Customer empowerment: Does it influence electronic government success? A citizen-centric perspective

Electronic Commerce Research and Applications xxx (2015) xxx–xxx

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Electronic Commerce Research and Applications

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Customer empowerment: Does it influence electronic governmentsuccess? A citizen-centric perspective

http://dx.doi.org/10.1016/j.elerap.2015.05.0031567-4223/� 2015 Elsevier B.V. All rights reserved.

⇑ Corresponding author. Tel.: +61 2 49217367.E-mail address: [email protected] (R. Chiong).

Please cite this article in press as: Alshibly, H., Chiong, R. Customer empowerment: Does it influence electronic government success? A citizen-centspective. Electron. Comm. Res. Appl. (2015), http://dx.doi.org/10.1016/j.elerap.2015.05.003

Haitham Alshibly a, Raymond Chiong b,⇑a Amman University College for Financial & Administrative Sciences, Al Balqa Applied University, Jordanb School of Design, Communication and Information Technology, The University of Newcastle, Australia

a r t i c l e i n f o a b s t r a c t

Article history:Received 10 September 2014Received in revised form 22 March 2015Accepted 11 May 2015Available online xxxx

Keywords:Electronic government successCustomer empowermentPersonalizationNet benefitTrust

Electronic government (or e-government) initiatives are widespread across the globe. The increasinginterest in e-government raises the issue of how governments can increase citizen adoption and usageof their online services. In this study, the fundamental argument is that citizens can be viewed as cus-tomers, and that e-government success can be measured by the extent to which customer net benefitsare positively influenced. Hence, the key consequents of e-government success are customer-related,and the antecedents of such success have to be considered from the customer viewpoint. We advocatethat government agencies must consider their customers’ perceptions of empowerment as a key causalmechanism in deriving value from e-government systems. However, the literature appears to lack thisperspective. This study aims to fill the gap by proposing a theoretical model and an associated evaluationtool that measures the e-government performance from a customer empowerment perspective. Themodel was validated by a survey method and analyzed using partial least squares. The results supportour argument and show that all paths in the proposed model are significant.

� 2015 Elsevier B.V. All rights reserved.

1. Introduction

Information and communication technologies (ICT) have revo-lutionized the processes, operations and structures of public sec-tors in both developed and developing countries (Alshibly andAl-Dmour 2011; Rana et al. 2015). The application of ICT to govern-ment is considered a cost-effective solution that improves commu-nication between government agencies and their constituents byproviding access to information and services online (Vragov andKumar 2013). Most government services are now established elec-tronically, ranging from license registration and renewal, tax filingand payment to online voting. As a result, governments in differentcountries have implemented beneficial electronic government(e-government in short) initiatives, and others are following suitto enable their citizens to access services and information throughthe Internet (Karkin and Janssen 2014).

Generally speaking, three types of e-government systems andservices exist: Government to Government (G2G), Government toCitizen (G2C), and Government to Business (G2B). Government formsand services, public policy information, employment and businessopportunities, voting, tax return, license registration or renewal,fine payments and so on fall under the category of G2C services

(Wang and Liao 2008). There has been a recent shift of focus oncreating a more citizen-centric e-government platform (Karkinand Janssen 2014), which provides services in line with citizens’needs and offers greater accessibility. However, this endeavor can-not be guaranteed until e-government initiatives are embraced andutilized by citizens (Alshibly and Al-Dmour 2011). Consequently,citizens’ e-governance needs have become one of the primary con-cerns of government decision makers.

Successful implementation of e-government depends on theability to develop services that match the goals and requirementsof citizens and stakeholders. However, limited previous researchstudies have investigated e-government success from acitizen-based perspective (Scott et al. 2009). Specifically, citizens’needs or perceived net benefits have not been adequatelyaccounted for, leaving a clearly evident gap between design andreality in e-government service provision (Al-Haddad et al.2011). To this end, the concept of ‘‘customer’’ (i.e., citizen) empow-erment is imperative for understanding how citizens value the ser-vices they use and what features of e-government systems andservices influence their perceptions (Alshibly et al. 2015).Customer empowerment can be defined as a positive subjectivestate evoked by the feeling of increased control over the produc-tion of desired outcomes and the prevention of undesired out-comes relative to existing or previous systems (Hunter andGarnefeld 2008).

ric per-

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In this study, we aim to investigate the impact of customerempowerment on e-government success. The fundamental argu-ment is that e-government success can be measured by the extentto which customer net benefits are positively influenced, andhence the key consequents of e-government success arecustomer-related. The antecedents of such success thus have tobe considered from the customer viewpoint, and we advocate thatgovernment agencies must consider their customers’ perceptionsof empowerment as a key causal mechanism in deriving valuefrom e-government systems. This research may potentially helpto further understand the relationship between e-governmentand customer empowerment. It may also provide a new researchavenue for customer empowerment.

2. Literature review and hypotheses development

2.1. E-government success

An often suggested surrogate indicator of information systems(IS) success is user satisfaction. Many IS researchers have regardeduser satisfaction as the most important proxy of IS success, and it isalso the most employed measure (Au et al. 2002; Petter et al. 2013;Zviran and Erlich 2003). Cyert and March (1963) were among thefirst to suggest the concept of user satisfaction as a surrogate ofIS success. They suggested that if an IS meets the requirementsand needs of its users, then user satisfaction would increase.Since then, IS user satisfaction has been the subject of extensiveresearch, reaching its peak in the late 1980s. According to Iveset al. (1983), user satisfaction is ‘‘a perceptual or subjective mea-sure of system success that provides a meaningful surrogate forthe criticism, but immeasurable results of an IS, such as, changesin organizational effectiveness and success’’. Zviran and Erlich(2003) suggested that measuring user satisfaction provides busi-nesses with information about the system itself, because IS userscould be considered as sensors that measure the attributes of theirIS. An IS will be considered effective in meeting user needs if theusers are satisfied. In an e-commerce environment,user-satisfaction is an essential criterion for gaining customer loy-alty (Kim et al. 2012). Thus, it is considered a significant variable inmeasuring customer judgment, either positive or negative (Auet al. 2002). As a result, satisfaction in e-commerce includes theentire user experience journey, starting from searching for infor-mation through to purchasing and payment (DeLone and McLean2003).

E-government is essentially a form of IS, which shares similarcharacteristics with e-commerce in terms of providing services toor acting as an interactive channel for external users (customersand citizens). In fact, there have been some major overlapsbetween the core functions of e-government and e-commerce,which aim at exchanging information and/or conducting businesstransactions in a more cost-effective manner (Lin et al. 2011).These overlaps have motivated the former to learn from experi-ences of the latter, and adapt some of the e-commerce success sto-ries in the e-government domain (Sørum et al. 2012).Consequently, many citizen-centric studies in the e-governmentdomain have borrowed their models from the IS or e-commercefield (Rana et al. 2015; Wang and Liao 2008).

However, it is debatable whether these ‘‘borrowed’’ models arereally appropriate, as e-government systems clearly have somedistinctive differences compared to general information ore-commerce systems. For e-commerce, the strategic objective ofevery private organization is profit-oriented. These organizationsare mainly interested in providing good services and products sothat they can gain a competitive advantage, and subsequentlyattract more customers (Al-Haddad et al. 2011; Fountain 2001).

Please cite this article in press as: Alshibly, H., Chiong, R. Customer empowermespective. Electron. Comm. Res. Appl. (2015), http://dx.doi.org/10.1016/j.elerap.

Otherwise, customers would turn to competitors and choose thosewho provide better services and products (Srinivasan et al. 2002).Hence, customer satisfaction is an important indicator of the suc-cess of an e-commerce application (DeLone and McLean 2004).On the contrary, in the e-government context, government agen-cies do not compete with each other, as each has its own specialty.They offer a variety of free public services targeting a bigger andmore heterogeneous population (i.e., having different characteris-tics, like literacy, gender, income, etc.) than that of e-commerce(Al-Haddad et al. 2011). Moreover, each government agency pro-vides a variety of services to the public. Hence, the purpose forwhich citizens use e-government applications varies widely fromthat of an e-commerce system. Therefore, it is important to sepa-rately consider the factors that determine the success of ane-government system.

E-government services and facilities aim to provide conve-nience to citizens as well as to save their time and effort(Johnson 2008). As such, e-government success should be mea-sured based on the value created through positive benefits experi-enced by a citizen using an e-government system. The concept of‘‘net benefit’’ success measure (Petter et al. 2013; Scott et al.2009) for e-government services provides a theoretical frameworkwithin which the broad dimensions of success can be conceptual-ized. This measure is considered preferable because it successfullycaptures the balance of both positive and negative impacts of thesystem on the user. When specifying the conceptual context ofnet benefits, DeLone and McLean (2003) recommended that thefollowing three issues be taken into account: what counts as ben-efit, the individual or organization that benefits, and the level ofanalysis where it is considered beneficial. The clear focus of thisstudy is an evaluation of the citizens’ perspective ofe-government success and it is thus the user that defines the con-text or frame of reference as called for by DeLone and McLean(2003). Similarly, the benefits are to be measured from the individ-ual’s perspective, defining the level of analysis for this study. Sincethe focus here is on measuring e-government system success fromthe citizens’ perspective, ‘‘net benefit’’ in this study refers to thecitizen-perceived net benefit evaluation toward using a specifice-government system. Citizens and taxpayers may feel that theyare not getting benefit for their money. They would like this benefitreflected in terms of cost/time savings and better e-governmentsystem performance. Thus, ‘‘net benefit’’ appears to be an impor-tant success measure of e-government systems. This is confirmedthrough the study by Wang and Liao (2008), who stated that the‘‘perceived net benefit’’ is an important success measure ofe-government.

The concept of public values, as defined by Karkin and Janssen(2014) in the e-government context, involves considering citizensas customers who should be served at the lowest possible cost.One of the main arguments put forward by the public values para-digm is that citizens who are the collective demanders and users ofpublic services should be the ones to decide what is valuable as apublic commodity or service, rather than those who actually pro-duce it (Cordella and Bonina 2012). Accordingly, in attempting tounderstand the antecedents of e-government success, we considerthe customer not only as an end user for IS supportinge-government, but also as a traditional customer. Thus, the modelconcerns variables used to describe the achievement ofcustomer-related objectives of a firm. The latter, we have sug-gested are encompassed in the concept of customerempowerment.

Customer empowerment is a customer’s subjective experiencethat they have greater ability than before to intentionally producedesired outcomes and prevent undesired ones, and that they arebenefiting from the increased ability (Hunter and Garnefeld2008). It is a positive subjective state, which results from a mental

nt: Does it influence electronic government success? A citizen-centric per-2015.05.003


H. Alshibly, R. Chiong / Electronic Commerce Research and Applications xxx (2015) xxx–xxx 3

comparison of a customer’s abilities (or beliefs that they have anability) relative to existing or previous abilities. As such, it is onlythe perception of increasing control that evokes empowerment,and empowerment may be experienced regardless of whether con-trol actually increases or not. This belief has been shown to be animportant factor in shaping individual satisfaction (Fuchs et al.2010; Hunter and Garnefeld 2008). Accordingly, such a positivebelief should influence other judgments, including those ofe-government net benefits. This motivates the hypothesis relatingcustomer empowerment to e-government success (net benefits):

H1. Customer empowerment positively influences e-governmentsuccess.

2.2. Customer empowerment antecedents

Theories of power underpin conceptualizations of empower-ment: ‘‘to empower means to give power’’ (Thomas andVelthouse 1990). Empowerment is the gaining of power in partic-ular domains of activities by individuals or groups and the pro-cesses of giving power to them, or processes that encourage andfacilitate their taking of power (Alshibly et al. 2015). The academicliterature dealing with power has used diverse approaches adoptedfrom disciplines including psychology, economics and manage-ment, and contains a corresponding range of views of the conceptsof power and empowerment. Two views relevant to any investiga-tion of customer empowerment are: the psychological view ofpower and empowerment; and the social relations view. Thispaper looks at these two theoretical bases for understanding theconcept of customer empowerment, and then draws them togetherto provide a preliminary model of customer empowerment.

2.2.1. The psychological view of power and empowermentThe psychological view of power concerns an individual’s abil-

ity (or belief that they have an ability) to produce desired changesor influences. It is considered as a human need that individualsseek to satisfy (Schiffman and Kanuk 2009). Thus, Greenbergeret al., (1989) has defined power as an individual’s beliefs, at a givenpoint in time, in his or her ability to influence a change, in a desireddirection, on the environment. Empowerment is the process ofsupplying the individual with the ability to produce such influ-ences, in order to satisfy the desire for power. Rappaport (1987)described empowerment as the process of becoming able or beingallowed to do some unspecified thing.

Power and empowerment are also linked with control.Schiffman and Kanuk (2009) defined power as an individual’sdesire to control his or her environment. Fatout (1995) definedempowerment as a process for providing individuals with morecontrol by placing boundaries around an area of potentially accept-able behavior and allowing the individual to test and experimentwith a variety of choices. Rappaport (1987) said that the conceptof empowerment ‘‘conveys both a psychological sense of personalcontrol or influence’’.

The psychological view of empowerment has been adopted andassessed extensively in the management field. Notions of empow-erment are derived from theories of participative management, inwhich managers share decision-making power with employees toenhance performance (Cook and Macaulay 1997). Therefore, staffempowerment concerns organizational behavior that givesemployees more power to serve customers; typically, staff empow-erment transfers power and responsibility to employees so that,within specified limits, they will be better able to provide the bestpossible service at their own judgment (Dahle 2000). In servicesmarketing, empowerment has been linked with both attitudinaland behavioral changes in employees, including increased


employee perceptions of satisfaction, loyalty, performance, levelof service delivery, and concern for others (Fulford and Enz 1995).

The customer behavior literature has themes that are part of, orclosely related to, the concept of power. These include perceivedcontrol, perceived locus of control, self-efficacy and choice (e.g.,Broniarczyk and Griffi 2014; Botti and Iyengar 2006). Notions ofcontrol or power have been used in several theoretical frameworks,with a common understanding that control/power is a human driv-ing force and the feeling of being in control over one’s environment(Botti and Iyengar 2006).

Consumer behavior researchers have associated the concept ofpower with that of perceived choice. Botti and Iyengar (2006) sug-gested that a perceived choice is one of the important types of con-trol, and Hui and Bateson (1991) argued that any behavioral oremotional effects caused by the availability of choices can be con-sidered as the outcome of perceived control. In this context, controlhas been defined as the freedom of choosing an alternative fromamong a choice set, instead of being assigned to the same alterna-tive by an external agent (i.e., other individuals or chance).However, Wathieu et al.’s study (2002) showed that the experienceof empowerment derives not from more choices, but from the sys-tem’s flexibility in allowing customers to define the choices avail-able to them. Their study also emphasized the importance ofequipping customers with tools to handle the variety of choicesand to discover the option that best meets their needs.Organizations offering large choices of products or services shouldmatch their offerings to their customers’ distinctive needs andwants. This has led us to suggest that the related concept of per-sonalization as an important factor in customer empowerment.

2.2.2. PersonalizationThe emphasis placed by the psychological view of empower-

ment on a user’s ability to shape the environment suggests thatthe related concept of personalization is also an important factorin the feeling of empowerment. Personalization is known as theprocess of customizing an IS’s functionalities, interfaces, and con-tents to a user’s demand based on knowledge obtained throughservices and user interactions (Park 2014; Pappas et al. 2014). Itis also widely known that personalization benefits users with moresuitable added value offerings (Kwon and Kim 2012).

Coner (2003) believed that personalization allows a firm to bet-ter match its products or services to each customer. If customersare better off with less variety, then the firm can increase profitsby reducing product lines. In a related study, Huffman and Kahn(1998) provided empirical evidence that customers are more satis-fied when they are allowed to specify their attribute preferences inselecting products. Huffman and Kahn found out that when cus-tomers were asked to choose items from a wide assortment, toomuch variation and complexity of choice could decrease customersatisfaction. However, by simplifying the choice process, they wereable to increase customer satisfaction. Huffman and Kahn alsoargued that, in order to increase customer satisfaction with theirshopping experience, firms need to focus on information presenta-tion as well as analyze customer inputs (to learn about availablealternatives). Another related study by Kwon and Kim (2012) pos-ited that using personalized services may increase customer loy-alty. For instance, when the recommendations are based onbrowsing histories and customers are able to make a purchase withjust a few clicks, they are more likely to complete that purchase.

In the e-government environment, personalization entails threefundamental aspects: first, adjusting information based on cus-tomer preferences and characteristics; second, portraying theinformation in a real-time environment; and third, estimatingand evaluating the behavior of customers (Gillenson et al. 1999).Personalized e-government services divide customers into groups(market segments), and are an important part of a computerized




one-to-one marketing system (Al-hassan et al. 2011). A workingdefinition of personalization concerns the extent to which informa-tion flow can be adapted to a customer according to his/her char-acteristics. The customer’s interaction with a firm is documented,interpreted and stored in a database for subsequent analytical use.

Personalization in e-government can also be seen as a specialform of differentiation that can transform a standard product orservice into a specialized solution for customers (Changchienet al. 2004), or as the adaptation of a particular online service toa single user based on user-related information (Al-hassan et al.2011). Al-hassan et al. (2011) believed that e-government person-alization has the potential to improve interactions between gov-ernments and citizens. Adopting personalization enablesgovernments to enhance their relationship with citizens and makeinteractions with them easier. With this, governments are able tomeet their citizens’ needs more effectively and efficiently.

Personalization helps customers get rid of unnecessary infor-mation or product options, thereby reducing the time and effortrequired for them to achieve what they aim for. When a customergets more relevant and targeted information, the customer’s expe-rience improves (Kwon and Kim 2012; Lee et al. 2012).Personalization can also create the perception of increased choiceby enabling a quick focus on what the customer really wants,according to Srinivasan et al. (2002). In addition, personalizationhas been claimed to signal better quality of service and help cus-tomers complete their transactions more efficiently (Pappas et al.2014; Srinivasan et al. 2002). The above discussion suggests thatpersonalization, by creating more personal, interesting and rele-vant services, can work to increase customer empowerment.However, we know of no empirical evidence for the relationshipwith e-government. Thus, the following hypothesized relationshipbetween personalization and customer empowerment will beinvestigated:

H2. Personalization positively influences customer empowerment.

2.2.3. The social relations view of power and empowermentIn sociology studies, it is generally agreed that power is an

important concept to describe and clarify the relationships amongsocial actors. Broadly speaking, the social relations view of powerassumes that individuals and groups are dependent upon eachother for the satisfaction of their needs. In other words, powerstems from a state of interdependence. This view is supported bythe work of Bacharach and Lawler (1980), who suggested thatpower is a function of dependence. More specifically, the powerof an actor is a function of the other person’s dependence on theactor.

Early sociology researchers, led by Russell (1938), Weber (1947)and Dahl (1957), have examined the concept of power in detail anddescribed its complexity. They were in consensus that the socialrelations view of power is about the ability of individuals or groupsto impose their will on others, or their ability to make others dosomething they would not have done otherwise (Russell 1938;Weber 1947; Dahl 1957). For example, Russell (1938) stated that‘‘power is the capacity of some persons to produce intended andforeseen effects on others’’. Weber (1947) said that in the relation-ship context, power is often related to an individual’s ability tomake others do what he/she wants, regardless of their own wishesor interests. Dahl (1957) concurred with Russell and Weber andsuggested that power is the ability to evoke a change in others’behaviors. He defined power as: ‘‘A has power over B to the extentthat A can get B to do something that B would not otherwise do’’(Dahl 1957). It appears that the power to control, or influencethe other, resides in control over things one values.


Building on Deutsch (1973), Coleman (2005) suggested thatpower is the ‘‘reciprocal interaction between the characteristicsof a person and the characteristics of a situation’’, where the indi-vidual has the capacity to influence outcomes in their personal,relational and/or environmental domains. Coleman viewed poweras an individual’s capacity to influence the outcomes of the indi-vidual him/herself, of another person, and of the individual’s envi-ronment. According to Coleman, such positive power means thatindividuals seek out each other’s abilities and appreciate theother’s contributions, negotiate with and influence each other toexchange resources that will help them both be more productive,and encourage each other to develop and enhance their valuedabilities.

In the commercial context, Ramsay (1995) said that buyers pos-sess power if they are able to produce intended changes in a sup-plier’s product specification, create a closer match between thesupplier’s specification and the buyer’s purchase specification,and incur increased sellers’ costs without increasing buyer costs.Therefore, in order to empower customers in a competitive mar-ketplace, vendors may try to increase the perceived attractivenessof their products by offering product specifications (quality) thatexceed the buyer’s purchase specification, or that exceed the spec-ifications offered by their competitors. In fact, anything thatincreases a vendor’s need or desire to deal with a specific customer,or that increases the customer’s freedom or their conversion capa-bility, tends to increase the customer’s power.

Luhmann (1979, 1988) noted that individuals are essentiallyindependent agents whose behavior is not always understandableor rational. Individuals have to consider an enormous number ofpotential behaviors other individuals and organizations might dis-play in their interactions. One important factor that determinesinteractions with others is ‘trust’. According to Luhmann, a coreaspect of human behavior is the need to control and predict thesocial environment: individuals have a need to predict how theirbehavior will influence the behavior of others, and hence affectthemselves. Rules and customs are instigated to reduce social com-plexity and improve predictability, but if these do not exist or arenot strongly enforced, people have to rely on trust. Luhmannviewed trust as an essential ingredient for any successful relation-ship; according to Luhmann’s theory, human beings try to createand support trust because they like to reduce their social uncer-tainty. They have a need to know in advance how their behaviorwill influence the behavior of others, and how the behavior ofothers will consequently affect themselves.

2.2.4. TrustThe social relations view of power and empowerment empha-

sizes the need to reduce complexity in social interactions, whichcan occur through trust. Urban et al. (2000) argued that investiga-tions into customer empowerment need to consider the role oftrust strategies, as they stated that trust building can be seen asa method to increase customer satisfaction.

Urban (2004) suggested that the increasing power of customerswill compel a new paradigm for marketing based on providingopen and honest information and advice. The argument is that cus-tomers’ increasing power decreases the effectiveness of old-stylemarketing strategies and, therefore, trust building becomes a crit-ical factor in the success of a business-customer relationship.

Luhmann (1979) noted that in the commercial sphere, trust isbuilt on information that reflects a seller’s reputation, policies,practices and performance history. Building trust leads tolong-term relationships and higher long-term profits (Corbittet al. 2003). Trust is an interpersonal determinant of behavior thatdeals with beliefs about the integrity, benevolence, ability, and pre-dictability of other people, according to McKnight et al. (2002),




who claimed that interpersonal interactions, or even cues relatingto them, are notably missing from e-commerce websites.

Hoffman et al. (1999) discussed the need for control in an onlineenvironment – they observed in particular that customers feel theylack control over the access that web merchants have to their per-sonal information and consequently, are hesitant to trust the mer-chants or firms. The importance of control in online environmentsincreases because customers are dealing with time asymmetry, inthe sense that payment is made before the receipt of a product orservice. Thus, empowering customers by providing them withmore control is likely to be important in building trust.Moreover, because the social relations view of empowerment sug-gests that regulations and conventions reduce complexity and riskin social interactions, regulations may provide control mechanismsthat empower customers (Cook 2002).

In the e-commerce literature, trust has been conceptualized invarious ways. McKnight et al. (2002), in an attempt to reconcile avast body of literature regarding the different types of trust, devel-oped a typology of trust consisting of five types: interpersonaltrust, trusting beliefs, system trust, dispositional trust, and deci-sion to trust. Interpersonal trust is about an actor’s willingness todepend on another actor with a feeling of security, even when neg-ative consequences are possible. Trusting beliefs are based on anindividual’s cognitive beliefs about the other party’s characteristics(e.g., competence, integrity). Dispositional trust is a person’s ten-dency to trust across a broad spectrum of situations and persons.Decision to trust refers to the intention to trust in particular situa-tions (McKnight et al. 2002). Collectively, these constructs providea reasonable definition of trust.

However, in the context of studying e-government success, acritical issue has hampered empirical investigations of the impactof customer trust on e-government success: the confusion betweeninterpersonal trust and system trust. With interpersonal trust,trust is with a person or business, whereas system trust is aboutthe reliability and security of the system. Pennington et al.(2003) defined system trust as ‘‘a belief that the proper impersonalstructures have been put into place, enabling one party to antici-pate successful transactions with another party’’. In the online set-ting, trust is not only affected by properties attributed to the firm,but also by the electronic system mediating the transaction.Pennington et al. (2003) noted that much of the conceptualizationof trust within the e-commerce literature focuses on the risk of asituation, rather than on the question of whether the user canactively manipulate the system and intervene in the process.Similarly, McKnight et al. (2002) noted that much of the discussionin the literature around trust deals with building trust in a physi-cal, interpersonal environment, and that little is known abouthow to create a trust-conducive environment based on interactivemedia systems. Building trust is a very important forward step inbuilding successful e-government projects, since users who trustthe e-government system are more willing to adopte-government services.

Although the lack of human interface may be considered as anegative aspect of e-government transactions for some citizens,others are motivated by the absence of human presence, as thismay enhance the feeling of empowerment, freedom and control.However, it very much depends on the anonymity of transactionsand protection of personal information. The past literature has dis-cussed different sources of risks in e-government transactions,especially the risk of privacy and security (Teo et al. 2008; Chauet al. 2007).

Pavlou et al. (2006) suggested that the spatial and temporalseparation between consumers and firms in the online environ-ment increases fear of vendor opportunism due to product andidentity uncertainty, and fear of customers’ personal informationbeing exploited by multiple parties not directly linked to the


transaction. This spatial and temporal separation reduces cus-tomers’ confidence in e-government activities, creating a barrierto e-government success (Teo et al. 2008).

The uncertainty of the online environment emphasizes theimportance of building the sense of trust. In this study, trust willbe viewed as a two-dimensional construct. The first dimension issecurity, relating to the security of a consumer’s interaction withthe e-government system as well as the security and protectionof personal information. The second dimension is privacy, relatingto the right of an individual to be left alone and able to control therelease of his or her personal information (Li 2014).

To sum up, the social relations view of empowerment empha-sizes the need to reduce risk in e-government interactions, whichcan be achieved by increasing trust. Most theorists are in agree-ment that trust is closely related to empowerment. Luhmann(1979) noted that trust is instrumental in reducing uncertainty;trust is also useful in enhancing the perceived probability ofdesired behavior. Castelfranchi and Falcone (2000) pointed out thattrust and control go hand in hand and claimed that trust is espe-cially needed when a consumer has inadequate power and controlover the system. Accordingly, the relationship betweene-government trust and customer empowerment is hypothesizedas:

H3. Trust positively influences customer empowerment.

Within the e-government context, the lack of physical contactinherent in the online experience causes customers to dependgreatly on information technology (IT) behind the e-governmentsystem. IS researchers generally regarded personalization to be ahighly important characteristic of all web-based ISs (Komiak andBenbasat 2006), independent of the specific application a systemwas designed to support. In the e-commerce context, Komiak andBenbasat (2006) found that personalization influences trust in apositive way by facilitating the perceived competence of the ITartifact and the user. In turn, this study suggests that the greaterthe extent to which an e-government website understands andrepresents the personal needs of the user and the degree to whichinformation is tailored to meet the needs of an individual user, thehigher is the customer’s trust (this is in agreement with the litera-ture reviewed above). However, there has been little empiricalresearch about the impact of personalization provided bye-government websites on trust. Therefore, this work contributesto the current knowledge about the impact of personalization ontrust by investigating the following hypothesis:

H4. Personalization positively influences trust.

In our research model, which is shown diagrammatically inFig. 1, e-government success is posited to be directly affected bycustomer empowerment. Since the focus of this study is on themeasurement of G2C system success from the perspective of citi-zens, the net benefit in this study refers to the citizen-perceivednet benefit evaluation of a specific G2C system. Citizens and tax-payers may feel that they are not getting benefit for their money.Thus, the ‘‘perceived net benefit’’ appears to be an important suc-cess measure of G2C systems.

3. Research methods

3.1. Sampling and data collection

In this study, a self-completion questionnaire was designed. Itwas decided that the questionnaire should be circulated to citizenswho are familiar with e-government services. Accordingly, purpo-sive sampling was used. A field survey carried out by the



Fig. 1. The research model.


Department of Statistics in Jordan in 2012 indicated that 43% ofJordanian families did not have computers due to financial con-straints, while 47% of Jordanian households were online. The fieldsurvey also indicated that only 6.6% of Jordanians were usinge-government services. Consequently, a sample size of 400 respon-dents was taken from the population of Amman – the capital andmost populous city of Jordan – between April and May 2014.

The questionnaire was accompanied by a cover letter explainingthe research objectives. Respondents were first asked whether theyhad used G2C applications through the Jordan e-government portal(www.jordan.gov.jo), a one-stop portal of the Jordaniane-government system where users can immediately and conve-niently access available G2C services online. If the response wasaffirmative, the respondents were invited to participate in the sur-vey. They were then given a copy of the questionnaire and wereasked to complete the questions as instructed at a time convenientto them.

Among the 400 questionnaires sent out, we received 221 ofthem back. However, 45 questionnaires were discarded becausethey had an unacceptable amount of missing data (Hair et al.2013). Therefore, 176 usable questionnaires were accepted for dataanalysis. The usable responses represent a 44% response rate,which is considered to be adequate for this type of study.

Table 1 lists the respondent demographics. From the table, wesee that male participants represent a slightly higher percentageof the effective sample (approximately 52%) compared to femaleparticipants (approximately 48%). The majority of the participantsare aged 23–45 years (approximately 69%). The completed sampleis composed of well-educated individuals: about 70% of them have

Table 1Demographic characteristics of the survey respondents (n = 176).

Characteristics Items

Gender MaleFemale

Age Less than 22 Y23 Y to less than 333 Y to less than 446 Y to less than 555 Y and more

User’s education level High school or beloDiplomaBachelorMasterPhD

Computer experience (years) Less than 1 Y1 Y to less than 5 YMore than 5 Y

E-government experience (years) Less than 1 Y1 Y to less than 3 YMore than 3 Y

Internet access DailyWeeklyMonthly


university or post-graduate qualifications. Around 77% of the par-ticipants have more than 5 years of experience in using computers.The table also reveals that the majority of the participants (92%)have used e-government services for more than 3 years, and that91% of the participants access the Internet daily.

3.2. Construct measurement

A total of four different constructs, as shown in the researchmodel in Fig. 1, have been used in this study. Each of the constructshas multiple items. To increase the validity and reliability of theresults, most of the items used for these constructs were adaptedfrom previously validated studies (Hair et al. 2013). Most of thescales that have been used were previously validated in the IS ormarketing field. Any changes required to fit the instruments tothe e-government context were appropriately performed. Thus, itwould be safe to assume that using these scales and items withsome contextual changes can help to address the research aim ofthis study.

Specifically, six items for personalization were adapted andrefined from the work of Burnham et al. (2003) and Park (2014).Five items for trust were adapted and refined from Egger’s(2003) and Bart et al.’s (2005). To measure customer empower-ment, four items were adopted and refined from instruments usedby Hunter and Garnefeld (2008). However, the e-government suc-cess (net benefits) construct had not been examined empirically inthe context of e-government. Consequently, we identified the mainindicators used by researchers to measure net benefits and facili-tate conditions from the IS literature, and ‘‘borrowed’’ them for

Frequency Percentage (%)

91 5285 48

42 23.92 Y 73 41.55 Y 48 27.25 Y 12 6.8

1 0.6

w 5 2.847 26.7

110 62.511 6.3

3 1.7

1 0.639 22.1

136 77.3

6 3.48 4.6

162 92

160 9110 5.6

6 3.4


http://www.jordan.gov.jo


Table 2Measurement items.

Constructs Survey items Sources

Personalization P1: I ‘‘set up’’ the e-government website to use it the way I want to Burnham et al. (2003)P2: The e-government website makes good suggestions about what kinds of things customersmight want

Park (2014)

P3: The e-government website offers services based on information that customers volunteerP4: The e-government website guides a customer through the service choicesP5: The e-government website enables me to gain access to personalized account information forknowing what I wantP6: The e-government website can provide me with personalized service tailored to my needs

Trust T1: I think the e-government website is secure Egger (2003)T2: I think the e-government website is reliableT3: The e-government website addresses my concerns as a customer about privacy Bart et al. (2005)T4: The e-government website implements security measures to protect its online customersT5: I think the e-government website is trustworthy

Customer empowerment Empo1: In my dealings with this e-government website, I feel I am in control Hunter and Garnefeld (2008)Empo2: The ability to influence the services of the e-government website is beneficial to meEmpo3: I feel better because of my ability to influence the choice set offered to me by the e-government websiteEmpo4: My influence over the e-government website has increased relatively compared to the past

E-government success (netbenefits)

EG1: Altogether, I think the e-government website meets all my expectations Oliver (2010)

EG2: I’ve had especially good experiences with this e-government website Etezadi-Amoli and Farhoomand(1996)

EG3: I think the e-government website saves my timeEG4: I think I made the right choice when I started using the e-government website


use in this study. Accordingly, the e-government success constructwas measured by using two items from Etezadi-Amoli andFarhoomand’s (1996) user performance scale and two fromOliver’s (2010) universal product/service consumption satisfactionscale. All of these items were measured using a 5-point Likert scaleranging from ‘‘strongly disagree’’ (1) to ‘‘strongly agree’’ (5), to indi-cate the respondent’s level of agreement and disagreement toward agiven statement.

After the measurement variables were developed, the facevalidity of these variables was tested. Three IS scholars and onemanagement scholar reviewed the measurement variables. Inaddition, ten IS graduate students were asked to review the mea-surement variables and provide feedback on the length and clarityof each item as well as ease of completion. They also assisted intranslating and validating the Arabic version of the survey ques-tionnaire distributed to e-government users. Based on the feedbackreceived, any question items that would cause confusion or weredeemed potentially difficult to understand were dropped orreplaced by new, easier-to-understand items. Table 2 presentsthe research constructs and related survey items used for the mea-surement of each of these constructs.

4. Research results

Partial Least Squares-Structural Equation Modeling (PLS-SEM)was used for data analysis and hypotheses testing through theSmartPLS software version 3.1.7 (Ringle et al. 2014). PLS-SEM cananalyze structural equation models involving multi-item con-structs, with direct and indirect paths. It works by extracting suc-cessive linear combinations of the predictors and is effective inexplaining both the response and predictor variations (Davcik2014).

More importantly, PLS-SEM has the ability to simultaneouslyevaluate the measurement model (the relationships between con-structs and their corresponding indicators) and the structuralmodel (the relationships among constructs), with the aim to min-imize the error variance (Chin 2010; Hair et al. 2014). It generatesloadings between reflective constructs and their indicators,


weights between formative constructs and their indicators, stan-dardized regression coefficients between constructs, and coeffi-cients of multiple determination (R2) for dependent variables(Davcik 2014). PLS-SEM is powerful for analyzing models becauseof its minimal demands on measurement scales, sample sizes,and residual distributions. In addition, it avoids two serious prob-lems: inadmissible solutions and factor indeterminacy (Martínez-López et al. 2013). SEM approaches, such as LISREL and AMOS,are not able to deal with non-normal distributions, and they canyield non-unique or otherwise improper solutions in some cases(Hair et al. 2014). PLS-SEM is useful in screening out factors thathave an insignificant effect on the dependent variable. Its emphasisis on predicting the responses as well as in understanding theunderlying relationship between variables (Monecke and Leisch2012).

In this study, we have chosen PLS-SEM as the primary data anal-ysis technique because of its minimal requirements regarding thesample size, as it does not assume multivariate normality andtakes into account the measurement error when assessing thestructural model. A rule of thumb for the required sample size inPLS-SEM is that the sample should be at least ten times the numberof independent variables in the most complicated multiple regres-sion of the model (Chin 2010). The sample size in this study metthe minimum sample size requirement. According to Hair et al.’s(2014) guidelines, the minimum number of respondents for thisPLS-SEM analysis should be 60 observations. Furthermore, for apower of 80% with 60 observations, the R2 for the respective latentvariable would have to be 0.50. The minimum acceptable N can becalculated by identifying the reflexive latent variable with the lar-gest number of the indicators, and multiplying that number ofindicators by 10. As per Table 2, this would be the personalizationlatent variable, with 6 indicator variables, and 10 � 6 is 60. Oursurvey had an N of 176 observations, which exceeds the generalrule requirement.

We applied PLS-SEM to validate the constructs of personaliza-tion, trust, customer empowerment, and e-government success,as well as to test the hypotheses. Here, PLS-SEM path modelingwas applied with a path-weighting scheme for the inside approxi-mation (Chin 2010). Then, the non-parametric bootstrapping




approximation was applied with 200 resampling to obtain thestandard errors of the estimates (Hair et al. 2013, Martínez-Lópezet al. 2013).

4.1. The measurement model assessment

The measurement model was assessed by reliability, conver-gent and discriminant validity. Reliability was tested byCronbach’s alpha and composite reliability (CR). Both Cronbach’salpha and CR provide an estimate of the reliability based on theinter-correlations of the observed indicator variables. Accordingto Hair et al. (2013), items have acceptable reliability if theCronbach’s alpha and CR values are greater than 0.70. As shownin Table 3, the Cronbach’s alpha and CR values for each of the fourconstructs – personalization, trust, customer empowerment, ande-government success – range from 0.85 to 0.95, which are abovethe suggested threshold of 0.7. Thus, the scale can be consideredreliable.

For construct validity, both the convergent and discriminantvalidity were examined. The convergent validity (i.e., the extentto which a measure correlates positively with alternative measuresof the same construct) was confirmed by examining the averagevariance extracted (AVE). As shown in Table 3, all of the AVE valuesare higher than the recommended level of 0.5, and thus the cut offvalue assures that at least 50% or more of the variances in theobserved variables are explained by the set of indicators (Chin2010). This shows that the convergent validity is good (Hair et al.2014).

The discriminant validity was assessed by examining whetherthe square root of the AVE for each construct is greater than thecorrelation between that construct and other constructs (Fornelland Larcker 1981). Table 4 lists the correlation matrix for the

Table 3Measurement model analysis (reliability and convergent validity).

Constructs Items Factor loadings Com

Personalization P1 0.70 0.89P2 0.81P3 0.74P4 0.78P5 0.77P6 0.74

Trust T1 0.84 0.92T2 0.75T3 0.90T4 0.86T5 0.81

Customer empowerment Empo1 0.88 0.94Empo2 0.88Empo3 0.90Empo4 0.88

E-government success EG 1 0.89 0.95EG 2 0.87EG 3 0.90EG 4 0.89EG 5 0.90

Table 4The AVE and squared correlations (discriminant validity).

E-government success C

E-government success 0.89Customer empowerment 0.74 0Personalization 0.56 0Trust 0.56 0

Note: The bold elements on the diagonal represent the square root of the AVE, and off-d


constructs. The diagonal elements in the ‘‘correlation construct’’(in bold) represent the square root of the AVE. Those off-diagonalelements are the correlations among constructs. As shown in thetable, the square root of the AVE for each construct is greater thanthe correlation between constructs, thus offering evidence of dis-criminant validity.

An alternative approach to assessing discriminant validityinvolves examining the cross loadings. Discriminant validity canbe established when an indicator’s loading on a construct is higherthan all of its cross loadings with other constructs. Table 5 showsthat the study construct indicator’s loadings are higher than allof its cross loadings. For example, the indicator Empo3 has thehighest value for the loading with its corresponding construct cus-tomer empowerment (0.90). All cross loadings with other con-structs have lower values (E-government success = 0.66;Personalization = 0.40; Trust = 0.64). The same finding holds forthe other indicators of customer empowerment (Empo1, Empo2& Empo4) as well as the indicators measuring e-government suc-cess, personalization, and trust. Thus, the discriminant validityhas been established.

4.2. Structural equation modeling

Two measures were used to assess the structural model: thestatistical significance (t-tests) of the estimated path coefficients,and the ability of the model to explain the variance in the depen-dent variables (R2). R2 results represent the amount of variancein the construct in question that is explained by the model (Chin2010). R2 attempts to measure the explained variance of thedependent variable relative to its total variance. Values of approx-imately 0.35 are considered substantial, values around 0.333 mod-erate, and values of approximately 0.190 weak (Chin 2010). To testthe significance of the hypotheses, the rule proposed by

posite reliability (<0.7) Cronbach’s alpha a (<0.7) Ave (<0.5)

0.85 0.57

0.89 0.70

0.91 0.78

0.93 0.79

ustomer empowerment Personalization Trust

.88

.48 0.75

.71 0.36 0.84

iagonal elements are the correlation estimates.



Table 5The loadings and cross loadings for the construct indicators.

Construct Items E-government success Customer empowerment Personalization Trust

E-government success EG1 0.89 0.72 0.50 0.62EG2 0.87 0.59 0.49 0.47EG3 0.90 0.74 0.47 0.52EG4 0.89 0.58 0.53 0.40EG5 0.90 0.61 0.51 0.45

Customer empowerment Empo1 0.68 0.88 0.45 0.62Empo2 0.66 0.88 0.46 0.60Empo3 0.66 0.90 0.40 0.64Empo4 0.60 0.88 0.38 0.68

Personalization P1 0.44 0.31 0.69 0.26P2 0.43 0.39 0.81 0.29P3 0.47 0.39 0.74 0.34P4 0.39 0.39 0.78 0.25P5 0.45 0.40 0.77 0.25P6 0.35 0.28 0.74 0.21

Trust T1 0.52 0.66 0.30 0.84T2 0.40 0.48 0.25 0.75T3 0.50 0.63 0.35 0.90T4 0.42 0.60 0.27 0.86T5 0.48 0.57 0.30 0.81

Note: The bold elements are indicators’ loadings.

Table 6Standardized coefficients (b), R2, and t-statistics.

Hypotheses b R2 t-statistics p-values Results

H1 Empowerment ? E-government success 0.736 0.541 18.969⁄⁄⁄ 0.000 SupportedH2 Personalization ? Empowerment 0.257 0.567 5.237⁄⁄⁄ 0.000 SupportedH3 Trust ? Empowerment 0.622 12.997⁄⁄⁄ 0.000 SupportedH4 Personalization ? Trust 0.357 0.127 4.248⁄⁄⁄ 0.000 Supported

***p < .001, based on a two-tailed test; t (p < 1%) = 2.58; t (p < 5%) = 1.96; t (p < 10%) = 1.65.

Fig. 2. Results of the research model tests.


Martinez-Ruiz and Aluja-Banet (2009) was followed. That is, thet-value > 1.65 is significant at the 0.05 level, and the t-value > 2 issignificant at the 0.01 level. As aforementioned, the statistical sig-nificance of each path was estimated using a PLS-SEM bootstrap-ping method utilizing 200 resamples to obtain the t-values.

Results of the analysis are shown in Table 6 and Fig. 2.The findings provide support for H1, which predicted positive rela-tionships between customer empowerment and e-government suc-cess. As can be seen, customer empowerment has significanteffects on e-government success (b = 0.736, p < 0.01), and the t-testsare significant (p < 0.01) for customer empowerment (t = 18.969).Therefore, consumer empowerment is making significant contribu-tions in explaining the variance in e-government success.

The two variables, personalization and trust, are found to havesignificant positive influences on customer empowerment


(b = 0.257, p < 0.01; b = 0.622, p < 0.01, respectively). The t-testsare significant (p < 0.01) for both personalization (t = 5.237) andtrust (t = 12.997). Therefore, both personalization and trust aremaking significant contributions in explaining the variance in cus-tomer empowerment. The standardized beta (b) values of 0.622 fortrust and 0.257 for personalization show that trust has moreimpact than personalization on customer empowerment. Theseresults provide support for H2 and H3.

Finally, the results also provide support for H4. Personalizationis found to have significant positive influences on trust (b = 0.357,p < 0.01). The t-tests are significant (p < 0.01) for personalization(t = 4.248). Therefore, personalization is making significant contri-butions in explaining the variance in trust.

In terms of the R2 value for each endogenous variable, customerempowerment explained 54.1% of the variance in e-government




success. In addition, personalization and trust explained 56.1% ofthe variance in consumer empowerment. According to the thresh-olds denoted by Chin (2010), the R2 results of customer empower-ment (R2 = 0.567) and e-government success (R2 = 0.541) aresubstantial. Fig. 2 shows the standardized path coefficients as wellas variances explained.

5. Discussion, implications, limitations, and future research

The goal of this study was to investigate the impact of customerempowerment on e-government success. We argued thate-government success can be considered as the extent to which‘‘net benefits’’ experienced by a citizen using an e-government sys-tem are positively influenced. This means it is important to takethe ‘‘customer’’ (i.e., citizen) viewpoint into consideration.Following this perspective, the concept of customer empowermentwas introduced as a key factor in determining e-government suc-cess. To the best of our knowledge, no previous studies in the IS lit-erature had examined or modeled the linkage between customerempowerment and e-government success.

5.1. Discussion of the results

The first conclusion of the study is that there is a positive rela-tionship between customer empowerment and e-government suc-cess. Our results have shown that customer empowerment couldexplain 54.1% of the variance in e-government success (seeTable 6). The findings suggest that customer empowerment canincrease e-government success by providing customers with theperception that they are in control when navigating throughe-government websites, the perception that they have the abilityto influence the services of e-government websites, the perceptionthat they have the ability to influence the choice set offered tothem by the e-government website, and the perception that theyhave an increasing influence over e-government websites.

The second conclusion relates to the impact of personalizationon customer empowerment. The literature has identified personal-ization as a major antecedent of customer empowerment (Wathieuet al. 2002). Personalization represents the psychological view ofempowerment, which emphasizes the ability to control and shapethe environment. The psychological view of empowerment high-lights the importance of equipping customers with tools to handlethe variety of choice; to help them sort through the variety anddetermine which option best fits their needs (Pappas et al. 2014).To do so, government agencies should provide customers withthe ability to customize e-government websites and use thesewebsites the way they intend to. A government website shouldmake suggestions about what kinds of things customers wouldwant, and tailor the way information is presented to them. Thisstudy’s overall results have shown that personalizede-government websites can foster customer empowerment byenabling customers to order tailor-made services, gain access topersonalized account information, guide customers through ser-vice choices, and make good suggestions about what kinds ofthings a customer might be interested in. These findings supporttheoretical suggestions from prior studies that personalizationhas a direct and positive impact on customer empowerment (e.g.,see Wathieu et al. 2002). As far as we know, however, none ofthe previous studies had empirically tested this in the context ofe-government success.

The third conclusion relates to the impact of trust on customerempowerment. Our results have suggested that trust increasescustomer empowerment by giving them the perception that theyare in control and by making them more confident in their interac-tions with e-government. Specifically, such trust is found to


depend on the extent to which the e-government addresses cus-tomer concerns about privacy and implements security measuresto protect its online customers. This line of reasoning is consistentwith the social relations view of empowerment, which emphasizesthe need to reduce complexity in social interactions, as can occurthrough increased trust. Pavlou et al. (2006) claimed that the spa-tial and temporal separation between customers and firms in thee-government context increases fear of vendor opportunism dueto product and identity uncertainty, as well as fear of personalinformation being exploited by parties not directly linked to thetransaction. This uncertainty then increases the importance ofbuilding the sense of trust. Of course, many antecedents may drivethese perceptions. In this study, trust has been viewed as atwo-dimensional construct. The first dimension is termed security,relating to the extent to which e-government secures interactionswith customers and provides reasonable assurance that personalinformation is kept secure and protected. The second dimensionis privacy, relating to the right of an individual to be left aloneand able to control the release of his or her personal information.In e-government, privacy and security policies help regulate theinteractions a consumer has with e-government services. Ourresults support the idea that the level of customers’ trust and theirbehavioral retention increases on integrating security dimensionsinto the design of e-government websites (Teo et al. 2008).

Finally, personalization is found to have significant positiveinfluences on trust. These findings support prior studies’ sugges-tions that the greater the extent to which an e-government websiteunderstands and represents the personal needs of the user, and thegreater the degree to which information is tailored to meet theneeds of an individual user, the higher the customer trust is (e.g.,see Komiak and Benbasat 2006).

5.2. Theoretical implications

The main novelty of this work being that it has introduced cus-tomer empowerment as a key causal mechanism in deriving valuefrom e-government systems, and established the relation betweencustomer empowerment and the success of e-government. Thisprovides a new research avenue for customer empowerment. Thedefinition and a conceptual model of consumer empowerment pre-sented here are an amalgamation of different but overlappingviews of power and empowerment from psychology and socialrelations, which suggest a complex concept of consumer empower-ment that may have wide applicability.

Our study has provided the first investigation into a formal con-ceptualization of consumer empowerment. In particular, we havesystematically synthesized a conceptual model of empowermentconsisting of factors that are frequently or not so frequently men-tioned in the literature, and then empirically tested it in thee-government success context. This identification has severalimplications for the theory of e-government success and theimportance of the concept of empowerment in both IS and market-ing fields.

While we have already known a great deal about directe-government users, the same cannot be said of customers as anew type of e-government user. As indicated earlier, limited previ-ous research studies have investigated e-government success froma citizen-based perspective. Specifically, citizens’ needs or per-ceived net benefits have not been adequately accounted for, leav-ing a clearly evident gap between design and reality ine-government service provision. This work has established a betterunderstanding of users in contemporary e-governmentenvironments.

Fountain (2001) argued that adoption of a customer serviceview might lead to increased political inequality by bucketingresources to the services based on market demand. She suggested




that customer service techniques and private sector tools appliedto government may lead to increased political inequality, evenwhen some aspects of the services are improved. The argumentwas proposed more as a projection of the public sector’s inabilityto adopt and perform in the new media environment. Andersen(2006) believed that the transition to viewing users as digital enti-ties is not only a management challenge, since major challengeswill occur in the technical development aspect too. Governmentswill need to rethink about new applications and consider theout-phasing of existing applications toward the external users(customers). After all, the citizen-centric perspective will implythat governments should formulate visions for online communica-tion as the norm and keystone in governance, and not as anexception.

5.3. Practical implications

The results of this study have also led to e-government designimplications, suggesting that customer empowerment practicesare relevant to e-government success. Therefore, e-governmentdevelopers and designers should consider the impact of customerempowerment practices. An initial focus on empowerment, ratherthan the traditional emphasis on information and system quality,may motivate development teams to consider additional featuresthat are customer-oriented for their e-government websites.

Given the importance of measuring e-government investmentsand e-government outcomes, it would be sensible to expect gov-ernments to employ advanced techniques for assessing metricsfor IT investments. Yet, in practice this is not the case (e.g., seeRana et al. 2015; Sørum et al. 2012). The e-government successmodel developed in this study has suggested that the net benefitis a surrogate indicator of e-government success, and that the crit-ical predictor of e-government success is customer empowerment.Understanding the role of customer empowerment in explaininge-government success gives practitioners the opportunity tounderstand how customers feel toward specific attributes ofe-government websites. Web designers could use this to predicta customer’s reaction and behavior, which in turn would helpimproving the effectiveness of e-government. We suggest, there-fore, that the e-government success model proposed in this studycan contribute to practice by helping governments to select met-rics of e-government success that can help explain relevant organi-zational outcomes.

5.4. Limitations and future work

To conclude, it is necessary to point out the limitations associ-ated to the research methods used in this study. Firstly, the currentstudy was conducted at one point in time (cross-sectional design).While it provided a useful snapshot and helped to understand thephenomenon we undertook, it could not explain possible changesin consumers’ attitude and behavior over time. It is generally rec-ognized that longitudinal studies (or at least a series ofcross-sectional studies) can detect attitude changes over timeand allow stronger inferences to be drawn about the dynamic ele-ments of behavior. Secondly, there were limitations arising fromthe sample used in this study, as the participants were not takenfrom a probability sample. Although there was no evidence of sam-ple bias compared to the population from which the sample wasdrawn, this possibility cannot be ruled out. Thirdly, the surveyquestions asked were more communication and design focusedrather than content or outcome focused.

Despite these limitations, this work has provided valuableinsights into the study of e-government success. The acknowl-edged limitations have led to suggestions for future research intwo broad areas: (1) further exploration of hypothesized


relationships, by using new methods of investigation; and (2)expansion of the consumer empowerment model, to include newantecedents or views of power and empowerment. An interestingquestion is whether consumers’ characteristics have an impacton their perception of empowerment and hence should be incorpo-rated into the model. Another related research avenue would be tolook at behavioral characteristics of empowered customers versusunder-empowered customers.

In summary, this study has explored the impact of customerempowerment on e-government. The detailed framework is thefirst rigorous research step toward understanding the importanceof customer empowerment on e-government success. The frame-work we built from theory and empirical research provides a foun-dation for future studies.

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Customer empowerment: Does it influence electronic government success? A citizen-centric perspective