Identifying Priority Using an Importance-Performance

International Journal of E-Adoption, 6(1), 1-15, January-March 2014 1

Identifying Priority Using an Importance-Performance Matrix Analysis (IPMA):

The Case of Internet Banking in Malaysia T. Ramayah, Operations Management Section, School of Management ,Universiti Sains Malaysia, Minden, Penang, Malaysia Lo May Chiun, Faculty of Economics and Business, Universiti Malaysia Sarawak, Kota, Samarahan,Sarawak, Malaysia Kamel Rouibah, Department of Quantitative Methods and Information Systems, College of Business Administration, Safat, Kuwait Oh Sook May, Technology Management Lab, School of Management, Universiti Sains Malaysia, Minden, Penang, Malaysia

ABSTRACT This study used the combined Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) as the theoretical underpinning to examine the adoption of Internet banking. Five factors (perceived ease of use, perceived usefulness, attitude, subjective norms, and perceived behavioral control) were identified to model their impact on intention to adopt Internet banking individual bank customers in Malaysia. Survey questions from prior studies were adopted and customized to collect data. A total of 239 customers responded to the survey. Partial least Square (PLS) SmartPLS M2 Version 2.0 was used for data analysis. Perceived ease of use significantly influenced perceived usefulness but did not impact attitude. Perceived usefulness was positively related to attitude and also intention to use. Attitude and subjective norm were significant predictors of intention to use while perceived behavioral control was not significant. Further to that the authors conducted an Importance-Performance matrix analysis to determine priority variables to focus on for the implications to practitioners. Keywords:

Importance-Performance Matrix Analysis (IPMA), Internet Banking, Intention to Use, Priority, Technology Adoption

INTRODUCTION Banks decide to invest in Internet banking for many reasons; among these are: pressures to cut costs, increase information richness for

customers, pressures to produce more without increasing costs, improve the quality of services in order to stay in business or to reach a wider audience. (Rouibah et al., 2009). The number of banks adopting Internet banking is increasing

DOI: 10.4018/ijea.2014010101 Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

2 International Journal of E-Adoption, 6(1), 1-15, January-March 2014

over the world even in less developed countries, e.g. but not limited to, Hong Kong (Liao et al., 1999; Wan et al., 2005), Singapore (Tan & Teo 2000; Wang et al., 2003), Finland (Pikkarainen et al., 2004), USA (Lassar et al., 2005) Korea (Suh & Han 2002), Taiwan (Shih & Fang 2004; Shih & Fang, 2006), and Estonia (Erriksson et al., 2005). There has been little prior research into critical influences on Internet banking services adoption in Malaysia (Suganthi & Balachandran, 2001; Ramayah et al., 2003, Ramayah et al., 2008; Guriting & Ndubisi, 2006; Rouibah et al., 2009) The banking sector in Asia including Malaysia is a rapidly growing market. Many western banks are very interested in establishing in this part of the world and building branches since there are 13 foreign banks among 23 in Malaysia. The banking systems have been investing millions of dollars in automation to reduce cost but are the users receptive? Therefore, it is important to understand the management actions and investment in Internet banking. To achieve this goal, this study reports important empirical data on the adoption of Internet banking services. This study will also explore the use of the Importance-Performance matrix analysis to identify priority factors that can be enhanced to increase usage.

Conceptual Foundation and Hypothesis This section will discuss the conceptual underpinning and also the hypothesis developed for the research.

Theory of Planned Behavior (TPB) TPB is an extension of the TRA (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975), due to the limitation of TRA to deal with behavior over which individuals have incomplete volitional control (Ajzen, 1991; 2001). According to TPB, people’s actions are determined by their intentions, which are influenced by their perceived behavioral control, besides attitude

and subjective norm. Perceived behavioral control refers to the perception of internal and external resource constraints on performing the behavior. Control beliefs reflect the perceived difficulty (or ease) with which the behavior may be affected with perceived facility acting as important weighting criteria (Ajzen, 1991; 2001). In the case of Internet banking, control beliefs refer to knowing how to perform transactions via Internet banking and facility refers to external resource constraints, such as time, money, andresources.

Technology Acceptance Model (TAM) Based on the TRA, Davis (1989) developed the TAM model. It states that an individual’s system usage is determined by BI, which, in turn, is determined by his attitude toward the behavior, which in turn is determined by two beliefs: perceived usefulness (PU) and perceived ease of use (PEOU). PU refers to the extent to which a person believes that using the system will improve his or her job performance (Davis et al., 1989). PEOU refers to the extent to which a person believes that using the system will be free of effort (Davis et al., 1989). TAM is the most widely applied model that information systems researchers have used to explain or predict the motivational factors underlying user acceptance of technology.

Combined TAM-TPB (C-TAM-TPB) Taylor and Todd (1995b) combined the predictors of TPB with perceived usefulness from TAM to provide a hybrid model i.e. C-TAMTPB. In C-TAM-TPB, the main variables are attitude toward behavior, subjective norm, perceived behavioral control and perceived usefulness. C-TAM-TPB attempted to find out whether the determinants of IT usage are the same for experienced and inexperienced users of a system. In fact, C-TAM-TPB model has been found to be able to predict the behavior of both experienced and inexperienced users of

Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


IT (Taylor & Todd, 1995b). See C-TAM-TPB model as proposed by Taylor and Todd (1995b).

Hypothesis Development Perceived Ease of Use (PEOU) Many studies in the past have evidenced that perceived ease of use would influence the actual usage and usage intention (Eriksson et al., 2005; Pallister, Wang, & Foxall, 2007; Rouibah, Ramayah, & Oh, 2009) either directly or indirectly through its impact on perceived usefulness. This is further supported by a recent study that PEOU have indirect impact on users’ behavioral intention to use towards perceived usefulness (Calisir, Cumussoy, & Bayram, 2009; Nasri & Charfeddine, 2012) and so leads to an understanding that users would intend to use the system more frequently when the system becomes easy to use. In addition to that, researchers found that PEOU is antecedent of trust and that it promotes positive impression towards the online seller in the early stage of the online service (Wu & Chen, 2005; Koufaris & Hampton-Sousa, 2002). Besides Internet, a recent study has also found that ease of use has positive impact on the usefulness of the citation database interface (Lin & Chou, 2009). Thus the following hypotheses are posited: H1: Perceived ease of use will be positively related to attitude H2: Perceived ease of use will be positively related to perceived usefulness

Perceived Usefulness (PU) Perceived usefulness (PU) is defined as a person’s belief that the adoption of a system would enhance his or her performance (Saade & Bahli, 2005). Individuals who believed that the usage of a system could subsequently lead to positive outcomes would have a more positive attitude towards them (Agarwal & Prasad, 1999; Davis, 1989; Davis et al., 1989; Guriting & Ndubisi, 2006; Calisir et al., 2009; Rouibah et al., 2009). Nonetheless, it was found that

even though user might evaluate the system as useful and have positive beliefs about the technology, he or she might not immediately lead to intention to use (Calisir et al., 2009). On the hand, some studies have found that PU relies on the users’ experience, acceptance of the Internet and past experience, as user gains experience and believed in internet, PU becomes stronger and that would subsequently bring in an increase in the number of exchanges and higher usage (Gefen, Karahanna, & Straub, 2003; Hernandez, Jimenez, & Martin, 2009; Nasri & Charfeddine, 2012). Thus the following hypotheses are posited: H3: Perceived usefulness will be positively related to attitude H4: Perceived usefulness will be positively related to intention

Attitude Fishbein and Ajzen (1975) defined attitude as an individual’s positive or negative feelings (evaluative affect) about performing a target behavior of banking (Tan & Teo, 2000). According to Tan and Teo (2000), attitude is related to behavioral intention because people form intentions to perform behaviors toward which they have positive affect. Several studies (Tan & Teo, 2000; Shih & Fang, 2004; Nasri & Charfeddine, 2012; Albarq & Alsughayir, 2013) have examined the relationship between attitude with intention to adopt Internet banking. Their results indicate that attitude show a significant relationship toward intention to adopt on-line banking services. Thus the following hypotheses are posited: H5: Attitude will be positively related to intention

Subjective Norms According to Fishbein and Ajzen (1975), subjective norms refer to “the person’s perception that most people who areimportant to him think he should or should not perform the behavior in



question.” In a review by Ajzen (1991), it was found that subjective norms were an important determinant of intentions and behaviors. While subjective norms were not included in the TAM, prior research (Igbaria et al., 1996; Igbaria et al., 2000; Chan & Lu, 2004) supports the premise social factors do influence behaviors related to technology. But a review of Tan and Teo (2000), subjective norms fails to play the role in intention to adopt on-line banking services. One possible reason is that relevant information is readily available from banks, thereby reducing the reliance of potential adopters on their friends, family or colleagues for information about these services. This was supported by Shih and Fang (2004), which also found that subjective norms to intention failed to achieve significance in either model. Recent research by Nasri and and Charfeddine (2012), and Albarq and Alsughayir (2013) on the other hand found subjective norm to be an important predictor of intention to use Internet banking in Tunisia and Saudi Arabia respectively. Thus the following hypotheses are posited:

H6: Subjective norms will be positively related to intention

Perceived Behavioral Control (PBC) Perceived behavioral control refers to the factors that may impede the performance of the behavior. According to Ajzen (1985, 1991), PBC reflects belief regarding access to the resources and opportunities needed to effect a behaviour. PBC appears to encompass two components. There are (1) self efficacy, which defined as an individual’s self-confidence in his or her ability to perform a behavior by Bandura (1977, 1982) and the (2) facilitating conditions, which reflect the availability of resources needed to engage in the behavior (Shih & Fang, 2004; Tan & Teo, 2000). Ajzen and Madden (1986) claim that the PBC is less likely to be related to intention (Shih & Fang, 2004). Several researchers (George, 2004; Nasri & Charfeddine, 2012; Albarq & Alsughayir, 2013) have found that perceived behavior control directly affected online behavior. Thus the following hypotheses are posited in Figure 1.

Figure 1. Research model



H7: Perceived behavioral control will be positively related to intention

Methodology The measures used to operationalize the constructs included in the investigated models and the questionnaires were mainly adapted from previous studies, with minor wording changes to tailor them to the Internet banking field. Items for PU and PEOU, and BI were adapted from Davis (1989). Items for attitude, subjective norms, and perceived behavioral control were from Taylor and Todd (1995b). Moreover, constructs common to the examined model were measured using the same scale, an approach suggested by Taylor and Todd (1995). All items were measured using a 7-point Likert-type scale with anchors on 1=strongly disagree and 7 =strongly agree respectively. (See Table 1). A pre-test was conducted prior to the actual data collection by using a collaborative pre-testing procedure. Target subjects were individual customers in Malaysia from the age of sixteen and above who had banking transactions, and who have concerns or have taken part in Internet banking.The questionnaires were distributed to bank branches, factories. Some of the questionnaires were handed over by hand, through e-mail, and also mail to quicken the collection process. The banks’ officers were requested to lend a helping hand by placing the questionnaires in the bank branches for customers to fill up willingly. For those questionnaires sent through mail, a cover letter and self-addressed stamped return envelope was also provided. The cover letter stated the purpose of study and ensured confidentiality and anonymity. Of the 300 questionnaires distributed, 242 were collected and returned back. Three of them were partially completed, and therefore discarded. Thus the effective response rate is 239, with a 79.67% response rate. The respondents’ profile is summarized in Table 2. The above table indicates the percentages of genders are almost equal, with an average age of 31 years; the majority of respondents is

Chinese and holds a university degree, working in the private sector and having an income level of USD 401 - 800. Besides, 90% of the respondents owned a personal computer and all were aware of Internet banking, while 46.0% of them were users of Internet banking.

Results To analyze the research model the authors used the Partial Least Squares (PLS) analysis using the SmartPLS 2.0 software (Ringle, et al., 2005). Following the recommended two-stage analytical procedures by Anderson and Gerbing (1988), the authors tested the measurement model (validity and reliability of the measures) followed by an examination of the structural model (testing the hypothesized relationship) (see Ramayah et al. 2011; 2013). The Smart PLS M2 Version 2.0 and two-step analysis approach was used to analyze the data. To test the significance of the path coefficients and the loadings a bootstrapping method (1000 resamples) was used (Gholami et al., 2013).

Measurement Model Convergent validity is the degree to which multiple items measuring the same concept are in agreement (Ramayah & Rahbar, 2013). The convergence validity of the measurement is usually ascertained by examining the loadings, average variance extracted and also the composite reliability (Gholami et al., 2013). The loadings were all higher than 0.7, the composite reliabilities were all higher than 0.7 and the AVE were also higher than 0.5 as suggested in the literature (see Table 3 & 4). The discriminant validity of the measures (the degree to which items differentiate among constructs or measure distinct concepts) (Ramayah & Rahbar, 2013) was examined by comparing the correlations between constructs and the square root of the average variance extracted for that construct (Gholami et al., 2013). As can be seen from Table 4, all the square root of the AVE was higher than the correlations values in the row



Table 1.Constructs and measurement items Construct Intention

Items

Sources

If Internet banking were available at your bank(s), how likely would you plan to experiment with or regularly use Internet banking during the next six months?

Davis (1989)

If Internet banking were available at your bank(s), how likely would you be interested in using wireless Internet banking (mobile banking) within the next six months? If Internet banking were available at your bank(s), how likely would you be interested in using securities trading via Internet banking within the next 6 months? If Internet banking were available at your bank(s), how likely would you be interested in using insurance services via Internet banking within the next 6 months? Attitude

I feel using Internet banking is a wise idea.

Taylor & Todd (1995b)

I feel using Internet banking is a good idea. I like to use Internet banking. Subjective

Most people who are important to me would think I should use Internet banking.

Norm

My family who are important to me would think I should use Internet banking.


My relatives who are important to me would think I should use Internet banking. My friends who are important to me would think I should use Internet banking. My superiors who are important to me would think I should use Internet banking. My co-workers who are important to me would think I should use Internet banking Perceived

I would be able to operate Internet banking.

Behavioral

I have the resources to use Internet banking.

Control

I have the knowledge to use Internet banking.


I have the ability to use Internet banking. Perceived

Using Internet banking would enable me to accomplish task more quickly.

Usefulness

Using Internet banking would improve the quality of the banking transactions performed.

Davis (1989)

Using Internet banking would make it easier to do my banking transactions. Using Internet banking would enhance my effectiveness on the transactions. Using Internet banking would increase my time availability. I find Internet banking useful in my life. Perceived

My interaction with Internet banking would be clear and understandable.

ease of use

It would be easy to get Internet banking to do what I want it to do.

Davis (1989)

Learning to operate Internet banking is easy for me. I would find Internet banking is flexible to interact with. It would be easy for me to become skillful at using Internet banking. I would find Internet banking easy to use.

and the column indicating adequate discriminant validity (Fornell & Larcker, 1981).

Structural Model Structural model shows the causal relationships among constructs in the model (path coefficients and the R2 value). Together, the R2 and the path

coefficients (beta and significance) indicate how well the data support and hypothesized model (Chin, 1998; Sang et al., 2010; Ramayah et al., 2011). Table 5 and Figure 2 show the results of the structural model from the PLS output. Perceived ease of use was positively related to perceived usefulness (β = 0.757, p < 0.01) but was not significantly related to attitude (β



Table 2. Respondent profile Respondents’ Demographic Gender Race

Educational Level

Income Level (USD)

Occupation

Age

Frequency

Percentage

Male

121

50.60

Female

118

49.40

Malay

43

18.00

Indian

19

7.90

Chinese

177

74.10

Secondary

19

7.90

Diploma

54

22.60

University Degree

145

60.70

Master & above

21

8.80

0.05). Perceived usefulness on the other hand was positively related to Attitude (β = 0.637, p < 0.01) and Intention (β = 0.204, p < 0.05). The R2 for perceived usefulness was 0.574 and attitude was 0.483 indicating that perceived ease of use explained 57.4% of the variance in perceived usefulness whereas perceived ease of use and perceived usefulness both explained 48.3% of the variance in attitude. The results supported H2, H3 and H4 whereas H1 was not supported. Attitude (β = 0.249, p < 0.01) and Subjective norm (β = 0.159, p < 0.05) were positively related to Intention but Perceived behavioral control (β = 0.072, p > 0.05) was not a significant predictor of Intention. Thus, H5 and H6 are supported whereas H7 is not supported.

31.29 7.01

Importance-Performance Matrix Analysis (IPMA) Next the authors conducted an ImportancePerformance Matrix Analysis. Hair et al. (2013) in their book mentioned that importance-performance matrix analysis is useful in extending findings from PLS which provides direct, indirect and total relationships and extract the analysis to include another dimension which includes the actual performance of each construct.

yirescaled =

(y − Minscale x  )* 100 (Maxscale Y  − Minscale Y  ) i

Since the scale used in this research is a 7 point Likert scale an example of this calculation is shown below:



Table 3.Item loadings Construct Attitude

Intention

Item

ATTITUDE

ATT1

0.820

ATT2

0.927

ATT3

0.866

INTENTION

INT1

0.857

INT2

0.836

INT3

0.892

INT4

0.855

INT5

0.859

PBC

Perceived

PBC1

0.910

Behavioral

PBC2

0.916

Control

PBC3

0.937

PBC4

0.929

PEU

Perceived

PEOU1

0.884

Ease of

PEOU2

0.889

Use

PEOU3

0.888

PEOU4

0.877

PEOU5

0.879

PEOU6

0.927

PU

Perceived

PU1

0.853

Usefulness

PU2

0.828

PU3

0.920

PU4

0.920

PU5

0.862

PU6

0.835

SN

Subjective

SN1

0.866

Norm

SN2

0.870

SN3

0.919

SN4

0.908

SN5

0.892

SN6

0.892

If the answer to Y1 is say 5, then the rescaled score for performance is 66.67. yirescaled =

(5 − 1) * 100 =66.67 (7 − 1)

The results from the rescaled analysis and the original analysis (total effects) are combined in Table 6. Next the values were plotted as shown in Figure 3. As can be seen from Figure 2, although the construct PBC has high performance but it is not an important variable in the prediction of



Table 4.Intercorrelations, AVE, CR, mean and standard deviation AVE

CR

ATTITUDE

0.761

0.905

4.870

Mean

1.290

SD

0.872

ATTITUDE

INTENTION

PBC

PEU

PU

INTENTION

0.740

0.934

4.320

1.350

0.521

0.860

PBC

0.852

0.958

5.060

1.430

0.522

0.390

0.923

PEOU

0.794

0.958

4.920

1.180

0.557

0.470

0.716

0.891

PU

0.758

0.949

5.180

1.210

0.693

0.504

0.637

0.757

0.870

SN

0.795

0.959

3.960

1.280

0.586

0.436

0.370

0.519

0.514

SN

0.891

Note: AVE = Average Variance Extracted; CR = Composite Reliability Values on the diagonals represent the square root of the AVE while the off diagonals are correlations

Intention. The 2 constructs which are more important are perceived usefulness and perceived ease of use. Perceived usefulness is already high in performance but perceived ease of use is slightly lower so the developers should focus on this issue in the Internet banking system to enhance usage.

Discussion This study found that perceived ease of use influenced perceived usefulness directly and influenced attitude indirectly through attitude and it was interesting to see that perceived ease of use was not directly influencing attitude. This is supported by past studies (Eriksson et al., 2005; Pallister et al., 2007; Rouibah et al., 2009) that perceived ease of use influences attitude either directly or indirectly through its impact on perceived usefulness. This is further supported by a recent study that PEOU have

indirect impact on users’ behavioral intention to use towards perceived usefulness (Calisir et al., 2009; Nasri & Charfeddine, 2012) and so leads to an understanding that users would intend to use the system more frequently when the system becomes easy to use. Next perceived usefulness was found to influence intention directly which is also supported by several prior research (Agarwal & Prasad, 1999; Davis, 1989; Davis et al., 1989; Guriting & Ndubisi, 2006; Calisir et al., 2009; Rouibah et al., 2009; Hernandez et al., 2009; Nasri & Charfeddine, 2012). This confirms the notion that as a technology is perceived to be useful the higher the intention to use the technology and otherwise. Attitude was alspsited to be a significant predictor of intention and it eas confirmed in this study. Several studies (Tan & Teo, 2000; Shih & Fang, 2004; Nasri & Charfeddine, 2012; Albarq & Alsughayir, 2013) have shown that

Table 5. Hypotheses testing Hypothesis

Relationship

Std. Beta

Std. Error

t-value

Decision

H1

PEU ----> ATTITUDE

0.074

0.090

0.820

H2

PEU ----> PU

0.757

0.034

22.269**

Not Supported Supported

H3

PU ----> ATTITUDE

0.637

0.079

8.061**

Supported

H4

PU ----> INTENTION

0.204

0.102

2.004*

Supported

H5

ATTITUDE ----> INTENTION

0.249

0.090

2.758**

Supported

H6

SN ----> INTENTION

0.159

0.088

1.812*

Supported

H7

PBC ----> INTENTION

0.072

0.094

0.767

Not Supported

**p< 0.01, *p< 0.05



Figure 2. Results of hypotheses testing

there is a positive relationship between attitude with intention to adopt Internet on-line banking. Thus attitude is very important in shaping the intention to use a particular technology. Subjective norm was also found to be a significant predictor of intention to use which is consistent with some of the recent researches on Internet banking, i.e. Nasri and Charfeddine (2012), and, Albarq and Alsughayir (2013), who found that subjective norm to be an important predictor of intention to use Internet banking in Tunisia and Saudi Arabia respectively. This

finding, points to the relevance of information from their friends, family or colleagues in their decision to use these services prior to adoption. Perceived behavioral control was found not to be a significant predictor of intention to use which contradicts previous literature (Shih & Fang, 2004; Tan & Teo, 2000; George 2004; Nasri & Charfeddine, 2012; Albarq & Alsughayir, 2013). One possible reason could be that the present sample have a relatively younger set of people who are very technology savvy and they are more adept to the technology

Table 6. Importance-performance results Construct

Importance

Performance

ATTITUDE

0.249

64.975

PBC

0.072

67.716

PEU

0.293

65.396

PU

0.362

69.732

SN

0.159

49.378



Figure 3. Importance-performance matrix analysis

mediated service deliver as compared to the older customers as such perceived control was not an important issue in their intention to use.

Implications For managerial implications, this finding is particularly important for managers as they decide how to allocate resources to retain and expand their current customer base (Albarq & Alsughayir, 2013). The banks should promote the advantages of online banking when compared to traditional ways of banking as perceived usefulness have been found to be a significant predictor of intention to use. The next action that can be taken is to make Internet banking to be easier to use as ease of use was a significant predictor of Internet banking adoption. This would be to look at the interface and support to make the use a less stressing one. The banks can also do demonstrations via video presentations at the bank branches to showcase the user-friendliness of such services and also do some road shows to enhance the awareness which is an important criterion in

helping potential adopters’ Internet; banking services (Tan & Teo, 2000). Attitude and subjective norm were also significant predictors of intention as such the banks should focus on tackling this issue by making advertisements that can mould positive attitude towards the use of Internet banking. These advertisements can also be the base for influencing social norms as when the awareness of the ease and usefulness of the Internet banking reaches the mass then more and more people would be influence by the positive word of mouth of their friends and family to use the technology.

Research Limitations The results of this study should be considered in light of the five limitations. First, the sample size is small (239) and is limited in scope. It covered respondents from one region in Malaysia (Island of Penang) while the other 12 regions were not included due to geographical distance. Second, the data of the study was collected conveniently just once, over a period of two months, under the non-contrived setting. This was deemed



suitable as it was nearly impossible to a hold of the list of banking customers due to limitations in regulations. Thus, the results obtained are narrowed in term of generalizability to the whole users in Malaysia as well to other studies in new technologies and systems. Third, the study excluded the actual usage behavior in the research models for comparison. However, this is not a serious limitation as previous comparative researchers had substantial empirical support for the causal link between intention and actual behavior (e.g. Taylor & Todd, 1995b; Mathieson 1991; Chau & Hu 2001). Fourth, as per Wang et al. (2003), since the study was conducted in snapshot, additional research efforts are needed to evaluate the validity of the investigated models and our findings across time. The user acceptance of Internet banking is important for further research in the future.

Suggestions for Future Research To overcome the limitations, this study encourages future research to delve into the three following directions. To improve the explanatory power of the three models, future research should incorporate additional constructs. In particular it emphasize the inclusion of trust since results of past studies found it has a direct positive effect on intention to use Internet banking adoption (Suh & Han 2002; Pikkarainen et al., 2004; Erriksson et al., 2005), e-shopping (Gefen et al., 2003), and self-efficacy that has a positive effect on intention to use Internet banking over PEOU, PU and trust (Wang et al. 2003), as well as on intention to use Internet information management (Celuch et al., 2004). The study also encourage to test the effect of past experiences in IT and training on Internet banking adoption as well as the effect of gender since they affect the intention to use, perceived behavioral control and social norm. Tan and Teo (2000) found that past experience influences positively the intention to use Internet banking. Taylor and Todd (1995a) have found that the effect of behavioral intention on actual usage was found to be stronger for the experienced users; while perceived behavioral control was found to

be stronger on system usage for inexperienced users. While Venkatesh and Morris (2000) found in the short term (with less experience) social norm for women is positively correlated to intention to use after initial training, but it did not play a significant role in determining intention to use among men. The effect of income on Internet banking is another perspective since past studies did also prove that it affects the intention to use Internet banking via the mediation of perceived usefulness and perceived ease of use (Lassar et al., 2005). Biometric techniques, such as fingerprint verification, iris or face recognition, retina analysis and handwritten signature verification are increasingly becoming basic elements of authentication and identification systems in order to be privacyfriendly, minimize the social risks and prevent misuse of biometric data (Kassim & Ramayah, 2010; Kassim & Ramayah, 2012).

CONCLUSION This research presents the findings about Internet banking adoption in Malaysia. The findings revealed that were significant in affecting users’ behavioral intention to use Internet banking. Results also revealed that attitude plays the most important role, followed by perceived usefulness and subjective norm. Also with the help of the importance-performance matrix it can be identified that the 2 most important variables that are important in the use of Internet banking are perceived ease of use and perceived ease of use. This information can be capitalized by the banks to re-look at this issue in enhancing the Internet banking system that they have.

ACKNOWLEDGMENT This chapter is a revised version of an earlier article, “Modeling User Acceptance of Internet Banking in Malaysia: A Partial Least Square (PLS) Approach” which was published in E-adoption and Socio-Economic Impacts: Emerging Infrastructural Effects



REFERENCES Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391. doi:10.1111/j.1540-5915.1999.tb01614.x Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.), Action-control: From cognition to behavior (pp. 11–39). Springer. doi:10.1007/978-3-642-69746-3_2 Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi:10.1016/07495978(91)90020-T Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52(1), 27–58. doi:10.1146/annurev.psych.52.1.27 PMID:11148298 Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall. Ajzen, I., & Madden, J. T. (1986). Prediction of goal directed behavior: Attitudes, intentions and perceived control. Journal of Experimental Social Psychology, 22(5), 453–474. doi:10.1016/0022-1031(86)90045-4 Albarq, A. N., & Alsughayir, A. (2013). Examining theory of reasoned action in Internet banking using SEM among Saudi consumers. International Journal of Marketing Practices, 1(1), 16–30. Anandarajan, M., Igbaria, M., & Anakwe, U. P. (2000). Technology acceptance in the banking industry: A perspective from a less developed country. Information Technology & People, 13(4), 298–312. doi:10.1108/09593840010359491

Celuch, K., Taylor, S. A., & Goodwin, S. (2004). Understanding insurance salesperson internet information management intentions: A test of competing models. Journal of Insurance Issues, 27(1), 22–40. Chan, S. C., & Lu, M. T. (2004). Understanding Internet banking adoption and use behavior: A Hong Kong perspective. Journal of Global Information Management, 12(3), 21–43. doi:10.4018/ jgim.2004070102 Chau, P. Y. K., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699–719. doi:10.1111/j.1540-5915.2001. tb00978.x Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern business research methods. Mahwah, NJ: Lawrence Erlbaum Associates. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. doi:10.2307/249008 Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. doi:10.1287/mnsc.35.8.982 Eriksson, K., Kerem, K., & Nilsson, D. (2005). Customer acceptance of Internet banking in Estonia. International Journal of Bank Marketing, 23(2), 200–216. doi:10.1108/02652320510584412 Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. doi:10.1037/0033-2909.103.3.411

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. JMR, Journal of Marketing Research, 18(1), 39–50. doi:10.2307/3151312

Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. Management Information Systems Quarterly, 27(1), 51–90.

Bandura, A. (1982). Self-efficacy mechanism in human agency. The American Psychologist, 37(2), 122–147. doi:10.1037/0003-066X.37.2.122 Calisir, F., Gumussoy, C. A., & Bayram, A. (2009). Predicting the behavioral intention to use enterprise resource planning systems: An exploratory extension of the technology acceptance model. Management Research News, 32(7), 597–613. doi:10.1108/01409170910965215

George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet Research, 14(3), 198–212. doi:10.1108/10662240410542634 Gholami, R., Sulaiman, A. B., Ramayah, T., & Molla, A. (2013). Senior managers’ perception on green information systems (IS) adoption and environmental performance: Results from a field survey. Information & Management, 50(7), 431–438. doi:10.1016/j. im.2013.01.004



Guriting, P., & Ndubisi, N. O. (2006). Borneo online banking: Evaluating customer perceptions and behavioral intention. Management Research News, 29(1/2), 6–15. doi:10.1108/01409170610645402 Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage. Hernandez, B., Jimenez, J., & Martin, M. J. (2009). Adoption vs acceptance of e-commerce: Two different decisions. European Journal of Marketing, 43(9/10), 1232–1245. doi:10.1108/03090560910976465 Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13(1), 127–143. Kassim, N. M., & Ramayah, T. (2010). Security policy issues in Internet banking in Malaysia. In E. Adomi (Ed.), Handbook of research on information communication technology policy: Trends, issues and advancements (pp. 667–687). Hershey, PA: IGI Global. doi:10.4018/978-1-61520-847-0.ch042 Kassim, N. M., & Ramayah, T. (2012). Biometrics technologies implementation in Internet banking reduce security issues? Procedia: Social and Behavioral Sciences, 65, 364–369. doi:10.1016/j. sbspro.2012.11.135 Koufaris, M., & Hampton-Sousa, W. (2002). Customer trust online: Examining the role of the experience with the web site. CIS Working Papers Series. Retrieved from http://cisnet.baruch.cuny.edu/ papers/cis200205.htm

Nasri, W., & Charfeddine, L. (2012). Factors affecting the adoption of Internet banking in Tunisia: An integration theory of acceptance model and theory of planned behavior. The Journal of High Technology Management Research, 23(1), 1–14. doi:10.1016/j. hitech.2012.03.001 Pallister, J. G., Wang, H. C., & Foxall, G. R. (2007). An application of the style/involvement model to financial services. Technovation, 27(1-2), 78–88. doi:10.1016/j.technovation.2005.10.001 Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224–235. doi:10.1108/10662240410542652 Ramayah, T., Jantan, M., Mohd Noor, M. N., Razak, R. C., & Koay, P. L. (2003). Receptiveness of Internet banking by Malaysian consumers: The case of Penang. Asian Academy of Management Journal, 8(2), 1–29. Ramayah, T., Lee, J. W. C., & Boey, J. C. I. (2011). Network collaboration and performance in the tourism sector. Service Business, 5(4), 411–428. doi:10.1007/s11628-011-0120-z Ramayah, T., Oh, S. M., & Omar, A. (2008). Behavioral determinants of online banking adoption: Some evidence from a multicultural society. Journal of Management, 2(3), 29–37. Ramayah, T., & Rahbar, E. (2013). Greening the environment through recycling: An empirical study. Management of Environmental Quality: An International Journal, 24(6), 782–801.

Lassar, W. M., Manolis, C., & Lassar, S. S. (2005). The relationship between consumer innovativeness, personal characteristics, and online banking adoption. International Journal of Bank Marketing, 23(2), 176–199. doi:10.1108/02652320510584403

Ramayah, T., Yeap, J. A. L., & Ignatius, J. (2013). An empirical inquiry on knowledge sharing among academicians in higher learning institutions. Minerva: A Review of Science. Learning and Policy, 51(2), 131–154.

Liao, S., Shao, Y. P., Wang, H., & Chen, A. (1999). The adoption of virtual banking: An empirical study. International Journal of Information Management, 19(1), 63–74. doi:10.1016/S0268-4012(98)00047-4

Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (beta). Retrieved from www.smartpls.de

Lin, P. C., & Chou, Y. H. (2009). Perceived usefulness, ease of use, and usage of citation database interfaces: A replication. The Electronic Library, 27(1), 31–42. doi:10.1108/02640470910934579 Mathieson, K. (1991). Predicting user intention: Comparing the technology acceptance model with the theory planned behavior. Information Systems Research, 2(3), 173–191. doi:10.1287/isre.2.3.173

Rouibah, K., Ramayah, T., & Oh, S. M. (2009). User acceptance of Internet banking in Malaysia: Test of three competing models. International Journal of E-Adoption, 1(1), 1–19. doi:10.4018/jea.2009010101 Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42(2), 317–327. doi:10.1016/j. im.2003.12.013



Sang, S., Lee, J. D., & Lee, J. (2010). e-Government adoption in Cambodia: A partial least squares approach. Transforming Government: People. Process and Policy, 4(2), 138–157.

Taylor, S., & Todd, P. A. (1995a). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. doi:10.1287/isre.6.2.144

Shih, Y., & Fang, K. (2006). Effects of network quality attributes on customer adoption intentions of Internet banking. Total Quality Management & Business Excellence, 17(1), 61–77. doi:10.1080/14783360500249661

Taylor, S., & Todd, P. A. (1995b). Assessing IT usage: The role of prior experience. Management Information Systems Quarterly, 19(4), 561–570. doi:10.2307/249633

Shih, Y. Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet Research, 14(3), 213–223. doi:10.1108/10662240410542643

Venkatesh, V., & Morris, M. G. (2000). Why don’t men stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. Management Information Systems Quarterly, 24(1), 115–139. doi:10.2307/3250981

Suganthi, B., & Balachandran. (2001). Internet banking patronage: An empirical investigation of Malaysia. Journal of Internet Banking and Commerce, 6(1).

Wan, W. W. N., Luk, C. L., & Chow, C. W. C. (2005). Customers’ adoption of banking channels in Hong Kong. International Journal of Bank Marketing, 23(3), 255–272. doi:10.1108/02652320510591711

Suh, B., & Han, I. (2002). Effects of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications, 1(3-4), 247–263. doi:10.1016/S1567-4223(02)00017-0

Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501–519. doi:10.1108/09564230310500192

Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of Internet banking. Journal of the Association for Information Systems, 1(5), 1–42.

Wu, I. L., & Chen, J. L. (2005). An extension of Trust and TAM model with TPB in the initial adoption of on-line tax: An empirical study. International Journal of Human-Computer Studies, 62(6), 784–808. doi:10.1016/j.ijhcs.2005.03.003


Identifying Priority Using an Importance-Performance

Identifying Priority Using an Importance-Performance

Suggest Documents

IDENTIfYING PRIORITY AREAS fOR ...

Identifying priority areas for island endemics using genetic versus ...

Identifying Priority Species Within the South-western

USING PRIORITY INHERITANCE TECHNIQUES

Identifying Sentiment Words Using an Optimization

Identifying Unsaturated Hydraulic Parameters Using an ... - CiteSeerX

Identifying Sow Lameness Using an Embedded ...

An approach for congestion control in sensor network using priority ...

Identifying priority areas for ecosystem service management in South ...

Identifying priority conservation landscapes and actions for the ... - PLOS

Identifying a network of priority areas for ... - Wiley Online Library

Identifying priority areas for European resource ... - Springer Link

Identifying Priority Areas for Conserving Biodiversity in a Multi-Use

Identifying research priorities for health care priority setting: a ...

Representing biodiversity: Data and procedures for identifying priority

Identifying priority conservation landscapes and actions for the ... - PLOS

The Priority of an Overcomer

Identifying and priority setting indicators of integration ...

Identifying priority investments in water in Myanmar's Dry Zone ... - MIMU

Identifying priority conservation areas from Baja California to the ...

Identifying priority investments in water in Myanmar's Dry Zone ... - MIMU

Identifying structural barriers to an effective HIV response: using the ...

An Artificial Immune System for Identifying people using EEG - wseas

Identifying Speculative Bubbles using an Infinite ... - Google Sites