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Journal of Systems and Information TechnologyEmerald Article: Internet banking in Jordan: The unified theory of acceptance and use of technology (UTAUT) perspectiveE. AbuShanab, J.M. Pearson

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Internet banking in JordanThe unified theory of acceptance and use of

technology (UTAUT) perspective

E. AbuShanabCollege of Information Technology and Computer Sciences,

Yarmouk University, Yarmouk, Jordan and

J.M. PearsonCollege of Business and Administration, Southern Illinois University,

Carbondale, Illinois, USA

Abstract

Purpose – The purpose of this research is to investigate the key determinants of the adoption ofinternet banking in Jordan. The paper also attempts to validate the appropriateness of the UnifiedTheory of Acceptance and Use of Technology (UTAUT) within the context of internet banking.

Design/methodology/approach – A questionnaire was developed based on previous work in theareas of technology acceptance and internet banking. The questionnaire was distributed through threebanks in Jordan to customers as they enter each bank’s main office. Multiple regressions were utilizedto evaluate the collected data.

Findings – The results of this study indicate that UTAUT provides a good foundation for futuretechnology acceptance research. The three main predictors relevant to this study (performanceexpectancy, effort expectancy, and social influence) were significant and explained a significantamount of the variance in predicting a customer’s intention to adopt internet banking. The results alsoindicate that gender moderated the relationships between the three independent variables and thedependent variable (behavioral intention).

Research limitations/implications – This study did not follow-up with respondents to determineif they actually adopted internet banking. Therefore, the results do not measure actual adoption.

Originality/value – This study is one of the first to utilize the Unified Theory of Acceptance andUse of Technology (UTAUT) to the technology acceptance domain. It also provides a broader view ofthe technology acceptance decision in that the study took place in a non-English speaking culture(Arabic – Jordan).

Keywords Internet, Banking, Jordan, Consumer behaviour

Paper type Research paper

IntroductionThe World Wide Web (WWW) has changed the world since it started facilitating internetactivities for both individuals and businesses. World Wide Web users have beenincreasing exponentially since its introduction in the early 1990s. Based on numberspublished by Internet World Stats (2007), there are approximately 1,086 million internetusers world wide. The estimated annual growth rate of internet users world wide is 202.9per cent, with an even higher rate projected in the Middle East (490.1 per cent). Arab usersof the WWW are projected to reached 19 million by the end of 2007.

Although many business analysts overestimated the impact of internet technologieswhen they were first introduced, we now know that the presence of these technologieshas and will continue to shape business practices for years to come. One industry that

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Journal of Systems and InformationTechnologyVol. 9 No. 1, 2007pp. 78-97q Emerald Group Publishing Limited1328-7265DOI 10.1108/13287260710817700

has witnessed a significant influence of WWW technology is the banking industry.The WWW provides banks with a new channel in reaching their customers.Previously, customers could perform banking transactions only at bank’s premises, orby ATM or telephone, but the WWW has opened new opportunities for banks tointroduce online banking services to their customers. According to Orr (2001), the totalnumber of customers performing one transaction per month has grown from 1 millionto 16 million since 1996. The same report indicated savings for banks of $5 pertransaction and a return on investment of approximately 60 per cent for banks utilizingthe WWW.

Customers also benefit from this new channel of delivery. Internet banking givesthem the option to perform banking transactions and other related activities fromhome, and the convenience of performing most banking transactions twenty-fourhours a day, 365 days a year. Unfortunately, given these advantages, customers offinancial institutions have not completely embraced internet banking. Like otherinnovations, internet banking faces many obstacles associated with its adoption. Sincethe success or failure of this technology will depend on the rate at which it is adopted,there is a need to determine which factors influence customers to adopt internetbanking. This research hopes to add to the body of knowledge in the area of technologyacceptance and specifically internet banking adoption by customers in developingcountries.

This paper is organized as follows. The next section provides an overview of theareas of technology adoption and internet banking. Then, the research model isdescribed and hypotheses presented. The results and analysis of data are discussednext and finally, limitations, implications, future research and conclusions in theconcluding section of the paper.

Literature reviewTechnology acceptanceThe technology acceptance domain is a well researched area in the informationsystems area. Several models and theories exist that try to predict an individual’sintention to adopt or not adopt a specific technology. This research has typicallyconcentrated on “usage” or “intention to use” as the key dependent variable. Researchin this area has explored topics such as the adoption of electronic commerce (Grandonand Mykytyn, 2004), software applications (Davis et al., 1989; Mathieson, 1991), e-mailusage (Szajna, 1996), telemedicine applications (Chau and Hu, 2001), computer usage(Compeau and Higgins, 1995), and internet banking (Tan and Teo, 2000). For acomprehensive review of the proposed models/theories of technology acceptance, seeVenkatesh et al. (2003).

Research in the area of technology acceptance generally has lacked the integratedview needed to understand the domain. The work proposed by Venkatesh et al. (2003)integrates previous technology acceptance models into a Unified Theory of Acceptanceand Use of Technology (UTAUT). The UTAUT hypothesized four main indicators asinfluencing behavioral intention and/or use behavior (see Figure 1). Theserelationships were suggested to be moderated by age, gender, experience andvoluntariness. Venkatesh et al. also suggested that behavioral intention significantlyinfluenced use behavior with no moderation effect assumed between intention and use.One of the main conclusions of Venkatesh et al. was that, contrary to many previous

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79

studies, self-efficacy and anxiety did not influence behavioral intention directly andsuggested these may be antecedents for one of the independent variables in theUTAUT model.

Anderson et al. (2006) were among the first to test the validity of the UTAUT in adifferent setting. They utilized the model in the context of business faculty at a largeuniversity and the adoption of Tablet PCs. Their findings moderately supported therelationships proposed by UTAUT as predictors of behavioral intention, withemphasis on performance expectancy and voluntariness as the most salient drivers ofacceptance. Anderson et al. concluded that UTAUT was a useful model in predictinguser adoption behavior, but that it should be tested in different contexts in order tofurther validate the proposed relationships.

Internet bankingThe level of service offered by banks varies, and studies that deal with what banksoffer and the channels utilized are rare. Sciglimpaglia and Ely (2002) reported a highrisk of banks losing customers when offering services via the net. They reported that32.9 per cent of all customers would be tempted to switch banks if they discovered abetter rate, and this percentage would increase to 40.5 per cent if customers have anexisting internet connection. Such results emphasize the importance of knowing whatcustomers want and addressing those needs. A study by Chung and Paynter (2002)reported that four factors that affect customer satisfaction in internet banking(security, download time, response time, and transactions free of technical problems).Chung and Paynter concluded that factors that are important to customers’ adoption ofinternet banking include age, education, being an existing phone customer, andperceiving the service as not being complicated. Table I presents a list of the importantadvantages and disadvantages of internet banking to customers.

Internet banking in the Middle EastAccording to Guru et al. (2003), internet banking in the Middle East is still in its earlystages as internet usage is limited. The study concluded that Islamic banks areprogressing towards transactional internet banking, but they are slower than theirconventional counterparts. The reasons cited for this lag include a lack of topmanagement support, security, and privacy issues. These findings were similar to astudy by Khalfan and Alshawaf (2004) that addressed e-commerce in the banking

Figure 1.Unified theory ofacceptance and use oftechnology (UTAUT)

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sector in Oman (a survey aimed at IS executive and managers). Other factors that wereless significant were power conflict in deciding to adopt internet banking and thegeneral lack of investment in e-commerce applications.

Jordan, like any other developing country, is witnessing a rapid movement towardsinternet banking. One of the few studies that has dealt with internet banking in Jordanwas a survey of the major banks in Jordan with respect to which service phase eachbank was at and the type of services provided (Awamleh et al., 2003). The resultsindicated that major Jordanian banks have introduced bank services primarily throughinformational website facilities. Only two banks out of the thirteen banks surveyedmaintained a transactional website. For this study, bank websites were visited duringJuly 2006 and it was found that the number of banks offering transactional internetbanking had increased significantly.

In another study related specifically to internet banking and Jordan, Al Sukkar andHasan (2005) utilized the technology acceptance model (TAM) as a foundation to studyintention to adopt internet banking. They extended this model to include measures onculture, trust, and online service quality. Al Sukkar and Hasan found support for thebasic structure of TAM, but suggested that it may be necessary to include othervariables (i.e. consumer specific constructs and service provider specific constructs)when this model is utilized in different countries.

Objectives of this researchIn this study, we are not testing the relationship between facilitating conditions anduser behavior as we are looking specifically at intention to adopt internet banking; weare not investigating actual usage. Based on that, we dropped perceived facilitatingconditions in the replication process. The first objective of this work was to determinethe validity of the UTAUT as proposed by Venkatesh et al. (2003) as it relates tointention to adopt. Thus, the study tested the three independent variables (performanceexpectancy, effort expectancy, and social influence) proposed by the UTAUT and theirrelationship to behavioral intention and three of the four moderating variables (age,gender, and experience). Figure 2 indicates the relationships that were tested in thisstudy.

The second objective of this study is to develop and validate an Arabic instrumentthat tests the UTAUT in the context of internet banking. Part of the processing ofvalidating and demonstrating the robustness of a proposed theory/model is testing it

Advantages Disadvantages

Reduced cost of transactions, like mailing andtransportation to the bankAvailability of the webEase of accessConvenienceEfficiency of operationsProfitability for the bankRetain customers

Extra cost of systems and connectionExtra cost or monthly charge, or bill payingchargeIdentity theftPrivacy policy problems and ambiguity

Sources: Adapted from: Consumer Report, 2002; Awamleh et al., 2003.

Table I.Advantages and

disadvantages of Internetbanking


81

within different contexts. The development of an Arabic instrument that facilitates thisprocess in Islamic countries is a valuable contribution to the validation process thatUTAUT (and all instruments/models/theories) should go through.

Research hypothesesPrevious research has indicated that the relationship between performance expectancy(PE) and behavioral intention is a strong predictor of technology acceptance. Incomposing performance expectancy, the UTAUT summed or aggregated alljob/performance related constructs. Aspects like usefulness, job fit, and relativeadvantage are important for individuals when they decide on using/adopting atechnology. Usefulness (or extrinsic motivation in the motivational model) was asignificant predictor of intention in most of the research related to TAM, TAM2,Decomposed Theory of Planned Behavior (DTPB) and Motivational Model (MM)(Davis, 1989; Davis et al., 1989; Taylor and Todd, 1995a, 1995b), the same is the case ofrelative advantage as proposed in the Innovation Diffusion Theory (IDT) (Moore andBenbasat, 1991), job-fit in the Model of PC Utilization (MPCU) (Thompson et al., 1991),and outcome expectations in the Social Cognitive Theory (SCT) (Compeau and Higgins,1995). Based on the findings of these previous studies, it is expected that PE will have asignificant and positive relationship with behavioral intention to use internet bankingin Jordan. Therefore, we propose the following hypothesis:

H1. Customers with high performance expectancy will have high intention to useinternet banking.

The relationship between effort expectancy (EE) and behavioral intention is frequentlydebated because of the effect of performance expectancy on behavioral intention. Theconstruct was aggregated in the UTAUT from perceived ease of use and complexity.Research conducted utilizing the TAM model has provided contradictory outcomes;however, when reviewing the research utilizing PEOU and complexity in studiesutilizing the TAM, IDT, and MPCU (Davis, 1989; Davis et al., 1989; Thompson et al.,1991; Moore and Benbasat, 1991), we concluded that a positive relationship betweenperceived ease of use and intention is expected. Thus, it is expected there will be a

Figure 2.Components of UTAUTtested in this study

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positive relationship between effort expectancy and behavioral intention related tointernet banking in Jordan.

H2. Customers with high effort expectancy will have high intention to use internetbanking.

Social influence has been explored in more than one model (Theory of ReasonedAction, Theory of Planned Behavior, and Decomposed Theory of Planned Behavior)and the results have been debated with respect to the importance of this construct inpredicting behavioral intention. The construct was composed in the UTAUT fromsubjective norms (SN), social influence/factors, and image. Based on the review of theliterature, it is expected that social influence will positively influence behavioralintention in the context of internet banking in Jordan.

H3. Customers perceiving high social influence from significant others will havehigh intention to use internet banking.

The UTAUT hypothesized that four factors would moderate the relationships depictedin their model. The moderators utilized were gender, age, voluntariness and experience.These variables have been shown to moderate the intention to adopt new technologiesin several studies (Agarwal et al., 2000; Pearson et al., 2002; Venkatesh et al., 2003;Al-Gahtani, 2004) Based on the nature of this study (internet banking and its focus onintention to adopt), we dropped voluntariness from the set of moderators and thus onlythree moderators were used to replicate the UTAUT. The following hypotheses arerelated to this effect:

H4a. Gender will significantly moderate the relationships between (PE, EE and SI)and behavioral intention in the UTAUT.

H4b. Age will significantly moderate the relationships between (PE, EE and SI) andbehavioral intention in the UTAUT.

H4c. Experience will significantly moderate the relationships between (EE and SI)and behavioral intention in the UTAUT.

Research methodsThe survey used in this study consisted of three sections: first, a description ofinternet banking and the services provided by Jordanian banks. Second, 15 itemswere used to measure the variables tested in this study. The items used in this studywere modified from the items utilized by Venkatesh et al. (2003). The items used foreach construct and their sources are provided in Table II. The third part of theinstrument solicited demographic information from each respondent about age,gender, education, etc.

Translation processThe instrument (the English version) was submitted to a certified translation office inJordan. At the same time, the same copy was reviewed and translated by two Arabgraduate students residing in the USA. After translation, the Arabic version wasreviewed for differences between the translations (differences were minor). The Arabicversion was then resubmitted to a second certified translation office and two other


83

graduate students to back translate the Arabic version to the English language(Brislin, 1976). Finally, the two English versions (the initial and the back-translated)were reviewed for consistency. The versions contained non-significant differences,which suggested the translation of the instrument was acceptable.

The Arabic survey was pilot tested using Arab students residing in the USA. Theconvenience of the pilot sample is important at this stage as the purpose of the pilottesting is the readability of the instrument and the logical flow of the questions. Thepilot survey included a “your comments” section to collect opinions of the respondentsbefore using the survey. The results of this stage indicated that the sequencing of theinstrument was appropriate and did not include mistakes or confusing items.

Data collectionThe population of interest in this study is Jordanian bank customers. The samplingprocess took place at the banks’ branches in three major cities in Jordan (Amman,Zarqa and Russaifa). The institutions chosen for this study were leading banks in theJordanian banking sector: The Housing Bank, The Jordan Islamic Bank, and The ArabBank. It was assumed that no differences exist between the three banks customers andthe customers residing in the three cities. The purpose of using three banks and threecities is to guard/reinforce the external validity of the study.

Behavioral intention1. I intend to use internet banking in the next few months UTAUT2. I predict that I would use internet banking in the next few months UTAUT3. I plan to internet banking in the next few months UTAUT

Performance expectancy1. I expect internet banking will be useful in my life UTAUT2. Using internet banking will enable me to accomplish transaction more

quicklyUTAUT

3. Using internet banking will increase my productivity UTAUT4. Using internet banking will increase my chances of getting a raise UTAUT

Effort expectancy1. I expect my interaction with internet banking will be clear and

understandableUTAUT

2. I expect it would be easy for me to become skillful at using internetbanking.

UTAUT

3. I expect internet banking to be easy to use UTAUT4. Learning to use internet banking will be easy for me UTAUT

Social influence1. People who influence my behavior thing that I should use internet

bankingUTAUT

2. People who are important to me think that I should use internet banking UTAUT3. Senior management at the bank has been very helpful in the use of

internet bankingUTAUT

4. In general, the bank has supported the use of internet banking UTAUT

Notes: UTAUT ¼ Unified Technology Acceptance and Usage Theory by Venkatesh et al. (2003).All items measured on a seven-point Likert scale: (1) Strongly disagree to (7) Strongly agree

Table II.Items utilized for TAMconstructs

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The study used a systematic random sampling (taken on intervals) of the customersentering the bank for a two week period of time. The necessary sample size wasestimated based on the number of independent variables. The total number ofindependent variables in this study was three. Based on the recommendations of Hairet al. (1998) the sample size should be 15-20 observations per variable forgeneralizability purposes. For power calculations and to detect significant differencesin R 2 with a power level of 0.8 (recommended by Hair et al., 1998), the sample sizeshould be greater than 100. The final sample collected was 940 cases.

The first step performed on the data was to check the data visually to detect anymissing data. The data contained 62 cases that were missing more than two responses.Those cases were deleted. The total number of usable responses after the visualinspection was 878 cases. Preliminary regression analysis was conducted using themean of the items representing each variable for each case, regardless of the reliabilityof the instrument and regression model accuracy. The purpose of the preliminarymultiple regression test(s) was to check for outliers and influential cases only. The testsincluded Mahalanobis distance, Cook’s D, leverage, standardized DFBeta, and thestandardized residuals. Cases which exceeded the limits on more than one measurewere deleted. Inspection of these measures resulted in deleting nine additional casesfrom the file (n ¼ 869). Table III provides the demographics of the sample used formodel validation (non-internet banking customers).

Factor analysisThe purpose of using factor analysis in this study was to confirm item loadings and tocheck the reliability of the measures used. The results of the analysis were used todevelop a set of summated measures representing the four variables (three independentand one dependent) used in this study. Hair et al. (1998) recommends a ratio of 1-to-10

Completed % Missing Total

BankJordan Islamic Bank 204 39.0 0 523Arab Bank 148 28.3Housing Bank 171 32.7

GenderMale 334 63.9 0 523Female 189 36.1

AgeLess than 30 301 57.6 0 52330-40 154 29.4More than 40 68 1.0

EducationHigh school and less 156 29.8 0 523Bachelor 296 56.6Graduate 71 13.6

Use of Internet bankingUsed 346 39.3 0 869Did not 523 60.7

Table III.Demographics of

non-Internet bankingcustomers


85

between the items to be factored and the number of cases used, with a minimum of1-to-5. Analysis was conducted on 15 items and the ratio of items to cases was 1-to-58.

The method used in the analysis was R-type factor analysis using an obliquerotation (oblimin). Preliminary checks on the results indicated the overall suitability offactor analysis based on Bartlett’s test of Sphericity with a x1275 ¼ 29; 783:7,p , 0.001. This indicates that correlations were adequate to conduct factor analysis.The Kaiser-Meyer-Olkin measure of sampling adequacy was used to check forexcessive correlations with a value equal to 0.9466 (recommended value of KMO isgreater than 0.5). This suggests the existence of small correlations between variables.Finally, a substantial part of the off-diagonal correlations in the anti-image matrix wereless than 0.1, while the diagonal correlations were all more than 0.5. This indicates thatfactor analysis was an appropriate technique for reducing the number of items used inthis study.

After running the initial factor analysis, we checked the suitability of the rotationmethod. Highly correlated factors are a concern, and low correlations indicate that anorthogonal rotation would be more appropriate than oblique. This was done throughthe inspection of the component correlation matrix. The matrix identified asignificant portion of the correlations between the extracted factors as significant andbetween 0.2 and 0.4 (Field, 2000). Based on these findings, an oblique rotation wasutilized for factor analyzing the data. One aspect of a confirmatory factor analysis isthe ability to limit the number of factors to be extracted. In this study, based on theresearch model and literature review, four factors were extracted. The cumulativevariance explained was 75.9 per cent. Table IV summarizes the explained variance ofthe extracted factors.

Factors extracted represented all the variables in the research model. Behavioralintention (BI), performance expectancy (PE), social influence (SE) and effort expectancy(EE) all loaded as expected on unique factors with significant loadings. Table V showsthe pattern matrix and the final factor loadings. Cronbach’s alpha was used as ameasure of the reliability of the scales. Values that have been used in the literature asacceptable Cronbach’s alpha range from 0.6 and above. The reliability measures arelisted at the bottom of Table V.

Testing the UTAUTIn order to test the validity of the proposed UTAUT relationships, it was necessary tosplit the data set into adopters (n ¼ 346) and non-adopters (n ¼ 523) of internetbanking. This part of the analysis utilized the non-adopters as this study investigatedthe intention to utilize internet banking. The analysis indicated significant results forall three variables (PE, EE and SI) with an R 2 value of 0.316 (adjusted R 2 ¼ 0:311),

Initial eigenvaluesComponent Total % of variance Cumulative %

1 7.162 47.749 47.7492 1.504 10.026 57.7753 1.462 9.745 67.5204 1.256 8.371 75.892

Table IV.Total variance explained

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with an F3;522 ¼ 76:84, p , 0.001. Table VI provides the output of the analysis.Performance expectancy was more important in predicting behavioral intention thanthe other two variables (based on the standardized betas).

Testing the interaction effects in the UTAUT. Interaction effects (or moderationeffects) were estimated using an additional term in the regression model that iscomputed through the multiplication of the independent variable and the moderator.Researchers recommend using dummy coding for categorical variables in theregression model. Dummy coding is a simple technique that represents the categoricalvariable with a set of K 2 1 factors, where K is the number of categories measuringthat variable. The regression model includes the independent variables, the categoricalmoderator (or dummy variable(s)), and the multiplication of the dummy variable by theindependent variables. In our case, we are measuring the moderation effect of age,gender and experience on the independent variables.

The analysis used only non-users for the regression of gender and age. Whentesting for the moderation effect of experience, this study contrasted users vs.

1 2 3 4

BI 1 0.254 0.192 0.167 0.825BI 2 0.184 0.175 0.205 0.875BI 3 0.227 0.167 0.194 0.824PE 1 0.749 0.219 0.207 0.308PE 2 0.855 0.230 0.171 0.193PE 3 0.854 0.216 0.190 0.160PE 4 0.811 0.235 0.212 0.181EE 1 0.217 0.740 0.193 0.215EE 2 0.228 0.829 0.187 0.183EE 3 0.245 0.827 0.211 0.025EE 4 0.158 0.748 0.209 0.215SI 1 0.161 0.139 0.781 0.254SI 2 0.146 0.166 0.799 0.228SI 3 0.138 0.256 0.758 0.130SI 4 0.259 0.198 0.702 0.035Cronbach’s 0.916 0.875 0.838 0.899

Notes: Extraction method: Principal Component Analysis; Rotation method: Oblimin with KaiserNormalization

Table V.Rotated component

matrix and reliabilitymeasures

Unstandardizedcoefficients Standardized coefficients

B Standard error Beta t-test Significance

Constant 20.597 0.916 20.652 0.515PE 0.302 0.040 0.329 7.449 0.000EE 0.136 0.044 0.138 3.102 0.000SI 0.201 0.040 0.222 5.041 0.000

Note: Dependent variable: Behavioral intention

Table VI.Testing the UTAUT

coefficients


87

non-users, thus utilizing the full data set to measure the variation of usage (experience).Tests were conducted using the block method. In the first block, the independentvariables were entered into the model (PE, SI, SE and the moderator). The second blockincluded the interaction variable, which included the multiplication of the moderatorby the independent variables. Note that when the moderator included two dummyvariables, the interaction effects were the result of multiplying the two dummyvariables by the predictor variables (resulting in two interaction parts in the model).

Gender effect. Gender was coded with males as the reference category (controlcategory). When testing the effect of gender as a moderator with performanceexpectancy, social influence, and effort expectancy as predictors, the results indicatedsignificant results for all interaction terms at the 0.05 level. Performance expectancyand effort expectancy were significantly moderated by gender with a beta value of20.449 ( p , 0.01) and 0.453 ( p , 0.01) respectively. Finally, social influence wasmoderated by gender with a beta value of 0.166 ( p , 0.05). Results indicated thatmales had stronger effect in the case of performance expectancy, and females hadstronger effect in the case of social influence and effort expectancy. Tables VII, VIIIand IX summarizes the results when moderated by gender. The results include themodel summary, the ANOVA table and the coefficient table.

Age effect. The case of age is slightly different than gender as there were threecategories which required two dummy variables. The independent variables weremultiplied by the two dummy variables, which resulted in six interaction terms in theregression model. Results indicated significant interaction terms for both performanceexpectancy and effort expectancy when moderated by age. The beta values were20.487 and 0.397 for performance expectancy and effort expectancy respectively( p , 0.05 for both). Social influence interaction terms were not significant in predictingbehavioral intention when moderated by age. The results indicated that the effect wasstronger for older customers in the case of performance expectancy, and stronger foryounger customers in the case of effort expectancy. Tables X, XI and XII summarizethe results of the interaction model using age as a moderator. The table includes themodel summary, the ANOVA table, and the coefficient table. Contrary to what wasproposed by Venkatesh et al. (2003), age did not significantly moderate the relationshipbetween social influence and behavioral intention.

Experience effect. Tables XIII, XIV and XV provide the results when usingexperience as a moderator, which includes the model summary, the ANOVA table, andthe coefficient table. Experience was a significant moderator in the relationshipbetween social influence and behavioral intention. The moderation effect was strongerfor non-users in the case of social influence. Experience failed to moderate therelationships between performance expectancy, effort expectancy and behavioralintention.

The previous results supported the finding of the UTAUT with respect to thesignificance of the independent variables in predicting behavioral intention. Therelationship between performance expectancy and behavioral intention was moderatedby gender and age, but not moderated by experience. The relationship between effortexpectancy and behavioral intention was moderated by gender and age, but notmoderated with experience. Finally, the relationship between social influence andbehavioral intention was moderated by gender and experience, but not by age (seeTable XVI).

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Table VII.Model summary of the

interaction effect ofgender


89

DiscussionPerformance expectancy ( behavioral intentionPerformance expectancy accounted for the largest unique contribution in explaining thevariance in behavioral intention. Customers with high performance expectancy had highintentions to use internet banking. Such a result supports the work of Venkatesh et al.(2003) in the UTAUT, Davis (1989) in TAM , Venkatesh and Davis (2000) in TAM2 and

Model Sum of squares df Mean square F Significance

1 Regression 861.9 4 215.5 209.7 0.000Residual 514.7 501 1.027Total 1376.6 505


Notes: Model 1 – Predictors: (constant), Female, PE, SI, EE; Model 2 – Predictors: (constant), Female,PE, SI, EE, Female *SI, Female *PE, Female *EE; Dependent variable: Behavioral intention

Table VIII.ANOVA table of theinteraction of effect ofgender

ModelUnstandardizedcoefficient Beta

Standarderror

Standardizedcoefficient Beta t Significance

1 Constant 0.155 0.215 0.718 0.473PE 0.606 0.042 0.500 14.276 0.000EE 0.074 0.046 0.057 1.637 0.102SI 0.217 0.032 0.215 6.847 0.000Female 21.346 0.094 20.394 14.321 0.000

2 Constant 0.325 0.254 1.280 0.201PE 0.730 0.055 0.603 13.169 0.000EE 20.052 0.059 20.040 20.881 0.379SI 0.170 0.040 0.169 4.270 0.000Female 21.898 0.440 20.555 24.314 0.000Female * PE 20.269 0.085 20.449 23.170 0.002Female * EE 0.297 0.091 0.453 3.254 0.001Female * SI 0.127 0.064 0.166 1.982 0.048

Note: Dependent variable: Behavioral intention

Table IX.Coefficient table of theinteraction effect ofgender

Change statistics

Model R 2 Adjusted R 2Standard errorof the estimate R 2 change F change df1 df2

Significantchange F

1 0.676 0.672 0.961 0.676 211.5 5 508 0.0002 0.687 0.681 0.949 0.012 3.2 6 502 0.004

Notes: Model 1 – (constant), Middle, EE, SI, PE, Young; Model 2 – (constant), Middle, PE, SI, EE,Young, Young *SI, Middle *SI, Young *PE, Middle *PE, Young *EE, Middle *EE

Table X.Model summary of theinteraction effect of age

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other replications of those models (Szajna, 1996; Tan and Teo, 2000). The results of thisstudy also supports the finding of the UTAUT with respect to the moderation effect ofgender. As males in Jordan are closer to decision making positions and have morecontrol in the society, they can manifest their intentions based on their usefulness.




Notes: Model 1 – (constant), Middle, EE, SI, PE, Young; Model 2 – (constant), Middle, PE, SI, EE,Young, Young *SI, Middle *SI, Young *PE, Middle *PE, Young *EE, Middle *EE

Table XI.ANOVA table of the

interaction of effect of age


Standarderror

Standardizedcoefficient Beta t-value Significance

1 Constant 1.016 0.223 4.553 0.000PE 0.601 0.040 0.485 15.047 0.001EE 0.139 0.043 0.104 3.270 0.001SI 0.162 0.030 0.158 5.405 0.000Young 22.151 0.131 20.635 216.409 0.000Middle 20.891 0.143 20.243 26.253 0.000

2 Constant 1.265 0.445 2.842 0.005PE 0.779 0.097 0.628 8.017 0.000EE 20.008 0.110 20.006 20.074 0.941SI 0.063 0.089 0.062 0.711 0.478Young 22.560 0.517 20.756 24.955 0.000Middle 21.157 0.633 20.316 21.827 0.068Young * PE 20.283 0.109 20.487 22.598 0.010Young * EE 0.251 0.122 0.397 2.053 0.041Young * SI 0.154 0.097 0.208 1.597 0.111Middle * PE 0.019 0.133 0.032 0.144 0.886Middle * EE 20.007 0.145 20.011 20.050 0.960Middle * SI 0.036 0.107 0.051 0.336 0.737

Table XII.Coefficient table of the

interaction effect of age

Change statistics

Model R 2 Adjusted R 2Standard error of

the estimate R 2 change F change df1 df2Significantchange F

1 0.615 0.614 1.057 0.615 335.6 4 839 0.0002 0.625 0.622 1.045 0.010 7.1 3 836 0.000

Notes: Model 1 – (constant), Nonusers, EE, SI, PE; Model 2 – (constant), Nonusers, PE, SI, EE,Nonusers *SI, Nonusers *PE, Nonusers *EE

Table XIII.Model summary of the

interaction effect ofexperience


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Age was another significant moderator that affected this relationship as the effectwas stronger for older customers than younger ones. This result was inconsistentwith the UTAUT findings as their findings supported a higher influence for youngerpeople. The result may indicate an inverse influence compared to the perception thatyounger customers will accept the technology more easily. One reason might be the


Standarderror

Standardizedcoefficient Beta t Significance

1 Constant 0.844 0.190 4.445 0.000PE 0.570 0.037 0.443 15.325 0.000EE 0.143 0.039 0.106 3.701 0.000SI 0.198 0.026 0.192 7.639 0.000Nonusers 21.311 0.076 20.378 217.187 0.000

2 Constant 0.295 3.945 0.000PE 1.163 9.715 0.000EE 0.664 0.068 0.515 0.000SI 0.123 0.067 0.091 1.844 0.065Nonusers 0.044 0.043 0.043 1.023 0.306Nonusers* PE 21.828 0.366 20.527 24.994 0.000Nonusers * 20.126 0.081 20.216 21.558 0.120EE 0.024 0.082 0.037 0.288 0.773Nonusers* SI 0.239 0.054 0.342 4.456 0.000

Table XV.Coefficient table of theinteraction effect ofexperience


1 Regression 1498.5 4 374.630 335.611 0.000Residual 936.5 839 1.116Total 2435.1 843Regression 1521.9 7 217.417 199.049 0.000Residual 913.1 836 1.092

2 Total 2435.1 843

Notes: Model 1 – (constant), Nonusers, EE, SI, PE; Model 2 – (constant), Nonusers, PE, SI, EE,Nonusers *SI, Nonusers *PE, Nonusers *EE

Table XIV.ANOVA table of theinteraction of effect ofexperience

Moderator Hypothesis No. Relationship Result

Gender H4a supported PE(BI Effect was stronger for malesEE(BI Effect was stronger for femalesSI( BI Effect was stronger for females

Age H4b partially supporteda PE(BI Effect was stronger for old customersEE(BI Effect was stronger for young customersSI(BI Not supported

Experience H4c partially supporteda PE(BI Not supportedEE(BI Not supportedSI(BI Effect was stronger for non-users

Note: a Interaction effect exists, not on all relationships, or different direction

Table XVI.Interaction results(UTAUT replication)

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confidence level in the system (as a new system) and the economical stability inJordan, and at the same time, older people generally have higher income so they canuse internet banking for their financial needs.

Finally, experience moderated the relationship between performance expectancyand behavioral intention. This was not tested by the UTAUT. This result contradictsthe TAM2 findings (Venkatesh and Davis, 2000), but supports the findings of Zinkhanet al. (1987). Users had stronger influence than non-users which indicates theaccumulating benefits of internet banking.

Effort expectancy ( behavioral intentionThis relationship was supported. Previous research supported this relationship basedon the association between the ease of the system and the higher intentions to use it(Davis, 1989; Venkatesh, 2000; Szajna, 1996). When replicating the UTAUT, genderand age significantly moderated the relationship. Gender supported the findings of theUTAUT, but age did not as younger customers had stronger effect (consistent withHarrison and Rainer, 1992 and Zinkhan et al., 1987). Experience was not a significantmoderator of this relationship as the ease of a system attenuates with experience andcustomers are more concerned by the usefulness and security of it (supports the resultof Szajna, 1996).

Social influence ( behavioral intentionThe results indicated a support of the UTAUT findings and other research (Venkateshand Davis, 2000; Riemenschneider et al., 2002). Customers with high social influencehad high intentions to use internet banking. Gender moderated this relationship as theeffect was stronger for females than males (consistent with the UTAUT). Age was nota significant moderator of this relationship, which fails to support the UTAUTfindings that older people had stronger influence than younger people. Previousresearch indicated a direct relationship between age and perceptions related totechnology (Venkatesh et al., 2002). Experience was a significant moderator in thereplication of the UTAUT as non-users showed stronger effect than users (supports theUTAUT findings). The result of this study can be explained as the effect tends to bestronger for people without previous experience as they depend more on the socialinfluence of others.

Implications for practiceInternet banking in Jordan is a recent technology that emerged from the needs ofbusinesses to better serve their customers and reduce their operational costs. Factorsthat affect customers’ acceptance of internet banking were explored and introduced inan integrated and summarized structure. In this study, bank customers wereinfluenced mostly by their perceptions about the performance of internet banking andthe benefits expected from it. This implies that advertising for internet banking inJordan should emphasize the benefits and advantages of the systems to customers andhow it can improve performance and productivity of users.

As a highly social society, Jordanians are influenced by others in their daily life, andalso when using internet banking, and thus banks’ efforts to advertise the benefits ofinternet banking can be amplified through the social influence on people. Providingincentives and promotions for customers’ referrals can be a strong technique to


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influence customers to use internet banking. Also, another factor that is important topractitioners is the trust level invested in internet banking by customers. This factor isclosely related to internet banking as previously supported by the literature (researchrelated to e-commerce and the internet). Internet banking is a service that requirespeople using it to trust the system when transacting because of the financial risksassociated. Banks need to emphasize the security levels imposed and the safeenvironment to influence customers’ levels of trusts so that customers use the system.Jordanians are willing to trust banks with their financial transactions and will continueto do that when a bank builds this trustworthy business image.

Practitioners need to put more emphasis on customers’ differences that existbetween different categories of bank customers (related to gender, age, and experience).The demographical and cultural differences combined are of great importance inaffecting users’ intentions to use internet banking. This cultural difference requiresbanks to design systems that take into consideration the values, needs and personaldifferences between customers. As an example, this study showed older customers hadhigher intentions to use internet banking with respect to the effect of performanceexpectancy, which contradicts with the UTAUT findings. Such result show thecultural difference as Jordanians start their professional life (earning money) later thantheir American counterparts.

Implications for researchThis study is one of the first to investigate the robustness of the model (UTAUT)proposed by Venkatesh et al. (2003). The UTAUT is a parsimonious model thatintegrates much of the previous work that has been done in the technology adoptiondomain. By validating the relationships that have been suggested by Venkatesh et al.,we have provided support for the validity of this model. However, our understandingof the relationships and interactions that influence the intention to adopt internetbanking (and technology adoption) still needs further exploration.

An important part of developing solid theoretical foundations within a discipline isto have proposed theories or models undergo a thorough validation process. We believethat this study is a first step in this validation process. Future studies shouldinvestigate the appropriateness and robustness of the UTAUT for differentapplications and within different cultures. Future studies should also investigate therole that other constructs can have in explaining the two dependent variables proposedby UTAUT – intention and usage.

The process of translating and validating an instrument is a long and often complexprocess, which suggests that future research should investigate the Arabic instrumentand validate it in further implementation. More research is encouraged to validate theinstrument and refine it (maybe add more items) to fit with Arabic/Mediterraneanculture. To generalize the findings of this study, more research is encouraged on othertechnologies in the Middle East and using an Arabic instrument.

Limitations of the studyAs stated in this study, behavioral intention is the closest construct that can be used asa surrogate for internet banking usage. Using behavioral intention is rich, but does notreplace exploring actual usage of a system. One limitation of this study is the methodby which respondents were solicited; although efforts were taken to strengthen

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external validity, the use of surveys to collect the data has obvious weaknesses.Another limitation of this study could be that through replication, the study may haveoverlooked constructs that could help explain a significant portion of the varianceassociated with predicting the intention to utilize internet banking. Finally, this studygeneralizes only to bank customers in Jordan, internet banking systems and for thosewho speak Arabic language.

ConclusionsThe results of this study extended the body of knowledge in the area of technologyadoption. The differences between the results of this study and previous researchconducted in North America call for additional research on cultural differences effectand in the technology acceptance domain. The advantage of using the UTAUTperspective is the integrated view of the major models in the area that emphasized thestrength of certain constructs compared to others. Finally, an Arabic instrument wasvalidated and can provide a mechanism for additional research in the area.

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Further reading

Venkatesh, V. and Morris, M.G. (2000), “Why don’t men ever stop to ask for directions? Gender,social influence, and their role in technology acceptance and usage behavior”, MISQuarterly, Vol. 24 No. 1, pp. 115-39.

Corresponding authorJ.M. Pearson can be contacted at: [email protected]


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