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e-Government Adoption Model (GAM): Differing service maturity levels

Mahmud Akhter Shareefa, Vinod Kumar b, Uma Kumar b, Yogesh K. Dwivedi c,a DeGroote School of Business, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4M4, Canadab Sprott School of Business, Carleton University, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canadac School of Business and Economics, Swansea University, Singleton Park, Swansea, SA2 8PP, UK

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

Available online 20 October 2010

Keywords:

e-Government (e-Gov)

Information and communication technology

(ICT)

Adoption

Citizens

Service maturity levels

This research has as its objective the discovery of the critical factors that enable citizens to adopt e-

Government (e-Gov) at different stages of service maturity. To accomplish the objective, this research has

explained the related concepts and theories and developed a research framework grounded on a strong

theoretical and literature review background. The empirical study was conducted in Canada, which is a leader

in providing mature e-Gov services. From our results, we have observed two ontological differences from the

present literature in the adoption behavior of e-Gov where organizational and nancial perspectives have

distinct implications over parsimonious technology adoption behavior. First, technology adoption model

(TAM),diffusion of innovation theory(DOI),and theoryof planned behavior (TPB) cannotcapture andspecify

the complete essence of e-Gov adoption behavior of citizens. Second, e-Gov adoption behavior also differs

based on service maturity levels, i.e., when functional characteristics of organizational, technological,

economical, and social perspectives of e-Gov differ. Our ndings indicate the critical factors that enable

citizens to adopt e-Gov at different stages of service maturity. Public administrators and policy-makers have

potential implications from the ndings of the adoption behavior of e-Gov at different maturity levels.

Crown Copyright 2010 Published by Elsevier Inc. All rights reserved.

1. Introduction

As a new and rapidly growing eld, the concepts and theories of e-

Government (e-Gov) are still in a premature stage. Researchers from

different disciplines address this phenomenal theme from their

respective speculations and conceptualize it in a scattered fashion

(Heeks & Bailur, 2007). e-Gov has several aspects, including social,

technical, economic, political, and public administrative. However,

most dominating concepts of e-Gov arise from the technical

perspective and a combination of the socio-economic and public

administrative perspectives. Nevertheless, all the denitions are

headed towards a single notion and encompass a generic and unique

mission of e-Govpresenting government systems using information

and communication technology (ICT) to serve citizens better (Al-

Mashari, 2007; Evans & Yen, 2006; Gil-Garcia & Martinez-Moyano,

2007; Reddick, 2006; Shareef, Kumar, Kumar, & Dwivedi, 2009;

Sprecher, 2000).

Though different countries' e-Gov implementationsextensively differ

in setting common missions and objectives, all of them contain the

similar fundamental essence of e-Gov value: it should be citizen focused.

Therefore, it may be signicant to observe that the most important toolfor implementation of e-Gov is the willingness of citizens to adopt it

(Evans & Yen, 2006; Shareef et al., 2009). While there is evidence for

substantial growth, development, and diffusion of e-Gov universally, it is

not clear whether citizens of all developed and developing countries are

ready to embrace those services (Carter & Blanger, 2005). The

acceptance, diffusion, and success of e-Gov initiatives are contingent

upon citizens' willingness to adopt these services.

Reviewing the existing literature on e-Gov adoption by citizens

and business organizations (Al-Adawi, Yousafzai, & Pallister, 2005;

Chen & Thurmaier, 2005; Ebrahim & Irani, 2005; Gilbert, Balestrini, &

Littleboy, 2004; Klievink & Janssen, 2009; Kumar, Mukerji, Butt, &

Persaud, 2007; Phang, Sutanto, Li, & Kankanhalli, 2005; Reddick, 2004;

Sakowicz, 2007; Schedler & Summermatter, 2007; Shareef et al., 2009;

Tung & Rieck, 2005; Wang & Liao, 2008), we can infer that the

adoption models offered so far in the academic literature are mainly

conceptual. Extensive empirical studies among the actual users to

validate and generalize the models are absent. Most of those who

have attempted to validate their models did not rigorously review the

literature and integrate discourses from technical, social, organiza-

tional, political, and cultural perspectives to develop their ontological

and epistemological paradigms of model validation doctrine. As

identied byHeeks and Bailur (2007)through an extensive literature

review of e-Gov, methodologically these models are not grounded on

a strong theoretical framework. While developing those models of

adoption, the generalization aspect is heavily ignored (Heeks & Bailur,

2007).

Government Information Quarterly 28 (2011) 17 35

Corresponding author. Fax: +44 1792 295626.

E-mail addresses:[email protected](M.A. Shareef),[email protected]

(Y.K. Dwivedi).

0740-624X/$ see front matter. Crown Copyright 2010 Published by Elsevier Inc. All rights reserved.

doi:10.1016/j.giq.2010.05.006

Contents lists available at ScienceDirect

Government Information Quarterly

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Despite the potentially signicant impacts of e-Gov systems on

public administrations, organizations, individuals, and society, so far

only a few systematic and thorough studies have been undertaken on

the subject to comprehensively integrate overall factors related to the

successful implementation of e-Gov (Jaeger, 2003; Kraemer & King,

2003). However, citizens' behavior in terms of adopting a new

technology-driven system is a very complex and robust subject. It is

expected that extensive research will focus on criteria necessary for

citizens to adopt technology that will enable successful implementa-tion of e-Gov. Understanding and estimating the effect of citizens'

adopting criteria, which leads to successful implementation of e-Gov,

would have important managerial implications. Therefore, this

research attempts to investigate the users' requirements for the

adoption of e-Gov and sets the rst objective:

1. To identify and conceptualize the critical factors that affect citizens

to adopt e-Government.

Implementation and successive upgrading of the e-Gov system

follow certain paths, levels of maturity, stages, or phases. Different

countries implementing e-Gov in their ICT framework certainly have

different missions and objectives; however, the gradual development

of an e-Gov system in any country follows some unique levels of

service maturity for evolution. Each of the service levels represents a

different service pattern, different levels of technological sophistica-

tion, different stakeholder orientation, different types of interaction,

different security requirements, and different reengineering processes

(Holden, Norris, & Fletcher, 2003; Moon, 2002; Dorner, 2009). It can

also be inferred that these levels describe the development of

maturity of service in a sequential manner.

Based on the conceptualization of service development stagesof e-

Gov by different researchers (Accenture, 2003; Andersen & Henriksen,

2006; Evans & Yen, 2006; Fang, 2002; Klievink & Janssen, 2009; Layne

& Lee, 2001), we dene levels of service maturity of e-Gov as the

pattern of service that a government develops, successively enhances

interactivity, and delivers for stakeholders' acceptance and usage with

upgrading of technological sophistication and functional characteristics.

Since this research is engaged in developing adoption concepts ofe-Gov by citizens at different levels of service maturity, we will put

more attention into the rst two functionally different levels of

maturity of service development of e-Gov: the static stage and the

interaction stage (Blanger & Carter, 2005; Chandler & Emanuels,

2002; Howard, 2001). The reason behind this is that these two levels

are widely developed in most of the countries. The third level is

described as transaction stage.However,most of thecountries arestill

struggling to attain this e-Gov service level, so this stage is not

considered for developing any comprehensive model. The next stages

of service maturity, such as vertical integration and horizontal

integration, are also not very important for this research, as these

stages are not fully achieved by most of the countries so far. Most

countries have failed to realize horizontal stage of e-Gov universally

across all public services in their countries.From the end users' perspectives, the two stages of services have

signicant differences in characteristics and functionality (Gottschalk,

2009). In the publishing or static stage, stakeholders can only view and

collect government information or download some forms and publica-

tions. This is one-way communication. Here the user cannot commu-

nicate with the government service system through this interface and

the government authority does not respond to the user electronically

(Accenture, 2005). In the next maturitylevel of servicethe interaction

phasetwo-way communication is established. Through the govern-

ment web page, at this stage, stakeholders can contact service providers

to resolve any issues in different electronic ways, such as sending e-

mails, using chat-room, etc. (Accenture, 2005).

However, differentiating and dening these two stages as gradual

service maturity of e-Gov do not mean that citizens useor adopt these

stages of e-Gov sequentially, i.e., rst static level and then interact

level. They can simply skip any beginning level and start adopting e-

Gov from the next matured level. It can be predicted that the various

development levels of e-Gov might differ in pursuing the intention to

adopt e-Gov for its successful implementation. Static and interaction

levels especially offer different modes of service with different levels

of association of technology. As a result, adoption criteria for different

stages by citizens might have signicant implications. However, no

literature so far has investigated these criteria while exploringadoption models for e-Gov. To investigate the users' requirements

for the adoption of e-Gov at different levels of service maturity (not

like Layne and Lee, 2001 who look at organizational growth), this

research paper sets its second objective as:

2. Arethese critical factorsthat affectcitizens' adoptionof e-Government

different at different levels of service maturity?

2. Design perspective

While investigating and revealing the theoretical perspectives of

independent variables of e-Gov adoption, this research explores two

major elds as theoretical background. The rst attempt is engaged in

reviewing the surrounding areas of literature addressing e-Gov

adoption, implementation, characteristics, and related issues. The

second attempt is the extensive cultivation of theories related to

technology adoption, public administration and organization, psy-

chology, sociology, political science, culture, and marketing. Several

researchers who have done a comprehensive literature synthesis on

different e-Gov issuesincluding e-Gov adoption by end users

asserted that e-Gov implementation and adoption concepts have a

signicant lack of theoretical synopsis (Heeks & Bailur, 2007; Titah &

Barki, 2005; Wang & Liao, 2008).

Different researchers (Al-Shehry, Rogerson, Fairweather, & Prior,

2006; Chen & Thurmaier, 2005; Kumar et al., 2007) emphasized that

e-Gov adoption is more than a technological matter as it is inuenced

by many factors, including organizational, human, economic, social,

and cultural issues. These perspectives provide important specula-

tions for analyzing the e-Gov structure that reects governmentnatureand its responsibility in society (Carter& Blanger,2004;Moon

& Norris, 2005). In addition, the adoption of e-Gov systems requires

analyzing the changes in social values over time (Ebrahim & Irani,

2005).Steyaert (2004)adopted a marketing perspective to analyze e-

Gov performance. He proposed an e-Commerce (EC)-based perfor-

mance model to evaluate e-Gov performance in terms of citizen

satisfaction.Parent, Vandebeek, and Gemino (2005) and Warkentin,

Gefen, Pavlou, and Rose (2002)investigated the effect of trust on the

adoption of e-Gov. Gilbert et al. (2004)proposed theintegrationof the

service quality, technology, and behavioral aspects of the e-Gov

adoption framework.Shareef, Kumar, and Kumar (2007) and Shareef

et al. (2009) investigated technological, behavioral, economic, and

service quality aspects of e-Gov adoption criteria in formulating and

validating a framework of e-Gov adoption. Titah and Barki (2005) andPhang et al. (2005) reviewed the adoption literature of e-Gov

extensively and they suggested that technological, organizational,

social, cultural, behavioral, and economic aspects should be consid-

ered in a comprehensive framework of e-Gov adoption. Therefore,

from our literature review, we perceive that technological, behavioral,

social, cultural, organizational, economic, political, and marketing

aspects might provide important insights while investigating explan-

atory variables for e-Gov adoption.

To delineate the theoretical paradigms, this research looks at the

core characteristics of e-Gov. We nd that e-Gov is affected by several

issues like, organizational reformation, cultural revolution, habitual

change, technology adoption, information and service modication,

speed of service, accessibility, availability, more participation, trans-

parency, cost effectiveness, democratization, and globalization (Evans

18 M.A. Shareef et al. / Government Information Quarterly 28 (2011) 1735


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& Yen, 2006; Kim, Kim, & Lee, 2009; Robin, Andrew, & Sasha, 2009;

Titah & Barki, 2005; Turner & Desloges, 2002; Wang & Liao, 2008).

Since e-Gov is a revolutionary reformation of organizational structure

and characteristics, its adoption might have close ties with organiza-

tional attributes. e-Gov offers enormous benets to its end users,

which include economic incentives and service improvement.

Therefore, marketing and economic behavior reects citizens'

preferences in adopting e-Gov. Transaction cost analysis (TCA) also

sheds light on these perspectives of e-Gov adoption criteria. From thecore principle of TCA, the motivation for behavioral intention to

interact with different organizational structures is signicantly

inuenced by economic parameters (Shelanski & Klein, 1995).

According to the theory of planned behavior (TPB) ( Ajzen, 1991)

and the theory of reasoned action (TRA) (Ajzen & Fishbein, 1980),

social and cultural values affect beliefs and attitudes and the adoption

of e-Gov operated through ICT. Beliefs and attitudes about e-Gov lead

to formation of behavioral intention to learn, accept, and use e-Gov

systems. Therefore, behavioral or attitudinal aspects of citizens are

very important in stimulating an adoption framework of e-Gov. If we

translate the core doctrine of socio-technical theory, which explains

the effect of social and technological aspects on a system, we get

thorough insights into integrating the social, organizational, and

technological aspects of the e-Gov adoption (Damodaran, Nicholls, &

Henney, 2005).

However, these perspectives, where we have concentrated our

investigation of critical factors for the adoption framework of e-Gov,

are not mutually exclusive phenomena. These are interrelated issues.

Adoption perspectives of e-Gov by different stakeholders at different

levels of service maturity of e-Gov are intertwined with different

explanatory variables. Therefore, our investigation for identifying

critical factors of e-Gov will not track those perspectives of e-Gov

adoption factors separately; rather, we will look for interdependent

and comprehensive effects. We will connect technological, behavioral,

social, cultural, organizational, economic, political, and marketing

aspects of consumers to develop a comprehensive e-Gov adoption

model.

3. Theoretical framework

As we mentioned in the Introduction section, the existing

literature on e-Gov has failed to present a comprehensive framework

of e-Gov adoption and performance at different phases of service

maturity of e-Gov implementation. Therefore, although, we are

attempting to identify allof the constructs from our detailed literature

review in conjunction with the insight from different theories related

to technology adoption, diffusion, and behavioral, social, and cultural

characteristics, the adoption behavior of e-Gov is in a very premature

stage (Heeks & Bailur, 2007). Consequently, this study has potentially

an exploratory nature. It means, we are conducting this research not

to test any specied theory of e-Gov adoption, rather we are

conducting this research in the hope of developing a theory of the

adoption of e-Gov at different service maturity levels. So, as we areadvancing from theory development to statistical analysis, we should

continue to rene our exogenous variables and also hypotheses to

develop our nal paradigms of adopting e-Gov at different service

maturity levels. For an exploratory study, this renement of variables

and hypotheses is typical and also a part of the theory development

process (Stevens, 1996).

3.1. Explanatory variables

According to information management principles for open

government adoption, a prime factor for adoption is creating

awareness among the stakeholders. This means informing the

citizens about the transformation of public administration, imple-

mentation of innovation, basic paradigms of the new system,

application of ICT, objectives and mission of e-Gov development,

comprehensive information about relative advantages and disad-

vantages of e-Gov, and the overall credibility of the system. A long

history of government service shows that citizens and business

organizations are traditionally habituated to use brick and mortar

government services for information collection, interaction, and all

types of transactions thatare basically operatedofine. Thehistoryof

e-Gov evolution is very new. Basically, most of the countries are just

at the beginning efforts of implementing e-Gov. Therefore, stake-holders are still not very aware of this new innovation of the

government system. As we learned from the TPB and TRA, beliefs

about a system turn to the attitude of using the system. However,

awareness of the system is important at the beginning to develop

beliefs (Limayem, Hirt, & Cheung,2007).

Before developing an attitude to adopt e-Gov, stakeholders need to

be aware of its complete characteristics, including the background of

the system, functional behavior, strategic benets, the safety and legal

environment, etc. Awareness of e-Gov has several different aspects:

political, marketing, behavioral, and social. Whencitizens are aware of

the political agenda of e-Gov, social values related to the strategic

implementation of e-Gov, service quality, and the competitive

advantages of e-Govi.e., the marketing paradigm and attitudinal or

behavioral motivation of the EG systemthey might then have an

intention to adopt the e-Gov system. Several researchers asserted

awareness as the signicant independent variable to create the

attitude to use an e-Gov system (Eggers, 2004; Parent et al., 2005). So

we categorize this predicted variable for adoption as the Attitudeto

use, since awareness is the primary stimulus of creating attitude.

Depending on the previous arguments, this research proposes:

H1. Perceived awareness (PA) has a positive relation with the

Adoption of e-Gov.

From TPB, diffusion of innovation theory (DOI), and TCA, a user will

not arrive at an intention to use an EG system, which requires computer

knowledge to get a competitive advantage, unless the user has

competence from experience in the use of modern ICT. From

technological, behavioral, economic, and organizational perspectives,it is anticipated that failing to get hands-on experience of technology

will not create in the user an attitude favorable to adopting the system.

Also, in the absence of computer knowledge, a user cannot perceive the

economic advantages of e-Gov. The organizational structure of e-Gov is

computer- and internet-based, while from the viewpoint of end users,

traditional government services do not require computer knowledge.

Therefore,fromorganizational perspectives, computer self-efcacyisan

important predictor of whether a user will adopt an e-Gov system

instead of using traditional government services.

Several researchers exploring the barriers of adoption of e-

Commerce and e-Gov revealed that users' computer self-efcacy

and experience of theinternet, ICT, andcomputers create a perception

of security in theusers' attitude toward using onlinesystems (Moon &

Norris, 2005; Tung & Rieck, 2005; van Dijk, Peters, & Ebbers, 2008)that affects their intention to use. Wang (2002) investigated the

relation of technology availability and computer self-efcacy with

behavioral intentionto adopt an onlinetax ling system and observed

a positive relation. Computer self-efcacy is concerned not with the

skills one has but with judgments of what one can do with whatever

skills one possesses. Therefore, we categorize this explanatory

variable of e-Gov adoption as the Attitudeto use.

Including others, the most important and dominating barriers for

adopting e-Gov, particularly in developing countries are scarcity of

electricity, telephones, computers, the internet, and related acces-

sories, and government supports like call-center, resource-center, and

cyber-cafs. If we examine the capability theory (Nussbaum & Sen,

1993), it highlights that citizens' use of e-Gov systems is limited

unless they have the freedom of utility required to use modern ICT-

19M.A. Shareef et al. / Government Information Quarterly 28 (2011) 1735


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based e-Gov. If a country cannot make the skills and resources

required for using e-Gov available to all citizens equally, the country

cannot expect the same capability from all citizens to adopt that

system. Therefore, without reducing the digital divide, promoting

equality in resources of using e-Gov and making available all

components of an e-Gov system and knowledge, the adoption of

e-Gov will not be successful. The availability of resources required for

the use of e-Gov has behavioral, economic, cultural, social, and

technological aspects. Generally, where computers, internet, andmodern ICT are not available, the citizens are economically poor, less

educated, unaware of modern technology, socially and culturally

unfamiliar with modern technology, and lack the necessary skills to

use technology. As a result, they also do not believe that they will

receive benets by using an e-Gov system. Therefore, there is an

obvious relation between availability of resources and the adoption of

e-Gov (van Dijk et al., 2008). We argue that AOR creates a belief in

using an e-Gov system operated through ICT, which, in turn, creates

an attitude to use e-Gov. Therefore, we categorize the construct AOR

as the Attitudeto use e-Gov. Drawing a conclusion from the above

arguments, we propose here:

H2. Computer-self efcacy (CSE) has a positive relation with

Adoption of EG.

H3. Availability of resources (AOR) has a positive relation with

Adoption Of EG.

The technology acceptance model (TAM) proposes that perceived

ease of use (PEOU) and perceived usefulness (PU) determine the

attitude towardadoption of ICT. This behavioral attitude,in turn, leads

to the intention to use ICT and the nal acceptance of the system

(Bhattacherjee, 2001; Davis, Bagozzi, & Warshaw, 1989; Lucas &

Spitler, 1999; Moon, 2002; Venkatesh, 2000). e-Gov fails if the users

do not have the ability to use the technology to access useful

information and services, and eventually do not perceive e-Gov as

useful. This would lead to a non-acceptance of the system by citizens

(Shareef et al., 2007). Numerous scholarly articles (Evans & Yen, 2006;

Gil-Garcia & Martinez-Moyano, 2007; Shareef et al., 2009) revealedthat PEOU and PU are potential indicators of user acceptance,

adoption, and motivation to use web services. However, some

researchers (Carter & Blanger, 2004) did not nd any signicant

relation between the adoption of e-Gov and PU. A plausible

explanation lies in the logic that inclusion of relative advantage as a

predicted variable in the adoption model explains enough of PU in the

adoption construct. So, at this stage, we will provide further insight

into PU and relative advantage for inclusion in the adoption model as

the predictor variables.

According to the DOI theory (Rogers, 1995), the rate of diffusion is

affected by an innovation's relative advantage, complexity, compati-

bility, trialability, and observability. Literature reviews suggest that

among those ve constructs, relative advantage, compatibility, and

complexity are the most relevant constructs to determine the adoptioncharacteristics of technology innovation (Gilbert et al., 2004; Moore &

Benbasat, 1991; Rogers, 1995; Tornatzky & Klein, 1982). These authors,

especially Tornatzky and Klein (1982)in their meta-analysis of research

on the adoption of innovations, argue that trialability and observability

are not related constructs for technology adoption. Therefore, in this

study we are not considering these two constructs.

Complexity, comparable to TAM's PEOU construct, captures the

perception of some pre-use complexities that seem to have very close

relation to the perceptions of complexities in using modern ICT,

internet, and computers. Therefore, we comprehend the generic

essence of the construct complexity with PEOU and introduce

perceived ability to use (PATU) as the predictor of adoption to reect

other aspects of e-Gov above and beyond technology. The construct

PATU has technological and organizational perspectives. Due to

revolutionary reengineering of the traditional government system,

the perception of online organizational structure, which is apparently

new, is an important aspect of the perceived ability to use the system.

Technology is a very predictable aspect to get insight into PATU. We

argue that PATU reects the ability of citizens to use an e-Gov system

and categorize it as the Abilityto use for e-Gov adoption.

Compatibility construct has cultural, behavioral,and socialaspects. It

is dependent both on individual characteristics such as avoiding

personal interaction, and social in

uence. Several researchers indicatedthat specic characteristic of e-Gov that allow citizens to avoid personal

interaction might create the perception of compatibility among citizens

to adopt an e-Gov system (Gilbert et al., 2004). Shedding light on the

TPB, TRA, and capability theory, the compatibility of an e-Gov system

with adopters' beliefs, values, and attitudes reects the behavioral

aspect. From the socio-technical and complementary theories, beliefs

and attitudes of adopters of a new technology system also have social

and cultural aspects. Several researchers use this construct as a

signicant predictor of EG adoption (Carter & Blanger, 2004; Chen &

Thurmaier, 2005; Shareef et al., 2007). This research argues that PC

creates citizens'attitudesto usean e-Gov systemand thus we categorize

this construct as the Attitudeto use.

Relative advantage captures the gain from receiving services and

information through e-Gov systems in comparison with that from

traditional government ofces. This denition expands the limited

concept of PU of the TAM, which captures only the absolute benets

from job performance. However, by adopting e-Gov systems, a user

can gain a lot of relative and absolute benets ranging from

effectiveness, efciency, availability, accessibility from anywhere,

comfort in use, time savings, cost savings, and convenience. The

combined effects of the two constructs, therefore, basically capture

the essence of absolute and comparable functional benets of the e-

Gov system. If we integrate both views of PU and relative advantage,

we can introduce perceived functional benet (PFB) as the predicted

variable of e-Gov adoption. This construct has economic, organiza-

tional, marketing, behavioral, and social perspectives. If we look at the

TCA, we can understand that PFB also captures the essence of time

efciency and price savings. Several researchers assumed that this

time constraint characteristic is also typical in EG because it is rationalto assume that citizens might adopt e-Gov systems as they save time

to perform tasks relative to the functions of a traditional paper-based

government ofce (Carter & Blanger, 2005; Gilbert et al., 2004;

Wagner, Cheung, Lee, & Ip, 2003). Another construct, price savings,

which is a measure of e-Gov efciency in terms of reduction in service

rendering cost, is also an overlapping concept of PFB (Tung & Rieck,

2005). This analysis shows that PFB captures the essences of the

behavioral, economic, and marketing aspects of adoption. It also has

an organizational aspect, because institutional theory asserts that

actors will accept organizational change as long as they perceive it to

be benecialto them(Lawrence & Suddaby, 2006). We argue that PFB

imparts reasons to use an e-Gov system, instead of using a traditional

government system, and, thus, it is categorized in this research as the

Adherence to adopt the use of e-Gov. We investigate the threedimensions PATU, PC, and PFB here since we assume that these might

affect EG adoption. Therefore, we propose:

H4. Perceived ability to use (PATU) has a positive relation with the

Adoption of e-Gov.

H5. Perceived compatibility (PC) has a positive relation with the

Adoption of e-Gov.

H6. Perceived functional benet (PFB) has a positive relation with the

Adoption of e-Gov.

In addition, we also include a new construct, Image, as proposed

by Moore and Benbasat (1991). According to the DOI, Image



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inuences the acceptance and use of an innovation. Image refers to

citizens' perceptions that adopting e-Gov makes them superior to

others in the society. Interaction with e-Gov systems, instead of

using traditional government ofces, is perceived to give these

citizens superior status. Several researchers have, therefore, includ-

ed this construct in their proposed model of EG adoption (Gilbert et

al., 2004; Phang et al., 2005; Tung & Rieck, 2005). Since the adoption

of e-Gov might reect the adopters familiarity with modern

technology, higher level of education, competence in using compu-ters and the internet, and perception of modernism, these phenom-

ena impart some degree of social values and prestige to adopters.

Therefore, this research argues that Image has social, behavioral, and

also cultural aspects and depends on an individual's personal

behavioral ideology. We also argue that the construct perception of

Image is a proponent of reasoning to use e-Gov and, thus, it is

categorized in this research as the Adherence to use for e-Gov

adoption. Therefore, we propose:

H7. Perceived image (PI) of using e-Gov has a positive relation with

the Adoption of e-Gov.

Content, organization, and presentation of informationi.e., infor-

mation quality, which includes accuracy, current information, relevan-

cy, fulllment, linkage, completeness, integration, organization,

timelinesare potential contributors to creating a perception of

reliability that inuences citizens to accept e-Gov (Collier & Bienstock,

2006; Kim, Kim, & Lennon, 2006; Kumar et al., 2007; Parasuraman,

Zeithaml, & Malhotra, 2005; Sebastianelli, Tamimi, & Rajan, 2006). The

assurance and condence in using e-Gov that information quality gives

citizens is characterized in this research as the Assurance tousefor e-

Gov adoption. Gilbert et al. (2004) conducted a survey in theUK among

users of e-Gov thatshowed thatinformationquality is a strongpredictor

of EG adoption. Another extensive empirical study conducted in

Australia (AGIMO, 2003) revealed that there is an obvious expectation

that information from government will be provided in accordance to

fullling citizen needs rather than serving the convenience of

government agencies. In order to ensure the success of an IS, Delone

and McLean (1992, 2003) proposed the Information System SuccessModel (IS Model). The model asserted that information quality is the

determinant of system use and user satisfaction that eventually leads to

regularadoption (Wang & Liao, 2008; Wangpipatwong, Chutimaskul, &

Papasratorn,2005). Information quality asserts service requirements of

consumers and the economic benets of viewing and collecting

information from a website in lieu of verbal interaction from traditional

government ofces. Therefore, information quality has a marketing

aspect.Lin and Lu (2000)asserted the conjecture that the features and

accuracy of information posted on a website signicantly affect users'

behavioral attitude. Grounded on the aforementioned arguments, we

propose:

H8. Perceived information quality (PIQ) has a positive relation with

Adoption of EG.

Several e-Gov researchers address customer service as one of the

important explanatory variables to satisfy customers and, thus, to

ensure a recurring use of e-Gov (Lee & Rao, 2003; Shareef et al., 2007;

Wangpipatwong et al., 2005). Service quality is a strong predictor to

differentiate performance of different organizations. From the

behavioral point of view, recurring users of e-Gov will achieve beliefs

and thus attitudes to adopt e-Gov if they perceive higher customer

service in e-Gov. Traditional government service has a different

approach than e-Gov. If citizens perceive a higher level of customer

service in e-Gov than that offered in a traditional government ofce,

they will purse the adoption of e-Gov. Kumar et al. (2007)proposed

that service quality leads to satisfaction that ensures regular use of e-

Gov. Service quality can be considered from different dimensions, like

technical or output quality, functional or process quality (Czepiel,

Solomon, Surprenant & Gutman, 1985; Grnroos, 1984; Lehtinen &

Lehtinen, 1982), and direct customer service from employees

(Shareef, Kumar, & Kumar, 2008). Since technical or output quality

is already being assessed in PATU and PIQand functional quality of the

system is incorporated in PFB, we will only discuss here the customer

service response to service quality. The reliability and assurance of

servicewhich are intertwined with trust, security, privacy, and risk

concepts of customer service

will also be discussed in a differentsection.

Due to the absence of any physical presentation, the service

response of e-Gov has different aspects and properties. In e-Gov, the

service response is generally assumed to be a recovery quality item.

When there are problems or concerns, stakeholders always expect

that customer service will resolve the problem promptly with

complete sympathy. If citizens feel that they do not nd any customer

service in e-Gov when they require it, that they are being treated

unfairly, or that the customer-oriented service policy of government

websites is not credible, they are less likely to adopt e-Gov; rather

they will go to a physical government ofce to seek services. A study

in a developing country conducted byShareef et al. (2009)also found

that service response has a signicant effect on citizens'adoption of e-

Gov. We argue that perceived service response (PSR) stimulates

citizens' adaptability and satisfaction in using e-Gov and thus ensures

recurring use. Therefore, this research categorizes the PSR construct as

the Adaptability to use for e-Gov adoption. Based on the above

arguments, we propose here:

H9. Perceived service response (PSR) has a positive relation with

Adoption of EG.

In an online transaction, since different physical cues are absent,

virtual transaction requires some extra facilities to perform transac-

tions for individuals withdifferent ethnic backgrounds. This criterion

is especially very important for a country that comprises multicul-

tural and multilingual groups. Additionally, e-Gov also has a global

aspect. Considering these aspects, the multilingual option of e-Gov

might enhance the adoption of e-Gov. The service quality research ofEC also nds this factor to be an important cause for consumers to

adopt a certain e-retailer'swebsite (Collier & Bienstock, 2006; Kim et

al., 2006; Parasuraman et al., 2005; Wolnbarger & Gilly, 2003).

Nantel, Sncal, and Mekki-Berrada (2005) conducted an empirical

study to determine critical factors of online purchase. Their research

captured a denite relation between the use of native language of a

user in a website as the medium of instruction and information and

adoptionof thewebsiteby thatuser. If an individual can interact with

a website using his/her primary language, he/she might feel more

cultural connection and have a more positive attitude to use of that

website. Through an extensive content analysis of 93 websites from

local companies in China, India, Japan, and the USA, Singh, Zhao, and

Hu (2005) conrmedthis statement. For citizens with less education,

a single language option other than mother tongue for viewing,collecting, interacting, and transacting with e-Gov websites might

create a signicant barrier. This is an important cultural aspect

(Michon & Chebat, 2004). If we borrow the speculations from the

TAMand DOI, the relativeadvantage of e-Gov will triggerinclusion of

a multilingual option in e-Gov web pages. However, beliefs in

competence with an e-Gov interactionwhich further promotes

attitude towards the adoption of e-Gov according to the TPBcan be

exaggerated if a known language option prevails in e-Gov websites.

Therefore, the multilingual option has behavioral aspects (Foo, Hui,

Leong, & Liu, 2000). If we look at the capability theory, the

multilingual option in e-Gov might create equal and competitive

capability, which ultimately enhances the economic power of

minority stakeholders. Therefore, from an economic perspective, a

multilingual option in e-Gov can create a level playingeld formajor



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stakeholder groups with a multilingual background. From the

marketing, technological, behavioral, and cultural perspectives, this

research argues that a multilingual option in e-Gov might enhance

the processing and understanding capability of e-Gov. Therefore,

since this criterion promotes the ability to use e-Gov by improving

service quality, we categorize this plausible explanatory variable of

e-Gov adoption as the Ability of stakeholders. This research thus

proposes:

H10. Multilingual option (MLO) has a positive relation with the

Adoption of e-Gov.

Many scholarly articles conducting research in e-Gov adoption

have shown that security, privacy, uncertainty, and risk are predom-

inant factors for adoption (Al-Adawi et al., 2005; Parent et al., 2005;

Shareef et al., 2007, 2008; Welch & Pandey, 2005).Blanger, Hiller,

and Smith (2002) found that pleasure, privacy, security, and web

features are matters related to the perceived trustworthiness of a

website. Research in e-Commerce and e-Gov found that uncertainty,

security, privacy, and risk are all antecedents of perceived trust ( Al-

Adawi et al., 2005; Balasubramanian, Konana, & Menon, 2003; Parent

et al., 2005; Soat, 2003).

Perceived security is crucial to users' condence regarding the

safety of a website. Based on previous research on security in e-

Commerce (Blanger & Carter, 2005; Schaupp & Blanger, 2005), this

current study visualizes perceived security as the protection of

customers from any type of nancial or non-nancial risk during

transactions on websites, such as any type of identity thefts including

abuse of credit card, overcharging, non-payment, etc. These security

factors are potential contributors in developing trust among citizens

as the authentication of e-Gov nancial transaction and protection of

disposed information in e-Gov. Therefore, we conjecture that

perceived security (PS) has a causal effect on perceived trust (PT).

Since transactions in e-Gov are basically virtual, no actual physical

transaction takes place during interaction and payment by the clients,

and the uncertainty construct of TCA can be a potential factor of non-

accepting virtual environment of e-Gov. This, in turn, is related to

perceived trust in e-Gov (Al-Adawi et al., 2005). In the virtualenvironment in particular, being able to place trust in a website is

critical to consumers' successful interaction with the system ( Gefen,

Karahanna, & Straub, 2003).Cox and Rich (1964)delineate perceived

risk as the perception of uncertainty in a particular interaction

situation that has a close relation to trust. Therefore, PT is dependent

on PU (uncertainty).

In e-Gov, citizens provide written information in technology

interface while interacting or receiving/paying through e-Gov web-

sites. As a result, users of e-Gov always feel a lack of privacy. Several

researchers (Angst & Agarwal, 2009; Shareef et al., 2008; Yoo

& Donthu, 2001), who conducted empirical studies regarding the

acceptance of the online environmente.g., e-Commerce and e-Gov

observed that perceived privacy is a major concern for internet

customers during interaction with websites. Customers are afraid thatwebsites can disclose, share, or misuse their personal information or

that hackers can intercept their secret information (Brown & Muchira,

2004; Ranganathan & Ganapathy, 2002). During interaction with

websites, customers may perceive there to be a privacy risk

(Parasuraman et al., 2005). So, perceived privacy (PP) is also related

to the condence of users on the web, which nally indicates

trustworthiness. Trusting the web can enhance the perceived

privacy feeling of the customers during interaction in e-Gov ( Kemp,

2000).

Warkentin et al. (2002)extensively discussed the impact of trust in

e-Gov adoption, and consequently proposed that institutional-based

trust, characteristics-based trust, and process-based trustwhich all

together capture the essence of perceived security, privacy, and less

uncertainty in e-Gov

will lead to the intention to adopte-Gov. Thomas

(1998)outlined the three aspects by which trust in the government is

produced. The rst aspect is related to behavioral attitude that is

supported by TPB. The second aspect of trust derives from institutional

credibility. The third component captures the trusting attitude on

typical outcomes of a process where the seller sells goods to buyers and

buyers in return will pay for that. Nye and Zelikow (1997)classied

these causal factors as social, cultural, economic, and political. From the

marketingaspect, if a customer does nothave trust in theinstitution and

process, he/she might not embrace that organization for interaction. So,we can conclude that PT in e-Gov has political, behavioral, social,

organizational, technological, marketing, and cultural perspectives.

Trust is an important factor in analyzing the adoption behavior of

consumers in virtual environment, because citizens have few tangible

and veriable cues regarding the service provider's credibility and

performance (Urban, Sultan, & Qualls, 2000). In light of the above

discussion, we argue that PT creates condence in the overall e-Gov

performance and, thus, categorize this construct as the Assuranceto

use e-Gov. Thus, we propose:

H11. Perceived trust (PT) has a positive relation with the Adoption of

e-Gov.

H11a. Perceived uncertainty (PU) (uncertainty) has a negative

relation with Trust in e-Gov.

H11b. Perceived security (PS) has a positive relation with Trust in

e-Gov.

H11c. Perceived privacy (PP) has a positive relation with Trust in

e-Gov.

The measuring scale items for all the exogenous constructs of the

proposed model, developed and operationalized based on existing

literature on e-Gov, e-Commerce, IS, marketing, and expert opinions,

are shown in Appendix A. Thequestionnaire was pretested by a group

of people comprised of two scholarly researchers from Sprott School

of Business, Carleton University, Canada who have expertise in

analyzing the online adoption behavior, and eight PhD studentsfrom the social science and natural science departments of Carleton

University who have extensive knowledge in using Canadian e-Gov to

verify the structure, constructs, and respective measurement items of

the questionnaire. For any exploratory research, if the surveyor

happens to organize a group comprised of a couple of people, the

participating pretesting method is better (Converse & Presser, 1986).

In our pretesting procedure, we followed the participating method. A

structured questionnaire was used to measure the independent and

dependent variables of the study with a 5-point Likert scale ranging

from 1 (strongly disagree/never) to 5 (strongly agree/always). The 5-

point Likert scale is used to increase the response rate and response

quality along with reducing the respondent's frustration level

(Babakus & Mangold, 1992).

3.2. Dependent variables

According to the marketing theory, adoption of a new product

begins with consumer awareness, leads to an attitude toward that

product, then further advances to an intention to use as a trial basis,

and nally ends as full acceptance and regular use with satisfaction

(Pavlou & Fygenson, 2006). A new system can replace an old system.

So, if an individual using traditional government systems perceives e-

Gov to be more advantageous from any perspectives, he or she might

adopt the online government systemse-Gov. This research encom-

passes the adoption of e-Gov as a continuous process starting from

awareness of the system, beliefs of the system benets, attitude

toward using it, intention to use, actual use, satisfaction, and recurring

use. Some researchers (Al-Shehry et al., 2006; Chen & Thurmaier,



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2005; Kumar et al., 2007; Moon & Norris, 2005; Schedler &

Summermatter, 2007; Shareef et al., 2007, 2009; Wangpipatwong

et al., 2005) have developed their adoption models and measuring

items for the adoption construct by considering complete acceptance

of the process.

Depending on the paradigms of the e-Gov adoption process, we

nd logical underpinnings on thepremise that the adoption process of

e-Gov involves the frequent and recurrent use of online services by

citizens not only for obtaining information but also for interactionwith government. Adoption construct has behavioral, organizational,

economic, technological, political, marketing, social, and cultural

perspectives. We have mentioned that this research is concerned to

explore the objective by investigating adoption criteria into two

different levels of service maturity: Static or Publishing stage and

Interaction stage. Therefore, we have differentiated the dependent

variable Adoptioninto two sub-groups:

Adoption 1: Decision to accept and use an EG systemto view, collect

information, and/or download forms for different government

services as the user requires with the positive perception of

receiving a competitive advantage. Adoption 2: Decision to accept and use an EG system to interact

with, and seek government services, and/or search for queries for

different government services as the user requires with the positiveperception of receiving a competitive advantage.

The endogenous/dependent variable Adoption was operationa-

lized in a way that ensures measurement of the causal effects of the

exogenous variables on the two levels of service maturity of e-Gov

and increases response rate. It is obvious that citizens can view and

interact with e-Gov for many tasks. Dening any specic task for

adoption in the proposed questionnaire might reduce the response

rate in terms of adoption. Therefore, this study formulates the

instruments of Adoption not for any specic tasks but for general

tasks to keep the questionnaire general for all respondents. A total of

six scale items were selected to measure those two dimensions of the

adoption construct (AGIMO, 2003; Gil-Garcia & Martinez-Moyano,

2007; Murru, 2003; Sakowicz, 2007; Turner & Desloges, 2002) (scaleitems are shown in Appendix B). Based on these arguments and

identication, a model of e-Gov adoption (GAM) to investigate the

plausible relations is proposed inAppendix C.

4. Methodology

The research methodologies we use in this research are those

typically used in empirical business research. Based on the sugges-

tions ofHeeks andBailur (2007) about e-Gov research, andtheoriesof

Campbell and Fiske (1959) and Bagozzi, Yi, and Philips (1991) about

reliability and validity of research, we designed our research

methodology. In this research, the respondents are the users of the

Canadian e-Gov system; anyone who has experience using Canadian

e-Gov system could participate in the survey. This study was

conducted in four large cities in Ontario, Canada. We selected the

venue for the following purposes:

1. Canada is one of the leading countries in terms of offering e-Gov

services. Canada's e-Gov implementation and offered services are

very mature, and have different services in the static, interaction,

and transaction stages. Therefore, in terms of the development

stage of e-Gov and mission, vision, and objectives, Canada can be

viewed as one the most focused countries for e-Gov development

(Cardin, Holmes, Leganza, Hanson, & McEnroe, 2006).

2. The adoption rate (29.8%) and maturity of servicesof EG in Ontario is

the highest in Canada according to the study byParent et al. (2005).

Since this research hasset its objectives in detectingadoption criteria

of citizens at different maturity levels of services offered by e-Gov,

Ontario is assumed to fulll the research objectives.

3. The selected four cities are the most populated and largest cities in

the respective regions of Ontario. These cities are also located

strategically in important position and are prominent in multicul-

tural assembly. Therefore, it is assumed that the sample should

have enough variability.

To test the model in the most realistic way possible, the study was

conducted through a survey (a self-administered questionnaire) of a

broad diversity of citizens at several communities. From our previousexperience, we assumed that the study would receive around a 10%

response rate. Since there are 11 primary exogenous variables/

constructs, the number of response shouldbe at least 220(20 samples

per independent variable) for regression and factor analysis (Stevens,

1996, p. 143). However, a sample size of a minimum of 200 is good for

structural equation modeling (SEM) (Kline, 2005, p. 110). Therefore,

the questionnaire was distributed among 2200 citizens (or residents)

in thepreviously mentionedfour citiesin Ontario, Canada, to meet the

targetand fulll the statistical specications. The specic way weused

to distribute the questionnaire was:

1. We maintained roughly the population ratio of the four cities,

distributing 100 questionnaires in Sudbury, 200 in London, 500 in

Ottawa, and 1400 in Toronto.

2. We divided all the cities into ve regions: east, west, north, south,

and center.

3. We then collected addresses from the Telephone White Pages of

each city; we included houses, condominiums, and apartments

located in the ve regions we identied. We also collected the

addresses of the residents living in the suburban areas in the east,

west, north, and south regions immediately outside the city.

4. We distributed the questionnaires by mail throughout the

suburban areas in the east, west, north, and south regions outside

each city. Onehalf of thetotal questionnaires allocated foreach city

were distributed in this way. The other half was distributed

physically to the houses, condominiums, and apartments in

different areas in the ve zones.

5. We distributed 50% of the questionnaires in houses and condo-

miniums and 50% in apartments.6. The survey was conducted over a three-month period.

We received a total of 241 questionnaires from the respondents.

Two returned questionnaires were blank. Therefore, the eligible

response number is 239. The response rate is around 11%. This is quite

satisfactory based on our previous knowledge and also considering

the length of the questionnaireeight pages, including a one-page

cover letter.

5. Statistical analysis

Several interrelated procedures were performed to organize, re-

arrange, and summarize the raw data and make it amenable for

analysis to get justied output. This section sequentially describesdata preparation and analysis techniques for statistical analysis of this

research.

5.1. Data reduction

Before performing any causeeffect relation, reliability, validity,

and normality tests, we rst conducted exploratory factor analyses

(EFA) on the preliminary 57 scale items measuring the latent

variables having direct causal relations to the adoption of e-Gov

excluding the measuring items of the constructs perceived uncer-

tainty (PU), perceived privacy (PP), and perceived security (PS),

which are not hypothesized to have direct relation with adoption in

our model. These three constructs are hypothesized to have causal

relations with perceived trust (PT), which is an exogenous variable



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for the adoption model. We have also done EFA on the 10 measuring

items of PU, PP, and PS separately because these three constructs are

widely used as the exogenous variables for PT (Shareef et al., 2008).

For EFA, we have used principal component analysis as the

extraction method and varimax rotation as the rotation method.

We used both the breaks-in-eigenvalues criterion (N1) and scree

plot to determine the number of factors to retain ( Stevens, 1996,

pp. 389390).

After conducting a series of EFAof those 57 measuring items, of the11 exogenous variables and also examining the correlation matrix we

found that nine constructs with 37 measuring items can be retained.

However, to support this renement in measuring items, we also

looked at the correlation matrix, analyzed convergence through CFA,

and thoroughly investigated theoretical aspects of those modica-

tions. For PA, AOR, CSE, PI, PIQ, MLO, and PFB constructs,

corresponding measuring items were loaded consistently (though

some items were removed because of low loading or cross loading).

Three PC items, three PATU items, and one PIQ item were loaded on a

single factor. Though the items loaded under this factor are the

measuring items from different hypothesized exogenous variables,

after close examination of those seven items loaded under a single

factor, we observed that all these items reect certain personal beliefs

and perceptions of ability of using e-Gov systems. This personal belief

denotes both physical and psychological perception (resembles to PC)

of ability to usee-Govsystems.Therefore, these seven items have very

close functional alignment. We veried the correlation between these

items andfoundmoderateto strongcorrelations. This also justiesthe

convergence of these items under a single factor. We also veried the

convergence of those seven measuring items in CFA by testing the

appropriateness of a single factor or two factor model (Appendix D).

Based on the functional meaning, we argued that PATU can still cause

those seven scale items and thus can be named PATU. However, the

denition of PATU should be edited adding new psychological

dimension. We have done that in Appendix E where all the

explanatory variables are dened. Four items of PT andve items of

PSR loaded under a single factor in EFA. We can explain this behavior

getting insight from the measuring items. On the one hand, perceived

trust of citizens is related to the credibility of e-Gov; on the otherhand, customer service of e-Gov also helps to enhance the perception

of trust and credibility, particularly in the virtual environment, among

the users of e-Gov. Therefore, we retained the name perceived trust

(PT) for the combined measuring items loaded under a single factor.

We also veried the correlation between these items and found

moderate to strong correlations. This also justies the convergence of

these items under a single factor. However, we also veried the

convergence of those nine measuring items in CFA by testing the

appropriateness of a single factor or two factor model (Appendix D).

We also veried all nine factors with the measuringitems individually

by CFA and observed conrmation of EFA results (Appendix D).

However, since 1 item of the construct AOR(AOR3) was loadedin CFA

with a loading factor of less than 0.50, we removed that item. In

addition, since we could retain only two items for CSE with highinternal correlations (more than 0.90) and two items for MLO with

high correlations (more than 0.95) from EFA, we could not perform

CFA for these two variables (since negative degree of freedom exists).

So, we took the average scores of the respective measuring items for

CSE and MLO respectively. Therefore, we retained a total of 34

measuring items with the nine exogenous variables(Appendix E).We

also retained two factors with nine indicators from the EFA of the 10

measuring items of PU, PS, and PP. However, four items of PS and two

items of PP were loaded under the same factor. Although as an

exploratory study we have hypothesized PS and PP as two different

exogenous variables for PT, several researchers used PS and PP as a

single construct by the name PS, because both the constructs are

related to security ofnancial transactions, identity, and personal in-

formation (Gummerus,Liljander, Pura, & van Riel, 2004; Wolnbarger

& Gilly, 2003). Therefore, we have provided the name of this construct

as PS, however, its denition was revised (Appendix E). We also

veried the correlation between these items and found moderate to

strong correlations. This also justies the convergence of these items

under a single factor. However, we also veried the convergence of

those six measuring items in CFA by testing the appropriateness of

a single factor or two factor model (Appendix D). So, for PT as

endogenous variable, we have retained two exogenous variables

namely, PU (uncertainty) and PS.The reliability scores for the constructs were measured by a

coefcient alpha, which justied the reliability of the items in each

dimension and thus internal consistency among the items in each

dimension. The reliability scores for all the nal exogenous and

endogenous variables are ranged from 0.706 to 0.974, which suggest

an acceptable internal consistency among the items in each

dimension (Nunnally & Bernstein, 1994). The CFA results suggest

that the scale items are reective indicators of their corresponding

latent constructs, which indicates construct validity (Chau, 1997;

Segars & Grover, 1993). In this data analysis, the average variances

extracted (AVE) for each factor and its measures all exceeded 0.50;

thus, convergent validity is achieved (Fornell & Larcker, 1981). We

also veried the correlation matrix of the items under each factor. All

the items individually under each factor have moderate to strong

correlation coefcients. This result also justied convergent validity.

We also examined multicolinearity, normality, and outliers.

5.2. Model testing: causal relationship by path analysis

We have used LISREL for path analysis, a family of structural

equation modeling (SEM), to test the causal relationships (the

hypotheses) of the model. Since we have measured all of the

exogenous and endogenous variables through Likert scale 1 5, data

gathered from this empirical study is not perfectly continuous.

Therefore, structural measurement through SEM by maximum

likelihood (ML) is not appropriate for this type of data (Kline, 2005,

p. 219). For structural measurement through SEM, one of the

fundamental requirements is that latent variables should be contin-

uous (Kline, 2005). Therefore, we took the average of the indicators ofeach of the latent variables individually for 239 cases and conducted a

path analysis (Kline, 2005) to nd out causeeffect relationships

between exogenous and endogenous variables. In path analysis, all of

the latent variables are treated as observed variables and their scores

represent the average of the scores of their respective indicators. We

have used the maximum likelihood procedure of LISREL for the

purpose of analysis. Since, the measuring items of PC and PATU were

integrated in a singleconstruct and themeasuring items of PT andPSR

were also integrated in a single item, we have now nine hypotheses to

test (with certain modications in the composition and denition of

PATU and PT constructs related to two hypotheses) from our

proposed 11 hypotheses having direct relations with adoption. Only

two hypotheses having direct relations with adoption were removed

during statistical renementin the previous section. Forpath analysis,we have used the correlation matrix as the input data for all the 11

exogenous variables (nine exogenous variableshaving direct relations

with adoption, i.e., PA, AOR, CSE, PATU, PFB, PI, MLO, PIQ, and PT and

two exogenous variables having indirect relations with adoption

through PT,i.e., PU and PS)and one endogenous variable (for example

ADOP1). Here PT is both an exogenous variable and endogenous

variable (like a mediator variable for adoption). Therefore, for two

models, i.e., GAM-S and GAM-I we have inputted two different

1212-correlation matrices. Final Path models and t indices are

shown inAppendices F and G. For path analysis we have tested 11

hypotheses, through path analysis for our research questions of this

research.

After conducting path analysis for the adoption of e-Gov at static

level, we nd that PA and PATU have signicant causal relations with



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ADOP1 with tvalues of 3.98 and 7.01 respectively. Therefore, these

two factors are signicant at the 0.05 level (z score for the 0.05 level is

1.96). Even these two factors are signicant at the 0.01 level (z score

for the 0.01 level is 2.576). PFB is signicant at the 0.1 level which has

a tvalue of 1.76 (z score for the 0.1 level is 1.645). PATU, PU, and PS

are signicant predictors on PT (Appendix F, Fig. 1).However,sincePT

is not a signicant predictor on ADOP1, subsequently PT and its

predictors are not related to adoption of EG. Apart from any issues

related to the adoption of e-Gov, separate causal relations of PT withother exogenous variablesare beyond thescope of this research.From

path analysis for adoption of EG at the interaction level, we nd that

PA, PI, PT, PIQ, and PATU have signicant causal relations with

ADOP2 at the 0.05 level. PATU, PU, and PS are signicant predictors on

PT (Appendix F, Fig.2). The nal accepted hypotheses for the adoption

of e-Gov at the static and interaction levels are listed inAppendix H.

Numerical formulation of the Path models for the static and

interaction levels are shown inAppendix I.

6. Discussion

As was previously mentioned, e-Gov implementation passes

through different phases of evolution as the services offered mature.

Though these phases are mutually exclusive and not distinctive,

however, as the services provided by e-Gov mature, its levels of

interaction improve from the static level to the interaction,

transaction, and integration levels. Especially in terms of technology,

organizational structure, service quality, reliability, security, and

privacy, the potential characteristics of the interaction level and

static level might be signicantly different. From the end users'

perspectives, thetwo stages of services havesignicant differences in

characteristics and functionality. ADOP1 indicates the adoption of EG

at the static stage. This is the rst stage of government service and

information presence online. Consequently, ADOP1 has a higher

mean between ADOP1 (3.673) and ADOP2 (3.2164). We found that

PA, PATU,and PFB are signicant predictors for the adoptionof e-Gov

at the static phase. Therefore, our hypotheses that PA, PATU, and PFB

have positive effects on the adoption of e-Gov at the static stage are

supported.When citizens are aware that there is an alternative sourceof brick

and mortar government service and information, for example e-Gov,

they might be interested in looking for this. After that, if they nd that

they have sufcient technological and psychological ability to use it

and also perceive that it provides absolute and relative advantages

they will most likely adopt it.

Since this is only the static stage of e-Gov, at this stage citizens can

only view, read, and collect government information relating to

government policies, services, rules and regulations, and different

other issues. Citizens do not adopt this stage to interact with

government agencies; rather they do that only to be informed of

government services that, instead, they could collect by physically

going to different government ofces. At this stage, availability

of resources (AOR), perceived information quality (PIQ), perceivedtrust (PT), computer self-efcacy (CSE), multilingual option (MLO),

and perceived image (PI) are not important for citizens to adopt

e-Gov. These constructs basically contribute very little to the

variances explained on ADOP1. Therefore, these hypotheses are not

signicant.

As Canada is a developed country and also advanced in modern

ICT, AOR might not be an important predictor for the adoption of

e-Gov. To adopt e-Gov, resources are mostly available. Here the

internet adoption rate is around 84% (Internet World Stats, 2008).

PIQ could be a potential predictor. However, since PFB is a signicant

predictor of e-Gov adoption, whencitizens perceive that using e-Gov

static webpage provides them with absolute and relative benets,

they are not concerned about PIQ. Same argument can be drawn for

CSE. From our demographic analysis, since more than 80% of the

respondents have at least undergraduate education and at least 80%

have online experiences formore than 3 years, CSE is not an issue for

citizens to adopt e-Gov. And, moreover, since PATU, which shared

some essence of CSE, has already contributed enough in ADOP1, CSE

is not a signicant predictor. Since adoption of the static stage of e-

Gov is very private, not an exposed matter to any other (as there are

no communications anywhere), perceived image (PI)is expectednot

to be a predictor of e-Gov at the static stage. At the static stage,

citizens are not interacting with government by any means, so theyare not disclosing any personal or nancial information in the virtual

environment. So,PT hasno signicantcausaleffect on theadoption of

e-Gov at this stage. MLO could be an important issue for citizens

whose mother tongue is not English when they need to collect

government information from published information in web pages.

However, from demographic analysis, we found that around 83% of

the respondents have mother tongue of English/French. Since

Canadian government web pages are written in English and French,

MLO cannot be an issue for use for respondents who speak both

English and French. Respondents who have a mother tongue other

than English or French are mostly new immigrants. Since the

Canadian immigration policy is highly dependent on level of

education, we found from cross tab analysis that around 90% of the

respondents whose mother tongues are not Englishor Frenchhave at

least an undergraduate education level. As we expected, language is

not a barrier for them to collect information from government web

pages that are written in English/French.

Now if we look at TAM (Davis, 1986) and DOI (Rogers, 1995), we

can get support for our analysis that PATU, which captures the

integrated view of PEOU of TAMand the complexity and compatibility

of DOI, and PFB, which captures the overlapping essences of PU

(perceived usefulness) of TAM and the relative advantage of DOI, are

the critical factors for the adoption of e-Gov, a system driven by

modern ICT. Since, government simply cannot abandon traditional

government ofces after launchinge-Gov andsincecitizenshave been

concerned about the traditional service system for decades, citizens

need to be aware of the alternative outlet of government service

system that is offered in e-Gov. Therefore, PA is also an important

predictor of e-Gov adoption that is not reected in simple technologyadoption as discovered byDavis (1986). From TPB (Ajzen & Fishbein,

1980), we know that a person's behavior is determined by the

person's intention to perform the behavior and that this intention is,

in turn, a function of the person's attitude and belief toward the

behavior. Therefore, adopting e-Gov at the static stage, which we can

view as the outcome of behavioral intention, is inuenced by the

beliefs of that person about the outcome of that behavior. We can

clearly see that PATU and PFB are those beliefs of positive attitude

toward adopting e-Gov, which in turn affect intention to use and,

nally, the adoption of e-Gov at the static stage. However, also

applicable in this theory that, for a system which has multidimen-

sional aspects (including technological, social, cultural, behavioral,

economic, organizational, and political) and which has an alternative

outlet reecting different behavioral outcomes (e.g., adoptingtraditional government services), for developing beliefs of the attitude

and intention to adopt the system, perceived awareness (PA) is an

important aspect to impart that beliefs.

From statistical analysis (see Appendices F and I), we observed

that at both stagesof service maturity of e-Gov, in theadoptionmodel,

perceived trust on e-Gov (PT) is affected by the causal effects of PATU,

PU, and PS. While in our model development, we have conjectured

that PU and PS have causal effects on PT; we did not make the

hypothesis that PATU also has a causal effect on PT. Our path analysis

has provided the result. However, since PT is not a potential factor for

the adoption of e-Gov at the static stage, we do not pay attention to

the causal relationships of PATU, PU, and PS with PT in this paragraph.

Rather, in the next section, we will do that while explaining the

signicance of ourndingsfor theadoptionof e-Gov at the interaction



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stage where PT is also a signicant predictor of adopting e-Gov at the

interaction stage. We also conducted multiple regression analyses to

verify our path analyses and got concrete support for the results

(Appendix J).

For the adoption of the interaction phase, we found that PA, PATU,

PI, PT, and PIQ are the signicant predictors. Therefore, our

hypotheses that PA, PATU, PI, and PT have positive effects on the

adoption of e-Gov at the interaction stage are supported. When

citizens are aware that instead of going to brick and mortargovernment departments for seeking service and information, they

can alternatively communicate with the respective departments

through e-mail, chat rooms, etc., which are integrated with e-Gov

websites, they develop an intention to use it. After that, if they nd

that they have the technological andpsychological ability to useit and

also perceive that this electronic communication is trustworthy, they

will most likely adopt it. Since, in the interaction stage, citizens have

some communication with others through modern ICT, they feel it to

be prestigious. Citizens feel that instead of going to a physical

government ofce, online communication with government depart-

ments can enhance their social status. As a result, PI was hypothesized

to have a positive relation to the adoption of e-Gov at the interaction

stage, and our ndings support that hypothesis.

PIQ is also a signicant predictor of e-Gov adoption at the

interaction phase. However, it has a surprisingly negative relation to

ADOP2, although our primary hypothesis predicted the causal relation

to be positive.Althoughregression analysis (Appendix J) supports this

nding, the correlation matrix shows a positive relation between

ADOP2 and PIQ. Basically PIQ has the lowest correlation coefcient

among the other signicant predictors of ADOP2 (0.239). From our

literature review of EC, we have found that PIQ has positive relation

with the use of EC. However, we should notice and consider some

subtle issues in this respect. For EC or general adoption criteria of e-

Gov (irrespective to service maturity level), better information quality

encourages consumers or users to use that online system. However,

the interaction stage of e-Gov has certain specic attributes. At this

stage, citizens generally do some queries to get specic information

that they do not nd on the e-Gov websites, or to provide some

information to the service providers. Therefore, if they nd thatinformation on the e-Gov websites is systematic, sequential, up-to-

date, effective, complete, and also provide sufcient links to

supplement information shortage, if any, citizens do not feel any

urge to contact or interact with government agencies through

electronic media. As a result, perceived information quality (PIQ) of

e-Gov automatically reduces the interaction of citizens with govern-

ment agencies through e-Gov websites. This argument justies thendings of the statistical analysis, which depicts negative causeeffect

relations between ADOP2 and PIQ.

Other exogenous variablesavailability of resources (AOR), com-

puter self-efcacy (CSE), multilingual option (MLO), and perceived

functional benets (PFB)are not important for citizens to adopt e-

Gov. These constructs, basically contribute very little to the variances

explained on ADOP2. Therefore, these hypotheses are not signicant.We have already explained the plausible reasons for the hypotheses

formed by the exogenous variables AOR, CSE, and MLO to be non-

signicant on theadoptionof e-Gov forCanadian users in theprevious

section. However, PFB wasfoundsignicant for ADOP1 while it isnon-

signicant for ADOP2. The hypothesis revealing a positive relation

between PFB and ADOP2 was not proven from the statistical analysis.

This is an interesting phenomenon. As we already explained, the

interaction stage of e-Gov has certain characteristics. This stage is

generally used to seek further information related to government

services, policies, and rules and regulations; to provide government

agencies with certain personal information; and to interact to ask

some queries. Citizens typically perform these interactions through e-

mail, chat rooms, etc. In the interaction stage, government agencies

might not communicate with citizens. Therefore, in this stage, citizens

are concerned mostly with the trustworthiness of e-Gov websites, not

PFB. Contacting through e-mail is so common in this era that doing

something (e-mail) through e-Gov websites does not create any

perceptions of functional benets among citizens as the plausible

reasons of using e-Gov websites at the interaction stage (Ong & Wang,

2009). Reasonably, after PATU, PT is the second inuential predictor,

and PFB is a non-signicant cause of the adoption of e-Gov at the

interaction stage.

Based on TAM (Davis, 1986) and DOI (Rogers, 1995) we cannotexactly explain the adoption criteria of e-Gov at the interaction stage.

The construct PATU, which captures the integrated view of PEOU of

TAM and complexity and compatibility of DOI, is the most contrib-

uting construct of EG adoption at the interaction stage. We also nd

that perceived image (PI) inuences the acceptance and use of e-Gov

at the interaction stage. This nding is supported by DOI proposed by

Moore and Benbasat (1991). Image refers to citizens' perceptions of

adopting e-Gov to present themselves as superior to others in the

society. Interaction with e-Gov systems, instead of using traditional

government ofces, reects a perception by citizens of superiorstatus.

PI exactly captures this superior status perception. In this phase, PA is

also a signicant predictor, like ADOP1 which is an addition to TAM

and DOI specic to e-Gov. Static and interaction stages of e-Gov are

the primary stages when a country rst launches e-Gov projects

(Accenture, 2005). Since these two phases are the rst introduction to

online service of government and are the alternative of traditional

government service systems, at these two levels of service of e-Gov,

citizens' awareness is presumably an important critical factor for

attitude and behavioral intention to use those systems. However, e-

Gov, particularly at the interaction stage, has more aspects than

simply adopting an innovation. Citizens' perceptions and expectations

differ remarkably at this stage from simply viewing government

information through static websites of e-Gov. Therefore, in this phase,

PT and PIQ are also additions to traditional TAM and DOI constructs,

which are introduced to capture the specic interaction character-

istics of e-Gov services. We have already explained the reason for the

negative impact of PIQ on ADOP2.

If we look at the sources of trust that citizens have while

interacting with the government, based onEaston (1965), we seethat the trust that develops the satisfaction of citizens with

governments due to their credible performance is very signicant.

This institutional-based trust is an important component while

interacting in e-Gov web pages (Parent et al., 2005). Thomas

(1998)also supported that citizens required trust while interacting

with e-Gov, a trust that derives from institutional credibility. If

actors of e-Gov do not feel institutional trust, according to the

institutional theory, they might not follow the institutional norms

of e-Gov. Therefore, PT is a reasonable addition to TAM and DOI in

predicting the adoption behavior of e-Gov at the interaction stage,

which is not reected in simple technology adoption as discovered

byDavis (1986). From TPB (Ajzen & Fishbein, 1980), we can clearly

see that PATU, PT, and PIQ are beliefs of attitude toward adopting e-

Gov, which in turn affect intention to use and, nally, the adoptionof e-Gov at the interaction stage. However, also related to this

theory is that for a system which has multidimensional aspects

(including technological, social, cultural, behavioral, economic,

organizational, and political) and which has alternative outlets

reecting different behavioral outcomes (e.g., adopting traditional

government services), PA and PI are important aspects for

developing beliefs of attitude and intention of adopting this

modern ICT-intense system.

From Appendix F we can see that at both stages of service

maturity of e-Gov, in adoption modelperceived trust on e-Gov

(PT)is affected by the causal effects of PATU, PU, and PS. We nd

that PS is the strongest predictor of PT. In all phases of e-Gov,

citizens have trust in e-Gov if they perceive that the e-Gov websites

ensure security ofnancial and personal information. Although we



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did not conjecture any relation between PATU and PT in our

hypothesis development process, we nd a strong causal relation of

PATU on PT. The perceived technological and psychological ability

to use e-Gov increases trust in e-Gov. This relation is suggested by

path analysis for better tness of the model and we nd several

logical aspects for this relation.Warkentin et al. (2002) and Parent

et al. (2005)both discovered that an important component of trust

is characteristic and process-based. If citizens perceive that they are

suf

ciently capable of handling online systems, their characteristicand process-based trust also increase (Warkentin et al., 2002). In

light of TPB, we also see that if a user believes that he/she is capable

of using an online system, he/she feels attitude and intention to use

that system, which implies his/her development of characteristic-

based trust. Therefore, the causal relation of PATU with PT has a

strong theoretical base. We conjectured that perceived uncertainty

(PU) has a negative relation to PT; however, surprisingly, we nd

that PU has a positive relation to PT (loading factor 0.25, signicant

at the 0.01 level). That is, the perception of uncertainty basically

increases the perception of trust in EG. Apparently, this result is

confusing. Therefore, we looked at the result from different

statistical and conceptual perspectives. The correlation matrix

shows that PU has a very weak positive correlation with PT

(0.109). PU has weak negative correlations with PATU and PS. We

have also done regression analysis of PU, PS, and PATU (indepen-

dent variables) with PT (dependent variable) (Appendix J). We see

that all these variables are signicant positive predictors of PT.

However, in stepwise regression, while only PU is inputted, we see

that PU has a negative non-signicant effect on PT. While inputting

PU, PS, and PATUeither in path analysis or in regression analysis

PU has the least positive effect on PT with very weak correlation

with PT. This simply signies that, while measuring the combined

effect of PATU, PU, and PS, PU has a positive effect on PT. In our

sample, more t

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