CHAPTER 8
Analysing Perceptions of Risk and Risk Management Dimensions and Issues: Inferential Statistical Analysis

This chapter is a continuation of Chapter 7 in analysing the quantitative data represented by the survey questionnaire. In this chapter inferential statistics are employed for exploring and analysing the opinions and attitudes of the respondents by providing a comparative analysis between several identified groups or respondent categories. In addition, the chapter considers some determinants and factors which contribute to the perception and knowledge of the respondents concerning risk management in Islamic banking.

As mentioned earlier in the research methodology chapter, the analysis in the present chapter employs several inferential statistics tools for non-parametric data analysis, ranging from cross-tabulation, the Friedman test, the Kruskall-Wallis test, and the Chi-Square test to factor analysis and MANOVA multivariate analysis of variance. Each of these statistical analyses will be used in the relevant section of the chapter; a brief description of it will be presented prior to its application, and the result will subsequently be interpreted. The chapter is divided into six broad sections in line with the main parts of the questionnaire and in accordance to the thematic division used in the interview analysis in Chapter 9. Each section is developed to find satisfactory answers to one or more of the main research questions and their sub-questions as previously explained in the book. This chapter concludes with a brief summary of the overall analysis and findings.

It should be noted that in order to avoid unnecessary detail, various analyses were brought together under one table to consolidate the analysis in a concise manner.

RISK PERCEPTION

It is highly expected that the respondents have different risk perceptions and understanding of risk management in Islamic banking according to their background, region, position within the organisation, nature of financial institution and other control variables. Therefore, this section analyses the respondents' opinions according to the selected category of their profile.

Risk Issues in Islamic Banks

Overall risks faced by Islamic banks   The first factor to be examined is the respondents' perceptions about the severity of risk facing IFIs. Descriptive statistics for Question 7, in Chapter 7, showed that Islamic and conventional bankers share similar views about the top risks facing IFIs, unlike non-bankers who adopted a more theoretical approach in their views. This section will investigate further to examine the difference in perceptions among different subgroups of respondents. For this purpose, the researcher has employed the Kruskall-Wallis (K-W) test for region, country, respondent's position, nature of Financial Institution, nature of activities and accounting standards

The first control variable is ‘Region’. The results from the K-W test for the entire research sample in Table 8.1 indicate that there is no statistically significant difference among various regions in risk perception (p-value > 0.05) except for corporate governance risk (p-value = 0.002), which is also evident from the mean ranking. With a ‘relaxation’ of the confidence level to 0.06, we can accept displaced commercial risk as significant as well.

TABLE 8.1 K-W test results by region for Question 7 for entire research sample

Risk Region N K-W Test
Mean Rank
Chi-Square Asymp.
Sig.
Credit Risk Americas  2 54   6.05 0.301
Europe 31 32.19
GCC 19 40.13
Other  2 22.25
Other Middle East 14 37.54
Southeast Asia  4 47.38
Total 72
Market Risk Americas  2 63   10.568 0.061
Europe 30 41  
GCC 19 29  
Other  2 31.75
Other Middle East 14 36.04
Southeast Asia  4 20.25
Total 71
Operational Risk Americas  2 38   4.496 0.48
Europe 31 35.58
GCC 19 41.68
Other  2 37.75
Other Middle East 14 28.39
Southeast Asia  4 46  
Total 72
Equity Investment Risk Americas  2 63   10.34 0.066
Europe 31 40.37
GCC 19 36.29  
Other  2 48    
Other Middle East 14 27    
Southeast Asia  4 21.75  
Total 72  
Liquidity Risk Americas  2 23   5.89 0.317
Europe 31 39.42  
GCC 19 38.18  
Other  2 43.25  
Other Middle East 14 26.79  
Southeast Asia  4 43.25  
Total 72  
ALM Risk Americas  2 24.5  3.482 0.626
Europe 31 39.53  
GCC 19 37.39  
Other  2 32.5  
Other Middle East 14 29.57  
Southeast Asia  4 41    
Total 72  
Displaced Commercial Risk Americas  2 36   11.002 0.051
Europe 29 28.64  
GCC 19 45.79  
Other  2 49.5   
Other Middle East 13 34.04  
Southeast Asia  4 25.25  
Total 69  
Shari'ah-Non-Compliance Risk Americas  2 42.5  4.49 0.481
Europe 31 39.11  
GCC 19 36.18  
Other  2 52.25  
Other Middle East 14 27.93  
Southeast Asia  4 36.88  
Total 72  
Concentration Risk Americas  2 41   5.869 0.319
Europe 31 37.9  
GCC 19 35.21  
Other  2 51    
Other Middle East 14 28.21  
Southeast Asia  4 51.25  
Total 72  
Reputation Risk Americas  2 33   3.644 0.602
Europe 31 34.68  
GCC 19 35.53  
Other  2 58.5  
Other Middle East 14 36.64  
Southeast Asia  4 45.5  
Total 72  
Fiduciary Risk Americas  2 33   4.978 0.419
Europe 30 34.12  
GCC 19 33.89  
Other  2 13.75  
Other Middle East 13 42.77  
Southeast Asia  4 42    
Total 70  
Corporate Governance Risk Americas  2 57   19.086 0.002
Europe 31 45.98  
GCC 19 29.21  
Other  2 49.25  
Other Middle East 14 26.07  
Southeast Asia  4 17.5   
Total 72  
Legal Risk Americas  2 26   2.067 0.84
Europe 31 38.19  
GCC 19 34.58  
Other  2 48.5   
Other Middle East 14 33.93  
Southeast Asia  4 40.75  
Total 72  

Repeating the K-W test with ‘Region’ as the control variable for different samples of data, in terms of the institutional setting of respondents, gives consistent results, as illustrated by Table 8.2, which confirms that there is a difference in risk perception of corporate governance risk among regions for fundamental market reasons. In other words, there is a significant difference between regions when institutional settings were also considered.

TABLE 8.2 K-W test results by region for Question 7 for selected sample data

Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Risk Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig.
Credit Risk  6.037 0.303 2.65 0.618 2.384 0.666
Market Risk  8.4   0.136 2.962 0.564 1.331 0.856
Operational Risk  4.181 0.524 4.241 0.374 1.222 0.874
Equity Investment Risk  9.399 0.094 3.188 0.527 6.795 0.147
Liquidity Risk  5.266 0.384 0.096 0.999 0.938 0.919
ALM Risk  2.404 0.791 0.894 0.925 3.006 0.557
Displaced Commercial Risk  9.785 0.082 7.992 0.092 7.219 0.125
Shari'ah-Non-Compliance Risk  4.609 0.465 1.387 0.846 6.283 0.179
Concentration Risk  7.318 0.198 2.751 0.6 4.077 0.396
Reputation Risk  4.388 0.495 2.795 0.593 2.223 0.695
Fiduciary Risk  5.846 0.322 3.128 0.537 9.058 0.06
Corporate Governance Risk 17.733 0.003 14.866 0.005 9.745 0.045
Legal Risk  2.656 0.753 2.904 0.574 3.398 0.494

In addition, examining the mean rankings across different regions for corporate governance risk confirms the existence of a structural pattern. As apparent from Table 8.3, the rankings do not change much when conducting K-W with different samples identifying different institutional settings. The inclusion of conventional banks and non-bankers in the test sample gives similar results. ‘Americas’ disappear when conventional banks are excluded from the test sample as there were no respondents from IFIs in the ‘Americas’ in this research sample. Also, the difference in values between the highest and the lowest mean rankings is noticeable, which confirms that the distribution of corporate governance risk is significantly different across regions.

TABLE 8.3 K-W test mean rankings for corporate governance risk for different sample data

Corporate Governance Risk Full Sample Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank
Americas 57    1st 47.75 1st N/A N/A N/A N/A
Europe 45.98 3rd 39.78 3rd 28.75 1st 19    1st
GCC 29.21 4th 24.74 4th 17.34 3rd 13.61 3rd
Other 49.25 2nd 41.25 2nd 27.75 2nd 18.75 2nd
Other Middle East 26.07 5th 22.25 5th 12.1  4th  8.3  4th
Southeast Asia 17.5  6th 14.88 6th 10.38 5th  7.13 5th

There is a pattern regardless of the nature of the respondents included in the sample, which implies that there are structural issues determined by the nature of the market, which can be explained by fundamental market reasons. Although corporate governance practices have material impacts on a bank's risk profile, IFIs do not generally have robust corporate governance frameworks in place particularly in the Gulf Cooperation Council (GCC), Middle East and Southeast Asia.

The same pattern could be identified, although to a lesser extent, when examining concentration risk, one of the main risks identified by respondents, as explained in the previous chapter. Table 8.4 confirms that there are fundamental market reasons for the difference in mean ranking among different regions. The mean ranking for the K-W test for the full sample ranks ‘Southeast Asia’ first (51.25), followed by ‘Other’ (51), then ‘Americas’ (41), while ‘Other Middle East’ comes last with mean rank of 28.21. This ranking changes little when conducting the K-W test for different samples using different institutional settings, which confirms that for concentration risk there is a significant difference between regions when institutional settings are also applied.

TABLE 8.4 K-W test mean rankings for concentration risk for different sample data

Concentration Risk Full Sample Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank
Americas 41    3rd 31.75 4th N/A N/A N/A N/A
Europe 37.9  4th 34.83 3rd 21.38 3rd 16.8  2nd
GCC 35.21 5th 26.58 5th 17.59 5th 12.28 3rd
Other 51    2nd 38.75 2nd 24.75 2nd 13.5  5th
Other Middle East 28.21 6th 22.25 6th 17.8  4th  8.8  4th
Southeast Asia 51.25 1st 40.13 1st 25.88 1st 14.88 1st

Furthermore, examining the mean rankings across different raw data for other significant risks like credit and liquidity risks (as identified by the respondents in Chapter 7) shows that rankings remain very similar between fully fledged Islamic banks and fully fledged Islamic banks combined with Islamic subsidiaries of conventional banks. However, adding conventional banks with no Islamic activities to the sample changes the rankings slightly as summarised in Tables 8.5 and 8.6. Under credit risk, for instance, when only fully fledged Islamic banks are included in the sample, ‘Southeast Asia’ ranks first (15.63), followed by ‘Other Middle East’ (13.2), ‘Europe’ (13.1), ‘GCC’ (13) and ‘Other’ (7). Also, the difference in values between the mean rankings is minimal, reflecting the close perception among different regions. When the institutional sample settings change to include Islamic subsidiaries as well, this pattern of mean rankings remains very similar. However, changing the institutional sample settings to include conventional banks changes the rankings and the gap between mean values becomes wider. Of note is the existence of the same pattern when non-bankers are also included in the sample. This shows that for credit risk there is a difference between regions when conventional banks and other non-banking respondents are also considered. Islamic and conventional bankers have different risk perceptions about credit risk across various regions.

TABLE 8.5 K-W test mean rankings for credit risk for different sample data

Credit Risk Full Sample Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank
Americas 54    1st 44.25 1st N/A N/A N/A N/A
Europe 32.19 5th 25.45 5th 18.58 4th 13.1  3rd
GCC 40.13 3rd 32.61 3rd 20.72 3rd 13    4th
Other 22.25 6th 18    6th 11    5th  7    5th
Other Middle East 37.54 4th 30.17 4th 21    2nd 13.2  2nd
Southeast Asia 47.38 2nd 38.75 2nd 24.63 1st 15.63 1st

Table 8.6 shows that the same trend exists for liquidity risk. K-W test results for different institutional samples indicate a similar pattern between samples of fully fledged Islamic banks and fully fledged Islamic banks combined with Islamic subsidiaries. Also, there is another similar pattern between the full sample and a sample comprising fully fledged Islamic banks, Islamic subsidiaries and conventional banks. This emphasises that Islamic and conventional bankers have different risk perceptions about liquidity risk across various regions, while the perceptions of Islamic subsidiaries is the same as that of fully fledged Islamic banks.

TABLE 8.6 K-W test mean rankings for liquidity risk for different sample data

Liquidity Risk Full Sample Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank
Americas 23    6th 17.75 6th N/A N/A N/A N/A
Europe 39.42 3rd 33.65 3rd 19.29 5th 12    5th
GCC 38.18 4th 30.58 4th 20.47 2nd 14.61 2nd
Other 43.25 2nd 34.75 2nd 20    3rd 12    3rd
Other Middle East 26.79 5th 22.67 5th 20.2  1st 12.3  1st
Southeast Asia 43.25 1st 34.75 1st 20    3rd 12    3rd

The findings indicate that there is an observed pattern which can be generalised to most of the risk categories. This can be explained only by market realities.

The K-W test was conducted in a similar manner according to ‘country’ as control variable; the results confirm those produced by the test conducted according to ‘Region’.

In addition, an attempt was made to test the impacts of ‘respondent's position’ and ‘accounting standards’ on risk perception; however, the results show that there are no significant differences, as summarised in Table 8.7.

TABLE 8.7 K-W test results by respondent's position and accounting standards for Question 7 for entire research sample

K-W According to Respondent's Position K-W According to Accounting Standards
Risk Chi-Square df Asymp. Sig. Chi-Square df Asymp. Sig.
Credit Risk 11.817 14 0.621 1.098 4 0.778
Market Risk 20.115 14 0.127 1.616 4 0.656
Operational Risk 15.095 14 0.372 3.472 4 0.324
Equity Investment Risk  7.749 14 0.902 6.584 4 0.086
Liquidity Risk 13.051 14 0.522 7.051 4 0.07
ALM Risk  7.108 14 0.93  5.677 4 0.128
Displaced Commercial Risk 15.899 13 0.255 5.266 4 0.153
Shari'ah-Non-Compliance Risk 22.246 14 0.074 6.074 4 0.108
Concentration Risk 16.891 14 0.262 5.79  4 0.122
Reputation Risk 13.971 14 0.452 4.421 4 0.219
Fiduciary Risk 17.288 14 0.241 0.525 4 0.913
Corporate Governance Risk 18.487 14 0.186 5.596 4 0.133
Legal Risk 11.305 14 0.662 0.668 4 0.881

Finally, conducting the K-W test to examine the significance of perceived differences among various risk groups for the entire research sample according to the ‘Nature of Financial Institution’ provided dispersed results. Table 8.8 shows that liquidity, ALM, Shari'ah-non-compliance, concentration, reputation and displaced commercial risks have significant p-values, while the remaining risks do not.

TABLE 8.8 K-W test results by nature of financial institution for Question 7 for entire research sample

Risk Chi-Square df Asymp. Sig.
Credit Risk  2.943 3 0.4  
Market Risk  6.238 3 0.101
Operational Risk  3.237 3 0.357
Equity Investment Risk  3.599 3 0.308
Liquidity Risk  8.818 3 0.032
ALM Risk  9.381 3 0.025
Displaced Commercial Risk 13.528 3 0.004
Sharia'a Non-Compliance Risk 15.674 3 0.001
Concentration Risk 16.629 3 0.001
Reputation Risk 11.257 3 0.01 
Fiduciary Risk  0.796 3 0.851
Corporate Governance Risk  1.511 3 0.68 
Legal Risk  4.146 3 0.246

Further examination of the mean rankings for risks with significant p-value, as summarised in Table 8.9, confirms the dispersion of data as no trend could be established. In general, fully fledged Islamic banks and conventional banks with Islamic activities have higher mean values than conventional banks alone and ‘Others', particularly for liquidity, Asset-Liability Management (ALM) and displaced commercial risks. This trend, nonetheless, slightly changes for concentration and reputation risks. Also of note is the proximity of mean value among fully fledged Islamic banks and Islamic subsidiaries, which reflects the similar perception of risks in Islamic banking. One possible reason for this is the similar knowledge and awareness of Islamic banking products and structures among those professionals with hands-on experience in Islamic banking. This confirms the findings of the section ‘Locating Risk Perception’ in Chapter 7.

TABLE 8.9 K-W test results by risk categories in relation to nature of financial institution for Question 7 for entire research sample

Risk Liquidity Risk ALM Risk Displaced Commercial Risk Shari'ah-Non-Compliance Risk Conc. Risk Rep. Risk
Nature of FI N Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank
Fully Fledged Islamic Bank 25 44.64 1st 45.18 1st 31.98 2nd 46.88 1st 48.48 1st 44.7  1st
Islamic Subsidiary 14 38.29 2nd 35.64 2nd 52    1st 25.46 4th 27.93 3rd 32.5  3rd
Conventional Bank 20 28    4th 27.05 4th 29.13 4th 27.73 3rd 35.85 2nd 25.93 4th
Others 13 32    3rd 35.27 3rd 30.36 3rd 41.92 2nd 23.69 4th 41.31 2nd
Total 72

Based on the above results, it can be concluded that three control variables (region, country and nature of FI) contribute to some significant differences about risk perception among respondents, but not for all risks. In addition, this can also be supported by the fact that there is no significant difference in perception levels between respondents from stand-alone Islamic banks and Islamic subsidiaries. Initially it was expected that respondents from stand-alone Islamic banks would have stronger perceptions compared to those from Islamic subsidiaries for two reasons: firstly, stand-alone Islamic banks have been in existence for much longer than Islamic subsidiaries, and, secondly, respondents from stand-alone Islamic banks have the advantage of dealing with only Islamic banking products and services, whereas Islamic subsidiaries still need to operate side-by-side with their respective conventional counterpart in sharing the same operating platforms and buildings. Nevertheless, the results have indicated otherwise. Differences could be spotted between perceptions of conventional banks and stand-alone Islamic banks, and more noticeably between the perceptions of bankers and non-bankers, represented by ‘Others’. This could be because bankers, whether Islamic or non-Islamic, have hands-on experience and better understanding of the Islamic banking model and its risk architecture than non-bankers, who tend to be more theoretical in their approach.

Islamic finance contracts   Questions 9 and 10 seek respondents' views on various Islamic modes of financing. Question 9 targets institutions that use Islamic finance contracts only. Therefore, when conducting the K-W test for Question 9, only stand-alone Islamic banks and Islamic subsidiaries were included in the data analysis.

Intensity of use of different Islamic finance contracts   Table 8.10 shows that regardless of the respondent's position or the nature of activities, banks use Islamic finance contracts in similar patterns; all products had p-value > 0.05. However, K-W test results according to ‘Region’ indicate that there is significant difference in the use of mudarabah across different regions. Moreover, there is significant difference in the use of wakala and salaam according to the nature of financial institution.

TABLE 8.10 K-W test results for Question 9 for selected sample data

K-W According to Region K-W According to Respondent's Position K-W According to Nature of FI K-W According to Nature of Activities
Contract Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig.
Murabahah  0.867 0.929  6.847 0.445 0.178 0.674 2.81  0.729
Wakala  1.273 0.866  6.472 0.486 6.875 0.009 6.946 0.225
Mudarabah 10.283 0.036  3.999 0.78  3.692 0.055 1.334 0.931
Ijarah  7.573 0.109 10.752 0.15  0.111 0.739 4.572 0.47 
Musharakah  2.085 0.72   5.511 0.598 0.727 0.394 2.85  0.723
Istisna'a  2.07  0.723  3.622 0.822 2.064 0.151 8.831 0.116
Salaam  4.794 0.309 10.661 0.154 4.729 0.03  3.073 0.689
Friedman test     0.00 

The Friedman test is used to find a tendency for some variables to receive higher ranks than others, i.e. to test whether the ranking is significant or not. The results of the test reflect that ranking for this question is significant.

TABLE 8.11 K-W test mean rankings for mudarabah according to region

Region N Mean Rank
Mudarabah Europe 12 17.71
GCC 16 16.47
Other  2 16.50
Other Middle East  5 32.50
Southeast Asia  4 27.13
Total 39

As can be seen in Table 8.11, ‘Other Middle East’ and ‘Southeast Asia’ use mudarabah the most, with mean values of 32.5 and 27.13 respectively, while ‘Europe’ (17.71) and the ‘GCC’ (16.74) rank lower on the use of mudarabah as financial institutions in these regions tend to rely more on murabahah, wakala and ijarah. This should be explained by the economies of the regions in question, as the lack of financial depth may necessitate greater use of equity financing.

As depicted by Table 8.12, Islamic subsidiaries (25.21) tend to use wakala to a greater extent than do fully fledged Islamic banks (17.08), while the picture is reversed for the use of salaam, where the disparity between mean values of the two groups is wide

TABLE 8.12 K-W test mean rankings for wakala and salaam according to nature of financial institution

Nature of Financial Institution N Mean Rank
Wakala Fully Fledged Islamic Bank 25 17.08
Conventional Bank with Islamic Activities/ Windows 14 25.21
Total 39
Salaam Fully Fledged Islamic Bank 25 22.86
Conventional Bank with Islamic Activities/ Windows 14 14.89
Total 39

In addition, Question 9 was re-tested, excluding Islamic subsidiaries from the sample. However, there were no significant differences between the use of different contracts across the different control variables: region, respondent's position, nature of activities and accounting standards.

Risk perception for different Islamic finance contracts   Unlike Question 9, which targeted financial institutions using Islamic finance contracts, Question 10 seeks risk perceptions for these contracts. The feedback of all respondents is valuable; therefore the K-W test is conducted on the entire research sample.

As can be seen in Table 8.13, the results of the Friedman test reflect that ranking for this question is significant, indicating that there is a significant difference between risk perceptions of Islamic contracts.

TABLE 8.13 K-W test results for Question 10 (risk seriousness) by region for entire sample

Contract Region N K-W Test Mean Rank Chi-Square Asymp. Sig.
Murabahah Americas  2 28.75 11.554 0.041
Europe 31 38.63
GCC 19 43.13 
Other  2 54   
Other Middle East 14 25.93 
Southeast Asia  4 20.63 
Total 72
Wakala Americas  2 33    4.682 0.456
Europe 31 32.26
GCC 19 37.29 
Other  2 33   
Other Middle East 14 42.14 
Southeast Asia  4 49.38 
Total 72
Mudarabah Americas  2 28.25   1.983 0.851
Europe 31 36.77
GCC 19 37   
Other  2 28.25 
Other Middle East 14 34.64 
Southeast Asia  4 46.75 
Total 72
Ijarah Americas  2 33.25   4.637 0.462
Europe 31 35.85 
GCC 19 34.08 
Other  2 39.5  
Other Middle East 13 43.88 
Southeast Asia  4 20.25 
Total 71
Musharakah Americas  2 36.75  3.503 0.623
Europe 31 39.85
GCC 19 33.05
Other  2 36.75
Other Middle East 14 37.64 
Southeast Asia  4 22.63 
Total 72
Istisna'a Americas  2 46.5    6.413 0.268
Europe 29 37.4  
GCC 19 33.21 
Other  2 46.5  
Other Middle East 14 27.5  
Southeast Asia  4 49.63 
Total 70
Salaam Americas  2 59.75   4.569 0.471
Europe 31 35.13 
GCC 19 35.53 
Other  2 28.75 
Other Middle East 13 33   
Southeast Asia  4 46.5  
Total 71
Friedman test 0.00 

K-W test results according to ‘region’, as illustrated in Table 8.13, indicate that murabahah is the only contract that reflects significant results across regions. This is expected because murabahah is extensively used globally. Moreover, mean rankings for murabahah, shown in Table 8.14, show that ‘Other’ regions, like Turkey and Pakistan, have a higher ranking (54.0) than the ‘GCC’ (43.13) and Europe (38.63), while the remaining regions follow. This can be attributed to two main reasons. First, the European and GCC markets are more sophisticated in their financial awareness of risk management, product structures and the use of risk-hedging techniques than are Turkey and Pakistan, a fact which has a direct impact on risk perception among those markets. Second, at the time of conducting this questionnaire, European and GCC markets enjoyed stable political environments and ‘relatively’ less volatile business cycles compared to ‘Others’.

TABLE 8.14 K-W test mean rankings for murabahah according to region for entire research sample

Contract Region N K-W Test Mean Rank Chi-Square Asymp. Sig.
Murabahah Americas  2 28.75 11.554 0.041
Europe 31 38.63
GCC 19 43.13 
Other  2 54.0  
Other Middle East 14 25.93 
Southeast Asia  4 20.63 
Total 72

Repeating the K-W test with ‘Region’ as the control variable for different institutional samples of data gives consistent results, as depicted by Table 8.15, which confirms that there is a difference in the risk perception of murabahah among regions in comparing according to institutional nature for fundamental market reasons.

TABLE 8.15 K-W test results by region for Question 10 for selected institutional data

Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig.
Murabahah 14.146 0.015 13.497 0.009 9.788 0.044
Wakala  4.339 0.502  3.369 0.498 3.927 0.416
Mudarabah  2.043 0.843  1.896 0.755 2.255 0.689
Ijarah  6.546 0.257  2.152 0.708 4.189 0.381
Musharakah  2.647 0.754  3.601 0.463 4.015 0.404
Istisna'a  7.964 0.158  3.241 0.518 5.859 0.21 
Salaam  5.065 0.408  1.954 0.744 1.937 0.747

In addition, examining the mean rankings across different regions for murabahah confirms the existence of a structural pattern. As apparent from Table 8.16, the rankings are similar when conducting the K-W with different raw data. The inclusion of conventional banks and non-bankers in the test sample gave similar results. The region ‘Americas’ disappears when conventional banks are excluded from the test sample as there were no respondents from Islamic banks in the Americas in this research sample.

TABLE 8.16 K-W test mean rankings for murabahah according to region for selected institutional data

Murabahah Full Sample Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region K-W Test Mean Rank Rank K-W Test Mean Rank Rank K-W Test Mean Rank Rank K-W Test Mean Rank Rank
Americas 28.75 4th 23    4th N/A N/A
Europe 38.63 3rd 34.48 2nd 23.83 2nd 17.1  2nd
GCC 43.13 2nd 34.42 3rd 22    3rd 14.78 3rd
Other 54    1st 43.5  1st 27    1st 19    1st
Other Middle East 25.93 5th 19.13 5th 10    4th  7.9  4th
Southeast Asia 20.63 6th 16    6th  9.5  5th  7.25 5th

As depicted by Table 8.16, there is a general pattern in terms of perception of murabahah-related issues. Such regional and institutional differences can be attributed to market conditions prevailing in each region.

Furthermore, using the entire research sample, attempts were made to test the impact of the respondents' positions, nature of financial institution, nature of activities and accounting standards on risk perception. However, the results, as depicted in Table 8.17, show that there are no significant differences except for murabahah contracts, which had significant risk perception according to accounting standards (p = 0.028), and nature of financial institution (0.03).

TABLE 8.17 K-W test results for Question 10 (perceived risk seriousness) for entire sample data

K-W According to Respondent's Position K-W According to Nature of Financial Institution K-W According to Accounting Standards K-W According to Nature of Activities
Contract Chi-Square Asymp. Sig Chi-Square Asymp. Sig Chi-Square Asymp. Sig Chi-Square Asymp. Sig
Murabahah 19.85 0.14 8.75 0.03 10.90 0.028 4.08 0.67
Wakala 17.98 0.21 2.23 0.53  3.87 0.42  2.68 0.85
Mudarabah 14.02 0.45 0.27 0.97  2.82 0.59  4.36 0.63
Ijarah 13.99 0.45 7.07 0.07  6.13 0.19  9.06 0.17
Musharakah 19.02 0.16 1.50 0.68  3.18 0.53  2.34 0.89
Istisna'a 19.46 0.15 0.49 0.92  4.87 0.30  1.69 0.95
Salaam 18.39 0.19 0.97 0.81  3.52 0.48  1.69 0.95

Additional risk issues facing IFIs   Question 11 aimed at exploring the perceptions of the participants in relation to a number of risks-related statements. For this, the K-W test was employed to determine if there were any statistically significant differences across the categories of respondent profiles.

Table 8.18 shows the K-W test results for the ‘Nature of financial institution’ variable. Statements 1, 2, 3, 4, 8, 10 and 11 are statistically significant, which reflects that there are significant differences in risk perception among respondents according to the nature of their financial institution. It should be noted that insignificant categories are eliminated and hence are not depicted in the table. Table 8.18 also breaks down the mean rankings for these statements.

TABLE 8.18 K-W test results for Question 11 for the full research sample according to nature of financial institution

Statement
1 2 3 4 8 10 11
Chi-Square 9.73  28.631 7.969 36.833 12.224 23.692 15.743
Asymp. Sig. 0.021  0.00 0.047  0.00   0.007  0.00  0.001
Nature of Financial Institution N Mean Rank
Fully Fledged Islamic bank 25 28.68 21.92 37.06 55.5 38.9 23.86 42.92
Conventional Bank with Islamic Activities 14 41.36 33 25 26.21 23.79 30.46 39.14
Conventional Bank 20 46.05 44.55 37.48 22.45 34.35 47.4 38.85
Others 13 31.62 55.92 46.31 32.65 48.88 50.54 17.69
Total 72

Note: Only statements with significant p-value are displayed in the table

Statements:

  1. Risks for Islamic banks should be managed using the same techniques used in conventional banking.
  2. Islamic banking is more risky by nature than conventional banking.
  3. Risk management for Islamic banks is more challenging than it is for conventional banks.
  4. There is naturally inherent conservatism in the principles of Islamic finance.
  5. In an Islamic bank, a low rate of return on deposits will lead to withdrawal of funds.
  6. Depositors would hold the bank responsible for a lower rate of return on their deposits.
  7. Variation among Shari'ah scholars' opinions represents a major risk to Islamic banking.
  8. Shari'ah-non-compliance could severely damage the reputation of an Islamic bank.
  9. AAOIFI and IFSB standards should be made mandatory for Islamic banks.
  10. Corporate governance is generally weak in Islamic banks.
  11. Islamic banking in its current state is a safer option than conventional banking.

Studying the mean ranking for each statement does not reveal a certain pattern governing the data; the data is widely dispersed with no clear trend of ranking according to nature of financial institution.

Repeating the K-W test for the entire research sample using other control variables (such as region, position of respondent, nature of activities and accounting standards) gives similar results; as seen in Table 8.19, which confirms that there is a significant difference in risk perception among various groups. With a ‘relaxation’ of the significance level to 0.06, more statements can be considered significant. An attempt was made to study the mean ranking for each statement within each test; however, the results did not reveal a certain pattern, and the data is widely dispersed with no clear ranking trend.

TABLE 8.19 K-W test results for Question 11 for the full research sample according to various control variables

Region Position of Respondent Nature of Activities Accounting Standards
Statement Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig.
1  9.695 0.084 18.272 0.195  7.736 0.258  9.534 0.049
2 29.87  0.00  32.308 0.004 31.623 0.00  22.755 0.00 
3 11.308 0.046 23.068 0.059  9.59  0.143  5.236 0.264
4 24.749 0.00  24.06  0.045 18.209 0.006 15.894 0.003
5  9.5   0.091 18.734 0.175  5.504 0.481  6.408 0.171
6 10     0.075 18.471 0.186  1.598 0.953  0.937 0.919
7 19.217 0.002 24.05  0.045 14.798 0.022  7.077 0.132
8  4.523 0.477 18.66  0.178 12.829 0.046  8.222 0.084
9 16.245 0.006 16.724 0.271 11.293 0.08   8.495 0.075
10 33.479 0.00  25.222 0.032 25.108 0.00  17.862 0.001
11 14.644 0.012 22.839 0.063 23.755 0.001 21.018 0.00 

Factor analysis for Question 11   In order to provide further statistical robustness to the analysis, factor analysis was conducted. Factor analysis seeks to discover if the observed variables can be explained largely or entirely in terms of a much smaller number of variables called ‘factors’.

As there are 11 statements for Question 11, all analysing the respondents' perceptions of different risk issues in Islamic banking, the researcher felt that reducing these statements into a more manageable number would enhance the analysis and would tell more about how respondents perceived these issues. Hence, factor analysis is deemed to be relevant in this respect as the main task of factor analysis is to cluster the related group of variables through their common variance (Pallant, 2007).

In order to test the factorability of the data in terms of sampling adequacy, there are two statistical measures available in the SPSS software that can be used: Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) test. As laid down in Pallant (2007), for the factor analysis to be considered appropriate, the Bartlett's test of sphericity value should be significant (p < 0.05), while for the KMO test, the suggested minimum outcome must be at least 0.6 (KMO score ranging from 0 to 1). The KMO test's benchmarks are as follows: for a KMO measure in the 0.90s the sampling is considered marvellous. If the outcome is in the 0.80s, then the sampling is considered meritorious, if it is in 0.70s then the sample is middling, if it is in the 0.60s then the sample is mediocre, if it is in 0.50s then the sample is deemed miserable and lastly if it is below 0.50 then the sample is unacceptable (Pallant, 2007).

Table 8.20 presents the results of KMO and also Bartlett's test for this factor analysis.

TABLE 8.20 KMO and Bartlett's test results for the 11 items combined

Kaiser-Meyer-Olkin Measure of Sampling Adequacy   0.760
Bartlett's Test of Sphericity Approx. Chi-Square 268.223
df  55    
Sig.   0.000

The outcome of the KMO measure for all 11 items combined, related to risk perception, produced the value of 0.760, which is higher than 0.60, therefore the factor analysis is appropriate for this study. In addition, the significant p-value as presented in the table of 0.000 is significantly lower than critical p-value of 0.05. Therefore, the identity matrix can be rejected. Based on the very encouraging results from both tests, factor analysis may be performed.

The second step is to choose the most suitable method of data extraction. As discussed in Chapter 6, the researcher selected principal component analysis (PCA) as it is deemed the most suitable method for the data at hand. PCA involves determining the patterns with the objective of studying the similarities and the differences among the components of the data set.

After determining the factors, the next step in order to facilitate the interpretation selection of rotation method is very important. In this regard, orthogonal (uncorrelated) and oblique (correlated) approaches are the two main techniques to rotation (Pallant, 2007). The results of the orthogonal rotation are easier to interpret, describe and report (Field, 2009). There are various rotational approaches in SPSS within both the orthogonal and oblique categories. Varimax, Quartimax and Equamax are the typical orthogonal approaches of rotation, whereas Direct Oblimin, Quartimin and Promax are the oblique methods. Varimax is the most commonly used orthogonal technique in order to reduce the number of variables whereas the Direct Oblimin technique is generally used for the oblique method. The researcher opted for Varimax rotation with Kaiser Normalization, as Table 8.21 suggests.

TABLE 8.21 Total variance explained for Question 11

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
1 3.732 33.926  33.926 3.732 33.926 33.926 3.103 28.214 28.214
2 1.887 17.151  51.076 1.887 17.151 51.076 2.013 18.296 46.509
3 1.439 13.078  64.155 1.439 13.078 64.155 1.941 17.646 64.155
4 0.982  8.929  73.084
5 0.675  6.136  79.219
6 0.571  5.190  84.410
7 0.443  4.023  88.433
8 0.370  3.366  91.799
9 0.349  3.171  94.970
10 0.299  2.722  97.692
11 0.254  2.308 100.000

Note: Extraction method: PCA

Table 8.21 presents the output of the number of factors that are retained according to Kaiser's criterion, in which all the eigenvalues are more than 1.0. In this situation, there are three factors that will be retained, since the eigenvalues are 3.732, 1.887 and 1.439 respectively. The screen plot, which is basically a graph of the eigenvalues, shows that the 11 variables could be reduced to only three as the graph slopes down steeply before becoming parallel to the horizontal line. It is therefore clear from the plot that there is only a three-factor solution to this question. Hence it was decided to retain the three factors.

According to Pallant (2007), the eigenvalue has to be greater than 1.0 to be regarded as significant and to be used in determining the factors. The assumption here is that the eigenvalues stand for the amount of total variation represented by the factors and this means that an eigenvalue of 1.0 or above indicates a high level of variation. Table 8.21 shows that there are three factors with an eigenvalue greater than 1.0. This means that the original 11 items can be simply reduced to three factors. The three-component solution explained 64.2% of the variance with component 1 contributing 33.9%, component 2 contributing 17.1% and component 3 contributing 13.1%. The explanatory power of the first factor is very high.

Table 8.22 further provides Rotated Component Matrix by distributing all variables to the identified three components. The factors in each component have some common characteristics and measure the same phenomenon, and therefore each component is named with a general description of the factors or variables it includes. For instance, factors in component 1 deal with the respondents' risk perception. The factors in component 2 deal with Shari'ah principles and their impact on the risk profile of an IFI, while the factors in component 3 deal with the rate of return paid by an IFI and the effect of this on depositors' behaviour and perception of how safe the IFI is. Thus the results indicate that all these statements can be explained with three main components. Figure 8.1 provides a screen plot of the factor analysis results for Question 11.

TABLE 8.22 Rotated component matrixa for Question 11

Component
1 Risk Perception 2 Shari'ah Compliance 3 Rate of Return
1 – Risks for Islamic banks should be managed using same techniques used in conventional banking   0.134 −0.770 −0.168
2 – Islamic banking is more risky by nature than conventional banking   0.806 −0.237   0.061
3 – Risk management for Islamic banks is more challenging than it is for conventional banks   0.521   0.301   0.400
4 – There is naturally inherent conservatism in the principles of Islamic finance −0.484   0.627 −0.289
5 – In an Islamic bank, a low rate of return on deposits will lead to withdrawal of funds   0.062 −0.012   0.882
6 – Depositors would hold the bank responsible for a lower rate of return on their deposits −0.010 −0.059   0.886
7 – Variation among Shari'ah scholars' opinions represents a major risk to Islamic banking   0.584 −0.448 −0.006
8 – Shari'ah-non-compliance could severely damage the reputation of an Islamic bank   0.038   0.647 −0.098
9 – AAOIFI and IFSB standards should be made mandatory on Islamic banks −0.693   0.419   0.112
10 – Corporate governance is generally weak in Islamic banks   0.790 −0.145   0.253
11 – Islamic banking in its current state is a safer option than conventional banking −0.693 −0.241   0.121

Notes: Extraction method: PCA.

Rotation method: Varimax with Kaiser Normalization.

a Rotation converged in five iterations

Screen plot depicting the graph of the total variance of the eigenvalues of 11 component variables with the graph sloping down gradually.

FIGURE 8.1 Screen plot for Question 11

After conducting factor analysis between groups, a MANOVA test was computed in order to investigate if there is any significant difference between the three component groups in relation to the same control variables. This will help to locate the impact or significance of each control variable on the established distribution.

The outputs of the relevant tests are presented in Tables 8.23 to 8.25 in terms of data conforming to the assumptions before the main MANOVA analysis. In this sense, the sig. value of Box's Test of Equality of Covariance Matrices should not be lower than 0.001 in terms of not violating the assumption (Tabachnick and Fidell, 2006). In this example, the output of Box's Test shows that there is no violation of assumption of homogeneity of variances of variance-covariance matrices since the sig. value of 0.248 is higher than the critical value of 0.001.

TABLE 8.23 Box's Test of Equality of Covariance Matricesa

Box's M  27.466
F   1.209
df1  18    
df2 544.584
Sig.   0.248

Notes: Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups.

a Design: Intercept + Region

TABLE 8.24 Levene's Test of Equality of Error Variancesa

F df1 df2 Sig.
Risk Perception 0.458 5 66 0.806
Shari'ah Compliance 1.818 5 66 0.121
Rate of Return 1.867 5 66 0.112

Notes: Tests the null hypothesis that the error variance of the dependent variable is equal across groups.

a Design: Intercept + Region

Additionally, the output of the Levene's Test of Equality of Error Variances is explored. The results in the Sig. column show that sig. values of ‘Risk Perception’ (0.806), ‘Shari'ah Compliance’ (0.121) and ‘Rate of Return’ (0.112) are higher than 0.05. Thus, there is no violation of the assumption of equality of variances for these three factors.

After performing Box's Test of Equality of Covariance Matrices and Levene's test, the set of multivariate tests was employed. Pallant (2007) states that multivariate tests of significance demonstrate if there are any significant differences among groups; the sig. value should be lower than 0.05 in order to find a statistically significant result. There are several statistics which are also used in the SPSS such as Pillai's Trace, Wilks' Lambda, Hotelling's Trace and Roy's Largest Root. In this research Wilks' Lambda result is taken into account since it is one of the most commonly used statistics (Tabachnick and Fidell, 2006). The results of the Wilks' Lambda show that there is a statistically significant difference between regions in relation to the perceptions of the three components since the sig. value of 0.00 is quite a bit lower than the critical level of 0.05.

TABLE 8.25 Multivariate Tests

Effect Value F Hypothesis df Error df Sig. Partial Eta Squared
Intercept Pillai's Trace   0.995 4017.889a  3.000  64.000 0.000 0.995
Wilks' Lambda   0.005 4017.889a  3.000  64.000 0.000 0.995
Hotelling's Trace 188.339 4017.889a  3.000  64.000 0.000 0.995
Roy's Largest Root 188.339 4017.889a  3.000  64.000 0.000 0.995
Region Pillai's Trace   0.568    3.083  15.000 198.000 0.000 0.189
Wilks' Lambda   0.472    3.693  15.000 177.077 0.000 0.222
Hotelling's Trace   1.036    4.330  15.000 188.000 0.000 0.257
Roy's Largest Root   0.951    12.550b  5.000  66.000 0.000 0.487

Notes

a Exact statistic

b Computed using alpha = 0.05

Since multivariate tests suggest that there is a statistically significant difference, a further investigation is conducted. This is in order to reveal if there is a difference in terms of region on ‘Risk Perception’, ‘Shari'ah Compliance’ and ‘Rate of Return’, or only to some extent. Tests of Between-Subjects Effects provide this information. Bonferroni adjustment, which is one of the most commonly employed methods, gives this information when the alpha level of 0.05 is divided by the number of dependent variables (Pallant, 2007). In this example, there are three dependent variables, therefore 0.05 is divided by three and the new alpha level is 0.0167. As can be seen in the Tests of Between-Subjects Effects in Table 8.26, the results indicate that the dependent variables ‘Risk Perception’ and ‘Shari'ah Compliance’ have significant values of 0.000, while ‘Rate of Return’ has a sig. value of 0.671, which is higher than the critical value of 0.0167 for this example.

TABLE 8.26 Tests of Between-Subjects Effects

Source Dependent Variable Type I Sum of Squares df Mean Square F Sig. Partial Eta Squared
Corrected Model Risk Perception  18.683a  5   3.737   11.212 0.000 0.459
Shari'ah Compliance  14.314  5   2.863    6.937 0.000 0.344
Rate of Return   1.537  5   0.307    0.638 0.671 0.046
Intercept Risk Perception 738.561  1 738.561 2216.046 0.000 0.971
Shari'ah Compliance 886.673  1 886.673 2148.655 0.000 0.970
Rate of Return 629.139  1 629.139 1305.938 0.000 0.952
Region Risk Perception  18.683  5   3.737   11.212 0.000 0.459
Shari'ah Compliance  14.314  5   2.863    6.937 0.000 0.344
Rate of Return   1.537  5   0.307    0.638 0.671 0.046
Error Risk Perception  21.996 66   0.333
Shari'ah Compliance  27.236 66   0.413
Rate of Return  31.796 66   0.482
Total Risk Perception 779.240 72
Shari'ah Compliance 928.222 72
Rate of Return 662.472 72
Corrected Total Risk Perception  40.679 71
Shari'ah Compliance  41.549 71
Rate of Return  33.333 71

Notes

a R Squared = 0.459 (Adjusted R Squared = 0.418)

Furthermore, Tests of Between-Subjects Effects provide the effect size. Partial Eta Squared is used to determine the impact of independent variables on dependent variables, and it signifies the percentage of the variance in the dependent variable which is explained by the independent variable (Pallant, 2007). In this question, the effect of ‘Region’ (independent variable) on ‘Risk Perception’ and ‘Shari'ah Compliance’ (dependent variables) can be evaluated by the Partial Eta Squared, which is depicted in the Tests of Between-Subjects Effects in Table 8.26. The importance of this impact is explored using the effect size values. Cohen (2005) categorises an effect size of 0.01 as a small effect and 0.06 as a medium effect whereas 0.14 is a large effect.

The effect size values for this case are 0.459 and 0.344, which are deemed large effect sizes using Cohen's. These results signify that 45.9% and 34.4% of the variances in ‘Risk Perception’ and ‘Shari'ah Compliance’ scores are explained respectively by the region.

MANOVA test according to nature of financial institution for Question 11   After conducting a MANOVA test with ‘Region’ as the independent variable, another MANOVA test was computed with ‘nature of financial institution’ as the independent variable in order to investigate if there is any significant difference between the three dependent factors identified by the factor analysis.

In this case, the output of Box's Test, as shown in Table 8.27, shows that there is no violation of the assumption of homogeneity of variances of variance-covariance matrices since the sig. value of 0.080 is higher than the critical value of 0.001.

TABLE 8.27 Box's Test of Equality of Covariance Matricesa

Box's M   29.551
F    1.497
df1   18
df2 9866.884
Sig.    0.080

Notes: Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups.

a Design: Intercept + Nature Financial Institution

Additionally, the output of the Levene's Test of Equality of Error Variances is explored in Table 8.28. The results in the Sig. column show that sig. values of ‘Risk Perception’ (0.753), ‘Shari'ah Compliance’ (0.427) and ‘Rate of Return’ (0.077) are higher than 0.05. Thus, there is no violation of the assumption of equality of variances for these three factors.

TABLE 8.28 Levene's Test of Equality of Error Variancesa

F df1 df2 Sig.
Risk Perception 0.400 3 68 0.753
Shari'ah Compliance 0.938 3 68 0.427
Rate of Return 2.386 3 68 0.077

Notes: Tests the null hypothesis that the error variance of the dependent variable is equal across groups.

a Design: Intercept + Nature Financial Institution

The results of the Wilks' Lambda in Table 8.29 show that there is a statistically significant difference according to nature of financial institution since the sig. value of 0.00 is quite a bit lower than the critical level of 0.05.

TABLE 8.29 Multivariate testsc

Effect Value F Hypothesis     df Error   df Sig. Partial   Eta Squared
Intercept Pillai's Trace   0.995 4340.694a 3.000  66.000 0.000 0.995
Wilks' Lambda   0.005 4340.694a 3.000  66.000 0.000 0.995
Hotelling's Trace 197.304 4340.694a 3.000  66.000 0.000 0.995
Roy's Largest Root 197.304 4340.694a 3.000  66.000 0.000 0.995
Nature of FI Pillai's Trace   0.621    5.921  9.000 204.000 0.000 0.207
Wilks' Lambda   0.478    6.335  9.000 160.777 0.000 0.218
Hotelling's Trace   0.890    6.396  9.000 194.000 0.000 0.229
Roy's Largest Root   0.534   12.100b 3.000  68.000 0.000 0.348

Notes

a Exact statistic.

b The statistic is an upper bound on F that yields a lower bound on the significance level.

c Design: Intercept + Nature Financial Institution

Since the multivariate test suggests that there is a statistically significant difference, a further investigation is conducted. Tests of Between-Subjects Effects provide this information. In this case, there are three dependent variables, therefore 0.05 is divided by three and the new alpha level is 0.0167. As can be seen in the Tests of Between-Subjects Effects in Table 8.30, the results indicate that the dependent variables ‘Risk Perception’ and ‘Shari'ah Compliance’ have significant values of 0.000, while ‘Rate of Return’ has a sig. value of 0.234, which is higher than the critical value of 0.0167 for this example. Furthermore, the effect size values as evaluated by the Partial Eta Squared for this case are 0.301 and 0.336, which are deemed large-effect sizes using Cohen's. These results signify that 30.1% and 33.6% of the variances in ‘Risk Perception’ and ‘Shari'ah Compliance’ scores are explained respectively by the nature of financial institution.

TABLE 8.30 Tests of Between-Subjects Effects

Source Dependent Variable Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
Corrected Model Risk Perception  12.240a 3   4.080    9.755 0.000 0.301
Shari'ah Compliance  13.952b 3   4.651   11.459 0.000 0.336
Rate of Return   2.014c 3   0.671    1.457 0.234 0.060
Intercept Risk Perception 715.613  1 715.613 1711.036 0.000 0.962
Shari'ah Compliance 799.581  1 799.581 1970.166 0.000 0.967
Rate of Return 570.736  1 570.736 1239.172 0.000 0.948
Nature of FI Risk Perception  12.240  3   4.080    9.755 0.000 0.301
Shari'ah Compliance  13.952  3   4.651   11.459 0.000 0.336
Rate of Return   2.014  3   0.671    1.457 0.234 0.060
Error Risk Perception  28.440  68   0.418
Shari'ah Compliance  27.597  68   0.406
Rate of Return  31.319  68   0.461
Total Risk Perception 779.240  72
Shari'ah Compliance 928.222  72
Rate of Return 662.472  72
Corrected Total Risk Perception  40.679  71
Shari'ah Compliance  41.549  71
Rate of Return  33.333  71

a R Squared = 0.301 (Adjusted R Squared = 0.270).

b R Squared = 0.336 (Adjusted R Squared = 0.306).

c R Squared = 0.060 (Adjusted R Squared = 0.019)

Conducting the MANOVA test according to ‘Region’ and ‘Nature of Financial Institution’ as independent variables provided consistent results. It can be concluded that ‘Risk Perception’ and ‘Shari'ah Compliance’ are significant dependent variables and have strong explanatory power, while ‘Rate of Return’ does not follow the pattern.

Capital Adequacy for Islamic Banks

This section addresses capital adequacy challenges facing IFIs. It tackles the controversial issues regarding the applicability of the Basel II and Basel III Accords to IFIs, and the appropriate capital requirement levels for Islamic banks.

The results of the K-W test in Table 8.31 show that all statements are statistically significant (p-value < 0.05) except for Statement 5 (p-value = 0.358) implying that regional differences in relation to capital adequacy are significant.

TABLE 8.31 K-W test results by region for Question 15 (capital adequacy) for entire research sample

Statement
1 2 3 4 5
Chi-Square 18.081 24.185 20.089 20.24  5.502
Asymp. Sig.  0.003  0.000  0.001  0.001 0.358
Mean Mean Mean Mean Mean
Region N Rank N Rank N Rank N Rank N Rank
Americas  2 59.25  2 22     2 27.5   2 57     2 28   
Europe 31 45.13 31 24.66 31 30.26 31 45.06 31 32.06
GCC 19 23.16 19 51.24 19 50.71 19 24.24 19 40.39
Other  2 18     2 48.5   2 59     2 28.75  2 28   
Other Middle East 14 34.54 14 42.86 14 33.29 14 32.39 14 39.93
Southeast Asia  4 37.75 4 37.25  4 21.88  4 36.38  4 48.88
Total 72 72 72 72 72

Statements as depicted in the following tables and their coding are:

  1. Basel II standards should be equally applied to Islamic banks without modification.
  2. IFSB standards on Capital Adequacy should be used by Islamic banks rather than Basel II.
  3. Basel II standards should be reviewed after failing to prevent the financial crisis.
  4. The proposed Basel III rules would be easily applicable to Islamic banks.
  5. Stricter capital, leverage and liquidity rules, as proposed under Basel III, are likely to prevent another financial crisis.

Conducting the K-W test with ‘Nature of Financial Institution’ as the control variable for the entire research sample gives different results, as illustrated by Table 8.32. All statements are statistically insignificant except Statement 5, which shows different views between bankers (whether Islamic or conventional) and non-bankers, which is also evident from the mean ranking. This implies that the nature of financial institution is not a statistically determining factor; and that the opinions of the respondents are rather similar.

TABLE 8.32 K-W test results by nature of financial institution for Question 15 (capital adequacy) for entire research sample

Statement
1 2 3 4 5
Chi-Square 5.611 5.127 5.781 4.07  9.79
Asymp. Sig. 0.132 0.163 0.123 0.254 0.02
Nature of
Financial Mean Mean Mean Mean Mean
Institution N Rank N Rank N Rank N Rank N Rank
Fully Fledged Islamic bank 25 30.92 25 37.9  25 37.4  25 31.86 25 39.98
Conventional Bank with Islamic Activities 14 33.93 14 45.71 14 46.14 14 34.57 14 40.75
Conventional Bank 20 45    20 32.05 20 32    20 39.73 20 38.78
Others 13 36.92 13 30.73 13 31.31 13 42.54 13 21.73
Total 72 72 72 72 72

Furthermore, repeating the K-W test with ‘Nature of Activities’ and ‘Respondent's Position’ as control variables for the entire research sample gives results consistent with those of K-W according to ‘Nature of Financial Institution’, as illustrated by Tables 8.33 and 8.34 respectively. For ‘Nature of Activities’, statements are statistically insignificant except for Statements 1 and 3, while as depicted by Table 8.34 for ‘Respondent's Position’, all statements are statistically insignificant except Statement 5. This reflects the difference in opinions among different groups regarding the newly developed Basel III capital and liquidity standards and their applicability to Islamic banking.

TABLE 8.33 K-W test results by nature of activities for Question 15 for entire research sample

Statement
1 2 3 4 5
Chi-Square 8.467 12.532 11.053 13.98 14.255
Asymp. Sig. 0.206  0.051  0.087  0.03  0.027
Nature of Mean Mean Mean Mean Mean
Activities N Rank N Rank N Rank N Rank N Rank
Commercial Banking 11 34.91 11 39.68 11 38.95 11 29.23 11 42.86
Integrated Banking  9 38.67  9 37.5  9 39     9 40.61  9 36.33
Investment Banking 11 44.09 11 23.86 11 24.23 11 48.32 11 37.91
Private Equity House  1 67     1 11.5  1 27.5   1 44     1 43   
Retail & Commercial Banking 17 37.35 17 39.79 17 38.35 17 33.26 17 45.71
Retail Banking 10 22.9  10 50.4 10 49.55 10 24.7  10 31   
Other 13 36.92 13 30.73 13 31.31 13 42.54 13 21.73
Total 72 72 72 72 72

TABLE 8.34 K-W test results by position of respondent for Question 15 for entire research sample

Statement
1 2 3 4 5
Chi-Square 12.056 12.503 16.546 19.49  29.835
Asymp. Sig.  0.602  0.566  0.281  0.147  0.008
Position of Mean Mean Mean Mean Mean
Respondent N Rank N Rank N Rank N Rank N Rank
Analyst  5 43     5 32.5   5 29.3   5 44     5 43   
Senior Analyst  4 37.75  4 27.75  4 21.88  4 44     4 13   
Auditor  2 42.25  2 42     2 16.25  2 57     2 13   
CEO  5 36.9   5 34.3   5 29.3   5 37     5 34.7 
CFO  2 9    2 64.5   2 59     2 13.5   2 13   
Consultant  2 27.25  2 42     2 43.25  2 23.75  2 13   
Director  6 39.17  6 22     6 29     6 48.33  6 31.92
General Manager 10 37.8  10 36.6  10 37.85 10 33.85 10 47.7 
Head of Investment Banking  1 3    1 64.5   1 59     1 3.5  1 13   
Head of Risk Management 11 33.27 11 37.95 11 36.91 11 39.91 11 33.64
Managing Director  8 37.81  8 38.88  8 40.44  8 28.75  8 48.06
Risk Manager 12 41.63 12 35.17 12 38.75 12 34.33 12 41.92
Senior Trader  2 41     2 38     2 59     2 44     2 57.25
Shari'ah Scholar  1 3     1 64.5   1 59     1 13.5   1 13   
Solicitor  1 51.5   1 32.5   1 27.5   1 44     1 43   
Total 72 72 72 72 72

The Credit Crisis and Islamic Banks

This section of the questionnaire seeks respondents' views on different issues relating to the recent global crisis. For this purpose, Question 16 of the survey presented nine statements to respondents. This part applied to all the respondents, which means replies from all institutional samples of data were obtained by asking respondents to answer using a five-point Likert scale (ranking from Strongly Agree = 5 to Strongly Disagree = 1). Table 8.35 employs the K-W test to examine the significant difference among respondents' perceptions according to ‘Region’.

TABLE 8.35 K-W test results by region for Question 16 for entire research sample

Statement 1 2 3 4 5 6 7 8 9
Chi-Square 11.052 19.879 27.446 14.571 10.877 4.863 18.587 11.171 3.695
Asymp. Sig.  0.05   0.001 0.00  0.012  0.054 0.433  0.002  0.048 0.594
Region Mean Rank
Americas 30.25 29     9    55    30.75 33    38    31    44.5 
Europe 28.39 27.08 26.1  42.5  33.89 34.16 25.11 30.05 39.06
GCC 44.76 50.87 51.37 38.05 47.42 43.16 49.32 48.29 29.5 
Other 52.5  41    56.5  17.25 52.75 51.25 43.5  31    32   
Other Middle East 38.86 41.93 35.43 26.71 29.29 33.57 41.89 38.04 38.93
Southeast Asia 47    23.75 54    17.25 24.88 27.63 40.75 30.63 39.63

Note: N for all statements = 72

Statements tested in this section and their coding are as follows:

  1. Islamic banks are more resilient to economic shocks than their conventional peers.
  2. The recent crisis would not have happened under a true Islamic banking system.
  3. Islamic finance could have solved the global crisis.
  4. Risk management must be embedded institutionally.
  5. Banks in general used to rely heavily on rating agencies.
  6. Islamic banks rely less on rating agencies than conventional banks.
  7. The Islamic finance industry should develop its own rating agencies.
  8. Islamic banks will emerge stronger from the crisis.
  9. Consolidation is needed among smaller Islamic banks.

The results in Table 8.35 show that most statements are statistically significant (p-value < 0.05). With a ‘relaxation’ of the confidence level to 0.06, we can accept all statements except Statements 6 and 9. Mean rankings reveal that, although there is no clear pattern that could be traced, the ‘GCC’ and ‘Other’ categories are usually ranked at the top for most statements. This emphasises the fact that respondents from these two regions are more aggressive than those from other regions in their views about the credit crunch and Islamic finance. Thus the findings indicate that there are statistically different and significant opinions among the respondents coming from different regions.

In addition, attempts were made to test the impacts of ‘Nature of Financial Institution’, ‘Nature of Activities’, ‘Accounting Standards’ and ‘Respondent's Position’ on the responses; the results are summarised in Tables 8.36 to 8.39.

TABLE 8.36 K-W test results by nature of financial institution for Question 16 for entire research sample

Statement 1 2 3 4 5 6 7 8 9
Chi-Square 4.614 1.698 12.818 4.531 5.573 5.631 2.965 4.369 4.238
Asymp. Sig. 0.202 0.637  0.005 0.21  0.134 0.131 0.397 0.224 0.237
Region Mean Rank
Fully Fledged Islamic bank 41.6  38.46 46.2  30.44 42.02 31.48 35.64 37.3  31.54
Conventional Bank with Islamic Activities 38.21 35.29 40.25 36.07 36.82 42.93 43.32 43.07 33.96
Conventional Bank 34.95 38.75 27.38 40.83 28.1  33.23 37.05 29.53 43.1 
Others 27.23 30.58 27.85 41.96 38.46 44.27 29.96 38.62 38.62

Note: N for all statements = 72

With the exception of Statement 3 (p = 0.005), there is no statistically significant difference among all other statements. Mean ranking for Statement 3, as seen in Table 8.36, shows that fully fledged Islamic banks are far more aggressive in their belief that Islamic finance could have solved the global crisis than other categories (46.2), followed by Islamic subsidiaries (40.25), then by Others and Conventional Banks. This is consistent with the K-W test result according to ‘Region’ as the control variable (Table 8.35) because respondents from the ‘GCC’ and ‘Other’ regions in this research sample are mainly fully fledged Islamic banks and Islamic subsidiaries.

The same statements are further investigated in relation to the nature of activities. As depicted by Table 8.37, only Statement 6 is statistically significant. With a relaxation of the confidence level to 0.06, one can also accept Statement 1. For both Statements 1 and 6, ‘Retail Banking’ is the most aggressive category according to mean rankings, and ‘Private Equity Houses’ is by far the least aggressive. Other categories fall in between, although no particular trend can be established. These results confirm the K-W test results according to ‘Region’ (Table 8.35), as out of the 10 retail banks included in the research sample, five are located in the ‘GCC’ and one is located in ‘Other’. It should be noted that the sole Private Equity House in this research sample is also located in the ‘GCC’. Furthermore, results from Table 8.37 are also consistent with the K-W test results according to ‘Nature of Financial Institution’ (Table 8.36) because 8 out of the 10 retail banks included in the research sample are fully fledged Islamic banks, and the other two are Islamic subsidiaries. The one Private Equity House is also a fully fledged Islamic bank.

TABLE 8.37 K-W test results by nature of activities for Question 16 for entire research sample

Statement 1 2 3 4 5 6 7 8 9
Chi-Square 12.156 8.402 11.446 12.029 4.862 15.608 10.263 6.527 2.581
Asymp. Sig.  0.059 0.21  0.076  0.061 0.562 0.016 0.114 0.367 0.859
Region Mean Rank
Commercial Banking 41.36 41.59 46.73 36.86 37.41 31.32 39.86 34.27 35.77
Integrated Banking 38.94 32.61 27.61 45.5  24.22 35.89 34.61 29.11 39.39
Investment Banking 27.55 25.95 29.45 42.95 33.68 22.68 28.32 29.82 36.27
Private Equity House  7    39.5  34 55    42.5   1.5  19    18.5  12.5 
Retail & Commercial Banking 39.29 40.85 39.88 30.03 39.38 37.59 37.35 40.38 38.21
Retail Banking 49.05 46    46.75 22.95 41.6  49.5  52.3  45.4  31.7 
Other 27.23 30.58 27.85 41.96 38.46 44.27 29.96 38.62 38.62

Note: N for all statements = 72

Table 8.38 shows that only Statements 3, 5 and 7 are statistically significant according to accounting standards. Mean rankings reflect that for these three statements, ‘AAOIFI’ and ‘International & AAOIFI standards’ are always top ranked, followed by other criteria. These results confirm the K-W results for the previous control variables in Tables 8.35 to 8.37; therefore the results indicate that the perceived views in relation to accounting standards are statistically significant for these three statements.

TABLE 8.38 K-W test results by accounting standards for Question 16 for entire research sample

Statement 1 2 3 4 5 6 7 8 9
Chi-Square 6.86  7.494 11.839 8.564 11.496 6.621 10.72 5.72  4.252
Asymp. Sig. 0.143 0.112  0.019 0.073  0.022 0.157  0.03 0.221 0.373
Region Mean Rank
AAOIFI standards 52.5  51.5  54.83 28.17 45.42 49.5  57    43.25 22.25
International & AAOIFI standards 40.9  49.1  54.5  45.6  58.9  29.7  49.8  52.6  33.1 
International standards 36.79 33.74 34.33 38.32 34.61 34.32 33.12 32.76 39.08
Local accounting standards 35.65 39.4  36    22.95 24.6  30.3  38.9  35.85 34.2 
N/A 27.23 30.58 27.85 41.96 38.46 44.27 29.96 38.62 38.62

Note: N for all statements = 72

The potential impact of the respondents' positions on the same statements is also investigated. The p-values in Table 8.39 show that there are no significant differences according to respondent's position. By relaxing the confidence level to 0.06, one can also accept Statements 5 and 7. No pattern could be concluded by studying the mean ranking. The only obvious conclusion is that Shari'ah scholars ranked the highest mean and solicitors had the lowest mean values for most, but not all, statements. This is expected because Shari'ah scholars tend to be more conservative in their views about Islamic banking and Shari'ah compliance, while solicitors usually focus more on legal structures rather than the Shari'ah side of transactions.

TABLE 8.39 K-W test results by respondent's position for Question 16 for entire research sample

Statement 1 2 3 4 5 6 7 8 9
Chi-Square 17.068 19.453 19.773 13.82 23.564 14.194 23.054 15.677 17.272
Asymp. Sig.  0.253  0.148  0.137  0.463  0.052  0.435  0.059  0.333  0.242
Region Mean Rank
Analyst 36.9  40.1  24 39.9  19 33 40.2  24.3  52
Senior Analyst 26.13 35 39.63 40.75 21.25 45.25 21.5  34.63 45.88
Auditor 30.25 51.5  14 55 63 33 47 25.75 57
CEO 32.1  33.3  39 39.9  46 24.1  18.5  26.4  38.1 
CFO 63.5  63.5  56.5  40.75 52.75 39.75 68 55.25 32
Consultant 41.25 51.5  41.5  31.5  63 51.25 43.5  55.25 29.25
Director 21 20.83 15.67 42.42 30.33 41.17 18.42 35.17 33.83
General Manager 30 41.2  34.5  27.65 30.75 31.75 40.8  33.5  28.1 
Head of Investment Banking 63.5  39.5  69 55 63 57.5  68 67 32
Head of Risk Management 38.23 36.59 41.27 37.77 43.55 32.55 39.14 40.82 30.45
Managing Director 46.94 45.5  41.5  25.44 28.94 49.81 40 43.31 34.06
Risk Manager 40 26.25 38.79 36.13 33.92 30.42 32.88 32.88 47.04
Senior Trader 30.25 29 34 55 42.5  33.75 47 37.5  22.25
Shari'ah Scholar 63.5  63.5  69 8 63 69.5  68 67 12.5 
Solicitor 19  8.5  34 55 42.5  33 38 18.5  12.5 

Note: N for all statements = 72

Factor analysis for Question 16 (credit crisis and Islamic finance)   To locate the perception of the participants regarding the credit crisis in relation to a number of issues related to Islamic finance, they were provided with a number of statements. The opinions are analysed through factor analysis.

As previously explained, factor analysis seeks to discover if the observed variables can be explained largely or entirely in terms of a much smaller number of variables called the factors.

As there are nine statements for Question 16, analysing the respondents' perceptions toward Islamic banking and the global credit crisis, the researcher felt that reducing these statements to a more manageable number would enhance the analysis and tell more about how respondents perceived these issues. Hence, factor analysis is deemed to be relevant in this respect as the main task of factor analysis is to cluster the related group of variables through their common variance.

In order to test the factorability of the data in terms of sampling adequacy, Table 8.40 presents the results of KMO and also Bartlett's Test for this factor analysis.

TABLE 8.40 KMO and Bartlett's Test results for the nine items combined

Kaiser-Meyer-Olkin Measure of Sampling Adequacy  0.844
Bartlett's Test of Sphericity Approx. Chi-Square 173.046
df 36
Sig.    0.000

The outcome of the KMO measure for all nine items combined, related to the respondents' perceptions, showed the value of 0.844, which is higher than 0.60, implying that factor analysis is appropriate for this study. In addition, the significant p-value of 0.000 is significantly lower than critical p-value of 0.05. Therefore, the identity matrix can be rejected. Based on the very encouraging results from both tests, factor analysis may be performed.

In the second step, PCA is used for data extraction, and then Varimax rotation is used in order to reduce the number of variables as in Table 8.41, which presents the output of the number of factors that are retained according to Kaiser's criterion, in which all the eigenvalues are more than 1.0. In this situation, there are three factors that will be retained, since the eigenvalues are 3.170, 1.356 and 1.332 respectively.

TABLE 8.41 Total variance explained for Question 16

Initial Eigenvalues Rotation Sums of Squared Loadings
Component Total % of Variance Cumulative % Total % of Variance Cumulative %
1 3.682 40.906  40.906 3.170 35.225 35.225
2 1.136 12.617  53.523 1.356 15.064 50.289
3 1.031 11.456  64.980 1.322 14.691 64.980
4 0.746  8.287  73.267
5 0.637  7.081  80.348
6 0.523  5.813  86.161
7 0.459  5.098  91.260
8 0.402  4.472  95.732
9 0.384  4.268 100.000

Note: Extraction method: PCA

The results indicate that these three components can explain 64.9% of the total variation, which satisfies the use of factor analysis.

Figure 8.2, which is basically a graph of the eigenvalues, shows that the nine variables could be reduced to only three as the graph slopes down steeply before becoming parallel to the horizontal line after the third component. It is clear from the plot that there is only a three-factor solution to this question. Therefore, it was decided to retain the three factors.

Screen plot depicting the graph of the eigenvalues of the nine component variables that could be reduced to only three as the graph slopes down steeply.

FIGURE 8.2 Screen plot for Question 16

Table 8.41 shows that there are three factors with an eigenvalue greater than 1.0; this means that the original nine items can be simply reduced to three factors. The three-component solution explains 64.9% of the variance with component 1 contributing 40.9%, component 2 contributing 12.6% and component 3 contributing 11.5%. The explanatory power of the first factor is very high.

Table 8.42 further provides a rotated component matrix by distributing all variables to the identified three components. The test results showed no component for factor 2; therefore the researcher accepted factors 1 and 3 only. The factors in each component have some common characteristics and measure the same phenomenon and therefore each component is named with a general description of the factors or variables it includes. For instance, factors in component 1 deal with ‘resilience of IFIs’. The factors in component three deal with ‘risk management must be embedded institutionally’. The former includes seven statements, while the latter includes only two components. Thus, the heavy weight is with the ‘resilience of IFIs’ component.

TABLE 8.42 Rotated component matrixa for Question 16

Component
1. Resilience of IFIs 2. 3. Risk management must be institutional
Islamic banks are more resilient to economic shocks than their conventional peers 0.776 −0.050 −0.238
The recent crisis would not have happened under a true Islamic banking system 0.764 0.081 −0.082
Islamic finance could have solved the global crisis 0.643 0.468 −0.146
Risk management must be embedded institutionally −0.100 −0.189 0.834
Banks in general used to rely heavily on rating agencies 0.552 0.447 0.358
Islamic banks rely less on rating agencies than conventional banks 0.454 −0.029 −0.583
Islamic finance industry should develop its own rating agencies 0.743 0.265 −0.075
Islamic banks will emerge stronger from the crisis 0.706 −0.007 −0.211
Consolidation is needed among smaller Islamic banks −0.012 −0.906 0.156

Notes: Extraction method: PCA.

Rotation method: Varimax with Kaiser Normalization.

a Rotation converged in nine iterations

MANOVA test according to region for Question 16   After conducting factor analysis between groups a MANOVA test was computed in order to investigate if there is any significant difference between the two factors in relation to same control variables. This will help to locate the impact or significance of each control variable on the established distribution.

The MANOVA test was conducted according to ‘Region’ as the independent variable with the objective of testing the significance of ‘Region’ on the identified two components. In this case, the output of the Box's Test in Table 8.43 shows that there is no violation of assumption of homogeneity of variances of variance-covariance matrices since the sig. value of 0.013 is higher than the critical value of 0.001.

TABLE 8.43 Box's Test of Equality of Covariance Matricesa

Box's M  24.157
F   2.342
df1   9    
df2 824.888
Sig.   0.013

Note: Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups.

a Design: Intercept + Region

Additionally, the output of the Levene's Test of Equality of Error Variances (Table 8.44) is explored. The results in the Sig. column show that sig. values of ‘Resilience of IFIs’ (0.681) and ‘Risk management must be institutional’ (0.236) are higher than 0.05. Thus, there is no violation of the assumption of equality of variances for these two factors.

TABLE 8.44 Levene's Test of Equality of Error Variancesa

F df1 df2 Sig.
Resilience of IFIs 0.625 5 66 0.681
Risk management is institutional 1.398 5 66 0.236

Notes: Tests the null hypothesis that the error variance of the dependent variable is equal across groups.

a Design: Intercept + Region

The results of the Wilks' Lambda in Table 8.45 show that there is a statistically significant difference according to the region since the sig. value of 0.01 is quite a bit lower than the critical level of 0.05.

TABLE 8.45 Multivariate testsc

Effect Value F Hypothesis df Error df Sig. Partial Eta Squared
Intercept Pillai's Trace  0.975 1255.030a  2.000  65.000 0.000 0.975
Wilks' Lambda  0.025 1255.030a  2.000  65.000 0.000 0.975
Hotelling's Trace 38.616 1255.030a  2.000  65.000 0.000 0.975
Roy's Largest Root 38.616 1255.030a  2.000  65.000 0.000 0.975
Region Pillai's Trace  0.370    2.992  10.000 132.000 0.002 0.185
Wilks' Lambda  0.646    3.175a 10.000 130.000 0.001 0.196
Hotelling's Trace  0.524    3.354  10.000 128.000 0.001 0.208
Roy's Largest Root  0.473    6.249b  5.000  66.000 0.000 0.321

Notes

a Exact statistic.

b The statistic is an upper bound on F that yields a lower bound on the significance level.

c Design: Intercept + Region

Since the multivariate test suggests that there is a statistically significant difference, a further investigation is conducted. Tests of Between-Subjects Effects provide this information. In this case, there are two dependent variables, therefore 0.05 is divided by two and the new alpha level is 0.025. As can be seen in the Tests of Between-Subjects Effects in Table 8.46, the results indicate that ‘Resilience of IFIs’ has significant values of 0.000, while ‘Risk management must be institutional’ has a sig. value of 0.242, which is higher than the critical value of 0.025 for this example. Furthermore, the effect size values as evaluated by the Partial Eta Squared for ‘Resilience of IFIs’ is 0.320, which are deemed large-effect sizes using Cohen's criteria. It can be concluded that these results, which signify 32% of the variances in ‘Resilience of IFIs’ scores, are explained respectively by region.

TABLE 8.46 Tests of Between-Subjects Effects

Source Dependent Variable Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
Corrected Model Resilience of IFIs   13.209a  5   2.642    6.221 0.000 0.320
Risk management is institutional    2.821b  5   0.564    1.384 0.242 0.095
Intercept Resilience of IFIs  313.934   1 313.934  739.239 0.000 0.918
Risk management is institutional  438.911   1 438.911 1076.963 0.000 0.942
Region Resilience of IFIs   13.209   5   2.642    6.221 0.000 0.320
Risk management is institutional    2.821   5   0.564    1.384 0.242 0.095
Error Resilience of IFIs   28.028  66   0.425
Risk management is institutional   26.898  66   0.408
Total Resilience of IFIs  877.837  72
Risk management is institutional 1292.250  72
Corrected Total Resilience of IFIs   41.237  71
Risk management is institutional   29.719  71

Notes

a R Squared = 0.320 (Adjusted R Squared = 0.269).

b R Squared = 0.095 (Adjusted R Squared = 0.026)

An attempt was also made to see the effect of ‘Nature of FI’ on the identified components in factor analysis through MANOVA. However, no significant results could be established.

RISK MANAGEMENT AND REPORTING

This part of the questionnaire examines the risk management and hedging techniques used within IFIs. Question 17 covers the frequency of producing risk management reports as perceived by the participants, and is applicable only to financial institutions.

As depicted by Table 8.47, the K-W test for fully fledged Islamic banks, conventional banks with Islamic activities, and conventional banks shows that, statistically, there is a significant difference among various regions in the frequency of producing risk reports (p-value < 0.05) except for Commodity Risk Report (0.094), Industry Concentration Risk Report (0.129), Credit Exposure Report (0.091) and Large Exposure Report (0.071). Hence, for the rest of the reports there are significant differences in the perceptions of the participants. Thus, for most of the reports region is a significant factor.

TABLE 8.47 K-W test results for Question 17 (risk reporting) by region for selected sample data

Frequency of Producing: Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Chi-Square df Asymp. Sig. Chi-Square df Asymp. Sig. Chi-Square df Asymp. Sig.
Capital Requirement Report 28.727 5 0.00  11.746 4 0.019  7.89  4 0.096
Operational Risk Report 18.01  5 0.003  3.534 4 0.473  2.208 4 0.698
Profit Rate Risk Report 20.859 5 0.001  8.04  4 0.09   4.539 4 0.338
FX Risk Report 19.469 5 0.002 10.321 4 0.035  9.646 4 0.047
Liquidity Risk Report 19.312 5 0.002  8.026 4 0.091  5.357 4 0.253
Commodity Risk Report  9.405 5 0.094  6.636 4 0.156  7.297 4 0.121
Country Report 11.58  5 0.041  6.554 4 0.161  5.218 4 0.266
Equity Mark-to-Market Report 12.611 5 0.027 11.406 4 0.022  6.464 4 0.167
Classified Accounts Report 16.91  5 0.005  9.651 4 0.047  5.386 4 0.25 
Industry Concentration Risk Report  8.537 5 0.129  3.168 4 0.53   3.153 4 0.533
Credit Exposure Report  9.479 5 0.091 10.937 4 0.027 12.452 4 0.014
Large Exposure Report 10.155 5 0.071  9.408 4 0.052  7.111 4 0.13 

Repeating the K-W test with ‘Region’ as the control variable for various institutional samples of data gives different results as the removal of conventional banks from the sample shows that the distribution of frequency of producing reports becomes the same across more reports, i.e. fewer risk reports show statistical significance in the frequency of production across regions. By removing Islamic subsidiaries from the sample and conducting the K-W test on fully fledged Islamic banks exclusively, only two reports (FX Risk Report and Credit Exposure Report) become statistically significant across various regions.

The results reflect the risk management culture difference between Islamic and conventional banks. By conducting the K-W test on fully fledged IFIs only, there was little significance between the responses across different regions. However, expanding the sample to include Islamic subsidiaries of conventional banks increased the significant difference in risk reporting across regions. When the sample was expanded further to incorporate conventional banks, the significance in difference becomes more noticeable.

Tables 8.48 to 8.55 examine the mean rankings for reports with statistically significant differences in frequency of production.

TABLE 8.48 Frequency of producing Capital Requirement Report

Region Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
N Mean Rank Rank N Mean Rank Rank N Mean Rank Rank
Americas  2 13.5  1st N/A N/A N/A N/A N/A N/A
Europe 20 18.3  2nd 12 13.46 1st  5  8    1st
GCC 19 32.03 4th 16 20.75 3rd  9 12.5  3rd
Other  2 23.5  3rd  2 14.75 2nd  2  8.75 2nd
Other Middle East 12 47.63 6th  5 31.9 5th  5 19.5  5th
Southeast Asia  4 37.5  5th  4 24.38 4th  4 14.38 4th
Total 59 39 25

TABLE 8.49 Frequency of producing Operational Risk Report

Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region N Mean Rank Rank N Mean Rank Rank N Mean Rank Rank
Americas  2 11.5  1st N/A N/A N/A N/A N/A N/A
Europe 20 19.5  2nd 12 15.17 1st  5  9.1  1st
GCC 19 33.08 4th 16 19.94 3rd  9 12.22 3rd
Other  2 27.5  3rd  2 17    2nd  2 10.25 2nd
Other Middle East 11 40.18 6th  4 24.13 5th  4 14.63 5th
Southeast Asia  3 38.17 5th  3 23.83 4th  3 13.83 4th
Total 57 37 23

TABLE 8.50 Frequency of Producing Profit Rate Risk Report

Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region N Mean Rank Rank N Mean Rank Rank N Mean Rank Rank
Americas  2 17 1st N/A N/A N/A N/A N/A N/A
Europe 20 18.95 2nd 12 13.67 1st  5  7.9  1st
GCC 19 33.11 4th 16 20.56 2nd  9 12.61 2nd
Other  2 32.5  3rd  2 21.5  3rd  2 12.75 3rd
Other Middle East 12 41.88 6th  5 27.5  5th  5 16.5  5th
Southeast Asia  3 33.83 5th  3 22.5  4th  3 13    4th
Total 58 38 24

TABLE 8.51 Frequency of producing FX Risk Report

Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region N Mean Rank Rank N Mean Rank Rank N Mean Rank Rank
Americas  2 13 1st N/A N/A N/A N/A N/A N/A
Europe 19 19.18 2nd 11 13.5  1st  5  8    1st
GCC 19 29.97 4th 16 17.66 3rd  9  9.67 2nd
Other  2 29.5  3rd  2 17.25 2nd  2 10.75 3rd
Other Middle East 11 39.91 5th  4 29.5  5th  4 18.75 5th
Southeast Asia  3 46 6th 3 27.5  4th  3 17.5  4th
Total 56 36 23

TABLE 8.52 Frequency of producing Liquidity Risk Report

Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region N Mean Rank Rank N Mean Rank Rank N Mean Rank Rank
Americas  2 18.5  1st N/A N/A N/A N/A N/A N/A
Europe 20 19.78 2nd 12 13.5 1st  5  7    1st
GCC 19 33.13 4th 16 22   3rd  9 14.44 3rd
Other  2 31.25 3rd  2 21   2nd  2 12.5  2nd
Other Middle East 12 41.25 6th  5 24.4 4th  5 14.8  4th
Southeast Asia  4 37.63 5th  4 25.5 5th  4 15.25 5th
Total 59 39 25

TABLE 8.53 Frequency of producing Country Report

Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region N Mean Rank Rank N Mean Rank Rank N Mean Rank Rank
Americas  2 16.5  1st N/A N/A N/A N/A N/A N/A
Europe 20 25.13 2nd 12 18.79 1st  5 13.9  3rd
GCC 19 27.47 3rd 16 16.53 2nd  9  9.17 1st
Other  2 33.75 4th  2 20.75 3rd  2 12.25 2nd
Other Middle East 12 39 5th 5 28.1  5th  5 16.9  5th
Southeast Asia  4 44.25 6th  4 27    4th  4 16    4th
Total 59 39 25

TABLE 8.54 Frequency of producing Equity Mark-to-Market Report

Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region N Mean Rank Rank N Mean Rank Rank N Mean Rank Rank
Americas  2 15.5  1st N/A N/A N/A N/A N/A N/A
Europe 20 22.55 2nd 12 15.13 1st  5  8.9  1st
GCC 19 30.34 3rd 16 18.03 2nd  9 11.94 3rd
Other  2 36.75 4th  2 23    3rd  2 13.25 4th
Other Middle East 12 39.96 6th  5 33.8  5th  5 19.8  5th
Southeast Asia  4 39.63 5th  4 23.75 4th  4 11.88 2nd
Total 59 39 25

TABLE 8.55 Frequency of producing Classified Accounts Report

Fully Fledged Islamic Banks, Conventional Banks with Islamic Activities and Conventional Banks Fully Fledged Islamic Banks and Conventional Banks with Islamic Activities Fully Fledged Islamic Banks
Region N Mean Rank Rank N Mean Rank Rank N Mean Rank Rank
Americas  2 8.5  1st N/A N/A N/A N/A N/A N/A
Europe 20 20 2nd 12 14.75 1st  5 11.9  3rd
GCC 19 33.95 4th 16 19.78 3rd  9 12.28 4th
Other  2 32.5  3rd  2 17    2nd  2  7.5  1st
Other Middle East 10 37.7  6th  4 32.5  5th  4 18.5  5th
Southeast Asia  4 37.25 5th  4 20.88 4th  4 10.25 2nd
Total 57 38 24

In this particular case mean ranking requires clarification. Since during coding ‘daily reporting’ was assigned value 1, and ‘never’ was assigned value 5, this has impact on the mean ranking. In other words, the better mean value here would be the lower value indicating better disclosure.

The results presented in this section so far indicate a particular pattern. The trend is obvious: conventional banks, concentrated in Europe and the Americas, produce risk reports more frequently than Islamic banks. Risk management and reporting is more advanced in conventional banking than in Islamic banking.

RISK MEASUREMENT

This section expands the descriptive analytical analysis conducted in Chapter 7 by examining the impact of various control variables on respondents’ views regarding the use of numerous techniques to measure and analyse risk. For this purpose, the researcher used K-W to determine if there were any statistically significant differences across the categories of respondent profiles, specifically region, respondent's position, nature of financial institution, nature of activities and accounting standards. Since this question targets financial institutions only, the sample used for this question is restricted to bankers.

‘Region’ and ‘Nature of Financial Institution’ are the control variables selected for analysis by mean ranking, being the control variables with the most significant results, and because these two variables are most essential to the difference in risk management techniques among banks. As can be seen in Table 8.56, ‘Region’ has five significant risk management techniques, and ‘Nature of Financial Institution’ has three significant techniques. Thus, they have more significant variables compared to others, which justifies their further analysis.

TABLE 8.56 K-W test results for Question 18 (risk measurement) for selected sample data according to various control variables

Region Respondent's Position Nature of Financial Institution Nature of Activities Accounting Standards
Risk Management Technique Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig.
Internal based ratings  6.223 0.285  9.79 0.459  1.612 0.447  3.067 0.69  3.699 0.296
Credit ratings by rating agencies  1.58  0.904  6.81 0.743  3.396 0.183  8.01  0.156 11.78  0.008
Gap analysis 17.56  0.004 10.8  0.372  0.516 0.773  6.884 0.229  7.119 0.068
Duration analysis 15.69  0.008 14.2  0.163  2.468 0.291  6.559 0.256  7.151 0.067
Maturity matching analysis  8.155 0.148  5.78 0.833  0.344 0.842 10.79  0.056  6.028 0.11 
Earnings at risk  8.58  0.127 10.0  0.438  7.754 0.021 10.14  0.071  4.029 0.258
Value at risk 10.98  0.052 13.0  0.222  1.926 0.382  5.731 0.333  5.134 0.162
Stress testing 17.48  0.004  9.70 0.466  4.91  0.086  7.604 0.179  5.687 0.128
Simulation techniques 14.60  0.012 19.2  0.038  6.64  0.036 13.05  0.023  7.708 0.052
RAROC 19.65  0.001 16.0  0.097 12.29 0.002 10.79  0.056  7.373 0.061

Table 8.57 shows that conventional banks in relation to their regional location, concentrated outside of the GCC and Middle East, use more advanced risk management techniques than Islamic banks. The ‘Americas’ are the most advanced across all techniques, followed often by ‘Other’ or ‘Europe’. The rest of the regional samples include mostly Islamic banks; their use of sophisticated risk measurements, however, is not as significant as in conventional banks in the Americas and Europe, as evidenced from mean ranking.

TABLE 8.57 K-W test mean rankings for risk measurement by region for selected sample data

Risk Management Technique Gap analysis Duration analysis Stress testing Simulation techniques RAROC
Nature of Financial Institution N Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank
Americas 2 36 1st 36.5  1st 41 1st 48.5  1st 42.5  1st
Europe 20 33.05 4th 35.03 3rd 36.58 3rd 36.7  2nd 33.65 3rd
GCC 19 34.45 3rd 31.84 4th 30.13 4th 28.32 4th 36.29 2nd
Other 2 36 1st 36.5 1st 41 1st 33.75 3rd 27.75 4th
Other Middle East 12 18.79 6th 19.29 6th 21.33 5th 21.46 5th 15.46 6th
Southeast Asia 4 21.25 5th 21.75 5th 11.5  6th 19 6th 20.38 5th
Total 59

Note: Only techniques with significant p-value are further analysed by mean ranking

These results in Table 8.58 confirm that there is a particular trend determined by the market realities. The use of risk management techniques in IFIs is not as sophisticated or as widely spread as in the conventional banking world. Fully fledged Islamic banks rank third across all techniques as not many IFIs use the more technically advanced risk measurement approaches, which is evidenced from the mean ranking in Table 8.58.

TABLE 8.58 K-W test mean rankings for risk measurement by nature of financial institution for selected sample data

Risk Management Technique Earnings at Risk Simulation Techniques RAROC
Nature of Financial Institution N Mean Rank Rank Mean Rank Rank Mean Rank Rank
Fully Fledged Islamic Bank 25 23.98 3rd 26.08 3rd 22.44 3rd
Conventional Bank with Islamic Activities 14 34.18 2nd 27.43 2nd 38.29 1st
Conventional Bank 20 34.6  1st 36.7  1st 33.65 2nd
Total 59

Note: Only techniques with significant p-value are further analysed by mean ranking

RISK MITIGATION

As previously discussed, risk mitigation and hedging are controversial issues in Islamic banking. Different mitigation techniques are subject to different interpretations by Shari'ah scholars. There have been substantial efforts in developing Shari'ah-compliant hedging instruments, which are the subject of this section. These include: on balance sheet netting, collateral arrangements, Islamic options, Islamic swaps, guarantees, Islamic currency forwards and parallel contracts. However, much of this progress remains localised with limited scope for cross-border application and further work is still needed as evident from the results of the K-W test in Table 8.59. Question 20 targets institutions that use Islamic finance contracts only; therefore, when conducting the K-W test, only stand-alone Islamic banks and Islamic subsidiaries were included in the raw data in relation to five control variables: region, respondent's position, nature of financial institution, nature of activities and accounting standards.

TABLE 8.59 K-W test results for Question 20 (risk mitigation) for selected sample data according to various control variables

Region Respondent's Position Nature of Financial Institution Nature of Activities Accounting Standards
Risk Mitigation Technique Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig.
On balance sheet netting  9.65 0.086 22.841 0.011 44.91 0.00  8.483 0.132  5.371 0.147
Collateral arrangements 11.25 0.047 22.177 0.014 53.14 0.00  8.599 0.126  5.248 0.155
Islamic options 16.94 0.005 20.417 0.026 46.59 0.00 15.034 0.01   9.811 0.02 
Islamic swaps 14.65 0.012 21.024 0.021 44.73 0.00 12.76  0.026 12.991 0.005
Guarantees  8.64 0.124 24.37  0.007 52.24 0.00 11.293 0.046  6.088 0.107
Islamic currency forwards  9.98 0.076 23.579 0.009 54.59 0.00  8.787 0.118  5.287 0.152
Parallel contracts 10.30 0.067 18.794 0.043 45.59 0.00 12     0.035  6.838 0.077

‘Nature of Financial Institution’ is the control variable selected for analysis by mean ranking as it has the highest number of significant results and because this variable is most essential to the difference in risk mitigation techniques among financial institutions, as illustrated in Table 8.60.

TABLE 8.60 K-W test mean rankings by nature of financial institution for selected sample data

Risk Mitigation Technique On Balance Sheet Netting Collateral Arrangements Islamic Options Islamic Swaps Guarantees Islamic Currency Forwards Parallel Contracts
Nature of Financial Institution N Mean Rank
Fully Fledged Islamic Bank 25 19.2 19.94 19.68 20.02 18.88 20.22 19.46
(2nd) (2nd) (2nd) (1st) (2nd) (1st) (2nd)
Conventional Bank with Islamic Activities 14 21.43 20.11 20.57 19.96 22 19.61 20.96
(1st) (1st) (1st) (2nd) (1st) (2nd) (1st)
Total 39

Note: Ordering in parentheses refers to mean ranking

These results confirm that there is a general trend determined by the market realities. With the exception of Islamic swaps and Islamic currency forwards, fully fledged Islamic banks fell behind Islamic subsidiaries in using all other risk mitigation techniques. The latter group tends to benefit from the already developed risk mitigation platforms at their conventional parents. However, of notice is that the difference in the value of mean ranking between the two groups is small, which reflects that IFIs are progressing in the use of risk mitigation but that still the use of risk mitigation techniques in IFIs is not as developed as in conventional banking.

ISLAMIC BANKING IN PRACTICE

This section examines the proposition that Islamic banking has been diverting from its roots by mimicking conventional banks. In doing so, a K-W test was conducted using the entire sample according to nature of financial institution.

This section aims to test the participants' perceptions in relation to the following statements. The coding of the statements as they appear in the tables is as follows:

  1. Islamic banks have been mimicking conventional models.
  2. Islamic finance provides an ethical banking alternative.
  3. There is a difference between current practice and the principles of Islamic banking.
  4. Islamic banks need to reform to be successful.

As depicted by Table 8.61, only Statement 3 is statistically significant, reflecting the similarities in views among respondents about the diversion between principles and current practices in Islamic banking. However, with a ‘relaxation’ of the confidence level to 0.06, Statement 1 can also be accepted as statistically significant. Furthermore, mean rankings reflect a pattern across all statements, with the exception of Statement 2. Non-bankers (Others) scored the highest mean, followed by fully fledged Islamic banks, conventional banks and Islamic subsidiaries respectively. This reflects the risk appetite of each group. Interestingly, Islamic bankers are more critical of the current practices in the industry than their conventional peers. This could be explained by the fact that Islamic bankers are more educated about the underlying principles of Islamic finance and have a better understanding of current structures than conventional bankers. The ‘Others’ category comprises Shari'ah scholars, consultants, researchers, etc., whose better understanding of the ideologies of Islamic banking is reflected in their lack of satisfaction with Islamic banking in its current state (highest mean ranking for three statements).

TABLE 8.61 K-W test results by nature of financial institution for Question 21 for entire research sample

Nature of Statement
Financial Institution 1 2 3 4
Chi-Square 7.566 4.589 12.812 7.171
Asymp. Sig. 0.056 0.205 0.005 0.067
Nature of Financial Institution N Mean Rank Rank Mean Rank Rank Mean Rank Rank Mean Rank Rank
Fully Fledged Islamic Bank 25 38.22 2nd 42.3  1st 40.94 2nd 37.24 2nd
Conventional Bank with Islamic Activities 14 26.79 4th 37.86 2nd 29.89 4th 27    4th
Conventional Bank 20 34.23 3rd 30.15 4th 27.18 3rd 35.2  3rd
Others 13 47.15 1st 33.65 3rd 49.42 1st 47.31 1st
Total 72

Repeating the K-W test with ‘Region’ as the control variable for the entire research sample gives different results, as illustrated by Table 8.62. All statements are statistically insignificant, except Statement 2, which shows the common dissatisfaction with the current status of Islamic banking across all regions.

TABLE 8.62 K-W test results by region for Question 21 for entire research sample

Statement
Region 1 2 3 4
Chi-Square 8.202 19.551 4.25  3.227
Asymp. Sig. 0.145 0.002 0.514 0.665
Mean Mean Mean Mean
Region N Rank Rank Rank Rank Rank Rank Rank Rank
Americas  2 51.75 1st 9.5  6th 24.75 6th 27.5  6th
Europe 31 40.45 2nd 29.24 4th 35.92 4th 38.42 2nd
GCC 19 26.92 5th 46.71 2nd 36.66 3rd 38    3rd
Other  2 51.75 1st 62.5  1st 60.5  1st 50    1st
Other Middle East 14 35.39 4th 42.5  3rd 34.11 5th 32.14 4th
Southeast Asia  4 40    3rd 23.75 5th 42.5  2nd 27.5  5th
Total 72

Despites the similarities between views of respondents across various regions (only Statement 2 has a significant p-value), the mean ranking results show dispersed results; no trend can be established across various regions.

In addition, an attempt was made to test the impact of the ‘Respondent's Position’ on the views; however, the results show that there are no significant differences as all p-value > 0.05.

THE NEXT CHAPTER IN ISLAMIC BANKING

The last section of the questionnaire is a forward-looking question that explores different strategies IFIs should follow in order to prepare for the day after tomorrow. For this, eight statements were provided to the respondents to disclose their opinion. The data was analysed through K-W test.

As shown in Table 8.63, ‘Nature of Financial Institution’ is the only control variable whose results had some statistically significant outcomes across different groups. ‘Organic growth in home market’ and ‘Standardisation’ had p-values of 0.036 and 0.015 respectively. The mean rankings of these two strategies according to ‘Nature of Financial Institution’ are examined in Table 8.64. As regards other control variables, the opinions do not show differences but rather convergence.

TABLE 8.63 K-W test results for Question 22 for the entire sample according to various control variables

Region Respondent's Position Nature of Financial Institution Nature of Activities Accounting Standards
Strategy Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig. Chi-Square Asymp. Sig.
Improved risk management  5.22 0.389 15.3  0.358 2.59 0.458  1.991 0.921 3.607 0.462
Enhanced morality – back to roots  9.10 0.105 11.82 0.621 5.49 0.139  9.002 0.173 3.799 0.434
Mergers and acquisitions  3.80 0.578 17.49 0.231 1.28 0.732  5.956 0.428 6.094 0.192
Organic growth in home market 10.83 0.055 13.98 0.451 8.57 0.036  2.965 0.813 2.216 0.696
Better risk mitigation  9.07 0.106 16.07 0.309 7.28 0.063  9.697 0.138 3.556 0.469
Innovation  1.42 0.921 12.13 0.596 3.04 0.385 12.17  0.058 1.435 0.838
Diversification – reduce concentration  5.09 0.404 17.36 0.238 5.06 0.167  6.979 0.323 6.118 0.191
Standardisation  7.14 0.21  21.59 0.087 10.5 0.015  5.246 0.513 5.301 0.258

TABLE 8.64 K-W test mean rankings by nature of financial institution for entire sample

Strategy Organic Growth in Home Market Standardisation
Nature of Financial Institution N Mean Rank Rank Mean Rank Rank
Fully Fledged Islamic Bank 25 31.68 3rd 38.88 2nd
Conventional Bank with Islamic Activities 14 28.86 4th 49.68 1st
Conventional Bank 20 46.6  1st 28.65 4th
Others 13 38.46 2nd 29.81 3rd
Total 72

As can be seen from Table 8.63, no particular pattern could be identified. For ‘Organic growth in home market’, conventional banks were more aggressive with a high mean value (46.6), followed by others (38.46), fully fledged Islamic bank (31.68) and finally Islamic subsidiaries (28.86). However, this trend was almost reversed for ‘Standardisation’ with Islamic subsidiaries having the highest mean value (49.68), which is much higher than the rest of the categories. Conventional banks rank last with a mean of 28.65.

CONCLUSION

This chapter represents the second part of the quantitative analysis for the questionnaire. The objective of this chapter was to gauge the perception of the respondents regarding different risk management and capital adequacy issues in Islamic banking, the effect of the recent global crisis on Islamic banking, and what the future holds for the industry. Various inferential statistical tools were employed to examine the relationship between the characteristics of the sample respondents and their risk perceptions. K-W analysis was the most-performed test to find out if there were any significant differences caused by the category to which the respondents belonged, and the results of testing were subsequently interpreted.

‘Region’ was the control variable that displayed the most statistically significant differences among respondents' perceptions for different parts of the questionnaire. Analysis according to ‘Nature of Financial Institution’, ‘Nature of Activities’ and ‘Respondent's Position’ also revealed some general trends that can be attributed to prevailing market conditions. ‘Accounting Standards’ was used as control variable as well; however, the results did not often provide much statistical significance for this category.

The differences among respondents' answers were scrutinised to test if there were significant differences related to characteristics. Chapter 9 takes the analysis one step further by qualitatively analysing the field interviews conducted with Islamic banking professionals, while further analysis was then carried out to make more sense of the available facts. Detailed analysis of the findings of this chapter, within the context of the findings of descriptive statistical analysis of the questionnaire and the interview analysis, is provided in an integrated manner in Chapter 10.

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