 2023). Furthermore, this divergence in results may be explained by the finite sample bias in difference GMM, which relies solely on differences across time periods and excludes the level data.”

Reviewer’s Suggestion 5: Are all the source data underlying the results available to ensure full reproducibility? Yes. The data sources are listed and the Zenodo link provides the bank-level data. The macroeconomic data sources are also publicly accessible. However, as noted above, clearer instructions on data merging are needed.
Authors’ Response: Thank you for the suggestion. The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 6: Are the conclusions drawn adequately supported by the results? Partly. The main conclusion regarding the positive impact of spin-offs on reducing financing risk is supported by the findings. However, the claim that larger spin-off banks are more exposed to risk needs further clarification. While the results show that size increases risk in general, it's not entirely clear from the presented analysis whether this effect is amplified after the spin-off, or if larger banks simply start at a higher risk level pre-spin-off. More nuanced analysis and discussion are needed to fully support this specific conclusion.
Authors’ Response: The conclusion that larger spin-off banks are more exposed to risk is derived from the interaction variables between the spin-off and size variables (spinoff*size), as detailed in the fifth paragraph of the "Empirical Results" section.
“Furthermore, these results are corroborated by the estimations of the interaction variable (spinoff*size), which significantly influences non-performing financing. Thus, larger Islamic banks that undergo spin-offs carry a higher financing risk compared to smaller banks. As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”

Specific Recommendations for Improvement:
Reviewer’s Suggestion 7: Literature Review: Organize the literature thematically and clearly articulate the research gap being addressed. Verify the accessibility of all cited works.
Authors’ Response: Please refer to response number 1.

Reviewer’s Suggestion 8: Methodology: Provide a stronger justification for the choice of oil price indicator. Elaborate on the specific GMM implementation details (lag selection, instrument details, and robustness checks).
Authors’ Response: Please refer to response number 2.

Reviewer’s Suggestion 9: Data and Replication: Provide clear definitions of all variables, including calculations. Include detailed instructions on how to merge the datasets used.
Authors’ Response: Please refer to response number 4.

Reviewer’s Suggestion 10: Statistical Analysis: Explore reasons for the inconsistent results of control variables. Provide more comprehensive GMM diagnostics.
Authors’ Response: Based on the literature review, our implementation of the GMM model adheres to the established guidelines. While inconsistencies in the estimation results between difference GMM and system GMM for some variables are common, the inconsistencies in the spin-off, size, and IPI variables might be attributed to endogeneity issues better addressed by system GMM or measurement errors in difference GMM. This can be explained by the inherent differences between the two estimators. Difference GMM uses first differences to eliminate fixed effects, whereas system GMM combines instruments from both first differences and levels, allowing it to capture more of the variability in the data (The preceding explanation offers a more in-depth analysis of response number 4).

Reviewer’s Suggestion 11: Conclusion: Refine the discussion about the impact of size on risk for spin-off banks. Provide clearer evidence to support the claim of increased risk for larger spin-off banks compared to smaller ones. Consider separating the impact of size on risk in general from the specific impact on spin-off banks.
Authors’ Response: we have explained separately in the last 2 paragraphs in the Results and Discussion section related to the general impact of size on NPF, followed by a specific examination of the interaction between spin-off and size on NPF. The positive interaction term between spin-off and size (spinoff*size) on Table 4 suggests an increased risk of NPF for larger spin-off banks. we have also added a supporting statement, “As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”
Competing Interests: none Close
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Azharsyah Ibrahim, Universitas Islam Negeri Ar-Raniry, Banda Aceh, Indonesia
Mutamimah Mutamimah, Universitas Islam Sultan Agung, Semarang, Indonesia
Eko Suprayitno, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Malang, Indonesia
Agus Widarjono, Universitas Islam Indonesia, Yogyakarta, Indonesia

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Reviewer Report

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13 May 2025 | for Version 3

Agus Widarjono, Universitas Islam Indonesia, Yogyakarta, Special Region of Yogyakarta, Indonesia

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Approved

The comments

INTRODUCTION
Thank you for responding to comments and providing feedback.

LITERATURE REVIEW
Thank you for responding to comments and providing feedback.

METHOD AND RESULT AND DISCUSSION
The author failed to respond to two robustness tests:

1. Changing variable of interest.
There is no single measurement to measure Islamic bank financing risk as a dependent variable. Two methods are widely used, namely NPF (non-performing financing) and FLP (financing loss provision). To get robust results, the author needs to change the variable of interest. FLP is an alternative measure of financing risk. For example, see the following reference:
Widarjono, A., Alam, M. M., Rafik, A., Afandi, A., & Sidiq, S. (2025). Nexus between competition, concentration and bank risk-taking in Indonesian Islamic banking. International Journal of Islamic and Middle Eastern Finance and Management, 18(3), 672–690. (refer to 1)

2. Changing estimation method
The dynamic panel model faces an endogeneity problem. The dynamic panel model is estimated by the GMM method. There are two GMM estimators, namely the first difference GMM (Arellano and Bond, 1991) and the system GMM (Arellano and Bover, 1995; Blundell and Bond, 1998) to address the endogeneity issue in the dynamic panel model using instrumental variables. The system GMM extends the first difference GMM to resolve the problems of instrument weakness so the system GMM generates more robust estimators than the first difference GMM. Therefore, authors must estimate using static panel regression as an alternative method for the robustness test. Furthermore, the dynamic panel method will generate a biased and inconsistent estimator if the cross-sectional object is small. The number of objects in this study is small, consisting of 35 Islamic banks. (Al-Muharrami & Murthy, 2016), see the following references:

Al-muharrami, S., & Murthy, Y. S. R. (2016). Interest banking spreads in Oman and Arab GCC. International Journal of Emerging Markets, 12(3), 532–549. (refer to 2 )

CONCLUSION
Thank you for responding to comments and providing feedback.

References

1. Widarjono A, Alam M, Rafik A, Afandi A, et al.: Nexus between competition, concentration and bank risk-taking in Indonesian Islamic banking. International Journal of Islamic and Middle Eastern Finance and Management. 2025; 18 (3): 672-690 Publisher Full Text
2. Al-muharrami S, Murthy Y: Interest banking spreads in Oman and Arab GCC. International Journal of Emerging Markets. 2017; 12 (3): 532-549 Publisher Full Text

Competing Interests

No competing interests were disclosed.

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Islamic finance and banking

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12 May 2025 | for Version 3

Azharsyah Ibrahim, Universitas Islam Negeri Ar-Raniry, Banda Aceh, Indonesia

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Approved

The author has addressed all comments satisfactorily.

Competing Interests

No competing interests were disclosed.

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Islamic business, economics, banking, and finance

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14 Apr 2025 | for Version 2

Agus Widarjono, Universitas Islam Indonesia, Yogyakarta, Special Region of Yogyakarta, Indonesia

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Approved With Reservations

INTRODUCTION
This study investigates the effect of the spin-off on NPF. The spin-off policy is a policy to improve the performance of Islamic banks in Indonesia. However, the author has not explained why NPF is used as a measure of the success of the spin-off policy. It needs to be explained why NPF is a serious problem in Islamic banking in Indonesia so that this spin-off policy can reduce financing risk.

LITERATURE REVIEW
Islamic bank financing is different from conventional bank financing. Islamic financing is broadly categorized as profit-loss sharing (PLS) financing and non-profit-sharing financing. PLS financings are subject to asymmetric information and moral hazard. As a result, PLS financing likely increases NPF. In this section, authors conduct the literature review regarding NPF but authors fail to address the impact of types of financing in Islamic banks on NPF. For example, see the following references :

Warninda T., et al., (2019) [Ref-1]
Sutrisno, S. et al., (2023) [Ref-2].

METHOD
The difference and system GMM are a family of dynamic panel regression. The author must perform the robustness test with another estimation method. The author can employ static panel regression considering the number of banks is small (35 banks) while GMM requires a large number of banks.

RESULT AND DISCUSSION
Authors need to perform some robustness tests:
1. Changing variable of interest
There is no single measure to calculate Islamic bank risk. To get robust results, the author needs to change the variable of interest. Financing loss provision (FLP) is an alternative measure of financing risk. For example, see the following reference:
Widarjono A., et al., (2025) [Ref-3]
2. Changing estimation method
Considering that the number of small banks is 35, the author needs to estimate the model with static panel regression.

CONCLUSION
In the conclusions, it is said that the spin-off policy is mandatory for every Islamic bank window. However, there is an important note from previous research that full spin-off likely decreases the performance of Islamic banks after several years of spin-off. See following references:

Al Arif M., et al., (2020) [Ref-4]
Trinugroho I, et al., 2021 [Ref-5]

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

References

1. Warninda T, Ekaputra I, Rokhim R: Do Mudarabah and Musharakah financing impact Islamic Bank credit risk differently?. Research in International Business and Finance. 2019; 49: 166-175 Publisher Full Text
2. Sutrisno, S., Widarjono, A., Mohamad, M.: Does Financing Diversification Improve Bank Risk? Evidence From Indonesian Islamic Rural Banks. International Journal of Economics and Finance Studies. 2023. Publisher Full Text
3. Widarjono A, Alam M, Rafik A, Afandi A, et al.: Nexus between competition, concentration and bank risk-taking in Indonesian Islamic banking. International Journal of Islamic and Middle Eastern Finance and Management. 2025. Publisher Full Text
4. Al Arif M, Mufraini M, Prabowo M: Market Structure, Spin-Off, and Efficiency: Evidence from Indonesian Islamic Banking Industry. Emerging Markets Finance and Trade. 2020; 56 (2): 329-337 Publisher Full Text
5. Trinugroho I, Santoso W, Irawanto R, Pamungkas P: Is spin-off policy an effective way to improve performance of Islamic banks? Evidence from Indonesia. Research in International Business and Finance. 2021; 56. Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Islamic finance and banking

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Author Response

21 Apr 2025

Zulfikar Bagus Pambuko, Islamic Economics Law, Universitas Muhammadiyah Magelang, Magelang, Indonesia

Dear Reviewer
We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1:
INTRODUCTION
This study investigates the effect of the spin-off on NPF. The spin-off policy is a policy to improve the performance of Islamic banks in Indonesia. However, the author has not explained why NPF is used as a measure of the success of the spin-off policy. It needs to be explained why NPF is a serious problem in Islamic banking in Indonesia so that this spin-off policy can reduce financing risk.
Authors’ response: We have added information at the end of the introduction with the following narrative:
“This is crucial given the pivotal role of financing risk management in impacting profitability, financing capacity, and overall financial stability (Pratami et al., 2023; Sriyana, 2015), where spin-offs hold the potential to mitigate such risks.”

Reviewer’s Suggestion 2:
LITERATURE REVIEW
Islamic bank financing is different from conventional bank financing. Islamic financing is broadly categorized as profit-loss sharing (PLS) financing and non-profit-sharing financing. PLS financings are subject to asymmetric information and moral hazard. As a result, PLS financing likely increases NPF. In this section, authors conduct the literature review regarding NPF but authors fail to address the impact of types of financing in Islamic banks on NPF. For example, see the following references :
Warninda T., et al., (2019) [Ref-1]
Sutrisno, S. et al., (2023) [Ref-2].
Authors’ response: Thank you for the positive feedback. We have added literature with the following narrative.
“Furthermore, a high concentration of financing has been shown to negatively impact Non-Performing Financing (NPF) (Sutrisno et al., 2023). This underscores the necessity for a comprehensive understanding of the distinct risk profiles associated with various Islamic financing instruments in effective credit risk management. For instance, Warninda et al. (2019) found that, contrary to some assumptions, Mudarabah financing is not inherently riskier than Musharakah financing.”

Reviewer’s Suggestion 3:
METHOD
The difference and system GMM are a family of dynamic panel regression. The author must perform the robustness test with another estimation method. The author can employ static panel regression considering the number of banks is small (35 banks) while GMM requires a large number of banks.
Authors’ response: Thank you for your valuable feedback. We will certainly incorporate these suggestions into our future research. However, within the context of this current research, the progression from difference GMM estimation to system GMM is already considered part of the robustness check process. This is because the nature of a robustness check is to test whether the main results of an analysis remain consistent and not solely dependent on a single analytical approach, thus enhancing their reliability and generalizability. On the other hand, there are fundamental differences between the two estimations: difference GMM uses first differences to eliminate fixed effects, whereas system GMM combines instruments from both first differences and levels, allowing it to capture more of the variability in the data.

Reviewer’s Suggestion 4:
RESULT AND DISCUSSION
Authors need to perform some robustness tests:
1. Changing variable of interest
There is no single measure to calculate Islamic bank risk. To get robust results, the author needs to change the variable of interest. Financing loss provision (FLP) is an alternative measure of financing risk. For example, see the following reference:
Widarjono A., et al., (2025) [Ref-3]
2. Changing estimation method
Considering that the number of small banks is 35, the author needs to estimate the model with static panel regression.
Authors’ response: Thank you for your valuable feedback. We will certainly incorporate these suggestions into our future research. However, within the context of this current research, the progression from difference GMM estimation to system GMM is already considered part of the robustness check process. This is because the nature of a robustness check is to test whether the main results of an analysis remain consistent and not solely dependent on a single analytical approach, thus enhancing their reliability and generalizability. On the other hand, there are fundamental differences between the two estimations: difference GMM uses first differences to eliminate fixed effects, whereas system GMM combines instruments from both first differences and levels, allowing it to capture more of the variability in the data.

Reviewer’s Suggestion 5:
CONCLUSION
In the conclusions, it is said that the spin-off policy is mandatory for every Islamic bank window. However, there is an important note from previous research that full spin-off likely decreases the performance of Islamic banks after several years of spin-off. See following references:
Al Arif M., et al., (2020) [Ref-4]
Trinugroho I, et al., 2021 [Ref-5]
Authors’ responses: Thank you for the feedback. It is true that several empirical studies reveal a decline in performance after spin-offs. However, from the perspective of financing risk management, we consider this policy to be viable. Moreover, with the issuance of the amendment to Law No. 21 of 2008 into Law No. 4 of 2023, which extends this policy, it increasingly indicates that this policy is not unfavorable. Nevertheless, we are refining the narrative in the conclusion as follows:
“Consequently, the implementation of a spin-off policy represents a viable approach. This is further supported by the amendment of Law No. 21/2008 to Law No. 4/2023, which continues to accommodate spin-offs as a policy to foster the growth and contribution of Islamic banking to the national economy.”

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Reviewer Report

19 Views

12 Apr 2025 | for Version 2

Eko Suprayitno, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Malang, East Java, Indonesia

19 Views Cite this report Responses(1)

Approved With Reservations

Abstract:
1. Should provide a more concise summary of policy implications.
2. Explain the role of GMM estimation more clearly.

Introduction:
��The justification for financing risk as the main focus should be stronger.
��The introduction is well-structured, presenting a logical flow from the importance of Islamic banking to the research gap.
��It could benefit from a stronger linkage between spin-offs and financing risk earlier in the section.
�� Regulatory changes should be introduced earlier for better framing.

Literature Review

The literature review covers a broad range of studies, but it does not deeply analyze conflicting findings from past research.
It could benefit from categorizing past studies (e.g., studies on spin-offs, studies on financing risk, and studies on policy implications).
Needs a conceptual framework diagram to show research positioning.
Could analyze contradictions in past research on non-performing financing (NPF).
The section needs a conceptual framework diagram to clarify key variables and hypotheses.

Methods
�� The methodology is rigorous and well-explained, but it assumes familiarity with GMM among readers.
��The rationale for choosing specific macroeconomic variables should be expanded.
��Justify why inflation and oil prices were included as control variables.
��Data collection methods are clearly documented, but more details on handling missing data would strengthen credibility. More details on how missing data was handled would strengthen credibility.

Results and Discussion
�� The interaction term (Spin-off × Size) needs a clearer interpretation.
�� Managerial and policy implications should be expanded beyond statistical results.
��The empirical results are robust and align with previous studies, but some explanations are too technical for non-expert readers.
��The discussion connects well with the literature, but it could further explain why larger spin-off banks face higher risks.
��Clarification of Interaction Term (Spin-off × Size): The interpretation of why larger spin-off banks face higher risk needs more depth.
��Policy recommendations could be better integrated into this section rather than leaving them mostly in the conclusion.

Conclusion
�� Should differentiate theoretical contributions from practical implications.
�� Future research suggestions are too general and need more specific directions.

References
�� Should add more recent studies (post-2022).
�� Include cross-country comparisons to strengthen global relevance.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Islamic banking, Islamic macroeconomics, Islamic finance

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Responses (1)

Author Response

10 May 2025

Zulfikar Bagus Pambuko, Islamic Economics Law, Universitas Muhammadiyah Magelang, Magelang, Indonesia

The justification for financing risk as the main focus should be stronger.

Authors’ Response: We have added information to last paragraph
“This is crucial given the pivotal role of financing risk management in impacting profitability, financing capacity, and overall financial stability (Pratami et al., 2023; Sriyana, 2015), where spin-offs hold the potential to mitigate such risks.”

The introduction is well-structured, presenting a logical flow from the importance of Islamic banking to the research gap.

Authors’ Response: Thank you

It could benefit from a stronger linkage between spin-offs and financing risk earlier in the section.

Regulatory changes should be introduced earlier for better framing.

Authors’ Response: We have added information to paragraph 2
“The growing importance of this matter is underscored by the fact that the amendment of Law No. 21/2008 into Law No. 4/2023 retains spin-off as a key strategy for the acceleration of Islamic banking in Indonesia, albeit with the necessity of proper alignment.”

Reviewer’s Suggestion 3:
Literature Review

The literature review covers a broad range of studies, but it does not deeply analyze conflicting findings from past research.
It could benefit from categorizing past studies (e.g., studies on spin-offs, studies on financing risk, and studies on policy implications).
Needs a conceptual framework diagram to show research positioning.
Could analyze contradictions in past research on non-performing financing (NPF).
The section needs a conceptual framework diagram to clarify key variables and hypotheses.

Authors’ response: We have added a new subsection (conceptual framework) to the literature review that addresses all the feedback as follows
"Conceptual Framework
Although numerous factors have been identified as influencing financing risk, the findings across studies tend to show inconsistencies. These discrepancies may be attributed to differences in research settings, observation periods, and data analysis techniques. Nevertheless, this study focuses on presenting findings that are theoretically grounded and relevant to the existing literature.
In line with this, the main objective of this research is to examine the impact of the spin-off decision on financing risk in Islamic banking in Indonesia—an aspect that has not been previously explored. The study also incorporates control variables, including internal bank conditions (bank size and market share) and macroeconomic indicators (inflation, economic growth, and oil price). The conceptual framework is illustrated in Figure 1, and the hypotheses are formulated as follows:
H₁: The spin-off decision has a negative effect on financing risk
H₂: Bank size has a positive effect on financing risk
H₃: Market share has a positive effect on financing risk
H₄: Inflation has a positive effect on financing risk
H₅: Economic growth has a negative effect on financing risk
H₆: Oil price has a negative effect on financing risk"

Reviewer’s Suggestion 4:
Methods

The methodology is rigorous and well-explained, but it assumes familiarity with GMM among readers.
The rationale for choosing specific macroeconomic variables should be expanded.
Justify why inflation and oil prices were included as control variables.
Data collection methods are clearly documented, but more details on handling missing data would strengthen credibility. More details on how missing data was handled would strengthen credibility.

Authors’ response: Thank you for your feedback. We have provided a relatively detailed explanation regarding the use of GMM, covering its advantages, lag selection, instrument validity, etc. Furthermore, we offer the following additional clarification:
“This approach was adopted to mitigate several underlying problems commonly encountered in panel data, specifically endogeneity, heteroskedasticity, and autocorrelation.”

Furthermore, the selection of inflation and economic growth as macroeconomic variables was based on their common usage in the context of Islamic banking. Meanwhile, oil price was chosen because this variable is very rarely associated with Islamic banking performance, including financing risk.

Subsequently, incomplete data were excluded from the analysis, leading us to employ an unbalanced panel data model. Consequently, only observations containing complete data were included in the estimation. This can be observed in Table 2 where the total data amounts to 1032 observations, while the final estimation in Table 4 only utilizes 948 observations.

Reviewer’s Suggestion 5:
Results and Discussion

The interaction term (Spin-off × Size) needs a clearer interpretation.
Managerial and policy implications should be expanded beyond statistical results.
The empirical results are robust and align with previous studies, but some explanations are too technical for non-expert readers.
The discussion connects well with the literature, but it could further explain why larger spin-off banks face higher risks.
Clarification of Interaction Term (Spin-off × Size): The interpretation of why larger spin-off banks face higher risk needs more depth.
Policy recommendations could be better integrated into this section rather than leaving them mostly in the conclusion.

Authors’ response: We have refined the Discussion section to enhance its accessibility for a broader audience, including non-expert readers. Specifically, we have incorporated additional explanations to clarify the impact of the interaction variable (as detailed in the fifth paragraph) and macroeconomic factors (the seventh paragraph) on financing risk. This was done by providing more intuitive interpretations of the statistical findings, drawing analogies to real-world scenarios, and simplifying complex economic relationships without sacrificing the rigor of our analysis.
“This is further supported by the significant positive effect of the interaction variable (spinoff*size), indicating that larger Islamic banks undergoing spin-offs face elevated financing risk compared to smaller ones. As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing. Consequently, substantial asset expansion and spin-off implementation carry inherent risks that can be amplified by insufficient managerial readiness, making it harder to maintain financing quality.”
“On the other hand, economic growth and inflation exhibit inconsistent results across different model specifications. Inflation generally exerts a positive influence on financing risk, as observed in models 2, 6, and 8. This finding aligns with previous studies, which suggests that inflation can reduce purchasing power, leading to increased loan defaults (Awdeh et al., 2024; Patiu & Eleazar, 2024). However, the impact of economic growth on financing risk is more nuanced, with negative effects observed in the difference GMM estimation and positive effects in the system GMM estimation. This discrepancy might be attributed to the non-linear relationship between economic growth and NPF. The difference GMM method may suggest short-term effects, potentially indicating that during periods of economic expansion, borrowers exhibit a higher propensity for timely repayment. Conversely, the system GMM method, which is considered to provide more robust estimates overall, may reveal longer-term effects. Over extended periods of strong economic growth, banks may exhibit increased lending appetite and potentially relax credit standards, leading to a higher incidence of future repayment difficulties (Castro, 2013; Huizinga & Laeven, 2019; Mongid et al., 2023). Furthermore, this divergence in results may be explained by the finite sample bias in difference GMM, which relies solely on the differenced data, discarding level information.”

We acknowledge the reviewer's valuable suggestion to elaborate on policy recommendations within the discussion section to provide immediate context and justification. However, we have made a deliberate decision to retain the policy recommendations in the conclusion section. Our rationale for this placement is to ensure that the recommendations are presented as a direct and concise culmination of the entire analysis. By positioning them at the end, we aim to offer policymakers and other readers a readily accessible summary of the key actionable insights derived from our research. This structure allows readers to first grasp the full scope of our findings and their implications before focusing on the specific policy implications. We believe this approach enhances the clarity and impact of our recommendations as a direct takeaway from the study's overall conclusions.

Reviewer’s Suggestion 6:
Conclusion

Should differentiate theoretical contributions from practical implications.

Authors’ response: We have refined the narrative, separating the empirical and theoretical contributions as follows:
“Empirically, our findings reveal that Islamic windows carry a higher risk compared to full-fledged Islamic banks that undergo spin-offs. We also discover that spin-off Islamic banks with larger assets are more exposed to risk than those with smaller assets. This notice is supported by the regression that financing risk increases with the growth of assets and market share. Theoretically, this research contributes to the broader understanding of Islamic banking dynamics by providing empirical evidence on the risk implications of organizational structure transformation. Specifically, it highlights the potential risk reduction associated with the increased autonomy and focused operational framework of independent Islamic banks post-spin-off.”

Future research suggestions are too general and need more specific directions.

Authors’ response: We have refined the narrative as follows:
“Furthermore, incorporating a broader range of internal (such as market concentration, market power, types of financing, operating expense ratio, etc) and external variables (exchange rate, money supply, etc), as well as expanding the analysis to include Islamic banks in countries that have implemented spin-offs, such as Saudi Arabia, Pakistan, Kuwait, etc., would provide a more comprehensive understanding of the phenomenon.”

Reviewer’s Suggestion 7:
References

Should add more recent studies (post-2022).
Include cross-country comparisons to strengthen global relevance.

Authors’ response: Thank you for your constructive feedback. We have now incorporated additional relevant references, including cross-country studies and publications from 2022 onwards, to further strengthen the theoretical grounding and contextual relevance of our findings.
Addou, K. I., Boulanouar, Z., Anwer, Z., Bensghir, A., & Ramadilli Mohammad, S. M. (2024). The impact of Basel III regulations on solvency and credit risk-taking behavior of Islamic banks. International Journal of Islamic and Middle Eastern Finance and Management, 17(5), 915–935. https://doi.org/10.1108/IMEFM-05-2024-0248
Nasir, M. S., Oktaviani, Y., & Andriyani, N. (2022). Determinants of Non-Performing Loans and Non-Performing Financing level: Evidence in Indonesia 2008-2021. Banks and Bank Systems, 17(4), 116–128. https://doi.org/10.21511/bbs.17(4).2022.10
Pratami, A., Afandi, A., Sriyana, J., & Feriyanto, N. (2023). The Role of Financing Models and Credit Risk on Islamic Bank Stability. Cuadernos de Economía, 46(131), 43–53.
Shah, S. A. A., Fianto, B. A., Sheikh, A. E., Sukmana, R., Kayani, U. N., & Bin Ridzuan, A. R. (2023). Role of fintech in credit risk management: an analysis of Islamic banks in Indonesia, Malaysia, UAE and Pakistan. Journal of Science and Technology Policy Management, 14(6), 1128–1154. https://doi.org/10.1108/JSTPM-06-2022-0104
Sriyana, J. (2015). Islamic banks’ profitability amid the competitive financing in Indonesia. International Journal of Applied Business and Economic Research, 13(4), 1695–1710.
Sutrisno, Widarjono, A., & Mohamad, M. (2023). Does Financing Diversification Improve Bank Risk? Evidence From Indonesian Islamic Rural Banks. International Journal of Economics and Finance Studies, 15(4), 103–124. https://doi.org/10.34109/ijefs.202315406
Warninda, T. D., Ekaputra, I. A., & Rokhim, R. (2019). Do Mudarabah and Musharakah financing impact Islamic Bank credit risk differently? Research in International Business and Finance, 49, 166–175. https://doi.org/10.1016/j.ribaf.2019.03.002

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none

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Reviewer Report

9 Views

06 Feb 2025 | for Version 2

Mutamimah Mutamimah, Universitas Islam Sultan Agung, Semarang, Central Java, Indonesia

9 Views Cite this report Responses(0)

Approved

Ok, thanks for your revision. This suite with my suggestion.
Good luck

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Economics, Management, Financial Management

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report

18 Views

10 Jan 2025 | for Version 1

Mutamimah Mutamimah, Universitas Islam Sultan Agung, Semarang, Central Java, Indonesia

18 Views Cite this report Responses(1)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Economics, Management, Financial Management

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Author Response

04 Feb 2025

Zulfikar Bagus Pambuko, Islamic Economics Law, Universitas Muhammadiyah Magelang, Magelang, Indonesia

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Competing Interests

none

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Reviewer Report

33 Views

07 Nov 2024 | for Version 1

Azharsyah Ibrahim, Universitas Islam Negeri Ar-Raniry, Banda Aceh, Indonesia

33 Views Cite this report Responses(1)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature? Partly. While the presentation is generally clear, some areas could benefit from more precise language and further elaboration. The literature review, while extensive, could be structured more thematically to better highlight the existing research gaps and the study's contribution. Some cited works appear very recent (2023-2024) and I am unable to verify their accessibility/existence given my current knowledge cutoff. It's important to ensure all cited works are publicly accessible.
Is the study design appropriate and is the work technically sound? Partly. The use of a dynamic panel model with GMM is appropriate for addressing potential endogeneity. However, the justification for using the OPEC basket price as a sole indicator of global oil prices could be strengthened. A broader measure or a combination of indicators might be more representative. Additionally, more detail on the specific GMM implementation (e.g., lag selection, instrument validity tests beyond Sargan) would enhance the technical soundness.
Are sufficient details of methods and analysis provided to allow replication by others? Partly. While the data sources are mentioned, the specific variables used within the model (e.g., how "market share" is calculated) could be more explicitly defined. The provided dataset link on Zenodo was checked and is accessible. However, clear instructions on how to merge the bank-specific data with the macroeconomic data are necessary for full replication. More detail on the GMM implementation is also needed (as mentioned above).
If applicable, is the statistical analysis and its interpretation appropriate? Partly. The interpretation of the results generally aligns with the findings. However, the discussion of the inconsistent results for economic growth and inflation could be improved. Exploring potential reasons for these inconsistencies or considering alternative model specifications might provide more robust insights. Furthermore, while the Sargan test is reported, more comprehensive diagnostics related to the GMM estimation would strengthen the analysis.
Are all the source data underlying the results available to ensure full reproducibility? Yes. The data sources are listed and the Zenodo link provides the bank-level data. The macroeconomic data sources are also publicly accessible. However, as noted above, clearer instructions on data merging are needed.
Are the conclusions drawn adequately supported by the results? Partly. The main conclusion regarding the positive impact of spin-offs on reducing financing risk is supported by the findings. However, the claim that larger spin-off banks are more exposed to risk needs further clarification. While the results show that size increases risk in general, it's not entirely clear from the presented analysis whether this effect is amplified after the spin-off, or if larger banks simply start at a higher risk level pre-spin-off. More nuanced analysis and discussion are needed to fully support this specific conclusion.

Specific Recommendations for Improvement:

Literature Review: Organize the literature thematically and clearly articulate the research gap being addressed. Verify the accessibility of all cited works.
Methodology: Provide a stronger justification for the choice of oil price indicator. Elaborate on the specific GMM implementation details (lag selection, instrument details, and robustness checks).
Data and Replication: Provide clear definitions of all variables, including calculations. Include detailed instructions on how to merge the datasets used.
Statistical Analysis: Explore reasons for the inconsistent results of control variables. Provide more comprehensive GMM diagnostics.
Conclusion: Refine the discussion about the impact of size on risk for spin-off banks. Provide clearer evidence to support the claim of increased risk for larger spin-off banks compared to smaller ones. Consider separating the impact of size on risk in general from the specific impact on spin-off banks.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Islamic management, banking, and finance

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Responses (1)

Author Response

04 Feb 2025

Zulfikar Bagus Pambuko, Islamic Economics Law, Universitas Muhammadiyah Magelang, Magelang, Indonesia

We would like to warmly thank you for reading our manuscript and your valuable comments which would substantially improve our paper. We have followed carefully all your comments in the preparation of the revised version of the paper. Our revisions are summarized below.

Reviewer’s Suggestion 1: Is the work clearly and accurately presented and does it cite the current literature? Partly. While the presentation is generally clear, some areas could benefit from more precise language and further elaboration. The literature review, while extensive, could be structured more thematically to better highlight the existing research gaps and the study's contribution. Some cited works appear very recent (2023-2024) and I am unable to verify their accessibility/existence given my current knowledge cutoff. It's important to ensure all cited works are publicly accessible.
Authors’ Response: We would like to express our gratitude for your valuable feedback in enhancing the quality of our article. In response to your suggestion, we have incorporated a dedicated section on literature review as follows:
“Literature Review
The management of bad financing or non-performing financing (NPF) presents a critical challenge for Islamic banking institutions in Indonesia (Imaduddin, 2008; Nugroho et al., 2018; Soekapdjo et al., 2018). Non-performing financing, characterized by loans or financing contracts in default or at risk of default, can have far-reaching implications on the stability and credibility of financial institutions (Hernawati et al., 2021). In traditional banking, its known as non-performing loan (NPL). The existence of financing risk can hinder the stability of Islamic banking entities, potentially jeopardizing their capacity to facilitate economic growth and foster financial inclusivity (Banna et al., 2022). Considering the distinct characteristics of Islamic finance, rooted in risk-sharing and asset-backed principles, a comprehensive exploration of how spin-off decisions influence financing risk, specifically non-performing financing, requires further investigation.
Bank-specific factors influencing financing risk
Research on NPF in Islamic banking and NPL in conventional banking reveals that various internal and external factors influence financing risk. For conventional banks, external factors such as macroeconomic conditions play a dominant role in shaping NPL levels, while in Islamic banks, both internal and external factors significantly affect NPF (Loang et al., 2023; Setiawan et al., 2017). Among bank-specific factors, an increase in NPF is associated with variables such as bank size, operating efficiency ratio, liquid asset ratio, financing-to-deposit ratio, and foreign ownership (Havidz & Setiawan, 2015; Hosen & Muhari, 2019; Osunkoya et al., 2023; Rahim & Zakaria, 2013; Saw et al., 2022; Zheng et al., 2019). Conversely, factors that contribute to a reduction in NPF include higher net operating profit, capital adequacy ratio, return on assets, cost-to-income ratio, deposit fund growth, return on equity, loan growth, and market concentration (Hartanto & Samputra, 2023; Hosen & Muhari, 2019; Muhammad et al., 2020; Rahim & Zakaria, 2013; Zheng et al., 2019).
Macroeconomic factors influencing financing risk
Macroeconomic conditions also significantly impact the level of financing risk in Islamic banks. Negative economic indicators such as exchange rate fluctuations, inflation, and economic policy uncertainty have been found to increase NPF levels (Firmansyah, 2014; Hosen & Muhari, 2019; Karadima & Louri, 2021; Osunkoya et al., 2023; Zheng et al., 2019). On the other hand, positive macroeconomic trends such as GDP growth and reduced unemployment contribute to a decrease in NPF (Ferhi, 2018; Hassan et al., 2019; Hernawati et al., 2021; Le & Diep, 2020; Zheng et al., 2019). Additionally, a surplus in foreign exchange reserves can trigger a financing boom, which may subsequently affect financing quality and contribute to increased NPF levels (Kuncoro, 2024).”

Reviewer’s Suggestion 2: Is the study design appropriate and is the work technically sound? Partly. The use of a dynamic panel model with GMM is appropriate for addressing potential endogeneity. However, the justification for using the OPEC basket price as a sole indicator of global oil prices could be strengthened. A broader measure or a combination of indicators might be more representative. Additionally, more detail on the specific GMM implementation (e.g., lag selection, instrument validity tests beyond Sargan) would enhance the technical soundness.
Authors’ Response:
We have added an explanation regarding the use of the OPEC basket price in the “Method” section.
“This price serves as a crucial benchmark in determining crude oil prices in the global market, including Indonesia, although it is merely one of many indicators influenced by various complex factors.”
Additionally, we have provided a more detailed explanation of lag selection in GMM estimation.
“A lag order of one (lag 1) was used for estimation”

Reviewer’s Suggestion 3: Are sufficient details of methods and analysis provided to allow replication by others? Partly. While the data sources are mentioned, the specific variables used within the model (e.g., how "market share" is calculated) could be more explicitly defined. The provided dataset link on Zenodo was checked and is accessible. However, clear instructions on how to merge the bank-specific data with the macroeconomic data are necessary for full replication. More detail on the GMM implementation is also needed (as mentioned above).
Authors’ Response: Thank you for the suggestion. Supplementary information regarding the variables has been provided in Table 1.
“Non-performing financing is the ratio of bad financing by total financing”
“Inflation is measured using the Consumer Price Index (CPI), which compares the total cost of a fixed basket of goods and services in the current period to the cost of the same basket in a base period”
“Economic growth is measured using the Industrial Production Index (IPI), which is calculated by dividing the value of production in a specific period by the value of production in the base year 2010.”
The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 4: If applicable, is the statistical analysis and its interpretation appropriate? Partly. The interpretation of the results generally aligns with the findings. However, the discussion of the inconsistent results for economic growth and inflation could be improved. Exploring potential reasons for these inconsistencies or considering alternative model specifications might provide more robust insights. Furthermore, while the Sargan test is reported, more comprehensive diagnostics related to the GMM estimation would strengthen the analysis.
Authors’ Response: Following the reviewer’s suggestion, we have enhanced the discussion of economic growth and inflation in the “empirical results” section. Furthermore, we have provided a more detailed statistical analysis to elucidate the differences between the difference GMM and system GMM estimators.
“On the other hand, economic growth and inflation exhibit inconsistent results across different model specifications. Inflation generally exerts a positive influence on financing risk, as observed in models 2, 6, and 8. This finding aligns with previous studies, which suggests that inflation can reduce purchasing power, leading to increased loan defaults (Awdeh et al., 2024; Patiu & Eleazar, 2024). However, the impact of economic growth on financing risk is more nuanced, with negative effects observed in the difference GMM estimation and positive effects in the system GMM estimation. This discrepancy might be attributed to the non-linear relationship between economic growth and NPF, where difference GMM captures short-term effects while system GMM captures long-term effects. The positive relationship in the system GMM, which is considered to provide more robust estimates, may reflect the potential for credit booms during periods of economic expansion, leading Islamic banks to loosen lending standards (Castro, 2013; Huizinga & Laeven, 2019; Mongid et al., 2023). Furthermore, this divergence in results may be explained by the finite sample bias in difference GMM, which relies solely on differences across time periods and excludes the level data.”

Reviewer’s Suggestion 5: Are all the source data underlying the results available to ensure full reproducibility? Yes. The data sources are listed and the Zenodo link provides the bank-level data. The macroeconomic data sources are also publicly accessible. However, as noted above, clearer instructions on data merging are needed.
Authors’ Response: Thank you for the suggestion. The data used in this study is a combination of panel data (internal data from Islamic banks) and time series data (macroeconomic data). For the macroeconomic data, we ensured that the estimation periods aligned, and duplicated the time series data for each Islamic bank observation. Given the unbalanced nature of the panel data, macroeconomic data was only added to complete Islamic bank observations.

Reviewer’s Suggestion 6: Are the conclusions drawn adequately supported by the results? Partly. The main conclusion regarding the positive impact of spin-offs on reducing financing risk is supported by the findings. However, the claim that larger spin-off banks are more exposed to risk needs further clarification. While the results show that size increases risk in general, it's not entirely clear from the presented analysis whether this effect is amplified after the spin-off, or if larger banks simply start at a higher risk level pre-spin-off. More nuanced analysis and discussion are needed to fully support this specific conclusion.
Authors’ Response: The conclusion that larger spin-off banks are more exposed to risk is derived from the interaction variables between the spin-off and size variables (spinoff*size), as detailed in the fifth paragraph of the "Empirical Results" section.
“Furthermore, these results are corroborated by the estimations of the interaction variable (spinoff*size), which significantly influences non-performing financing. Thus, larger Islamic banks that undergo spin-offs carry a higher financing risk compared to smaller banks. As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”

Specific Recommendations for Improvement:
Reviewer’s Suggestion 7: Literature Review: Organize the literature thematically and clearly articulate the research gap being addressed. Verify the accessibility of all cited works.
Authors’ Response: Please refer to response number 1.

Reviewer’s Suggestion 8: Methodology: Provide a stronger justification for the choice of oil price indicator. Elaborate on the specific GMM implementation details (lag selection, instrument details, and robustness checks).
Authors’ Response: Please refer to response number 2.

Reviewer’s Suggestion 9: Data and Replication: Provide clear definitions of all variables, including calculations. Include detailed instructions on how to merge the datasets used.
Authors’ Response: Please refer to response number 4.

Reviewer’s Suggestion 10: Statistical Analysis: Explore reasons for the inconsistent results of control variables. Provide more comprehensive GMM diagnostics.
Authors’ Response: Based on the literature review, our implementation of the GMM model adheres to the established guidelines. While inconsistencies in the estimation results between difference GMM and system GMM for some variables are common, the inconsistencies in the spin-off, size, and IPI variables might be attributed to endogeneity issues better addressed by system GMM or measurement errors in difference GMM. This can be explained by the inherent differences between the two estimators. Difference GMM uses first differences to eliminate fixed effects, whereas system GMM combines instruments from both first differences and levels, allowing it to capture more of the variability in the data (The preceding explanation offers a more in-depth analysis of response number 4).

Reviewer’s Suggestion 11: Conclusion: Refine the discussion about the impact of size on risk for spin-off banks. Provide clearer evidence to support the claim of increased risk for larger spin-off banks compared to smaller ones. Consider separating the impact of size on risk in general from the specific impact on spin-off banks.
Authors’ Response: we have explained separately in the last 2 paragraphs in the Results and Discussion section related to the general impact of size on NPF, followed by a specific examination of the interaction between spin-off and size on NPF. The positive interaction term between spin-off and size (spinoff*size) on Table 4 suggests an increased risk of NPF for larger spin-off banks. we have also added a supporting statement, “As highlighted by Trinugroho et al., (2021), the multifaceted nature of financing contracts in Islamic banking substantially elevates the risk of non-performing financing.”

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Competing Interests

none

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Influence of spin-off decision on financing risk: Empirical insight from Indonesian Islamic banks

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

Table 1. Regression variables.

(1)

(2)

Results and Discussion

Descriptive analysis

Table 2. Descriptive analysis of variables.

Empirical results

Table 3. Correlation matrix.

Table 4. Determinants of Islamic banks’ performance.

Conclusion

Ethical considerations

Data availability

Underlying data

Extended data

Software availability statement

References

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