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Rabu, 10 Maret 2010


Nuradli Ridzwan Shah Bin Mohd Dali
Universiti Sains Islam Malaysia
71800 Bandar Baru Nilai
Negeri Sembilan

Hanifah Abdul Hamid
Universiti Sains Islam Malaysia
71800 Bandar Baru Nilai
Negeri Sembilan


This study is conducted to analyze the factors influencing the banking preferences between Islamic Banking, Conventional and both banking users by using factoring analysis and multinomial logistic regression. Result shows that there are three factors that contributing to the banking preferences for Islamic banking and conventional banking users. However, there are only two factors are significant for Islamic Banking users while there is one factor is significant for Conventional banking users when using multinomial logistic regression. This research supports the earlier research by Nuradli et al stating that banking users choose the conventional banking because of service quality.

Keywords: Islamic Banking, Conventional Banking, Selection Factors, Factor Analysis, Logistic Regression


Worldwide, the Islamic banking is currently in enormous growth. Islamic banking is governed by the shariah laws with the objective of achieving fairness and balance between the parties in an agreement. This makes it a huge different from conventional banking as conventional banking does not have the element of religion or belief in the governance. In terms products, both type of banks offered equivalent facilities. This provides consumers with a wide range of choices in selecting a bank that best suite their needs and economic benefits. Researches have identified several factors of consumers’ preferences of bank selection, for example, the cost and benefit (Metawa & Almossawi (1998), Abbas, Hamid et al (2003)), service quality (Ahmad & Haron (2002), Asyraf & Nurdianawati (2007), staff factors (Laroche et al (1986), Azura et al (2006)) and mass media advertising (Haron, Ahmad et al (1994), Othman & Owen (2002)).

This research is motivated by the fact of choices the consumers have in their hands. Although there are many researches have been conducted to discover the preferences of consumers in selecting banks, this research tries to uncover the factors influencing between Islamic banking and conventional by using factoring analysis and logistic regression. From 191 respondents from the states in Malaysia, the result shows that promotion and convenience are positively significant to the Islamic banking selection whereby the service quality is negatively significant to the Islamic banking selection or in other word more towards the conventional banking.

Objectives of the study
The objective of the study is:
a) To identify the factors which influence the conventional and Islamic banking users’ selection

1.2 Motivation/Significance of the study
Several researches have been conducted based on the selection of Islamic banking. However, in this research we will identify the factors using a different approach i.e. using online questionnaire to capture respondents from many states in Malaysia. Most of the previous research collected samples from either a single state or several states. It is expected that the result from this research is consistent with result from the previous literature.

Naser et al (1999) in their study, has made an attempt to assess the degree of customer awareness and satisfaction towards an Islamic bank in Jordan. A sample of 206 respondents took part in this study. The analysis of their responses revealed a certain degree of satisfaction of many Islamic banks facilities and products. The respondents expressed their dissatisfaction with some of the Islamic banks services. Although the respondents indicated that they are aware of a number of specific Islamic financial products like murabaha , musharaka and mudaraba , the study shows that they do not deal with them.

Erol, and El-Bdour, (1989) who conducted a research in Jordan, showed that the customers were actually more profit-oriented than religious-oriented. In other words, religious motive was not a dominant factor to consider shari’ah bank, yet the strongest motive was based on profit-oriented motive.

Another study conducted by Ahamad and Haron (2002) on business attitudes towards Islamic banking found that economic factors, such as profitability and the quality of services, were more significant for Malaysian corporate customers than religious reasons. However, one qualifying factor could be that the majority of respondents were non-Muslims who were generally less aware of the existence of Islamic banks and the substitutability of Islamic finance methods for conventional bank products and services. In fact, most respondents, both Muslim and non-Muslim, had a low level of knowledge about Islamic finance, especially most of the business financing methods. As with the work on individual consumer preferences in Malaysia, this study recommended that Islamic financial institutions in Malaysia need to better market their products and services.

A research conducted in the Indonesia by Iwan Triyuwono et al. (2000) revealed that there were seven factors that contribute to the selection of Islamic banking which are information and rational consideration, religious and moral orientation, age and life cycle stage, preference group (family), location, life style, and belief and attitude.

A research conducted by Asyraf Wajdi Dusuki and Nurdianawati Irwani Abdullah (2006) revealed that the selection of Islamic banks appears to be predominantly a combination of Islamic and financial reputation and quality service offered by the bank. Other factors perceived to be important include good social responsibility practices, convenience and product price. questionnaires involving a sample of 750 respondents from four different regions in Malaysia. The Islamic banking criteria ranking as perceived by the respondents are analyzed using Friedman Test and exploratory factor analysis
is employed. (Asyraf Wajdi Dusuki & Nurdianawati Irwani Abdullah, 2006).

Nor Azurah Mad Kamdari, Remali Yusuf and Shaherah Abdul Malik (2007) have investigated the Islamic banking selection comparing Bank Islam (M) Bhd and other conventional banks users in Malacca. The total respondents are 300. The factors identified by them are reputation/ images, location, service quality, convenience and confidently for BIMB users. Overall, their findings revealed that customers and depositors of Islamic banks in Malacca have generally positive views of selection factors. One of the most important reflections of their positive attitude is that reputation and image factor are shown as important criteria in their banking selection. However their scope of study is limited to only Islamic banking users in a small state in Malaysia.

Nuradli, Hanifah and Izlawanie (2009) have used the online survey to capture the banking selection preferences comparing the Islamic banking and conventional users in Malaysia. They employed factor analysis and logistic regression and found out that there are three factors that contribute significantly to the banking selections. The factors are promotion, service quality and convenience. However, the researchers only compared between the conventional and Islamic banking users only excluding the users who use both banking services. Therefore, in this paper the researchers will compare the three groups of banking users using multinomial logistic regression and will try to compare the result with the previous paper.

Other research in the banking selection criteria are Erol and El Bdour, (1989), (1990); Haron, Ahmad et al., (1994); Gerard and Cunningham, (1997); Metawa and Almossawi., (1998); Naser, Jamal et al., (1999); Othman and Owen., (2001), (2002); Ahmad and Haron, (2002); Abbas, Hamid et al., (2003), Azura et al (2006) Nuradli, Hanifah and Izlawanie (2009) as tabulated in the following table.

Criteria in Banking Selection
Literature A B C D E F G H I J
Naser, Jamal et al (1976) + + + + + + + + n/a n/a
Aven (1979) n/a n/a n/a n/a n/a + n/a n/a n/a +
Ringgal (1980) n/a n/a n/a n/a n/a n/a n/a n/a n/a +
Tan & Chua (1986) n/a n/a n/a n/a n/a n/a n/a + n/a n/a
Laroche et al (1986) n/a + n/a n/a + + n/a + n/a +
Kaynak (1986) n/a n/a + + n/a n/a n/a n/a n/a n/a
Javalgi et al (1989) n/a + + + n/a n/a n/a n/a n/a +
Erol & El Bdour (1989) - + + + + + + + - n/a
Haron, Ahmad et al (1994) - + + + + + + + + n/a
Gerrard & Cunningham (1997) + + + + + + + + + n/a
Metawa & Almossawi (1998) + + n/a n/a n/a + n/a + n/a n/a
Othman (2001) - n/a n/a n/a n/a n/a n/a n/a - n/a
Othman & Owen (2002) + + + + n/a + + n/a + n/a
Ahmad & Haron (2002) - + + + + + n/a n/a n/a n/a
Abbas, Hamid et al (2003) + + n/a + + + + + n/a n/a
Azura et al (2006) + + + + + n/a n/a n/a n/a +
Asyraf & Nurdianawati (2006) n/a - + + + + n/a n/a n/a -
Nor Azurah et al (2007) n/a n/a + + n/a + + n/a n/a +
Nuradli et al (2009) n/a n/a - n/a + + n/a
Notes: + indicate positive result; +/- indicate equivocal results, - indicates negative result and n/a indicates variables were not investigated.
A: Religious factor F: Convenience
B: Cost and Benefit G: Confidentiality
C: Service Quality H: Friends’ and Relatives’ Influences
D: Size and Reputation I: Mass Media Advertising/Promotion
E: Staff Factors J: Location
(Source Adapted and Improvised from Nor Azurah Mad Kamdari, Remali Yusuf and Shaherah Abdul Malik, 2007)


The methods employ in this paper are rarely used in economics and finance research but are widely used in the medical research, sociology, marketing and international business. The methods are underutilized in applied behavioral research of economics and finance as well as Islamic finance mainly because lack of interest and initiatives from the researchers or because of the dynamics and robustness of the methods making the combination of these methods are popular in other areas. This study employs the exploratory factor analysis . Factor analysis will be used to reduce and consequently group the independent variables. Primary data will be collected for the purpose of analysis which will be further discussed in the collection of data section.

Nasser et al (1998) used logistic regression and factor analysis. Qian et al. (2004) explore methods to develop uncorrelated variables for epidemiological analysis models. Both of the methods are also used in the Preventive Veterinary Medicine research conducted by Thorne and Hardin (1997). The methods were also employed in microbiology and diary veterinary research was conducted by Kramsky et al. (2000), Grant et al. (2001), Berghausa et al. (2005), and Collins and Morgan (1992).

In addition, in the area of diabetes care, Wang et al. (2004) also uses the same methods. Not limited to diabetes, the same methods are also employed in cardiovascular research was conducted by Nakamura et al. (2006). Geriatric Psychiatry researchers (2003) also employ the factor analysis and logistic regression in their research.

A research conducted by Iwan Triyuwono et al. (2000) used factoring analysis as well as logit/tobit analysis in determining the relationship of the Islamic banking selection in Indonesia.

The factor analysis is used to define customers’ selection criteria of Islamic banks in Malaysia which is in line with the analytical style used by Haron et al. (1994), Gerrard and Cunningham (1997, 2001); Almossawi (2001), Asyraf Wajdi Dusuki and Nurdianawati (2006) and Nor Azurah Kamdari, Remali Yusuf and Shaherah Abdul Malik (2007). Exploratory factor analysis with varimax rotation, is conducted because the varimax criterion centers maximizing possible simplification is reached if there are only 1s and 0s columns (Hair et all, 2006). The Kaiser-Meyer-Olkin (KMO) is to quantify the degree of intercorrelations among the variables and the appropriateness of factor analysis (Hair et all, 2006) In addition, Bartlett’s Test of sphericity measure the presence of correlations among the variables.

There are two methods are available for rotation of factors, orthogonal and oblique rotation. The former ensures the factors produced will be unrelated to each other, while the latter produces factors, which are correlated (Hair et al., 1998). However, no specific rule has been developed to guide the researchers in selecting a particular orthogonal or oblique rotational technique. In this study, Varimax orthogonal rotation is used because the study seeks to ensure that the factors produced will be independent or unrelated to each other.

The data will be analyzed using the multinomial logistic regression comparing two groups with

Collection of Data
Questionaires were posted on the internet and emails were sent to the potential respondents redirecting them to the website. Then, the data were downloaded from the website database and were converted to SPSS compatible data form. The data were used for analysis using the factoring analysis and logistic regression. There are approximately 209 respondents who have participated in the survey comprise from the Islamic banking and conventional banking users. However, in this research we excluded the user who uses both banking. Therefore only 191 respondents will be used in this research. The number of samples is adequate for factor analysis since it is at least 5 times greater than the numbers of variables (Hair et al, 2006). In this case, the ratio of respondents to variables is 8:1. The demographic profiles of the respondents are illustrated in the table 3.1.

Frequency Percent Valid Percent Cumulative Percent
Valid Islamic Banking 131 62.7 62.7 62.7
Conventional Banking 18 8.6 8.6 71.3
Both 60 28.7 28.7 100.0
Total 209 100.0 100.0

Table 3.1: Distribution of Respondents (n = 209)
Valid Percent
Banking Preferences Islamic Banking 62.7
Conventional Banking 8.6
Both 28.7
Gender 1) Male 41.1
2) Female 58.9
Age Group 1) Below 20 1.4
2) 21-25 45.5
3) 26-30 20.1
4) 31-35 19.1
5) 36-40 5.7
6) 41-45 4.3
7) 46-50 2.9
8) Above 50 1
Marital Status 1) Single 55.5
2) Married 44
3) Divorced 0.5
Monthly Income 1) Below RM2000 44
2) RM 2001 - 2500 11
3) RM 2501-3000 9.6
4) RM 3001- 3500 10
5) RM 3501 - 4000 6.2
6) RM 4000 – 4500 5.3
7) RM 4501 – 5000 2.9
8) Above 5001 11
Education level School 4.3
Postgraduate 21.5
University 74.2
State Selangor 29.7
Wilayah Persekutuan 16.3
Negeri Sembilan 15.8
Kelantan 7.2
Pahang 5.3
Perak 4.3
Johor 3.8
Kedah 3.8
Pulau Pinang 3.8
Terengganu 3.3
Melaka 3.3
Perlis 1.4
Sabah 1.4
Labuan 0.5

Based on Table 3.1, it is found that almost two third of the respondents prefer to have Islamic banking, while 29% prefer to have both Islamic and conventional banking and less than 10% opt for conventional banking alone. Majority of the respondents are female (59%) compared to male (41%). Respondents are primarily at the age range of 21-25 (45%), followed by 26-30 (20%) and 31-35 (19%), and small percentage from 36-40 (6%), 41-45 (4%), 46-50 (3%), below 20 and above 50 (1% respectively).More than half of the respondents are still bachelor (55%), while 44% of them are married and the rests are divorced. Based on their income, majority of them (44%) earn below RM2000 monthly, followed by RM2001-2500 and above RM5001 (11% respectively), RM2501-3000 and RM3001-3500 (10% respectively), RM3501-400 (6%), RM4000-4500 (5%) and RM4501-5000 (3%). Three quarter of the respondents are university graduates, while 21% possess postgraduate studies and the rests finished their secondary school. Out of 209 respondents, almost 30% of the respondents are from Selangor followed by Kuala Lumpur and Negeri Sembilan, and the rests of the states in Malaysia.


This section will highlight the factors that contribute practically to the selection of banking users. Overall there are five factors that are found to be contributing practically but do not necessary significant to the Islamic banking and conventional selection factors using component analysis.

The Measurement of Sampling Adequacy (MSA) Test is 0.878 which is higher than 0.5 which enable the factor analysis to be further analyzed. Furthermore the Bartlett’s Test of Sphericity is significant at 0.00 level which means that there are intercorrelations among the variables.

Table 4.1: Overall Measures of Intercorrelation
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .878
Bartlett's Test of Sphericity Approx. Chi-Square 2042.93
df 253
Sig. .000

In extracting the numbers of factors, latent root criterion is used. This is the most commonly used technique and simple to apply either to component analysis or common factor analysis. In this criterion, only the factors having latent roots or eigenvalues of greater than 1 will be considered as significant. All factors which is having eigenvalues lower than one will be disregarded. This could be shown in the Scree Plot as below.

However, the total variance explained by the five factors do not exceed the minimum requirement needed of 0.60. The overall percentages of the five factors are 59% which do not pass the minimum requirement of 0.6.

The factor loadings for the variables of the five factors are also significant and positive except for X10, X23, X4 and X7 because their factor loadings are lesser than 0.5. Overall the communalities for significant factors are above than 0.5 except for X9, X15 and X19. Even though these here variables have low communalities but they are greater than one half of variance of each variable. Therefore all the three variables would be remained in the analysis.

Table 4.2 Rotated Component Matrix
Promotion Customer Service Pricing Service Quality Convenience Communalities
X10 0.52
X23 0.46
X4 0.40
X7 0.57
X8 0.76 0.65
X6 0.70 0.67
X1 0.81 0.69
X3 0.70 0.60
X2 0.62 0.55
X20 0.57 0.61
X11 0.77 0.70
X12 0.74 0.74
X5 0.65 0.51
X9 0.63 0.47
X13 0.53 0.52
X15 0.52 0.46
X19 0.52 0.48
X22 0.70 0.63
X21 0.52 0.48
X17 0.70 0.68
X16 0.69 0.65
X18 0.58 0.55 0.73
X14 0.54 0.50
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 8 iterations.

Based on the varimax rotated component analysis, there are five factors that were extracted by the techniques. The researchers have used their intuitions in naming the factors and the factors are named promotion, customer service, pricing, accessibility and service quality.

All of the five factors would be analyzed in order to determine the factors that contribute significantly in the selection of banks using multinomial logistic regression Factors that are deemed significant by this procedure are promotion, convenience and service quality. The promotion and convenience are positively significant to the Islamic banking selection whereby the service quality is negatively significant to the Islamic banking selection or in other word more towards the conventional banking. The result is as follows:

Model Fitting Information
Model -2 Log Likelihood Chi-Square df Sig.
Intercept Only 359.033
Final 329.024 30.009 10 .001

Chi-Square df Sig.
Pearson 380.964 404 .789
Deviance 327.637 404 .998

Pseudo R-Square
Cox and Snell .134
Nagelkerke .163
McFadden .083

Likelihood Ratio Tests
Effect -2 Log Likelihood of Reduced Model Chi-Square df Sig.
Intercept 433.700 104.676 2 .000
Promotion 339.505 10.481 2 .005
Customer Service 331.669 2.645 2 .266
Pricing 329.084 .060 2 .970
Service Quality 341.465 12.441 2 .002
Convenience 336.967 7.944 2 .019

Parameter Estimates
B Std. Error Wald df Sig. Exp(B) 95% Confidence Interval for Exp(B)

Question 1-I prefer to bank with Lower Bound Upper Bound
Islamic Banking Intercept .796 .162 24.043 1 .000
Promotion -.361 .168 4.621 1 .032 .697 .502 .969
Customer Service .266 .166 2.560 1 .110 1.305 .942 1.808
Pricing -1.341E-02 .162 .007 1 .934 .987 .719 1.354
Service Quality .392 .168 5.455 1 .020 1.480 1.065 2.057
Convenience -5.739E-02 .164 .123 1 .726 .944 .685 1.302
Conventional Banking Intercept -1.719 .394 19.067 1 .000
Promotion .468 .326 2.060 1 .151 1.597 .843 3.027
Customer Service .129 .261 .242 1 .622 1.137 .682 1.898
Pricing -6.952E-02 .283 .060 1 .806 .933 .536 1.623
Service Quality -.502 .319 2.478 1 .115 .605 .324 1.131
Convenience .737 .308 5.726 1 .017 2.090 1.143 3.822

Observed Islamic Banking Conventional Banking Both Percent Correct
Islamic Banking 122 0 9 93.1%
Conventional Banking 13 2 3 11.1%
Both 56 0 4 6.7%
Overall Percentage 91.4% 1.0% 7.7% 61.2%
The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.

The result from the multinomial logistic regression model supported our previous findings (Nuradli, 2009) in which we claimed that the Islamic banking is not efficient in service quality a s compared to the conventional banking. The result from the multinomial regression explained that the Islamic banking users choose Islamic banking because of the promotion and convenience factor while the Conventional banking users choose because of the service quality.


The Islamic banking is undoubtedly is growing at a tremendous phase and the liberalization of the financial service sector as part of the Malaysian financial plan will open the financial services sectors to conventional foreign banks and Islamic foreign bank. Thus this will create competition for the existing local Islamic banks and to ensure their competitiveness the Islamic banking should focus on promotion and convenient factos as these two factors are their strengths. The conventional banking will be benefiting if the Islamic banking neglects the service quality factor.


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