2013 abushanabtrust dimensions and the adoption of e government in jordan (1)

International Journal of Information Communication Technologies and Human Development, 4(1), 39-51, January-March 2012 39

Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Keywords: E-GovernmentAdoption,Jordan,PathAnalysis,PerceivedRisk,TrustinGovernment,TrustinInternet

1. INTRODUCTION

Trusting an e-government Website is a crucial step towards the adoption of such paradigm. It is important to build this cumulative trust in e-government to facilitate and encourage the interaction between government and their cus-tomers (citizens and business). E-government can be defined as the use of information and communication technology (ICT) and particu-larly the Internet to deliver information and services by the government to its customers (citizens and businesses). E-government gained

popularity in the last few years and adoption in the last years (Papadopoulou, Nikolaidou, & Martakos, 2010). Such increase in adoption came as a result of the gains to government, citizens and businesses. But despite these gains, e-government didn’t reach its full potential yet. Factors like trust are still overlooked.

E-government is an open domain that can be accessed by anybody in the world, which leads to a high level of uncertainty and risk in the services it delivers to citizens. Horst, Kutt-schreuter, and Gutteling (2007) argue that the risk of e-government services comes from the information sent and stored electronically. Such information can be easily copied, modified, de-

Trust Dimensions and the Adoption of

E-Government in JordanEmadAbu-Shanab,YarmoukUniversity,Jordan

AmeenAl-Azzam,YarmoukUniversity,Jordan

ABSTRACTE-governmentprojectutilizationdependsonusers’adoptionofthesystem,wheretrustisacrucialfactorinforcingtheintentionstousesuchsystems.Thisresearchutilized105usableresponsesfromcitizenswhousedandexplorede-governmentservices.Itwashypothesizedthattrustine-governmentandtrustintheInternet,alongwithperceivedrisk,willsignificantly influence trust inE-government,andfurther intention tousethesystem.Usingpathanalysis,resultssupportedtrustingovernmentandtheInternetanddidnotsupportperceivedrisk.Also,trustine-governmentsignificantlyinfluencedintentiontousethesystem.Finally,pathanalysisindicatedasignificantmediationoftrustinE-government,wheredirectandindirecteffectswereestimated.Conclusionsandfutureworkarestatedattheend.

DOI: 10.4018/jicthd.2012010103

40 International Journal of Information Communication Technologies and Human Development, 4(1), 39-51, January-March 2012


stroyed, or accessed by others without citizen’s approval. Alsaghier, Ford, Nguyen, and Hexel (2009) argue that trust plays an important role in the adoption of e-commerce and specially e-government by improving confidence between citizens and government.

Many factors can influence trust of citizens in e-government such as: trust in technology, trust in government, perceived ease of use, perceived usefulness, risk perception, privacy concerns, security, information provided by governments, and demographics of citizens us-ing e-government. Trust in e-government is an important factor in the adoption of e-government initiatives, lower trust in e-government leads to lower interaction with e-government portals and less satisfaction with e-government services.

The main objective of this research is to explore the dimensions of trust as an important factor to build confidence and satisfaction between citizens and e-government, and to build a framework of trust in e-government. The paper will review previous work related to trust in e-government, followed by a description of research method and sample used. Finally, the paper will end with recommendations and future work related.

2. BACKGROUND OF E-GOVERNMENT

E-government is a discipline that comes from many areas of research like information technology, political science, public and busi-ness administration. Such issue influenced the definition of e-government, where many definitions where proposed based on the view of the research domains they belong to. The literature indicates some similarities and differ-ences between e-government and e-commerce field. Carter and Belanger (2004) argue that both e-government and e-commerce depend on the Internet technology in delivering their services. Differences between e-government and e-commerce are the following: citizens interact with government in a much richer set of different contexts and life episodes than with

a single e-commerce vendor. Second, citizens have no choice to choose between service pro-viders while e-commerce customers have many services providers. Third, citizens have stronger trust in e-government and in the technology used than in e-commerce (Riedl, 2011).

Evans and Yen (2006, p. 209) defined the e-government as “e-government means thecommunicationbetweenthegovernmentanditscitizensviacomputersandaweb-enabledpres-ence.Theadvantagesintimeliness,responsive-ness,andcostcontainmentareoutstanding”.

Bhatnagar (2004, p. 22) defined e-government as “a process of reform in theway government works, shares informationanddeliversservicestoexternalandinternalclients. Specifically, e-government harnessesinformation technologies (suchaswideareanetworks,theInternetandmobilecomputing)totransformrelationswithcitizens,businessesandotherarmsofgovernment.Thesetechnolo-giescanserveavarietyofends:betterdeliveryofgovernmentservices tocitizens; improvedinteractionswithbusinessandindustry;citizenempowermentthroughaccesstoinformation,or more efficient government management.Theresultingbenefitscanbelesscorruption,increasedtransparency,greaterconvenience,revenuegrowthand/orcostreductions”. Also, Yildiz (2007, p. 650) defines e-government “astherelationshipsbetweengovernments, theircustomers(businesses,othergovernments,andcitizens),andtheirsuppliers(again,businesses,othergovernments,andcitizens)bytheuseofelectronicmeans”.

In this paper we define e-government as the use of information and communication technol-ogy (ICT) and particularly the Internet to deliver information and services by the government to its customers (citizens and businesses).

E-government efforts are categorized into GovernmenttoCitizens(G2C), where citizens can access services from home. Information that citizens need to carry out their transactions is available on the government web site. They can pay bills for telephone and electricity and other utilities. The second category is GovernmenttoBusiness(G2B), where governments serve busi-



nesses through automated transactions because of the large size of these transactions such as e-procurement, e-auctions and tax collection. Finally, Government to Government (G2G), where services and transactions are related to government agencies and public employees (Hiller & Bélanger, 2001).

On the other hand, research in the e-gov-ernment area focused on the stages followed by these initiatives. Models related to this issues ranged from a four to seven stages models. Bhatnagar (2004) adopted a four stage model, where e-government initiative transforms from web presence, to limited interaction, to transac-tion, and finally transformation. Another model proposed a five stage model emphasizing the dif-ference between web-presence and publishing information The United nations e-government readiness report proposed the following five stages: where the services of e-government transform from emerging information services, to enhanced information services, to transac-tional services, and finally connected services (UN Report, 2010).

3. TRUST CONSTRUCT

Trust is a social connection by individuals to surmount the complexity and uncertainty in interacting with another party (Dashti, Benba-sat, & Jones, 2010). Trust has been explored extensively and defined differently in numerous research studies. Trust is a highly complex, multi-dimensional and context-specific phe-nomenon (Papadopoulou et al., 2010). Ac-cording to Ridings, Gefen, and Arinze (2002) the definition of trust in online environment is complex because people don’t meet in face-to-face setting.

The concept of trust discussed in many areas like philosophy, psychology, sociology, economics, and organizational theory (Chopra & Wallace, 2002; Colesca, 2009b). Colesca (2009b) proclaims that the existence of many definitions for trust in the literature comes as a result of two main reasons: first, trust is an abstract concept, used instead of many concepts

such as reliability, safety and certainty, and this leads to an unclear definition which makes it challenging for researchers to agree on a uni-fied definition. Second, trust is a psychological concept with many facets, including cognitive, emotional and behavioral dimensions.

According to Aljazzaf, Perry, and Capretz (2010, p 165) trust is defined as “thewilling-nessofapartytobevulnerabletotheactionofanotherpartybasedontheexpectationthattheotherwillperformaparticularactionimpor-tanttothetrustor,irrespectivetotheabilitytomonitororcontrolthatotherparty”. Another definition by Belanger and Carter (2008, p. 166) describes trust as “anexpectancythatthepromiseofanindividualorgroupcanbereliedupon.” AL-Omari and AL-Omari (2006, p. 843) define the trust as “acentraldefiningaspectofmanyeconomicandsocialinteractions.Itis thebelief that theotherpartywillbehaveasexpectedinasociallyresponsiblemanner;andindoingso,itwillfulfillthetrustingparty’sexpectations”.

Research emphasize the cumulative pro-cess in building trust, where the levels of trust in earlier stages affect the later stages and im-pact the development of trust in a longer-term relationship (Colesca, 2009a, 2009b). Also, such contradiction in trust segmentation makes this research more important. Yaghoubi, Khani, and Esmaeali (2011, p. 415) proclaimed that we need to develop a multidimensional model taking into account the kind of research and its nature to provide a basis for practical and fundamental research in understanding trust in electronic domain. A study that utilized 302 Jordanian students and aimed at testing e-voting systems in a student council election setup, concluded that trust is a major predictor of such systems, and also, security and privacy were not significant in such settings (Abu-Shanab, Knight, & Refai, 2010).

Literatures of sociology, psychology, man-agement, and information systems research, have utilized five major antecedents of trust, which also are called “trusting bases”, which provide the foundation for trust.



Knowledge-Based Trust

The confidence that desired behavior comes from the experience and the direct interaction with trustee (Dashti et al., 2010). According to Wang (2010) knowledge-based trust comes from the familiarity with other party and the familiarity can build the trust between two par-ties through reducing the uncertainty.

Institution-Based Trust

According to Dashti et al. (2010) institution-based trust means that citizens believes that laws, rules and regulations will guarantee that the government will behave as expected. Lean, Zailani, Ramayah, and Fernando (2009) indi-cated that if citizens believe that the Internet lacks the proper rules, laws and regulations, they will not have high level of institution-based trust.

Calculative-Based Trust

Trust based on people’s calculations of the benefits and costs the other party will face if engaged in an opportunistic behavior. So citizens tend to trust when the other party has nothing to gain, or the cost is higher than the benefit of an opportunistic behavior (Dashti et al., 2010; Li, Hess, & Valacich, 2008).

Personality-Based Trust

This construct is based on the belief of the other party, that is, the other party has specific attributes (Wang, 2010) such as competency, the belief that people assess whether the other party has the skills, abilities, expertise to satisfy their needs; integrity, the belief that the online trader will behave in an honest way and will adhere to principles and standards; benevolence, people assess whether the vendor focuses on making profit or on customers’ interest (Lean et al., 2009).

Cognitive-Based Trust

Cognitive-based trust describes how people build their trust based on the first impression

rather than the direct interaction with trustee (Wang, 2010). When people lack the information and they haven’t any experience with the trustee, they will use their cognition or first impression (Li et al., 2008). The researchers also argued that cognitive-based trust is a substitute for the knowledge-based trust when we deal with an unfamiliar trustee.

4. THE CONCEPTUAL MODEL

Based on the literature related to trust in the e-government area, and the previous discussions, a conceptual model is proposed based on three factors: Trust in technology, Trust in govern-ment and Perceived risk. Figure 1 represents the proposed trust model.

Trust in Internet

According to Lee, Kim, and Ahn (2011), users might face uncertainty related to the use of the Internet technology. Individuals will have concerns about the reliability and security of online transaction especially if they did not use e-government before. To overcome these concerns users may rely on their past experience with e-commerce. So uncertainty can be reduced if users have a positive experience with new technology. Trust in the Internet was explored by and found to be a significant predictor of perceived risk, which significantly predicted ITU. The study utilized 260 responses and concluded that ITU will be influenced directly by PR, trust in e-filer, and optimism bias.

Based on that, trust in technology has two risk dimensions: privacy and security risks (Teo, Srivastava, & Jiang, 2008). Security risks are related to technology used; the risks occur when citizens feel that technology is not safe and they worry about important information like ID, password and financial information. Privacy and security issues were significant predictors of e-government website adoption (Abu-Shanab & Abu-Baker, 2011). The study utilized 300 Jordanian students and concluded that usability, accessibility and privacy/secu-rity were major predictors of the adoption of



e-government websites. Information quality is important to build trust in e-government ser-vices, and to adopt such services. Such conclu-sions were reached based on a study conducted in Lebanon utilizing 245 responses and reached conclusions regarding the influence of govern-ment influencing capabilities and accuracy of government information, which both build in trust in government construct (Chalhoub, 2010).

The privacy risks occur when citizens are concerned about their personal information not to be safe and they lack control over their information. Privacy is classified into three categories: access to personal information, control over personal information, and freedom from judgment or inspection by others (Whitley, 2009). Governments collect a great deal of per-sonal information when they deliver services to citizens (Leonetti, 2010). If citizens feel that their information is unsafe, or the government is not protecting personal information they will lose confidence in e-government services. McLeod and Pippin (2009) defined security as citizen’s belief that his information will be safe from all types of intrusion (viruses and hacking). So if citizens feel that privacy and security is low in the technology used they will not trust in e-government website.

Trust in Government

Trust in government is defined as the confidence of citizens in their government (both politicians and public officials) to do the right things and act appropriately and honestly on behalf of the public (Bannister & Connolly, 2011). Trust in

government leads to the success of e-govern-ment website (Teo et al., 2008). The authors indicated that government-citizen relationship plays a vital role in perceiving government websites trustworthy. Colesca (2009a) argued that trust in government is the ability of the government to provide online services. Trust in government is related to citizens’ expecta-tion and knowledge of government (Cullen & Reilly, 2007).

Trust in government appears from three factors (Bannister & Connolly, 2011): first, the characteristics of the individual (i.e., his or her social–cultural background); second, professional standards and public statements of ethical standards (institutional trust); finally, experience (process trust). Trust in e-filer, used by Schaupp and Carter (2010), can be related to trust in government as in Jordan, this fil-ing process is done mainly for individuals by government employees. Finally, adopting e-government and using it might improve citizens’ trust in the performance of the public agency, but will not lead to greater trust in federal government overall (Morgeson et al., 2010).

Perceived Risk

Perceived risk is defined as “consumers’psy-chologicalperceptionof risks in theprocessof online shopping, the subjective forecastabout the likelihood and the seriousness ofloss” (Wang, Wang, & Dong, 2010, p. 342). Liu and Zhou (2010) indicated that there is a strong relationship between trust and risk. Many dimensions of perceived risk are men-

Figure1.Theproposedresearchmodel



tioned in the literature like: Financial risk, the loss of money through online shopping; Time risk, the loss of time in consumer search or ordering; Functional risk, in case the func-tion of a product purchased over Internet does not perform as expected; Physical risk, is the danger of harm or injury to the customers or others while using a product or service; Social risk, is the potential change of status in one’s social group as the result of adopting a product or service; and finally, privacy risk (Ruizhong, Xiaoxue, & Zixian, 2010; Rotchanakitumnuai, 2007; Wang et al., 2010).

It is reported that perceived risk has a negative influence on the intention to exchange information and complete transaction in e-com-merce (Belanger & Carter, 2008). The authors indicated that the integration of disposition to trust, trust in the Internet, trust in government and perceived risk are important to the adoption the e-government initiatives, they also indicate that government agencies should budget trust building to their financial plans in addition to the technical staff and software.

Research that mixed trust, privacy and se-curity tried to build different perspectives of the three constructs. Yaghoubi, Kord, and Shakeri (2010) proposed that privacy, security, and trust will influence what they labeled “perceived risk” and PR will influence ITU. Results indicated that all four relationships were significant. The model included other predictors. Based on the previous review, the following hypotheses are proposed. The hypotheses are based on the trust in e-government adoption model (Figure 1).

H1: Trust in government will positively influ-ence the trust in e-government.

H2: Trust in internet will positively influence the trust in e-government.

H3: Perceived risk will negatively influence the trust in e-government.

H4: Trust in e-government will positively influ-ence the adoption of e-government.

5. RESEARCH METHODOLOGY

This empirical study utilized a questionnaire that consisted of two parts. Part one gathered general information about respondents like: gender, age, education, occupation, income per month, and other usage related data. Part two asked the respondents to answer 17 likert scale questions to measure to what extent does citizens trust e-government and how such trust in e-government impact the intention to use the services of e-government. The items of the instrument were adapted from previous research (Karavasilis, Zafiropoulos, & Vrana, 2010; Colesca, 2009a, 2009b; Belanger & Carter, 2008), and adjusted to fit with the Jordanian environment. This included an arbitration process utilizing 15 master students in an e-government graduate course. A pilot test was conducted in order to assess the questionnaire’s comprehension and eliminate potential problems, so the preliminary questionnaire was administered to a group of master students at Yarmouk University.

The population of this study was defined as “Jordanian citizens with regular access to Internet.” A paper-based survey was distributed to 150 citizens in Jordan, 120 surveys were col-lected, with 105 usable surveys. The sampling process was done randomly within Yarmouk University and on voluntary bases. Respon-dents were asked if they knew e-government website and services, and then requested to fill the survey. Mater students were the majority of the sample (70.5%), employed respondents were (37%). Table 1 shows the demographics of the sample.

6. DATA ANALYSES AND DISCUSSION

The proposed model needs to be tested using path analysis or structural equation modeling techniques. The method adopted in this research was path analysis and according to Baron and Kenny proposed steps (1986). The steps com-



prise multiple/single regression set of tests, where direct and indirect effects of variables are estimated. Tables 2, 3, and 4 below are the coefficient tables of the multiple/single regres-sion test results, where trust in e-government (TEG) and intention to use E-government (ITU) were used respectively in the analyses as dependent variables.

The tables indicate the non-significant effect of perceived risk on either ITU or TEG. Thus this predictor was dropped from the model. On the other hand, the two independent variables trust in government (TG) and trust in the Internet (TI) explained 34.8% of the vari-ance in trust in E-government (TEG). Also,

TEG explained 21.9% of the variance in ITU. Table 2 shows the coefficient of the regression were trust in e-government was the strongest predictor of ITU (beta = 0.377). Also, as men-tioned PR failed to predict ITU as beta was too small (beta = 0.042, p>0.05).

To complete the Baron and Kenny steps, we need to conduct a multiple regression between the second level dependent variable (mediator) with the predictors; results are shown in Table 2. Results indicate a similar relationship level as in Table 2. These steps indicate a mediation effect of TEG between ITU and the predictors (TG & TI). Based on that, an analysis of the direct and indirect effects is needed.

Table1.Demographicsofthesample

Gender Frequency %

Male 52 49.5%

Female 53 50.5%

Total 105 100%

Education Frequency %

High school or less 9 8.6%

Bachelor 74 70.5%

Master 17 16.2%

PhD 1 1%

Other 4 3.8%

Total 105 100%

Age Frequency %

18-25 years 62 59%

26-35 years 24 22,9%

36-45 years 14 13.3%

More than 45 years 5 4.8%

Total 105 100%

Occupation Frequency %

Private sector 12 11.4%

Public sector 27 25.7%

Student 60 57.1%

Unemployed 5 4.8%

Retired 1 1%

Total 105 100%



To consider the mediation of TEG between the predictors and ITU, it should be noted that effects should be significant between the pre-dictors and the mediators and also between the mediator and the dependent variable. To esti-mate the paths and the direct and indirect effects the mediator TEG and the dependent variable were regressed and shown to be significantly related and as shown in Table 4. Based on that and when using path analysis, the direct and indirect effects resulting from this model are listed in Table 5.

Results of Table 5 are important as they indicate the direct effects of predictors (TG & TI) on the predictor ITU through the mediator TEG. Results show that ITU is influenced in-directly by the predictors through TEG with an effect of (0.178) from TG, and (0.154) by TI.

The effects shown in Table 5 are all me-dium and large effects, where a 0.01 effect is considered small; 0.09 effect is considered medium; and 0.25 effect is considered large (Cohen, 1988). Also, the paths shown in the table are translated into a path diagram, where

Table2.CoefficienttablewhereITUisthedependentvariable

Unstandardized Coefficients Standardized Coefficients t Sig.B Std. Error

(Constant) 1.144 0.605 1.891 0.061

Trust in Government (TG) 0.463 0.127 0.377 3.638 0.000

Trust in Internet (TI) 0.331 0.132 0.258 2.500 0.014

Perceived Risk (PR) -0.052 0.107 -0.042 -0.489 0.626

Dependent Variable: ITU

Table3.CoefficienttablewhereTEGisthedependentvariable


(Constant) 0.949 0.482 1.969 0.052

Trust in Government (TG) 0.419 0.102 0.381 4.131 0.000

Trust in Internet (TI) 0.377 0.106 0.328 3.574 0.001

Perceived Risk (PR) -0.143 0.085 -0.130 -1.685 0.095

Dependent Variable: TEG

Table4.CoefficienttablewhereITUisthedependentvariable(singleregression)


(Constant) 2.109 0.351 6.001 0.000

Trust in E-Government (TEG) 0.523 0.097 0.468 5.375 0.000

Dependent Variable: ITU



the betas are used as path coefficients. Figure 2 summarizes the results of the regression effects.

Finally, to consider the total effect of TG and TI on ITU, we need to add the betas result-ing from Table 2 and the indirect effect in Table 5. It is important to stick to the conceptual model rather than utilizing methods like step-wise regression or any compatible tools to search for the largest explanation of variance in ITU. Based on that, this research will consider the research model results only without any fishing process for other propositions.

7. CONCLUSION

This research focused on the risk and trust constructs related to adopting e-government initiatives in Jordan. Results indicated a signifi-cant support of trust influence on ITU, which

is aligned with studies like Abu-Shanab et al. (2010) and Teo et al. (2008). Thus, three hypoth-eses were supported as shown in Table 6, and the only rejected hypothesis, was the perceived risk relationship to trust in E-government. The PR construct was tested against both DVs: TEG and ITU and yielded insignificant results, such result is similar to the study of Abu-Shanab et al. (2010), but contradicts with the results of Liu and Zhou (2010), Belanger and Carter (2008) and the study of Abu-Shanab and Abu-Baker (2011), where risk and privacy were major predictors of e-government adoption. On the other hand, TG and TI significantly predicted TEG, which means the high value Jordanian citizens put on trust in E-government. Also, TEG significantly mediated the relationship between TG/TI and ITU. Such mediation was proposed to explore how citizens conceptualize

Table5.Thedirectandindirecteffectsofthepredictorsinthemodel

TG Analysis

Path Effect

TG - TEG 0.381**

TEG - ITU 0.468**

Indirect effect 0.178*

Direct effect TG - ITU 0.377**

TI Analysis

Path Effect

TI - TEG 0.328**

TEG - ITU 0.468**

Indirect effect 0.154*

Direct effect TI - ITU 0.258**

** Large effect * Medium effect

Figure2.Theresultedpathmodel



the concept of overall trust in E-government. Path analysis facilitated the process of estimat-ing the effects of variables on the DVs. Results indicated significant direct and indirect effects, which indicates a partial mediation process.

Trust in E-government significantly influ-enced intention to use E-government with an R2=21.9%, beta value = 0.468, p < 0.001. Also, both trust in Internet and trust in government predicted TEG with an R2=34.8%, p < 0.001.

This study implies that to gain more trust in e-government initiatives, governments need to work on two major directions: the technology and the government image. It is important for citizens to trust technology (the Internet in this case) and build mechanisms for security and privacy protection. Also, speeding up related laws and regulations related to e-services and adhering with such privacy recommendations by the government will enhance citizens’ trust in e-government. On the other hand, governments need to work more on their image, and especially in developing countries. Such conclusions are shown clearly from the UN report related to e-government survey. The report indicates that trust is the cornerstone of government-citizen relationship and was a critical factor in escap-ing the last financial crises (UN Report, 2010). Also, the report allocated a special chapter for transparency and trust in public services. It is important for Jordanian government to utilize this chance of e-democracy paradigm and leverage the level of participation and reach citizens’ empowerment.

For researchers, it is important to explore more this field as one of the important factors failed to show significant results when entered to the model (i.e., perceived risk). Also, to explore

the distinction between risk and privacy and in-vestigate the influence of each, more research is needed to confirm results from previous research related to privacy and risk (Ruizhong, Xiaoxue, & Zixian, 2010; Rotchanakitumnuai, 2007; Wang et al., 2010; Abu-Shanab et al., 2011). Finally, the trust construct is a disputed one as we’ve seen from the literature where many studies defined trust in different manners, this calls for more attention to this important factor.

Future research is recommended to explore the reasons behind the insignificance of risk fac-tors, and to validate the instrument with a larger sample. Larger sample size is recommended to improve the reliability of the instrument and confirm the results.

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Table6.Hypothesesresults

Relationship Result

H1 Trustingovernmentwillpositivelyinfluencethetrustine-government Supported

H2 Trustininternetwillpositivelyinfluencethetrustine-government Supported

H3 Perceivedriskwillnegativelyinfluencethetrustine-government Not-Supported

H4 Trustine-governmentwillpositivelyinfluencetheadoptionofe-government Supported



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EmadAbu-ShanabreceivedhisPhDinbusinessadministration,majorinMISareain2005fromSouthernIllinoisUniversity,Carbondale,USA.HeisanassistantprofessorattheMISdepart-mentinYarmoukUniversity,Jordan,whereheteachesgraduateandundergraduatecourses.Heauthoredabookinarabicintheareaofe-government,andpublishedmanyjournalandconferencepapers.Hispublicationsandresearchinterestsareinareassuchase-government,technologyacceptance,e-learning,GDSS,ERPandstrategicissuesofinformationsystems.

AmeenAl-AzzamisamasterstudentinmanagementinformationsystemsdepartmentatYarmoukUniversity.Hereceivedhisbachelordegreeinmanagementinformationsystems,in2006.Hisresearchinterestsaretechnologyacceptanceande-government.

2013 abushanabtrust dimensions and the adoption of e government in jordan (1)

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