Do Islamic versus conventional banks progress or regress in productivity level?

Ribed Vianneca W. Jubilee; Fakarudin Kamarudin; Ahmed Razman Abdul Latiff; Hafezali Iqbal Hussain; Khar Mang Tan Article Do Islamic versus conventional banks progress or regress in productivity level? Future Business Journal Provided in Cooperation with: Faculty of Commerce and Business Administration, Future University Suggested Citation: Ribed Vianneca W. Jubilee; Fakarudin Kamarudin; Ahmed Razman Abdul Latiff; Hafezali Iqbal Hussain; Khar Mang Tan (2021) : Do Islamic versus conventional banks progress or regress in productivity level?, Future Business Journal, ISSN 2314-7210, Springer, Heidelberg, Vol. 7, Iss. 1, pp. 1-22, https://doi.org/10.1186/s43093-021-00065-w This Version is available at: https://hdl.handle.net/10419/246672 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Jubileeetal. Futur Bus J (2021) 7:22 https://doi.org/10.1186/s43093-021-00065-w RESEARCH Do Islamic versusconventional banks progress orregress inproductivity level? Ribed Vianneca W. Jubilee1,2, Fakarudin Kamarudin3* , Ahmed Razman Abdul Latiff1, Hafezali Iqbal Hussain4,5 and Khar Mang Tan6 Abstract This study assesses the differences between Islamic and conventional bank’s productivity. Earlier studies on bank productivity focused on conventional banks, but few have been done on Islamic banks. Therefore, the present study attempts to close the gap in the literature by investigating the productivity of Islamic and conventional banks in the context of the Middle East, Southeast Asia and South Asia regions. The sample is comprised of 385 banks (66 Islamic banks and 319 conventional banks) from 18 countries with data observations from 2008 to 2017. Panel data techniques with DEA-based MPI will be employed to investigate the impact of selected important factor and bank productivity as indicated by total factor productivity changes (TFPCH). Based on the results, Islamic banks are more productive than conventional banks and the results from t test are further confirmed by the results from nonparametric tests. These results are attributed to the progress in EFFCH. However, the mean difference between Islamic and conventional banks TFPCH is not statistically significant in all regions. The main benefit is that this work will hopefully provide additional insight and complement the existing studies on bank productivity of Islamic and conventional banks that are important to the banks, regulations, investors and researchers. Keywords: Total factor productivity change, Malmquist productivity index, Islamic bank, Conventional banks, Middle East, Southeast Asia, South Asia JEL Classifications: G21, G28 © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Introduction Nowadays, the banking industry continues to grow, at least, until another form of banking becomes available, and Islamic banking begins to gain further attention from Islamic and contemporary economists. Islamic finance is important for Muslims who require financial instruments that follow the Islamic legal code called Shariah [50]. Moreover, Islamic banks also have the potential to reduce risks endemic to financial transaction which have implication for economic growth [28]. The inception of Islamic banking began more than 30 years ago, and since this time, the aggregate of banks offering financial services has significantly grown to over 300 banks nowadays in over 75 countries from only one bank in 1975 [24]. Islamic finance is important for Muslims who require financial instruments that follow the Islamic legal code called Shariah [50]. Also, as the need for Shariah-compliant financial products and services increases, this sector will continue to rapidly grow in Muslim and non-Muslim market segments [13]. Referencing Table1, it can be seen that within 9 years between 2009 and 2018, the total Shariah compliance assets significantly increased. Iran, Saudi Arabia, Malaysia, the United Arab Emirates and Kuwait remain the primary markets for Islamic financial banking based on total Shariah compliance assets. Notably, South Asia and Southeast Asia countries, including the Middle East, dominate Islamic financing from a global perspective [4]. Therefore, taking these findings into Open Access Future Business Journal *Correspondence: [email protected]; fakarudink[email protected] 3 School of Business and Economics, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia Full list of author information is available at the end of the article Page 2 of 22 Jubileeetal. Futur Bus J (2021) 7:22 account, it is important to comprehend the productivity and nature of both conventional and Islamic banking in these countries. In principle, Islamic financial system abolishes interest, gambling, speculation, excessive uncertainty (gharar) and illegitimate transactions that are related to alcohol, tobacco, pornography and other activities which considered to be detrimental to the society [27]. Theoretically, Islamic banks which offer Shariah-compliant products can be separable from conventional bank associated with the difference in complexity, agency cost, level of maturity and development [11]. Further differences are evident concerning risk-taking, the price of money, income, the size of banks and so forth [26]. As Islamic banking is viewed as one of the rapidly rising markets, the industry is exposed to credit risk by lengthening borrowing via Murabaha and Ijarah in generating greater profitability. Furthermore, with non-standard financial agreements and varied methods of funding and intricacies related to managing risk brought about via the introduction of Shariah present further issues regarding Islamic banking to remain stabilised [36]. Islamic banking has the opportunity to utilise profit and loss sharing with respect to liabilities which intensifies credit risk [44]. Moreover, on the balance sheet’s liability side, Islamic banks receive deposits founded on profit and loss sharing where they need to pay a profit while investing funds identified on the asset side. Given insufficient investment opportunities, Islamic banks typically have excessive liquidity assets, so liquidity risk is minimal [29]. While Islamic and conventional banks can be distinguished from each other, both banks have a same objective that prioritise the profitability. Simply put, productivity in generating profitability is an important area for both Islamic and conventional banks. In light of the importance of profitability to Islamic and conventional banks, several studies have focused on analysing the efficiency of Islamic finance as a means of measuring the performance of banks [39, 50, 51, 57, 58, 62], whereas scant research has been undertaken to examine the productivity levels of the Islamic and conventional banking sector as intermediaries [39]. Therefore, this paper will provide a better understanding and contribute to the literature relevant to productivity in banking sector. Siddiqi [53] mentions that Islamic economic and finance theories are still underdeveloped. Therefore, this study proposes using real-life data to validate the foundational theories in terms of productivity perspective. Productivity is one of the crucial dimensions to measure the firm’s performance [15]. The financial performance is a broad concept that takes into consideration of the productivity, profitability and growth. Profitability is the overall efficiency of the company that shows the ability of the firm to earn a profit. One of the primary goals of banks is to look after the interests of shareholders by maximising their return on investment and optimising profits. In order for the bank to get high profit, they need to be productive. However, some of study found that the lower level of productivity can lead to lower profitability of the banks [15]. So, this becomes the main issue. The productivity becomes the main issue because productivity can lead to the lower and higher profitability. So, banks cannot ignore the productivity if they want to increase their profit. In the context of Islamic banks, similar goals are afforded as to those of conventional banks in maximising profits, although as mentioned previously, both differ as intermediaries. Conventional banks operate on an interest-based principle, whereas Islamic banks adhere to the Table 1 Ten leading countries for Shariah-compliant assets. Source: The Asian Banker [59] and IFDI (2019) Year 2009 2018 Rank Country Shariah-Compliant Assets ($ billion) Rank Country ShariahCompliant Assets ($ billion) 1 Iran 236.43 1 Iran 488 2 United Arab Emirates 67.31 2 Saudi Arabia 390 3 Malaysia 56.22 3 Malaysia 214 4 Saudi Arabia 55.01 4 United Arab Emirates 194 5 Kuwait 55.01 5 Kuwait 100 6 Bahrain 38.58 6 Qatar 97 7 Qatar 19.01 7 Turkey 39 8Turkey 13.76 8 Bahrain 35 9 Sudan 6.12 9 Bangladesh 36 10 Bangladesh 7.5 10 Indonesia 28 Page 3 of 22 Jubileeetal. Futur Bus J (2021) 7:22 interest-free principle by replacing it with profit and loss sharing in performing day-to-day operations founded on Shariah rules and principles [7], although to maximise profits, banks need to be productive, which is directly linked to their efficiency by converting inputs such as capital, raw materials and labour into outputs. Accordingly, as the throughput of outputs increases more rapidly compared to inputs, the efficiency and productivity of banks will also increase. In this situation, productivity determines the level of efficiency in utilising resources (input) to produce outputs. Globally, the market share of Islamic banking in the financial industry remains low, but in many regions is quickly growing, mainly in the Asian and Middle East regions [33]. According to Houben [31], Southeast Asia, with its ever-increasing Muslim population, receives minimal attention globally. This fact is also supported by Kamarudin etal. [39]. As Islamic finance is increasingly becoming an institutionalised part of the global capital market, it has the distinct potential to rapidly expand and contribute to economic growth [32]. In this context, it is therefore important for Islamic banks to remain productive, in order to remain competitive and contribute towards economic growth. Therefore, instead of just focusing on conventional banks, it is more justifiable if not beneficial, to compare the productivity of Islamic banks. The lack of comprehensive study on productivity of Islamic and conventional banks inspires this study to investigate the level of productivity amid Islamic and conventional banking sector in the three regions, consisting of South Asia (SA), Southeast Asia (SEA) and Middle East (ME) regions. This study also serves as a continuation to the ongoing debate on whether Islamic banks are more productive relative to conventional banks and vice versa. Hence, this paper focuses on the question whether the productivity of Islamic bank differs from the productivity of conventional bank? This paper begins with a brief review of related studies and followed by data and methodology, empirical results and conclusion. Literature review The role of banking sector as a form of financial intermediation which is part of the financial institution cannot be readily ignored, given it leads to stable economic growth and development. Endogenous growth theory argues that economic growth is primarily result of internal forces rather than external ones through the channel of productivity can be tied directly to faster innovation and more investments in human capital [61]. Business companies wish to be in a position to regulate their spending and overheads, in order to generate greater profits for stakeholders, similarly, conventional and Islamic banks also seek to improve their productivity given the contribution they make towards economic growth and sustainability. The Islamic banking system plays a similar role but differs slightly compared to the conventional banking system. Islamic banks are considered as a replacement or an alternate option in the provision of banking product and services in accordance with Islamic (Shariah) principles. Nevertheless, the theory in this area fails to make any obvious prediction as to whether Islamic banks should be more efficient or productive compared to conventional banks [11]. Although productivity is extremely important in Islamic banking in order to gain high profitability, given financing decisions are based on the productivity of the investment in the selected project. Moreover, the Shariah Advisory Committee plays a key part in this respect Islamic banks, in affirming the behaviour stakeholders’ in following the principles that govern Islamic law. Moreover, the Shariah Advisory Committee is ultimately responsible for minimising information asymmetries and agency costs within an Islamic bank. According to Jensen and Meckling [34], the existence of conflicts of interest between the principal and agent can influence the performance of organisations. In this regard, information asymmetry and agency conflicts should be less in Islamic banks compared to their conventional counterparts [48, 60]. Therefore, with the intervention of the Shariah Advisory Committee in monitoring the operations of Islamic banks, conflicts between the principal and agent(s) can be prevented and reduce the agency costs. Ang etal. [6] found that external monitoring produces lower agency costs and thereby increasing the efficiency of banks, leading to high productivity. On the other hand, the opposite may occur given the effect of various productivity determinants are distinctly different in Islamic banks compared to conventional banks such the complexity, level of maturity and development. Kopleman [43] defines productivity as the relationship between the amount of one or more physical output/s to the associated physical inputs used in production. In other words, they asserted that total production (output) is influenced by the amount of capital invested and the amount of involved labour. Productivity can also be broken down into smaller segments according to Fare etal. [23], based on changes in efficiency or fluctuations in order to compensate for lost ground and also through innovative technological changes, assuming that the outputs are equal equivalent to outputs, and the growth index total factor productivity captures the advancements or changes in technology. Therefore, total factor productivity can be considered equivalent to changes in Page 4 of 22 Jubileeetal. Futur Bus J (2021) 7:22 technology (from a technical perspective) which can be gauged as a shift in performance, which can accordingly be adjusted by altering the chosen input. Fundamentally, higher productivity will lead to higher bank profitability [39, 54, 56]. In other words, in the context of the banking industry, when the productivity level is increased, the additional output can be therefore be produced from the given amount of input. Accordingly, Cobb–Douglas Production Functions theory will be employed in this study to examine the productivity level between the Islamic and conventional banking sector in the ME, SEA and SA regions. A few studies have found that Islamic banks are significantly more productive compared to conventional banks, other studies have revealed contrary findings and a few studies have revealed that they are similar (no variation) regarding productivity. More recently, Saleh et al. [52] reveal that the Gulf Cooperation Council (GCC) banks encountered a decline in productivity after the global financial crisis of 2008– 2009. They also indicate that the disparity in inefficiency between Islamic and conventional banks has narrowed substantially and that Islamic banks have been able to cross and reduce the gap with conventional banks over the study period from 2005 to 2014. This finding is also supported by Alsharif etal. [5] in which six GCC countries were studied between 2005 and 2015. The findings suggest that Islamic banks are less productive than conventional banks. Moreover, the findings suggest that the Basel III agreement hampered the productivity of the GCC banks, and this detrimental impact is greater for Islamic banks. Another study by Alexakis etal. [3] reported that in 2008/09, both Islamic and conventional banks experienced a decline in productivity with conventional banks more negatively impacted. The global financial crisis can possibly be connected with this decline in the Gulf Cooperation Council (GCC) banking sector, which was supported by Maredza and Ikhide [45]. The results of this study indicated that there were differences in technological and technical efficiency in Islamic banks, which may be historically related to these banks in the GCC. While this sector is still developing, there are a number of mature Islamic banks in the GCC, although this mix is possibly varied given the assortment of financial products, bank status, clients and innovation. On the other hand, Rodoni etal. [49] undertook a comparison of the productivity and efficiency of the Islamic banking sector between 2009 and 2013 in Pakistan, Indonesia and Malaysia; the data included 31 banks across these three countries. Using the Malmquist productivity index (MPI) to gauge productivity and Data Envelopment Analysis (DEA) in gauging efficiency the findings from the study found that the efficiency of the sector in Malaysia was far better compared to Indonesia while in Pakistan, the rate of efficiency was near to 100% during this period. Kamarudin et al. [39] compared the productivity of 29 Islamic banks in SEA (Malaysia, Indonesia and Brunei) between 2006 and 2014. Using nonparametric DEAbased MPI methods, the researchers approximated the total factor productivity of the banks found that statistically, there was nil variance between the productivity and efficiency and productivity of local and international managed banks given the similar technologies and population. In another study by Doumpos etal. [21], they investigated the financial robustness of 52 Islamic windows, 347 conventional banks and 101 Islamic banks between 2000 and 2011 by considering 57 member countries of the Organisation of Islamic Cooperation (OIC). The findings from the study indicated that each bank varied concerning the employment of financial ratios, although no difference was evident from a statistical viewpoint for overarching financial robustness between the banks. Nevertheless, Mobarek and Kalanov [46] examined performance comparatively between Islamic and conventional banks 18 Organisation of Islamic Conference (OIC) countries regarding the pre-global financial crisis period and the actual global financial crisis period between 2004 and 2006 and between 2007 and 2009, respectively. The research was founded on the crosssectional data of 307 conventional banks and 101 Islamic banks employing DEA and stochastic frontier analysis (SFA) methods in measuring efficiency. The study indicated that the efficiency of conventional banks was higher compared to Islamic banks between 2006 and 2009, which may be due to the mean value of the efficiency score being larger in conventional banks making such a comparison non-equivalent. Kamarudin etal. [38] investigated the profit, revenue and costs efficiency of 74 banking institutions (47 conventional banks and 27 Islamic banks) in the GCC region between 2007 and 2011. Here, the efficiency level was gauged using the DEA technique by employing the intermediation method. The researchers found that conventional banks displayed higher levels of efficiency based on three determinants: revenue, profit and cost. Moreover, they suggest that the primary determinant with respect to the profit efficiency level was the efficiency level associated with revenue. Therefore, in summary, most studies documented varied and mixed findings on the level of efficiency level amid Islamic banks and conventional banks globally, whereas on the other hand, little has been undertaken, if anything, to explore Islamic and conventional banks Page 5 of 22 Jubileeetal. Futur Bus J (2021) 7:22 productivity levels. Also, there is less evidence to suggest the productivity level of both types of banks has been conducted in the Asian region, given the strong presence of Islamic banks [39]. Hence based on this deficiency, this research aims to offer evidence empirically mainly on the level of productivity in the Islamic and conventional banks sector. Methods Sources ofdata The dataset used in this study consisted of Islamic banks and conventional banks from Middle East (ME), Southeast Asia (SEA) and South Asia (SA) countries between the period 2008 and 2017 given these three regions are representative of Islamic banking and finance globally [42]. The global financial crisis that occurred between 2008 and 2009 is also taken into consideration in this study, which applies a ‘dummy’ variable to represent this period to avoid any possible biasedness. The sample size of the study consisted of Islamic and conventional banks from 18 countries, (11 from the ME, 4 from SEA and 3 from SA). The data source employed in this study comprised of information relating to Islamic and conventional banks collected from within the ME, SEA and SA regions between 2008 and 2017. All data were obtained from the Fitch Connect database produced by Fitch Solutions. Fitch connect is an online repository of data that comprises financial reports, accounting ratios and credit ratings of more 30,000 banks globally including the Islamic and conventional banking sectors. To compare the chosen banking institutions across the three regions (ME, SEA and SA), the currency will be depicted in US dollars. In total, 385 banks (66 Islamic banks and 319 conventional banks) from 18 countries with dual-banking system are selected in this study as represented in Table2. Also, to maintain homogeneity, all investment banks, insurance companies and finance companies were excluded in this study. The data of Islamic banks are obtained on the basis of an Islamic subsidiary. The country income level is taken from World Bank Database. DEA-based Malmquist Productivity Index (MPI) The data envelopment analysis (DEA) technique was developed by Charnes etal. [18] in which they proposed that the greater the output generated from inputs, the greater the efficiency level associated with the production process. Emrouznejad and Yang [22] affirmed that there had been exponential growth in DEA-related studies, especially given the work of Charnes etal. [18]. Therefore, this confirms that DEA has been acknowledged as a contemporary tool in measuring performance in the diversified fields of management science. Table 2 Sample data. Source: Fitch Connect database No. Country Income group Region No. of Islamic bank No. of conventional bank 1 Bahrain High Middle East 8 12 2 Egypt Lower Middle Middle East 1 23 3 Iran Upper Middle Middle East 1 8 4 Iraq Upper Middle Middle East 1 3 5 Jordan Upper Middle Middle East 2 11 6 Kuwait High Middle East 1 4 7 Lebanon Upper Middle Middle East 2 31 8 Oman High Middle East 2 7 9 Qatar High Middle East 3 5 10 Saudi Arabia High Middle East 3 8 11 UAE High Middle East 7 14 12 Brunei High South East Asia 1 1 13 Indonesia Lower Middle South East Asia 8 92 14 Malaysia Upper Middle South East Asia 13 31 15 Singapore High South East Asia 1 8 16 Bangladesh Lower Middle South Asia 4 37 17 Pakistan Lower Middle South Asia 7 23 18 Maldives Upper Middle South Asia 1 1 Total 66 319 Page 6 of 22 Jubileeetal. Futur Bus J (2021) 7:22 In the context of this study, efficiency and productivity in an organisation are interrelated, although efficiency is static given it fails to consider the time taken for production, which is important. Accordingly, this shows that when productivity measures alter or change, the level of efficiency also changes. Therefore, it is vital to measure productivity. Fundamentally, the ratio between inputs and outputs can be used to determine productivity. MPI is occasionally referred to as Total Factor Productivity (TFP), which can assess any change of efficiency and frontier technology in terms of progress or regress over time. Moreover, MPI has been used in many studies for DEA analysis of efficiency changes in diversified fields of management science across various industries and countries. Output-based MPI was used to understand and gauge the change in the productivity of banks and also to determine the change in TFP Change (TFPCH) to Technical Change (TECHCH) and Efficiency Change (EFFCH). According to Fare etal. [23], the changes in Scale Efficiency Change (SECH) and Pure Technical Change (PTECH) resulted from changes in EFFCH. Figure 1 illustrates the interactive relationship among the efficiency indices Here, MPI measures the productivity change from period t to t + 1, which reflects to a reference period technology. Thus, the MPI in relation to technology in period t is: Corresponding output based MPI concerning technology in period t + 1 is: Given it is complicated in choosing among period t and t + 1 as a benchmark period, an output-based MPI is defined as the geometric mean of Eqs.(1) and (2), [23]: (1) Mt 0=D t 0  x t+1 ,y t+1 Dt 0 xt,yt . (2) Mt+1 0=D t+1 0  x t + 1 ,y t + 1 Dt+1 0 xt,yt . Accordingly, this can be broken down into efficiency change  EFFCH t,t+1 as well as technological change ( TECHCHt,t+1) . As proposed by Fare et al. [23], an equivalent way of writing the MPI index is given below: where M represents the level of productivity change as measured by a shift in frontier measured at years’ t and t + 1 in which most of the recent production point  x t+1 ,y t+1 correspond to the previous production point (xt,yt) . However, when M > 1 it says that period (t + 1) productivity is higher compared to period t productivity. Although when M < 1, it says (t + 1) productivity is less compared to period t productivity which says that productivity regress and M = 1.000 correspond to inaction (no TFP change). Lastly, output distance functions are represented by D’s. The interrelation among the MPI and its two sub-indices can be shown as: where Fare etal. [23] proposed that the index for efficiency change can additionally be broken down into its agreed detailed parts of PTECH ( PureEfft,t+1 ) measured in relation to the VRS technology and part of SECH ( �Scalet,t+1) , which captured the change in the variation among the constant returns to scale (CRS) variable and returns to scale (VRS) technologies which can be described as given below: (3) M t,t+1 0  xt+1,yt+1,xt,yt  =  Dt 0  xt+1,yt+1  Dt 0  xt,yt  ×  Dt+1 0  xt+1,yt+1  Dt+1 0  xt,yt  1/2 . (4) Mt,t+1 0  xt+1,yt+1,xt,yt  =Dt+1 0xt+1,yt+1 Dt 0xt,yt   EFFCHt,t+1 × Dt 0xt+1,yt+1 Dt+1 0  xt+1,yt+1  ×Dt 0xt,yt Dt+1 0  xt,yt  1/2   TECHCH t,t+1 (5) M t,t+1 0 = Efficiency Change × Technical Change (6) Efficiency Change =D t+1 0  x t + 1 ,y t + 1 Dt 0 xt,yt  (7) Technical Change =  Dt 0  xt+1,yt+1  Dt+1 0 xt+1,yt+1  ×  Dt 0  xt,yt  Dt+1 0 xt,yt  1/2 . Fig. 1 Interactive relationship among the MPI efficiency indices Page 7 of 22 Jubileeetal. Futur Bus J (2021) 7:22 where The analysis can also be used to examine the losses or gains relating to the productivity sources to compare the values of both TECHCH and EFFCH. When EFFCH > TECHCH, it then means that the gains in productivity are primarily from the improvement in efficiency. While when EFFCH < TECHCH, the gains in productivity are mainly resulting from technological progression. Therefore, to summarise the analysis of the first stage the TFPCH of banks were determined by employing output-based MPI. Next, VRS technology was used to measure TFPCH ( Mt,t+1 0) relative to efficiency change (EFFCH) and technical change (TECHCH) as given in Eq.(5). Moreover, as proposed by Fare etal. [23], efficiency change (EFFCH) was then broken down further into the element of pure technical change (PTECH) which was determined based on the VRS technology. Accordingly, this was part of the scale efficiency change (SECH) employed to capture the variance among the constant returns to scale (CRS) and variable returns to scale (VRS) technologies as shown in Eq.(8). The scores representing efficiency were constrained in order to remain amid zero and one and the year 2007 was used as the reference year. The MPI and its constituent parts started with a value of 1.000. Therefore, an efficiency value less (higher) than one for a bank in the following years meant that it was performing below (above) the frontier. Also, the value representing efficiency showed the radial distance from the estimated production frontier to the decision-making unit (DMU) being under consideration. Specification ofbanks input andoutputs In order to study productivity, data envelopment analysis (DEA) is employed in this study as a primary tool because it is widely used and still relevant for measuring the productivity given it has been proved to be sustained over time for 40years with more than a thousand papers published in a year [22]. (8) Efficiency Change = PureEfft,t+1 × Scalet,t+1 (9)  PureEfft,t+1= Dt+1 VRS  xt+1 j,yt+1 j  Dt VRS  xt j,yt j  (10) � Scalet,t+1= Dt+1 CRS  xt+1 j,yt+1 j  /Dt+1 VRS  xt+1 j,yt+1 j  Dt CRS  xt j,yt j  /Dt VRS  xt j,yt j . Accordingly, the intermediation approach was adopted in this study in classifying the input and output of banks, as supported in many studies [12]. This approach has been widely adopted as the initial stage of DEA, given the significant part that banks enact in providing financial intermediation. In this study, the selection of inputs and outputs was steered by the process as depicted in several studies [3, 19, 39, 55]. All variables employed in the nonparametric DEA were based on the MPI model as part of the initial stage of analysis, as depicted in Table3. According to Banker and Datar [10] and Cooper etal. [20], an approximation [assumption] is made in selecting the number of inputs and outputs in that the size of the sample needs to meet this assumption prior to progressing with the measurement of DEA as shown: where n = number of decision-making unit (DMUs), m = number of inputs, s = number of outputs. Results The DEA-based MPI method is employed to examine the objective of this study, which is, to investigate the total factor productivity levels amid Islamic and conventional banking sector in South Asia, Southeast Asia and Middle East regions. The results are then tested using a parametric (t test) and nonparametric (Mann–Whitney [Wilcoxon] and Kruskal–Wallis) test in order to determine the variances in the productivity (y-axis) of Islamic and conventional banks. This test is widely used in prior banking productivity studies [14, 37]. Table4 presents the summary statistics of data used to construct the productivity frontiers for Islamic and conventional banks. All the variables are measured in US$m. Furthermore, Table 5 shows the details results on productivity of Islamic and conventional banks and its decompositions. In addition, the results on Productivity of Islamic and conventional banks using the bank n≥max{m×s,3 (m+s)} Table 3 Variables of outputs and inputs. Source: Hassan et al. [30] and Johnes et al. [35] Variable Symbol Variable Name Definitions Outputs y1 Loan Net loans y2 Investment Total securities Inputs x1 Deposits Total deposits, money market and short-term funding x2 Labour Personnel expenses x3 Physical capital Book value of fixed assets Page 8 of 22 Jubileeetal. Futur Bus J (2021) 7:22 number, specific years, specific regions and different income groups are illustrated in Tables6 and 7 (see “Appendix”). All the graph levels of productivity in Middles East, Southeast Asia, South Asia and all regions by specific years, specific regions and different income groups are depicted in Figs.2, 3, 4, 5, 6, 7, 8, 9, 10 and 11. Discussion Productivity ofIslamic andconventional banks andits decompositions Table5 shows the geometric mean scores of the total factor productivity change (TFPCH) and its component, which is the Technical Change (TECHCH) and Efficiency Change (EFFCH) that can be decomposed into Pure Technical Efficiency Change (PTECH) and Scale Efficiency Change (SECH) for all banks (Panel A), conventional banks (Panel B) and IBs (Panel C). This analysis helps to understand the performance of the banks for each year. Referencing Panel A in Table5, it can be seen that all banks have, on average, exhibited a lower TFPCH regress of −15.2% (0.848). The results show that all banks exhibited TFPCH regress of −35.9% (0.641) in 2017. During the period of study, the −15.2% (0.848) regress in TFPCH of all banks could be attributed mainly to the −13.4% (0.866) decrease in TECHCH, as the EFFCH seems to have a decrease in the rate of −2.1% (0.979). The decomposition of the EFFCH index consists of PTECH and SECH components indicating that the reason for the decrease in all banks EFFCH was mainly attributed to PTECH rather than SECH. Therefore, considering these findings, all banks are less efficient in the management of cost control, even though they have been operating at the optimal scale of operations. Panel B of Table5 depicts the results for conventional banks. As can be seen in the table, the conventional banks’ average exhibited a TFPCH regress of −14.7% (0.853). The results indicate 2017 represents a TFPCH regress of −35.2% (0.648) for conventional banks. The decomposition of the TFPCH index into its TECHCH and EFFCH components reported that the regress in conventional banks’ TFPCH was solely attributed to a −12.1% (0.879) decrease in TECHCH, as the EFFCH decreased at a rate of −3.0% (0.970). The decomposition of the EFFCH index into its PTECH and SECH components indicates that the dominant sources that regress in conventional banks’ EFFCH were mainly managerially rather than of an operational scale. Similarly, Panel C of Table5 shows the MPI average results of Islamic banks. The empirical findings seem to suggest that the Islamic banks’ TFPCH decreased by −11.2% (0.888) higher compared to their conventional banks’ counterparts. The results show that for 2017 the TFPCH has a regress of 39.3% (0.607) and progress of 36.1% (1.361) in 2016 for Islamic banks. It can also be seen that within the period of study, the −11.2% (0.888) regress in TFPCH of all banks could be attributed to the −11.0% (0.890) decrease in TECHCH, as the EFFCH seems to have a decline in the rate of −0.2% (0.998). The decomposition of the EFFCH index into its PTECH and SECH components indicates that the decrease in EFFCH was mainly attributed to managerial factors rather than the scale. Therefore, in summarising the findings, the TFPCH regress in conventional banks and Islamic banks mainly Table 4 Summary statistics of outputs and input variables in the DEA model (US$m) y1 (total of short-term and long-term loans); y2 (total securities); x1 (total deposits, money market and Short-term Funding); x2 (personnel expenses); x3 (fixed assets) Variables Output Input Total loans ( y1 ) Total Investment ( y2 ) Deposits ( x1 ) Labour ( x2 ) Capital ( x3 ) Mean Conventional banks 7426.564 2787.981 10505.271 106.314 115.847 Islamic banks 4019.186 938.938 5214.859 56.666 76.628 Minimum Conventional banks 0.500 0.019 0.800 0.008 0.008 Islamic banks 0.344 0.054 0.979 0.067 0.029 Maximum Conventional banks 241,732.006 86,833.757 314,909.471 2113.571 3834.810 Islamic banks 62,276.160 11,346.560 74,287.733 750.373 2095.493 SD Conventional banks 21,110.972 7431.583 27,668.656 229.472 274.103 Islamic banks 7274.636 1626.269 8977.123 99.315 177.676 Page 15 of 22 Jubileeetal. Futur Bus J (2021) 7:22 Table 6 Number and percentage of conventional and Islamic banks with productivity progress and regress Period Productivity change Technological change Efficiency change Pure Efficiency change Scale efficiency change (TFPCH) (TECHCH) (EFFCH) (PECH) (SECH) Progress Regress No ∆ Progress Regress No ∆ Progress Regress No ∆ Progress Regress No ∆ Progress Regress No ∆ # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) Panel A: All banks 2007–2008 114 (34.44) 89 (26.89) 128 (38.67) 77 (23.26) 228 (68.88) 26 (7.85) 220 (66.47) 88 (26.59) 23 (6.95) 231 (69.79) 77 (23.26) 23 (6.95) 155 (46.83) 131 (39.58) 45 (13.60) 2008–2009 123 (37.27) 143 (43.33) 64 (19.39) 40 (12.12) 283 (85.76) 7 (2.12) 199 (60.30) 119 (36.06) 12 (3.64) 175 (53.03) 137 (41.52) 18 (5.45) 170 (51.52) 107 (32.42) 53 (16.06) 2009–2010 134 (39.30) 155 (45.45) 52 (15.25) 182 (53.37) 157 (46.04) 2 (0.59) 152 (44.57) 181 (53.08) 8 (2.35) 190 (55.72) 134 (39.30) 17 (4.99) 99 (29.03) 202 (59.24) 40 (11.73) 2010–2011 158 (45.93) 146 (42.44) 40 (11.63) 137 (39.83) 206 (59.88) 1 (0.29) 194 (56.40) 136 (39.53) 14 (4.07) 194 (56.40) 126 (36.63) 24 (6.98) 165 (47.97) 128 (37.21) 51 (14.83) 2011–2012 170 (48.71) 145 (41.55) 34 (9.74) 94 (26.93) 254 (72.78) 1 (0.29) 205 (58.74) 132 (37.82) 12 (3.44) 191 (54.73) 135 (38.68) 23 (6.59) 147 (42.12) 154 (44.13) 48 (13.75) 2012–2013 198 (55.62) 148 (41.57) 10 (2.81) 196 (55.06) 159 (44.66) 1 (0.28) 173 (48.60) 170 (47.75) 13 (3.65) 181 (50.84) 156 (43.82) 19 (5.34) 133 (37.36) 181 (50.84) 42 (11.80) 2013–2014 191 (51.62) 175 (47.30) 4 (1.08) 82 (22.16) 288 (77.84) 0 (0.00) 209 (56.49) 148 (40.00) 13 (3.51) 202 (54.59) 147 (39.73) 21 (5.68) 158 (42.70) 161 (43.51) 51 (13.78) 2014–2015 177 (47.97) 187 (50.68) 5 (1.36) 148 (40.11) 220 (59.62) 1 (0.27) 214 (57.99) 145 (39.30) 10 (2.71) 191 (51.76) 157 (42.55) 21 (5.69) 181 (49.05) 139 (37.67) 49 (13.28) 2015–2016 174 (48.20) 180 (49.86) 7 (1.94) 127 (35.18) 232 (64.27) 2 (0.55) 181 (50.14) 165 (45.71) 15 (4.16) 173 (47.92) 166 (45.98) 22 (6.09) 139 (38.50) 173 (47.92) 49 (13.57) 2016–2017 137 (37.64) 225 (61.81) 2 (0.55) 81 (22.25) 281 (77.20) 2 (0.55) 167 (45.88) 192 (52.75) 5 (1.37) 149 (40.93) 205 (56.32) 10 (2.75) 166 (45.60) 191 (52.47) 7 (1.92) Panel B: Conventional banks 2007–2008 92 (31.83) 77 (26.64) 120 (41.52) 67 (23.18) 200 (69.20) 22 (7.61) 191 (66.09) 80 (27.68) 18 (6.23) 203 (70.24) 67 (23.18) 19 (6.57) 134 (46.37) 116 (40.14) 39 (13.49) 2008–2009 107 (37.15) 122 (42.36) 59 (20.49) 36 (12.50) 247 (85.76) 5 (1.74) 178 (61.81) 103 (35.76) 7 (2.43) 156 (54.17) 120 (41.67) 12 (4.17) 154 (53.47) 95 (32.99) 39 (13.54) 2009–2010 115 (39.79) 123 (42.56) 51 (17.65) 154 (53.29) 133 (46.02) 2 (0.69) 134 (46.37) 151 (52.25) 4 (1.38) 166 (57.44) 113 (39.10) 10 (3.46) 89 (30.80) 169 (58.48) 31 (10.73) 2010–2011 127 (43.79) 125 (43.10) 38 (13.10) 119 (41.03) 170 (58.62) 1 (0.34) 162 (55.86) 119 (41.03) 9 (3.10) 164 (56.55) 111 (38.28) 15 (5.17) 137 (47.24) 109 (37.59) 44 (15.17) 2011–2012 143 (48.64) 119 (40.48) 32 (10.88) 84 (28.57) 210 (71.43) 0 (0.00) 176 (59.86) 109 (37.07) 9 (3.06) 163 (55.44) 114 (38.78) 17 (5.78) 124 (42.18) 128 (43.54) 42 (14.29) 2012–2013 162 (55.29) 122 (41.64) 9 (3.07) 160 (54.61) 132 (45.05) 1 (0.34) 143 (48.81) 140 (47.78) 10 (3.41) 150 (51.19) 129 (44.03) 14 (4.78) 104 (35.49) 154 (52.56) 35 (11.95) 2013–2014 165 (53.92) 137 (44.77) 4 (1.31) 68 (22.22) 238 (77.78) 0 (0.00) 180 (58.82) 116 (37.91) 10 (3.27) 172 (56.21) 120 (39.22) 14 (4.58) 135 (44.12) 132 (43.14) 39 (12.75) 2014–2015 147 (48.20) 153 (50.16) 5 (1.64) 128 (41.97) 176 (57.70) 1 (0.33) 181 (59.34) 117 (38.36) 7 (2.30) 164 (53.77) 126 (41.31) 15 (4.92) 144 (47.21) 122 (40.00) 39 (12.79) 2015–2016 133 (44.04) 162 (53.64) 7 (2.32) 101 (33.44) 199 (65.89) 2 (0.66) 144 (47.68) 145 (48.01) 13 (4.30) 135 (44.70) 150 (49.67) 17 (5.63) 117 (38.74) 148 (49.01) 37 (12.25) 2016–2017 114 (37.75) 186 (61.59) 2 (0.66) 63 (20.86) 237 (78.48) 2 (0.66) 140 (46.36) 159 (52.65) 3 (0.99) 126 (41.72) 171 (56.62) 5 (1.66) 138 (45.70) 159 (52.65) 5 (1.66) Panel C: Islamic banks 2007–2008 22 (52.38) 12 (28.57) 8 (19.05) 10 (23.81) 28 (66.67) 4 (9.52) 29 (69.05) 8 (19.05) 5 (11.90) 28 (66.67) 10 (23.81) 4 (9.52) 21 (50.00) 15 (35.71) 6 (14.29) 2008–2009 16 (38.10) 21 (50.00) 5 (11.90) 4 (9.52) 36 (85.71) 2 (4.76) 21 (50.00) 16 (38.10) 5 (11.90) 19 (45.24) 17 (40.48) 6 (14.29) 16 (38.10) 12 (28.57) 14 (33.33) 2009–2010 19 (36.54) 32 (61.54) 1 (1.92) 28 (53.85) 24 (46.15) 0 (0.00) 18 (34.62) 30 (57.69) 4 (7.69) 24 (46.15) 21 (40.38) 7 (13.46) 10 (19.23) 33 (63.46) 9 (17.31) 2010–2011 31 (57.41) 21 (38.89) 2 (3.70) 18 (33.33) 36 (66.67) 0 (0.00) 32 (59.26) 17 (31.48) 5 (9.26) 30 (55.56) 15 (27.78) 9 (16.67) 28 (51.85) 19 (35.19) 7 (12.96) 2011–2012 27 (49.09) 26 (47.27) 2 (3.64) 10 (18.18) 44 (80.00) 1 (1.82) 29 (52.73) 23 (41.82) 3 (5.45) 28 (50.91) 21 (38.18) 6 (10.91) 23 (41.82) 26 (47.27) 6 (10.91) 2012-2013 36 (57.14) 26 (41.27) 1 (1.59) 36 (57.14) 27 (42.86) 0 (0.00) 30 (47.62) 30 (47.62) 3 (4.76) 31 (49.21) 27 (42.86) 5 (7.94) 29 (46.03) 27 (42.86) 7 (11.11) 2013–2014 26 (40.63) 38 (59.38) 0 (0.00) 14 (21.88) 50 (78.13) 0 (0.00) 29 (45.31) 32 (50.00) 3 (4.69) 30 (46.88) 27 (42.19) 7 (10.94) 23 (35.94) 23 (35.94) 12 (18.75) 2014–2015 30 (46.88) 34 (53.13) 0 (0.00) 20 (31.25) 44 (68.75) 0 (0.00) 33 (51.56) 28 (43.75) 3 (4.69) 27 (42.19) 31 (48.44) 6 (9.38) 37 (57.81) 17 (26.56) 10 (15.63) Page 16 of 22 Jubileeetal. Futur Bus J (2021) 7:22 Table 6 (continued) Period Productivity change Technological change Efficiency change Pure Efficiency change Scale efficiency change (TFPCH) (TECHCH) (EFFCH) (PECH) (SECH) Progress Regress No ∆ Progress Regress No ∆ Progress Regress No ∆ Progress Regress No ∆ Progress Regress No ∆ # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) 2015–2016 41 (69.49) 18 (30.51) 0 (0.00) 26 (44.07) 33 (55.93) 0 (0.00) 37 (62.71) 20 (33.90) 2 (3.39) 38 (64.41) 16 (27.12) 5 (8.47) 22 (37.29) 25 (42.37) 12 (20.34) 2016–2017 23 (37.10) 39 (62.90) 0 (0.00) 18 (29.03) 44 (70.97) 0 (0.00) 27 (43.55) 33 (53.23) 2 (3.23) 23 (37.10) 34 (54.84) 5 (8.06) 28 (45.16) 32 (51.61) 2 (3.23) Productivity growth: TFPCH > 1, Productivity Loss < 1, Productivity Stagnation: TFPCH = 1 Page 17 of 22 Jubileeetal. Futur Bus J (2021) 7:22 Table 7 Summary of parametric and nonparametric test on conventional and Islamic banks Region No. of Obs. Parametric test Nonparametric test t test Mann–Whitney [Wilcoxon Rank Sum] test Kruskal–Wallis Equality of Population t(Prb >t) z(Prb >z) X2( Prb >X2) TFPCH TECHCH EFFCH PECH SECH TFPCH TECHCH EFFCH PECH SECH TFPCH TECHCH EFFCH PECH SECH Panel A: year 2007–2008 ALL 331 − 1.449 − 1.445 0.140 − 0.620 0.232 − 1.798c− 1.253 − 0.041 − 0.419 − 0.219 3.234c1.570 0.002 0.176 0.048 Mean Diff. 0.347 0.076 (0.030) 0.127 (0.017) 27.580 19.800 (0.650) (6.630) 3.450 27.580 19.800 (0.650) (6.630) 3.450 ME 142 − 0.421 0.670 0.105 − 0.394 − 0.651 − 0.038 − 0.402 − 0.135 − 1.279 − 0.909 0.001 0.162 0.018 1.635 0.825 Mean Diff. 0.132 (0.043) (0.035) 0.148 0.057 0.360 (3.910) (1.310) (12.44) 8.830 0.360 (3.910) (1.310) (12.440) 8.830 SEA 132 − 2.153c− 3.589a− 0.230 − 0.378 0.307 − 3.644a− 3.072a− 0.301 − 0.416 − 0.350 13.276a9.434a0.090 0.173 0.123 Mean Diff. 1.076 0.384 0.088 0.109 (0.049) 41.550 36.990 3.620 5.010 (4.210) 41.550 36.99 3.620 5.010 (4.210) SA 57 0.381 1.396 − 0.094 − 0.629 0.383 − 0.286 − 1.317 − 0.905 − 1.113 − 0.274 0.082 1.736 0.820 1.239 0.075 Mean Diff. (0.118) (0.111) 0.042 0.177 (0.056) 1.640 (7.58) 5.210 6.430 (1.580) 1.640 (7.580) 5.210 6.430 (1.580) Panel B: year 2008–2009 ALL 330 0.100 0.533 − 0.267 − 0.167 − 0.299 − 0.235 − 1.047 − 0.075 − 0.127 − 0.416 0.055 1.095 0.006 0.016 0.173 Mean Diff. (0.016) (0.024) 0.059 0.027 (0.765) (3.680) (16.490) (1.190) 2.010 (6.540) (3.680) (16.490) (1.190) 2.010 (6.540) ME 135 − 0.416 0.021 − 0.597 − 0.888 0.021 − 0.373 − 0.418 − 0.025 − 0.848 − 0.963 0.139 0.174 0.001 0.719 0.927 Mean Diff. 0.113 (0.001) 0.238 0.225 (0.004) 3.620 4.040 (0.240) 8.210 (9.310) 3.620 4.040 (0.240) 8.210 (9.310) SEA 137 0.090 − 0.392 0.501 0.457 0.073 − 0.615 − 0.268 − 0.386 − 0.694 − 0.015 0.378 0.072 0.149 0.482 0.001 Mean Diff. (0.025) 0.039 (0.184) (0.139) (0.009) (7.020) (3.100) (4.460) (8.030) (0.170) (7.02) (3.100) (4.460) (8.030) (0.170) SA 58 0.362 2.168c− 0.374 0.425 − 1.155 − 0.588 − 2.419b− 0.401 − 0.227 − 0.093 0.346 5.851b0.161 0.051 0.009 Mean Diff. (0.073) (1.147) 0.079 (0.088) 0.129) (3.440) (13.200) 2.360 (1.330) 0.540 (3.440) (14.200) 2.360 (1.330) 0.540 Panel C: year 2009–2010 ALL 341 3.075a0.796 2.538b1.086 0.174 − 1.887c− 0.318 − 1.384 − 1.032 − 1.355 3.559c0.101 1.917 1.066 1.836 Mean Diff. (0.368) (0.026) (0.332) (0.229) (0.021) (27.960) (4.720) (20.550) (15.330) (20.11) (27.960) (4.720) (20.550) (15.330) (20.110) ME 146 2.601b0.353 2.423b1.980b− 0.414 − 1.530 − 0.463 − 1.407 − 1.036 − 0.870 2.340 0.214 1.979 1.073 0.756 Mean Diff. (0.499) (0.019) (0.493) (0.446) 0.093 (13.990) 4.230 (12.870) (9.480) (7.960) (13.990) 4.230 (12.870) (9.480) (7.960) SEA 139 0.841 0.216 0.742 0.444 0.733 − 0.781 − 0.350 − 0.564 − 0.518 − 0.093 0.610 0.122 0.318 0.268 0.009 Mean Diff. (0.252) (0.009) (0.239) (0.139) (0.106) (8.260) (3.740) (6.040) (5.550) (0.980) (8.260) (3.740) (6.04) (5.550) (0.980) SA 56 0.890 0.896 0.533 − 0.276 1.125 − 0.267 − 1.284 − 0.214 − 0.107 − 1.458 0.072 1.648 0.046 0.011 0.046 Mean Diff. (0.323) (0.052) (0.202) 0.069 (0.245) (1.520) (7.300) (1.220) 0.610 (8.280) (1.520) (7.300) (1.220) 0.610 (8.280) Panel D: year 2010–2011 ALL 344 − 0.645 0.141 − 0.666 − 0.088 0.089 − 0.843 − 0.426 − 0.839 − 0.926 − 0.299 0.710 0.182 0.704 0.182 0.089 Mean Diff. 1.149 (0.004) 0.162 0.019 (0.011) 12.410 (6.290) 12.370 13.640 4.390 12.410 12.410 12.370 12.370 4.390 ME 145 − 0.161 − 0.671 − 0.112 0.080 0.450 − 0.199 − 0.660 − 0.330 − 0.086 − 0.548 0.040 0.435 0.109 0.007 0.300 Page 18 of 22 Jubileeetal. Futur Bus J (2021) 7:22 Table 7 (continued) Region No. of Obs. Parametric test Nonparametric test t test Mann–Whitney [Wilcoxon Rank Sum] test Kruskal–Wallis Equality of Population t(Prb >t) z(Prb >z) X2( Prb >X2) TFPCH TECHCH EFFCH PECH SECH TFPCH TECHCH EFFCH PECH SECH TFPCH TECHCH EFFCH PECH SECH Mean Diff. 0.064 0.028 0.045 (0.029) (0.083) (1.840) 6.090 (3.050) 0.800 (5.050) (1.840) 6.090 (3.050) 0.800 (5.050) SEA 139 0.214 − 0.519 0.486 0.249 0.857 − 0.646 − 0.289 − 0.897 − 0.210 − 0.913 0.418 0.083 0.805 0.044 0.834 Mean Diff. (0.074) 0.029 (0.190) (0.099) (0.162) (6.540) 2.940 (9.120) (2.140) (9.280) (6.540) 2.940 (9.120) (2.280) (9.280) SA 60 − 1.522 2.237b− 2.129b− 1.551 − 1.330 − 2.761a− 2.168b− 3.210a− 2.637a− 2.596a7.622a4.702b10.302a6.952a6.737a Mean Diff. 0.623 (0.126) 0.937 0.340 0.365 16.090 (12.640) 18.700 15.370 15.030 16.090 (12.640) 18.700 15.370 15.030 Panel E: year 2011–2012 ALL 349 − 0.782 1.740c− 0.986 0.245 − 0.910 − 0.011 − 0.597 − 0.371 − 0.334 − 0.808 0.001 0.356 0.138 0.111 0.652 Mean Diff. 0.154 (0.043) 0.187 (0.046) 0.256 0.170 (8.840) 5.510 4.940 (11.950) 0.170 (8.840) 5.510 4.940 (11.950) ME 145 − 0.619 0.269 − 0.649 − 0.135 − 0.680 − 0.207 − 0.013 − 0.450 − 0.456 − 0.577 0.043 0.001 0.203 0.207 0.333 Mean Diff. 0.204 (0.011) 0.197 0.042 0.079 1.910 (0.120) 4.160 4.200 (5.320) 1.910 (0.120) 4.160 4.200 (5.320) SEA 144 0.224 1.444 0.067 0.341 0.600 − 0.145 − 0.650 − 0.453 − 0.643 − 0.266 0.021 0.422 0.205 0.414 0.071 Mean Diff. (0.057) (0.080) (0.016) (0.112) (0.107) 1.520 (6.820) 4.770 6.770 (2.790) 1.520 (6.820) 4.770 6.770 (2.790) SA 60 − 0.733 1.647 − 0.919 − 0.028 − 0.936 − 0.083 − 0.240 − 0.277 − 0.111 − 1.187 0.007 0.058 0.077 0.012 1.410 Mean Diff. 0.377 (0.049) 0.503 0.005 1.139 (0.460) (1.350) 1.560 (0.630) (6.660) (0.460) (1.350) 1.560 (0.630) (6.660) Panel F: year 2012–2013 ALL 366 − 1.082 − 2.465b− 0.756 0.199 − 0.984 − 0.458 − 1.566 − 0.067 − 0.222 − 1.143 0.210 2.452 0.004 0.049 1.307 Mean Diff. 0.256 0.066 0.161 (0.053) 0.084 6.550 22.380 (0.960) (3.170) 16.330 6.550 22.380 (0.960) (3.170) 16.330 ME 145 − 0.938 − 1.433 − 0.705 − 0.130 − 0.713 − 1.337 − 1.155 − 0.932 − 0.283 − 1.138 1.789 1.333 0.868 0.080 1.295 Mean Diff. 0.317 0.058 0.263 0.074 0.063 (11.820) 10.200 8.230 2.500 10.050 11.820 10.200 8.230 2.500 10.050 SEA 148 − 0.851 − 1.193 − 0.596 0.467 − 1.039 − 0.075 − 0.040 − 0.094 − 0.275 − 0.771 0.006 0.002 0.009 0.076 0.595 Mean Diff. 0.429 0.058 0.288 (0.122) 0.189 0.750 0.400 0.940 2.720 7.630 0.750 0.400 0.940 2.720 7.630 SA 63 0.517 − 2.390b0.940 1.395 − 0.139 − 1.240 − 2.208b− 1.732c− 1.699c− 0.477 1.537 2.885b3.001c2.885c0.228 Mean Diff. (0.153) 0.104 (0.282) (0.298) 0.020 (7.080) 12.600 (9.890) (9.690) 2.710 (7.080) 12.600 (9.890) (9.690) 2.710 Panel G: Year 2013–2014 ALL 370 0.003 − 0.953 − 0.008 − 0.051 − 0.430 − 1.117 − 0.276 − 1.461 − 1.087 − 1.318 1.249 0.076 2.133 1.182 1.736 Mean Diff. (0.001) 0.025 0.002 0.010 0.057 (16.430) 4.060 (21.470) (15.980) (19.350) (16.430) 4.060 (21.470) (15.980) (19.350) ME 149 1.573 − 1.852c 2.423b1.000 1.718c− 0.309 − 1.458 − 1.268 − 0.688 − 1.691c0.096 2.125 1.608 0.473 2.860 Mean Diff. (0.369) 0.101 (0.600) (0.379) (0.184) (2.760) 13.010 (11.320) (6.150) (15.070) (2.760) 13.010 (11.320) (6.150) (15.070) SEA 151 − 0.830 1.223 − 1.055 − 0.597 − 0.801 − 1.217 − 0.611 − 0.862 − 1.157 − 0.005 1.481 0.373 0.743 1.340 0.001 Mean Diff. 0.375 (0.053) 0.627 0.265 0.481 (12.060) (6.050) (8.540) (11.470) (0.050) (12.06) (6.050) (8.540) (11.470) (0.050) Page 19 of 22 Jubileeetal. Futur Bus J (2021) 7:22 Table 7 (continued) Region No. of Obs. Parametric test Nonparametric test t test Mann–Whitney [Wilcoxon Rank Sum] test Kruskal–Wallis Equality of Population t(Prb >t) z(Prb >z) X2( Prb >X2) TFPCH TECHCH EFFCH PECH SECH TFPCH TECHCH EFFCH PECH SECH TFPCH TECHCH EFFCH PECH SECH SA 70 − 0.192 − 0.258 − 0.258 − 1.000 2.691a− 0.717 − 0.725 − 0.662 − 0.304 − 1.046 0.514 0.525 0.439 0.092 1.093 Mean Diff. 0.071 0.013 0.099 0.344 (0.206) (4.620) (4.670) (4.270) (1.960) (6.740) (4.620) (4.670) (4.270) (1.960) (6.740) Panel H: year 2014–2015 ALL 369 0.734 3.334a− 0.198 0.659 − 1.891c− 0.898 − 2.960a− 0.131 − 1.139 − 2.253b0.807 8.763a0.017 1.297 5.077b Mean Diff. (0.146) (0.104) 0.038 (0.152) 0.197 (13.180) (43.410) 1.920 (16.700) 33.000 (13.180) (43.410) 1.920 (16.700) 33.000 ME 151 1.379 2.695a0.868 0.941 − 0.818 − 1.493 − 2.846a− 0.727 − 0.777 − 0.244 2.228 8.100a0.529 0.604 0.059 Mean Diff. (0.481) (0.135) (0.288) (0.395) 0.079 (13.490) (25.720) (6.570) (7.020) 2.200 (13.490) (25.720) (6.570) (7.020) 2.200 SEA 147 − 0.821 0.481 − 1.206 − 0.864 − 0.103 − 0.521 − 0.003 − 0.744 − 0.076 − 0.704 0.272 0.001 0.553 0.006 0.495 Mean Diff. 0.239 (0.024) 0.329 0.293 0.013 5.130 0.020 7.330 (0.750) 6.930 5.130 0.020 7.330 (0.750) 6.930 SA 71 0.262 3.080 − 0.583 1.212 − 2.104c− 0.572 − 2.498b− 0.349 − 1.644c− 3.545a0.328 6.239b0.122 2.703c12.568a Mean Diff. (0.098) (0.191) 0.246 (0.431) 0.796 (3.620) (15.820) 2.220 (10.410) 22.410 (3.620) (15.820) 2.220 (10.410) 22.410 Panel I: year 2015–2016 ALL 361 − 2.698a− 1.584 − 2.478b− 2.076b− 0.349 − 3.665a− 1.330 − 2.872a− 3.378a− 0.195 13.432a1.769 8.248a11.413a0.038 Mean Diff. 0.639 0.046 0.645 0.536 0.030 54.440 19.760 42.660 50.170 2.890 54.440 19.760 42.660 50.170 2.890 ME 148 − 2.421b− 1.661c− 2.192b− 1.987b− 0.565 − 3.155a− 1.351 − 2.515b− 2.887a− 0.027 9.956a1.824 6.325b8.337a0.001 Mean Diff. 1.076 0.081 1.081 0.914 0.076 28.790 12.320 22.50 26.350 (0.250) 28.790 12.320 22.50 26.350 (0.250) SEA 146 − 1.449 − 0.064 − 1.495 − 1.844c0.733 − 2.180b− 0.722 − 2.172b− 2.429b− 0.107 4.752b0.521 4.717b5.900b0.011 Mean Diff. 0.356 0.003 0.366 0.439 (0.114) 21.320 7.060 21.250 23.760 (1.050) 21.320 7.060 21.250 23.760 (1.050) SA 67 − 0.110 − 0.631 − 0.070 0.873 − 0.909 − 0.370 − 0.070 − 0.220 − 0.669 − 0.397 0.137 0.005 0.048 0.448 0.157 Mean Diff. 0.044 0.043 0.030 (0.327) 0.202 2.470 0.470 (1.470) (4.470) 2.640 2.470 0.470 (1.470) (4.470) 2.640 Panel J: year 2016–2017 ALL 364 0.393 − 3.022a3.753a2.865b1.534 − 0.225 − 2.446b− 1.113 − 1.240 − 0.184 0.050 5.984b1.239 1.538 0.034 Mean Diff. (0.088) 0.144 (0.980) 0.144 (0.138) (3.290) 35.880 (16.330) (18.200) (2.700) (3.290) 35.880 (16.330) (18.200) (2.700) ME 148 2.095b− 2.355b3.597a2.226b0.547 − 0.698 − 2.142b− 1.469 − 1.427 − 0.460 0.487 4.588b2.157 2.037 0.212 Mean Diff. (0.512) 0.142 (1.469) (1.273) (0.101) (6.280) 19.280 (13.210) (12.840) (1.140) (6.280) 19.280 (13.210) (12.840) (1.140) SEA 147 − 1.050 − 3.341a1.549 0.721 1.867c− 0.709 − 2.552b− 0.176 − 0.291 − 0.326 0.502 6.513b0.031 0.084 0.106 Mean Diff. 0.435 0.275 (0.760) (0.579) (0.257) 6.970 25.120 1.740 2.860 (3.210) 6.970 25.120 1.740 2.860 (3.210) SA 69 0.510 0.243 0.960 2.222b− 0.117 − 0.602 − 0.024 − 1.005 − 1.282 − 0.705 0.362 0.001 1.011 1.644 0.496 Mean Diff. (0.097) (0.026) (0.410) (0.599) 0.016 (3.840) 0.160 (6.400) (8.170) 4.490 (3.840) 0.160 (6.400) (8.170) 4.490 Page 20 of 22 Jubileeetal. Futur Bus J (2021) 7:22 Abbreviation DEA: Data envelopment analysis; TFP: Total factor productivity; TFPCH: TFP change; GFC: Global financial crisis; SA: South Asia; SEA: Southeast Asia; ME: Middle East; SAC: Shariah Advisory Committee; GCC : Gulf Cooperation Council; MPI: Malmquist productivity index; OIC: Organisation of Islamic Cooperation; SFA: Stochastic frontier analysis; TECHCH: Technical change; EFFCH: Efficiency change; SECH: Scale efficiency change; PTECH: Pure technical change; CRS: Constant returns to scale; VRS: Variable returns to scale; DMU: Decision-making unit. Acknowledgements Not applicable. Authors’ contributions RVWJ structured the theoretical, research framework and analysed the data. FK conceived the idea and clarified the main issues in the research to ensure the novelty contribution and represent as the corresponding author. ARAL collects and provides data for this study. HIH performed the interpretation from the analysis results obtained. KMT ensures all the previous literature cited related to the issues discussed in the study and keeps research flow accordingly. All authors read and approved the final manuscript Funding This research is a result of a scholarship by (1) Universiti Putra Malaysia Grant Putra Vot No. 9632100 sponsored by Universiti Putra Malaysia that paid fees of data collection expenses from the research institution; (2) Fundamental Research Grant Scheme (FRGS) Vot No. FRGS/1/2015/SS01/UPM/02/1 5524716 sponsored by Malaysian Ministry of Higher Education that funded the travelling data collection expenses; (3) Universiti Putra Malaysia Grant IPM Vot No. 9473700 sponsored by Universiti Putra Malaysia that supported the nonparametric DEA software econometric; (4) Universiti Putra Malaysia Grant IPS Vot No. 9651500 sponsored by Universiti Putra Malaysia that paid the English professional service to polish and improvise the flow of the article and (5) Xiamen University Malaysia Research Fund Vote No. ISEM/0021 sponsored by Xiamen University Malaysia that funded the remunerators wages for the data collection. Availability of data and material The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Competing interests The authors declare that they have no competing interests. Author details 1 Putra Business School, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia. 2 Labuan Faculty of International Finance, Universiti Malaysia Sabah, Jalan Sungai Pagar, 87000 Federal Territory of Labuan, Malaysia. 3 School of Business and Economics, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia. 4 Taylor’s Business School, Faculty of Business and Law, Taylor’s University, Taylor’s Lakeside Campus, 1 Jalan Taylor’s, 47500 Subang Jaya, Selangor Darul Ehsan, Malaysia. 5 University of Economics and Human Science, Okopowa 59, 01-043 Warsaw, Poland. 6 School of Economics and Management, Xiamen University Malaysia, 43900 Sepang, Selangor Darul Ehsan, Malaysia. Received: 24 April 2020 Accepted: 13 April 2021 References 1. Aluko O, Ajayi MA (2018) Determinants of banking sector development: evidence from Sub-Saharan African countries. Borsa Istanb Rev 18(2):122–139 2. Abedifar P, Hassan I, Tarazi A (2016) Finance-growth nexus and dualbanking systems: relative importance of Islamic banks. J Econ Behav Organ 132:198–215 Table 7 (continued) Region No. of Obs. Parametric test Nonparametric test t test Mann–Whitney [Wilcoxon Rank Sum] test Kruskal–Wallis Equality of Population t(Prb >t) z(Prb >z) X2( Prb >X2) TFPCH TECHCH EFFCH PECH SECH TFPCH TECHCH EFFCH PECH SECH TFPCH TECHCH EFFCH PECH SECH Panel K: all years and regions ALL 3515 − 1.488 − 1.700c0.152 0.765 − 1.034 − 0.605 − 0.408 − 0.090 − 0.202 − 0.044 0.366 0.166 0.008 0.041 0.002 Mean Diff. 0.100 0.019 (0.012) (0.066) 0.049 28.360 19.100 (4.240) (9.480) 2.070 28.360 19.100 (4.240) (9.480) 2.070 ME 1454 0.007 − 1.340 0.910 0.737 − 0.044 − 0.161 − 0.847 − 0.503 − 0.004 − 0.779 0.026 0.717 0.253 0.001 0.607 Mean Diff. (0.000) 0.024 (0.120) (0.119) 0.002 4.640 24.440 (14.510) 0.110 (22.470) 4.640 24.440 (14.510) 0.110 (22.470) SEA 1430 − 2.014b− 2.957a− 0.328 0.044 − 0.060 − 0.897 1.945c− 0.105 − 0.112 − 0.223 0.805 3.783c0.011 0.012 0.050 Mean Diff. 0.255 0.059 0.044 (0.006) 0.004 29.030 63.000 (3.420) 3.610 (7.240) 29.030 63.000 (3.420) 3.610 (7.240) SA 631 − 0.251 2.160b− 0.790 1.210 − 1.629 − 0.103 − 3.019a− 0.590 − 0.776 − 1.401 0.011 9.116a0.348 0.602 0.011 Mean Diff. 0.028 (0.050) 0.099 (0.101) 0.236 (1.950) (57.140) 11.160 (14.680) 26.470 (1.950) (57.140) 11.160 (14.680) 26.470 a , b, c indicate significance at the 1%, 5% and 10% levels, respectively In bracket indicates the productivity mean of conventional banks is higher than Islamic banks Page 21 of 22 Jubileeetal. Futur Bus J (2021) 7:22 3. Alexakis C, Izzeldin M, Johnes J, Pappas V (2018) Performance and productivity in Islamic and conventional banks: evidence from the global financial crisis. Econ Model 1–14 4. Alqahtani F, Mayes D, Brown K (2017) Islamic bank efficiency compared to conventional banks during. J Int Financ 51:58–74 5. Alsharif M, Nassir A, Kamarudin F, Zariyawati M (2019) The productivity of GCC Islamic and conventional banks after Basel III announcement. J Islam Account Bus Res 10(5):770–792 6. Ang J, Cole R, Lin J (2000) Agency costs and ownership structure. J Finance 55(1):1–39 7. Ariff M (1988) Islamic banking. Asian-Pac Econ Literat 2(2):48–64 8. Asian Development Bank (2017) Meeting Asia’s infrastructure needs. Asian Development Bank, Manila 9. Bader MK, Mohamad S, Ariff M, Hassan T (2008) Cost, revenue, and profit efficiency of Islamic versus conventional banks: international evidence using data envelopment analysis. Islam Econ Stud 15(2):23–76 10. Banker R, Datar S (1989) Sensitivity, precision, and linear aggregation of signals for performance evaluation. J Account Res 27(1):21–39 11. Beck T, Kunt AD, Merrouche O (2013) Islamic vs. conventional banking: business model, efficiency and stability. J Bank Finance 37:433–447 12. Bhatia V, Basu S, Mitra SK, Dash P (2018) A review of bank efficiency and productivity. OPSEARCH 55:557–600 13. Bitar M, Madies P, Taramasco O (2017) What makes Islamic banks different? A multivariate approach. Econ Syst 41(2):215–235 14. Bitar M, Pukthuanthong K, Walker T (2020) Efficiency in Islamic vs. conventional banking: the role of capital and liquidity. Global Finance J 46(C):1–40 15. Bottazi G, Secchi A, Tamagni F (2008) Productivity, profitability and financial performance. Ind Corp Change 17(4):711–751 16. Boukhatem J, Moussa FB (2018) The effect of Islamic banks on GDP growth: Some evidence from selected MENA countries. Borsa Istanb Rev 18(3):231–247 17. Bourkhis K, Nabi MS (2013) Islamic and conventional banks’ soundness during the 2007–2008 financial crisis. Rev Financ Econ 22(2):68–77 18. Charnes A, Cooper W, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444 19. Colwell R, Davis E (1992) Output and Productivity in Banking. Acand J Econ 94:S111–S129 20. Cooper W, Seiford L, Tone K (2002) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Kluwer Academic Publishers, Boston 21. Doumpos M, Hasan I, Pasiouras F (2017) Bank overall financial strength: Islamic versus conventional banks. Econ Model 64:513–523 22. Emrouznejad A, Yang G-L (2018) A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Soc Econ Plan Sci 61:4–8 23. Fare R, Grosskopf S, Norris M, Zhang Z (1994) Productivity growth, technical progress, and efficiency change in industrialized countries. Am Econ Rev 84(1):66–83 24. Ganouati J, Essid H (2017) The sources of productivity change and efficiency in Islamic banking: application of Malmquist productivity index. Cent Eur Rev Econ Manag 1(4):35–67 25. Ghosh A (2016) Banking sector globalization and bank performance: a comparative analysis of low income countries with emerging markets and advanced economies. Rev Dev Finance 6(1):58–70 26. Habib SF (2018) Chapter 3: Islamic banking versus Conventional banking. In: Habib SF (ed) Fundamentals of Islamic finance and banking. Wiley, New York, pp 47–78 27. Hassan M, Aliyu S (2018) A contemporary survey of islamic banking literature. J Financ Stab 34:12–43 28. Hassan M, Aliyu S, Paltrinieri A, Khan A (2018) A review of Islamic investment literature. Econ Pap 38(4):345–380 29. Hassan M, Khan A, Paltrinieri A (2019) Liquidity risk, credit risk and stability in Islamic and conventional banks. Res Int Bus Finance 48:17–31 30. Hassan T, Mohamad S, Bader MK (2009) Efficiency of conventional versus Islamic banks: evidence from the Middle East. Int J Islam Middle Eastern Finance Manag 2(1):46–65 31. Houben V (2003) Southeast Asia and Islam. Ann Am Acad Polit Soc Sci 588:149–170 32. Imam P, Kpodar K (2016) Islamic banking: good for growth? Econ Model 59:387–401 33. International Monetary Fund (2015) IMF annual report 2015: tackling challenges together. IMF 34. Jensen M, Meckling W (1976) Theory of the firm: managerial behavior, agency costs and ownership structure. J Financ Econ 3(4):305–360 35. Johnes J, Izzeldin M, Pappas V (2014) A comparison of performance of Islamic and conventional banks 2004–2009. J Econ Behav Organ 103(Supplement):S93–S107 36. Kabir M, Worthington A, Gupta R (2015) Comparative credit risk in Islamic and conventional bank. Pacific Basin Finance J 34:327–353 37. Kamarudin F, Anwar NM, Nassir A, Sufian F, Tan KM, Hussain HI (2020) Does country governance and bank productivity Nexus matters? J Islam Market 1–52 38. Kamarudin F, Nordin BA, Muhammad J, Hamid MA (2014) Cost, revenue and profit efficiency of Islamic and conventional banking sector: empirical evidence from Gulf Cooperative Council countries. Glob Bus Rev 15(1):1–24 39. Kamarudin F, Sufian F, Loong FW, Anwar NM (2017) Assessing the domestic and foreign Islamic banks efficiency: insights from selected South east Asian countries. Future Bus J 3:33–46 40. Khan F (2010) How ‘Islamic’ is Islamic Banking? J Econ Behav Organ 76(3):805–820 41. Komijani A, Hesary FT (2018) An Overview of Islamic Banking and Finance in Asia. ADBI working paper series, 853, 1–14 42. Khan M, Bhatti M (2008) Islamic banking and finance: on its way to globalization. Manag Finance 34(10):708–725 43. Kopleman R (1986) Managing productivity in organizations: a practical, people-oriented perspective. McGraw Hill Book Company, New Delhi 44. Lassoued M (2018) Comparative study on credit risk in Islamic banking institutions: the case of Malaysia. Q Rev Econ Finance 70:267–278 45. Maredza A, Ikhide P (2013) Measuring the impact of the global financial crisis on efficiency and productivity of the banking system in South Africa. Mediterr J Soc Sci 4(6):553–568 46. Mobarek A, Kalanov A (2014) Comparative performance analysis between conventional and Islamic banks: empirical evidence from OIC countries. Appl Econ 46(3):253–270 47. Mohamad S, Hassan T, Bader MK (2008) Efficiency of conventional versus Islamic banks: international evidence using the stochastic frontier approach (SFA). J Islam Econ 4(2):107–130 48. Paltrinieri A, Dreassi A, Rossi S, Khan A (2020) Risk-adjusted profitability and stability of Islamic and conventional banks: Does revenue diversification matter? Glob Finance J (In Press, Corrected Proof), 1–16 49. Rodoni A, Salim M, Amalia E, Rakhmadi R (2017) Comparing efficiency and productivity in Islamic banking: case study in Indonesia, Malaysia and Pakistan. J Islam Econ 9(2):227–242 50. Rosman R, Wahab NA, Zainol Z (2014) Efficiency of Islamic bank during the financial crisis: An analysis of Middle Eastern and Asian countries. Pac Basin Finance J 76–90 51. Said A (2013) Risks and efficiency in the Islamic banking systems: the case of selected Islamic banks in MENA region. Int J Econ Financ Issues 3(1):66–73 52. Saleh AS, Moradi-Motlagh A, Zeitun R (2020) What are the drivers of inefficiency in the Gulf Cooperation Council banking industry? A comparison between conventional and Islamic banks. Pacific Basin Finance J 60: 53. Siddiqi M (2006) Islamic banking and finance in theory and practice: a survey of state of the art. Islam Econ Stud 13:1–48. https:// doi. org/ 10. 4197/ islec. 17-1.4 54. Sufian F (2012) For which option is credit risk more representative on China banks’ total factor productivity: efficiency change or technological progress? China Finance Rev Int 2(2):180–202 55. Sufian F, Habibullah MS (2014) Banks’ total factor productivity growth in a developing economy: does globalisation matter? J Int Dev 26:821–852 56. Sufian F, Kamarudin F (2015) Determinants of revenue efficiency of Islamic banks. Int J Islam Middle Eastern Finance Manag 8(1):36–63 57. Sufian F, Kamarudin F (2017) Forced mergers on bank efficiency and productivity: evidence from semi-parametric Malmquist productivity index. Glob Bus Rev 18(1):19–44 58. Sufian F, Kamarudin F, Md. Nassir A (2017) Globalization and bank efficiency nexus: empirical evidence from the Malaysian Banking Sector. Benchmark Int J 24(5):1–8 59. The Asian Banker (2009, 2018) 100 Largest Islamic Banks. Retrieved from The Asian Banker: http:// www. theas ianba nker. com/ works pace. html? Page 22 of 22 Jubileeetal. Futur Bus J (2021) 7:22 mod= defau lt& method= downl oad&p= xgUgP kACJO GkKwB jdyYP 7aK7F wG% 2BHsG OmfQt jrWZH F0W% 2FMpR qXgVF OsItE aCLOx oLFSk BhMoJ vS% 2Bd12 z88Lv 6w% 3D% 3D 60. Toumi K, Louhichi W, Vivian J-L (2012) Alternative financial decision principles: theoretical foundations of islamic banks’ capital structure. In: Barnett W, Jawadi F (eds) Recent developments in alternative finance: empirical assessments and economic implications (international symposia in economic theory and econometrics. Emerald Group Publishing Limited, pp 157–172 61. Valderrama D (2003) Financial development, productivity, and economic growth. Retrieved from FRBSF Economic Letter: https:// www. frbsf. org/ econo micresea rch/ publi catio ns/ econo micletter/ 2003/ june/ finan cialdevel opmentprodu ctivi tyandecono micgrowth/ 62. Wanke P, Azad M, Barros CP, Hassan M (2016) Predicting efficiency in Islamic banks: an integrated multicriteria decision making (MCDM) approach. J Int Financ Mark Inst Money 45:126–141 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.