The Adoption of Online Banking in Malaysia: An Empirical ... - Informit

The Adoption of Online Banking in Malaysia

Research Paper

ISSN 1985-692X

Received 03/05/2010 Accepted 02/06/2010 SAFA = 0.71 Chief Editor: Mohammad Safa Special Issue Editor: Keng-Boon Ooi & Alain Yee-Loong Chong

The Adoption of Online Banking in Malaysia: An Empirical Analysis a

Garry Wei-Han Tan ♣, aChee-Keong Chong, aKeng-Boon Ooi and bAlain Yee-Loong Chong aFaculty

of Business and Finance, Universiti Tunku Abdul Rahman, Malaysia of Industrial and System Engineering, The Hong Kong Polytechnic University, Hong Kong

bDepartment

Abstract: Although millions of dollars have been spent in developing online banking infrastructures, findings revealed that consumers have yet to adopt the systems in spite of their availability and convenience. As such, the paper aims to investigate the factors that affect the adoption of online banking in Malaysia. Using a self-administered questionnaire, 231 online banking services users were tested. The finding of the study indicated that social influences, perceived usefulness, trust, perceived ease of use were positively associated with the intention to adopt online banking. Interestingly, social influences are found to be the most influential factors, contradicting with many past studies. However perceived financial cost and perceived security risk were found insignificant in this study. The results provide valuable information for both bankers and policy makers especially when formulating online banking marketing strategies. Keywords: Online banking, Technology Acceptance Model (TAM), Malaysia

INTRODUCTION The advancement of technology especially the internet has changed the way how organizations conduct their business. Nowhere has the revolution of internet been more apparent than in the banking and financial services industry. Gone were the days, when traditional banks were the only mean to conduct banking transactions. Today, through online banking, customers could conduct a wide range of banking services electronically, anytime and anywhere (Sonja and Rita, ♣

[email protected]

International Journal of Business and Management Science, 3(2): 169-193, 2010

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2008). Online banking brings about speed, convenience and round-the-clock availability of banking services (Cheng, Lam and Yeung, 2006; Abu Shanab and Pearson, 2007). Online banking in this context of study is defined as an internet portal, through which customers can use different kinds of banking services (Teo and Tan, 2000; Pikkarainen, Pikkarainen, Karjaluoto and Pahnila, 2004). The services include balance enquiries, transfer funds, ordering check, requesting credit card advances and bill payment (Chou and Chou, 2000; Ainin, Lim and Wee, 2005). From the bank’s perspective, online banking offers lower operating costs (Sathye, 1999; Polasik and Wisniewski, 2009). For example, Booz and Hamilton (1997) explained that internet banks can operate at an expense ratio of 15-20 percent compared to 50-60 percent for the average banks. Lichtenstein and Williamson (2006) on the other hand, associate online banking with improvement on consumer banking services, consumer retention and higher market share. In essence, online banking has become a ‘one stop service and information unit’ that promises great benefits for both banks and consumers (Teo and Tan, 2000). Despite of the advantages of online banking, recent views suggest that online banking may not achieve the levels of transformation as predicted (Cuevas, 1998; Canniffe, 2000). Likewise, similar sentiment was echoed by White and Nteli (2004), in which the adoption of online banking in many countries has not risen as strongly as expected. According to Wang, Wang, Lin and Tang (2003), only 1 to 3 percent of banking transactions in Taiwan were conducted via internet while in Hong Kong the penetration rate remains low at 23.8 percent (Cheng et al., 2006). Furthermore the adoption rate for online banking in Germany and Greece is at 40 and 10 percent respectively (Meyer, 2006). However, according to Alsajjan and Dennis (2010), there are 17.0 millions online banking users in the United Kingdom in 2006. Studies also revealed positive adoption rate in Brazil (Hernandez and Mazzon, 2007), Nordic countries (Celik, 2008) and Australia (Sathye, 1999). As the results are mixed, there is a need to understand the elements which could influence the embracement of online banking. Through a clearer understanding, the appropriate marketing strategies could be implemented to promote new forms of online banking systems (Wang et al., 2003). This is vital so that online banking can be embraced by a majority instead of only a few techno-savvy consumers (Kolodinsky, Hogarth and Hilger, 2004). Although a number of discussions have examined issues related to the adoption of online banking such as in the United States (Pikkarainen et al., 2004; Kolodinsky et al., 2004), United Kingdom (Bradley and Stewart, 2003), Spain (Joaquin, Carlos, Carla and Silvia, 2009), Finland (Pikkarainen et al., 2004), Hong Kong (Cheng et al., 2006), Taiwan (Wang et al., 2003; Shih and Fang, 2004), India (Geetika and Ashwani, 2008) there have been little scholarly research pertaining to the adoption of online banking from the specific context of Malaysia apart from Poon (2008), Lallmahamood (2007), Ainin et al. (2005), Suganthi, Balachandher and Balachandran (2001), and etc. Moreover, the results remain inconclusive due to the mix results (Ndubisi and Sinti, 2006). As reports also have shown that online banking adoption in Malaysia is still relatively low (Ndubisi and Sinti, 2006) and at infancy stage, we hope to close the gap by

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presenting a study on the adoption determinants in accepting online banking among consumers in Malaysia. In particularly, the critical drivers which could predict the intention to use online banking are identified. By ranking the factors, the results from this study will not only allow decision makers in bank to develop better online banking systems but also provide the knowhow, to attract potential non-users. The following is the structure of this paper. First, the paper begins with a brief overview of online banking. Second by reviewing the literature, the hypothesis and research model is presented. This is follow by the research methodology and discussion section. While in the final section, we will include the conclusion, implication and suggestion for future research.

LITERATURE REVIEW In order to gain a clearer understanding on the adoption of online banking in Malaysia, two sections which are related to the research topic have been included. The paper firstly presents an overview of the online banking adoption from the Malaysia’s perspective. This is follow by the factors that drive online banking adoption based on past studies. Online Banking in Malaysia Online banking can be defined in many ways. Daniel (1999) defined online banking as the provision of information and services by a bank to its customers via electronic wired or wireless channels. In the financial sector, online banking sometimes is use interchangeably with electronic banking (e-banking) or internet banking. The Central Bank of Malaysia (Bank Negara Malaysia, BNM) categorized online banking into three different websites namely the informational websites, communicative websites and transactional websites (Shanmugam and Balachandher, 2003). As online banking in Malaysia is forecasted to grow sharply in the near future (Poon, 2008), many scholars have attempted to study the adoption of online banking in Malaysia. Poon (2008) has identified ten factors which are significant to the adoption of online banking services in Malaysia. They are the convenience of usage, accessibility, features availability, bank management and image, security, privacy, design, content, speed, and fee and charges. Similarly, Suganthi et al. (2001) found seven factors which are significant in the adoption of online banking in Malaysia. They are accessibility, reluctance, costs, trust in one’s bank, security concerns, convenience and ease of use. Sohail and Shanmugham (2003) on the other hand, indicated that internet accessibility, awareness, attitude towards change, computer and internet access costs, trust in one’s bank, security concerns, ease of use and convenience are equally important.


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Factors That Drives Online Banking Adoption For many years, scholars have been investigating the factors which could influence the acceptance of new technologies. Among frameworks that have been developed based on the past studies includes the Technology Acceptance Model, TAM (Davis, 1989), Theory of Reasoned Action, TRA (Fishbein and Ajzen, 1975), Theory of Planned Behaviour, TPB (Ajzen, 1991), Innovation Diffusion Theory, IDT, (Masrom and Hussein, 2008) and the Unified Theory of Acceptance and Use of Technology, UTAUT (Venkatesh, Morris, Davis and Davis, 2003) TAM was developed by Davis (1986) to explain the computer-usage behaviour. According to the model, in explaining the adoption of any information system use, perceived ease of use (PEOU) and perceived usefulness (PU) are the two most important determinants. Perceived usefulness refers to the ‘degree to which a person that using a particular system would enhance or improve his or her job performance’ while perceived ease of use refers to the ‘degree to which a person that using a particular system would be free from effort’ (Davis, 1986). According to Masrom and Hussein (2008), the adoption of whether to use an information system for a particular individual is very much dependent on the perceived usefulness and perceived ease of use of the information system. Figure 1 shows the links between all the factors found in TAM. The Theory of Planned Behaviour on the other hand, is derived from the Theory of Reasoned Action (TRA). The framework was developed by Fishbein and Ajzen (1975). TRA explained that the actual behaviour follows from behavioural intention and that behavioural intention is formed by one’s attitude towards behaviour and subjective norm (Masrom and Hussein, 2008). In TPB (Ajzen, 1985) a third factor called perceived behavioural control is added. It suggests that the actual behaviour of a person is influenced by behavioural intention, and in term it is influenced by either attitude, subjective norms or perceived behavioural control, or all the factors mentioned above. Attitude refers to the ‘degree to which the person has a favourable or unfavourable evaluation of the behaviour in the study’; subjective norm refers to the ‘perceived social pressure to perform or not to perform the behaviour’ while perceived behavioural control refers to the ‘individual’s belief in the ease to execute behaviour’ (Ajzen, 1985). Figure 2 depicts the relationship between the various factors in TPB. IDT model describes the stages of innovation development over time and was developed by sociologist Rogers (Rogers, 1995). Similar, the model has been constantly used by scholars to explain the adoption of information technology system. According to Rogers, the study has identified five categories of adoption. They are the consumer innovators, early adopters, early majority, late majority, and laggards (Rogers, 1995). The primary intention of IDT is to provide an account of how innovation moves from early adoption to widespread use (Dillon and Morris, 1996). The UTAUT (Venkatesh et al., 2003) consists of four determinants: performance expectancy, effort expectancy, social expectance and facilitating conditions. According to Zhou, Lu and Wang (2010), the UTAUT was built

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based on eight theories. Although it is widely used in the field of IT adoption but it has a few shortcomings: (i) it focuses solely on individual perceptions of external factors that lead to behavioural intention and actual behaviour; (ii) UTAUT’s application is context-dependent and it considers only one individual’s behaviour and it cannot be extended to other behaviours (Genuardi, 2004) (cited by Masrom and Hussein, 2008). The frameworks above have both pros and corns. The research therefore applies the methodology of using a modified TAM and TPB to have a more precise forecast on the factors influencing the adoption of online banking in Malaysia. Therefore, the TAM model was retained and we deliberately include four additional variables namely perceived security risk, social influences, trust and perceived financial cost.

Perceived Usefulness Attitude towards Behavior

External Variables

Behavioral Intention to Use

Actual System Use

Perceived Ease of Use Figure 1: Original Technology Acceptance Model (TAM) Source: Davis (1989)

Attitude Concerning

Behavior Subjective Norm

Behavioral Intention

Actual Behavior

Perceived Behavioral Control Figure 2: Theory of Planned Behaviour (TPB) Source: Ajzen (1985)


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RESEARCH MODEL AND HYPOTHESES DEVELOPMENT Based on the literature review, a model on the intention to use online banking was developed. The proposed constructs and hypothesis as below have an effect on acceptance of online banking in Malaysia. Perceived Usefulness Perceived Ease of Use Perceived Security Risk Social Influence

Adoption of Online Banking/Intention to Use Online Banking

Perceived Financial Cost Trust Figure 3: The Research Model

Perceived Usefulness Perceived Usefulness (PU) is the ‘degree to which a person believes that using a particular system would enhance his or her job performance’ (Davis, 1989). Similarly, Mathwick, Malhotra and Rigdon (2001) perceived usefulness as the extent to which a person deems a particular system to boost his or her job performance. In another word, it refers to effectiveness at work, time saving and the relative importance of the system for the individual’s work (Joaquin et al., 2009). The effect of perceived usefulness on usage intention has been validated in many existing studies such as from Argarwal and Karahanna (2000), Hu, Chau, Sheng and Tam (1999) and Venkatesh and Morris (2000). According to Nysveen, Pedersen and Thornbjornsen (2005) as cited by Rao and Troshani (2007) a system that does not help people perform their jobs is not likely to be received favorably. Therefore in this research, if the banking websites enhances user’s productivity, it is more likely to be accepted by users. We therefore test the following hypothesis: H1: Perceived usefulness of online banking services will have a positive effect on online banking services use in Malaysia. Perceived Ease of Use Perceived Ease of Use (PEOU) is the ‘degree to which a person believes that using particular system would be free from effort (Davis, 1989). Likewise,

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Mathieson (1991), perceived ease of use as the consumer’s perception that online banking involves minimum effort. Studies over the past decades report on the influence of perceived ease of use has on usage intention whether direct or indirectly (Hu et al., 1999; Sohail and Shanmugham, 2003; Eriksson, Kerem and Nilsson, 2005; Cheng et al., 2006; AbuShanab and Pearson, 2007; Lallmahamood, 2007). According to Rogers (1995), the complexity of one particular system will discourage the adoption of innovation. Therefore, the internet banking system must be easy to learn and easy to use (Wang et al., 2003). By applying these into the online banking context, if the banking web sites are perceived to be easier to use and learn than another, is more likely to be accepted by users. Thus, the following hypothesis is proposed: H2: Perceived ease of use has a positive effect on online banking services use in Malaysia. Perceived Security Risk The importance of security risk has been acknowledged in many banking studies (Sohail and Shanmugham, 2003; Mannan and van Oorschot, 2007). Likewise it is also recognised as one of the barrier in the adoption of electronic banking (Ndubisi and Sinti, 2006; Aladwani, 2001). For example, security was found to be an important obstacle in the adoption of online banking in Australia and the UK (Sathye, 1999; White and Nteli, 2004). Poon (2008) in her studies found that security is important regardless of age group, education and income level in determining the adoption of online banking in Malaysia. According to Manzano, Navarre, Mafe and Blas (2009), security risk is associated with the loss of bank account, passwords, etc., which could lead to the loss of money. For example, if any authorized individuals is able to access a user online banking portfolio, a considerable amount of information may be jeopardised, which could result to a financial implications (Sonja and Rita, 2008). Similarly, Gerrard and Cunningham (2003) said that hackers may gain access to customer’s internet account if the internet banking security is weak. Therefore, in this context of study, ‘perceived security risk’ is defined as users’ perception of protection against threats when transmitting private information over the internet banking. Thus, the perceived security risk that people have in the system predicts their acceptance of online banking. Therefore test the following hypothesis: H3: Perceived security risk will have a positive effect on online banking services use in Malaysia. Social Influence Social Influence (SI) is defined as ‘the degree to which an individual user perceived the importance others believes he or she should use an innovation (Venkatesh et al., 2003) and have been explored in the Theory of Planned Behaviour (TPB) and Theory of Reasoned Action (TRA) in predicting behaviour


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intention and usage (Rao and Troshani, 2007; AbuShanab and Pearson, 2007). Lu, Yu, Liu and Yao (2003) on the other hand, added that social influence as equivalent to subjective norm in which is defined as an individual’s belief about whether significant others think that one should engage in the activity. Triandis (1979) (cited by Rao and Troshani, 2007) further explained that individuals learn and use behaviours based upon which they see in their social groupings. Similarly Davis, Bagozzi and Warshaw (1989) perceived that in certain cases, people might use a system to comply with other’s mandates rather than their own feelings. Based on the Innovation Diffusion Theory (DOI) however, Rogers (1995) distinguishes social influences between two forms. They are namely mass media and interpersonal influence. According to mass media influence are such as newspapers, magazines, academic journals, television, radio, internet and etc while interpersonal influence refers to word-of-mouth by referent groups such as peers, friends, superiors, computer and technology experts (Rao and Troshani, 2007). Given that strong empirical support for social influences, the following proposition is proposed: H4: Social Influence has a positive effect on online banking services use in Malaysia. Perceived Financial Cost Perceived financial cost is another barrier to adoption of online banking. Studies on m-commerce adoption so far have found negative influence between perceived financial cost and behavioural intention (Luarn and Lin, 2005; Anil, Leow, Lim and Goh, 2003). This is because financial costs such as handset, subscription, service and communication fees influence consumer intention to use the service (Wang, Lin and Luarn, 2006). Mathieson, Peacock and Chin (2001) perceived financial resources to be a significant antecedent of the behavioural intention in adopting information system. This led to believe that financial cost also plays a critical role in the determining the adoption of online banking in Malaysia (Suganthi et al., 2001). Sathye (1999) supported the relationship which he explained that cost is one of the reasons that prevents consumers from Singapore and Australia in using online banking. Suganthi et al. (2001), further associate online banking with two costs, namely the connection and internet charges and the bank fees charges. According to Poon (2008), services fees and charges are critical to the success of e-banks as they have negative impact on the acceptance of online banking. Therefore in this context of studies, the cost factor is tested in ‘Perceived Financial cost’ construct which is defined as the extent to which individual perceive that using online banking is costly. Thus if the cost associated with online banking is perceived to be inexpensive, it is more likely to be accepted by users. Therefore, we test the following hypothesis: H5: Perceived financial cost will have negative effect on online banking services use in Malaysia.

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Trust In online banking, Amin (2007) commented that trust is the ‘heart of the system’. The concept however has been defined differently by different scholar disciplines (Sonja and Rita, 2008). For example, in the online banking literature, Mayer, Davis and Schoorman (1995) and Rousseau, Sitkin, Burt and Camerer (1998) defined trust as, ‘a psychological state which leads to the willingness of customer to perform banking transactions on the internet, expecting that the bank will fulfil its obligations, irrespective of customer’s ability to monitor or control bank’s actions’. However in the e-commerce literature, trust is the belief that allows consumers to willingly become vulnerable to the online retailers after having considering the retailers’ characteristics (Pavlou, 2003). Studies so far have shown that trust is an important element in stimulating online banking operations (Kim and Prabhakar, 2000; Mukherjee and Nath, 2003; Kassim and Abdullah, 2006). Likewise they have a positive influence on behavioural intention (Sohail and Shanmugham, 2003). Yousafzai, Pallister and Foxall (2009) explained that the survivability of online banking depends on whether the bank is perceived as trustworthy by the customers. With greater trust, people can resolve uncertainty regarding their motives, intentions, and prospective actions of others on whom they depend (Kramer, 1999). Thus, when an individual has confidence with the reliability and integrity of another exchange partner, trust exists (Morgan and Hunt, 1994). Past studies have considered confidence to be a multidimensional construct that includes dimensions such as honesty, benevolence and competence (Ganesan, 1994; Sirdeshmukh, Singh and Sabol, 2002). The definition is also consistent with the construct of trust which includes goodwill trust (benevolence) and credibility (honestly, reliability, integrity) (Pavlou, 2003). As the construct of trust is multidimensional, often contradictory and confusing (Mayer et al., 1995; Gulati and Sytch, 2008), for this research purposes we simplified ‘trust’ as a dimension of honesty. Thus, trust is defined as the extent to which an individual believes that banks are sincere in keeping promises. Therefore we test the following hypothesis: H6: Consumer trust in online banking will have a positive effect on online banking services use in Malaysia.

RESEARCH METHODOLOGY The research methodology section presents the methods used in the collection of data and a brief overview of the variables adopted for this study. Sampling and Data Collection The main purpose of this study is to investigate and analyze users’ perception towards the adoption of online banking in Malaysia. In order to test the above mentioned hypothesis, a self-administered questionnaire was developed. Since


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the main purpose of this research is to test the users’ perception towards the adoption of online banking in Malaysia, the target population would mainly be any online banking users from Malaysia. As such, the survey was conducted at one of the private university in Malaysia. The university was chosen, as it has a fair coverage and representative of all individuals from Malaysia. After two weeks of survey period, a total of 235 respondents participated. Out of the 235 questionnaires distributed, 4 samples were rejected due to partial response and/or missing data, thus leaving a total response of 231 samples. This constitutes a response rate of 98.3%. Variable Measurement The variables of the model have been discussed in the following section. The sources of questionnaire and number of items are presented in Table 1 to ease the understanding of model construction (Table 1). Independent variables The independent variables used in this study were derived and adopted from existing literatures. All the variables and questions are tabulated in Table 1. There are six independent variables used in this study namely, perceived usefulness, perceived ease of use, perceived security risk, social influence, perceived financial cost and trust. Each of these variables described have between four to six questions. Hence, a total of 30 questions were developed for the six factors that are being studied. Each of the questions was measured via a five-item, Likert 5-point scale, ranging from strongly disagree = 1, to strongly agree = 5. Table 1: Questionnaires sources and number of items Constructs Perceived Usefulness (PU)

Perceived Ease of Use (EU)

Trust (TR) Perceived Financial Cost (FC) Perceived Security Risk (PR)

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Number Sources of items 5 Pikkarainen et al. (2004), Cheng et al. (2006), AbuShanab and Pearson (2007) and Joaquin et al. (2009) 5 Pikkarainen et al. (2004), Wei et al. (2009), Cheng et al. (2006), AbuShanab and Pearson (2007) and Joaquin et al. (2009) 5 Flavian, Guinaliu and Torres (2005) and Joaquin et al. (2009) 4 Sohail and Shanmugham (2003), Chong, Ooi, Lin and Tan (2010), Chong and Ooi (2008) and Poon (2008) 5 Cheng et al. (2006), Lallmahamood (2007) and Joaquin et al. (2009)



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Table 1 continues….. Constructs Social Influence (SI) Intention to Use (IU)

Number Sources of items 6 Wei et al. (2009) and Chong, Darmawan, Ooi and Lin (2010) 5 Cheng et al. (2006), Wang et al. (2006) and Wei et al. (2009)

Dependent variable A total of five questions were developed to measure the users’ intention towards the adoption of online banking. Each of the questions are also measured using the five-point Likert scale, where “1” denotes as strongly disagree, up to “5” that denotes as strongly agree. The survey data were analyzed by employing correlation and the multiple regression analysis.

DATA ANALYSIS The section begins with an overview of the sample profile of the current study. This is follow by the analysis of the results. Sample Profile The demographic profile of the survey respondents is presented in Table 2 which includes gender, age, the highest level of academic qualification and marital status. The gender distribution of the survey respondents shows that 57.14% are males, while 42.86% of them are females. The results also show that majority of the respondents fall into the age group of 21-25 years (80.95%) while only 19.05% are below 20 years old. Out of all the respondents, 48.05% had achieved at least a bachelor degree or professional qualifications. Finally most of the respondents are still single (95.2%). Table 2: Demographic profile of respondents Variables Gender Age

Highest level of academic qualifications

Group Male Female ≤ 20 Years Old 21 - 25 > 25 years No College Degree

Frequency 132 99 44 187 0 66

Diploma/Advanced Diploma Bachelor Degree/Professional Qualifications

54 111

Percentage (%) 57.14 42.86 19.05 80.95 0 28.57


23.38 48.05

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Table 2 continues…. Variables Marital Status

Group

Frequency 220 11

Single Married

Percentage (%) 95.2 4.8

Factor Analysis and Scale Reliabilities A principle component factor analysis with varimax rotation was applied on the six adoption factors for online banking (perceived usefulness, perceived ease of use, trust, perceived financial cost, perceived security risk and social influences) comprising of 30 items; intention to use consist of 5 items respectively. The item loading for each factor was rather high with a minimum loading of 0.617 (trust). Rollins (1992) commented that a loading of 0.4 or greater is generally considered good in statistical terms. Hence, the survey instrument had been validated to have construct validity. The scale reliabilities of the independent variables (Adoption factors) and the dependent variable (i.e. intention to use internet banking) exceeded 0.70, which coincide with the recommendation made by Nunnally and Bernstein (1994). The results of factor analysis and reliability analysis are summarised in Table 3. Table 3: Instrument reliability and validity Variable PU

EU

TR

FC

PR

SI

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Item PU4 PU3 PU5 PU1 PU2 EU3 EU5 EU4 EU1 TR3 TR2 TR4 TR1 FC1 FC2 FC4 FC3 PR2 PR5 PR1 SI1 SI2

Factor Loading 0.828 0.819 0.746 0.711 0.679 0.828 0.742 0.668 0.646 0.771 0.754 0.661 0.617 0.795 0.762 0.754 0.698 0.817 0.699 0.679 0.800 0.800

Set of Items 5

Cronbach’s Alpha 0.869

4

0.810

4

0.791

4

0.837

3

0.774

2

0.781



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Correlation Analysis: Relationships between Independent Variables Since multiple items were measured using the questionnaire, the average score of the multiple items for each of the construct was computed for further analysis. Table 4 displayed the results of the correlation analysis of the study variables. In this research, the Pearson correlation analysis was used to examine the correlation between the two variables. Accordingly to Wong and Hiew (2005), the correlation coefficient value (r) range from 0.10 to 0.29 is considered weak, from 0.30 to 0.49 is considered medium and from 0.50 to 1.0 is considered strong. Correlation coefficient however should not exceed 0.8 to avoid multicollinearity (Bryman and Cramer, 1997; Wei, Marthandan, Chong, Ooi and Arumugam, 2009; Chong, Ooi and Sohal, 2009; Chong, Ooi, Lin and Tang, 2009; Ooi, Arumugam, Teh and Chong, 2008; Wong, Sim, Lam, Loke and Darmawan, 2010; Khang, Arumugam, Chong and Chan, 2010). In this research, since the highest coefficient of correlation is 0.445, which is below the cut-off of 0.8, hence no multicolinearity among the variables is being distinguished. Table 4: Pearson’s correlation coefficient Variables

Perceived Trust Usefulness

Perceived 1.000 Usefulness Trust 0.369** Perceived Financial -0.246** Cost Perceived Security 0.053 Risk Social Influence 0.169** Perceived Ease of 0.445** Use

Perceived Financial Cost

1.000 -0.148**

1.000

0.301**

0.288**

0.144** 0.260**

Perceived Security Risk

-0.033 0.007

Social Influence

Perceived Ease of Use

1.000 0.195** 0.101

1.000 0.126

1.000

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

Multiple Regression Analysis The relationship between adoption factor for online banking and consumer intention to adopt online banking were tested using a multiple regression analysis. According to Hair, Anderson, Tatham and Black (1998), the multiple regression analysis is useful to analyse the relationship between a single dependent variable and several interdependent variables. The sample size of the data is one of the vital considerations in multiple regression analysis (Hair et al., 1998). The sample size should have an estimated parameter ratio of 15:1 to 20:1 to achieve meaningful estimation of sample size (Hair et al., 1998). Since the sample size of this study has an estimated parameter of ratio 38.5:1, we conclude the sample sizes to be adequate. Based on this method, the size main independent variables (adoption factor for online banking) namely, perceived usefulness, trust, perceived financial cost, perceived security risk, social influence and International Journal of Business and Management Science, 3(2): 169-193, 2010

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perceived ease of use and dependent variable (consumer intention to adopt online banking) were entered together. The summary of the regression findings is depicted in Table 5. The results indicated that the model had no serious multicollinearity problem (Hair et al., 1998) as statistics value shows tolerance for all variables of more than 0.1 while the Variation Inflation Factors (VIF) are all lesser than 10. The Durbin-Watson index of 1.889 also falls within the acceptable range of (1.50