Salient beliefs that influence the acceptance or rejection of public e - services in Lebanon

Abstract

This paper examines the reasons citizens would choose

to accept or non-accept/reject public e-services. The

approach taken was based on the model of acceptance

of technology in households (MATH) and on the two

factors theory. The research model was measured with

data gathered in two phases, via interviews with open-

ended questions in the first stage, and through survey

with questionnaire in the second phase. Results of the

qualitative and the quantitative studies show that only

a small percentage of Lebanese intended to accept

government e-services. Perceived usefulness (PU),

perceived government support (PGS), computer self

efficacy (CSE), and perceived government influences

(PGI) are the key drivers of the e-services acceptance

intention (AI). For the non-intenders, barriers like fear

of government control (FGC), lack of trust in the secu-

rity (TSEC), lack of trust in the privacy (TPRI), lack of

support (PGS), and lack of knowledge (CSE) were

most significant. In the two studies, the fear of gov-

ernment control (FGC) was the most important deter-

minant, both in terms of frequency and in terms of

importance. The willingness to use the public e-

services will be present if governments can develop

trust relationships with individuals, assure them that

their financial details are secure, that these services

will respect the privacy of citizens, and she will not use

e-services in order to increase control.

Key-words:

E-government, e-services acceptance, MATH, TPB,

ICT acceptance intention, ITA e-Gov Model.

Salient beliefs that

influence the

acceptance or rejection

of public e-services in

Lebanon

Référence : 22

Antoine HARFOUCHE Maitre de Conférence au CREPA

Université Paris-Dauphine

Place du Maréchal de Lattre de Tassigny

75775 PARIS Cedex 16

Tél. : +33 (0)1 44 05 44 05

Fax : +33 (0)1 44 05 49 49

[email protected]

Stephane Bourlitaux-Lajoinie Maitre de Conférences à l‟IAE de Tours, Labora-

toire CERMAT

Directeur du M2 Marketing des Services

Tel : + 33 (0)2 47 36 10 42

[email protected]

mailto:[email protected]

mailto:[email protected]

Big Brother is Watching You: inhibitors and enablers of public e-services

Antoine HARFOUCHE and Stéphane Bourliataux-Lajoinie

Introduction

Research in information systems (IS) is concerned with

identifying the factors that facilitate or impede the accep-

tance and rejection (or non-acceptance) (Bagozzi 2007)

of e-services by citizens. Researchers seek to understand

the user‟s behavior by placing the individual at the center

of the inquiry. Understanding why people accept or reject

e-services or other information and communication tech-

nologies (ICTs) is one of most challenging issues in this

domain.

While ICTs acceptance in the workplace and at home has

been studied extensively, little systematic research has

been conducted to understand the determinants of accep-

tance of online public services by citizens. Government e-

service acceptance by citizens is a substantial global

problem. Indeed, research shows that providing e-service

access and creating conditions for its usage does not

guarantee e-service acceptance by the citizens (Dwivedi

et al. 2009; UNDESA 2008; UNPAN 2005). Until today,

studies have shown that e-government initiatives have

failed to engage citizens. Despite incentives and media

campaigns that encourage citizens to go online for gov-

ernment transactions, most citizens of developing coun-

tries do not use government e-services and prefer to use

traditional face-to-face services (UNDESA 2008;

UNPAN 2005). Therefore, the success of e-government

will depend on whether governments are able to entice

citizens to accept and use online public services.

Today, in addition to the face-to-face service delivery

system, the Lebanese government is introducing the vir-

tual channel of service delivery system (VCSDS). This

multichannel of service delivery system will allow the

government to offer two types of public service: tradi-

tional services and online services. As a result, the Leba-

nese government is appreciating the need to increase citi-

zens‟ awareness regarding the transition to online deli-

very of public services. Therefore, the Lebanese govern-

ment needs to better understand the factors that affect the

e-services acceptance or rejection (or non-acceptance)

intention.

According to van Dijk et al. (2008) a theory of the accep-

tance of public e-services is lacking. They also asserts

that this kind of theory have to be derived from a general

theory of acceptance and use of ICTs applied to the spe-

cial context of the government to citizens (van Dijk et al.

2008, p. 383). In this article, we begin working on such a

theory. Therefore, this paper develops an integral model

of individuals‟ intention to accept or reject e-government

services (ITA e-Gov Model). It captures the influence of

different external and internal factors (enablers and inhi-

bitors) on government e-services acceptance/ rejection at

the first stages of the acceptance process. Based on the

model of acceptance of technology in households

(MATH) conceptualized and measured by Brown and

Venkatesh (2001; 2005) and on Cenfetelli (2004) two

factors theory, the ITA e-Gov Model focuses on the asso-

ciation between (1) public e-services perceived outcomes

(2) citizens‟ personal variables, (3) social influences, and

(4) contextual factors, and their evaluations in the first

stages of the acceptance process. Developed from the

theory of planned behavior (TPB, e.g. Ajzen 1991) and

the decomposed theory of planned behavior (DTPB, e.g.

Taylor and Todd 1995a), MATH is suggested as an ideal

framework for understanding ICT acceptance outside the

workplace (Hsieh et al. 2008).

The ITA e-Gov Model was designed to capture the rea-

sons for acceptance or rejection (or non-acceptance) of

public e-services in the Lebanese context. In order to

examine the inhibitors and enablers of the citizens‟ inten-

tion to accept e-services, data was collected in a first

stage from 188 randomly chosen potential Lebanese pub-

lic e-service users. In Phase 1, open-ended questions

were asked about government e-services acceptance/non-

acceptance intention, and about reasons for such accep-

tance or non-acceptance. Then, the 188 answers were

double coded based on a start list of beliefs (Miles and

Huberman 1984) that included variable definitions se-

lected from prior research. After identifying salient be-

liefs of public e-services acceptance in Lebanon, the re-

search model and the questionnaire were developed.

In Phase 2, we used a quantitative method. We surveyed

210 Lebanese potential public e-services users. The aim

was to understand the weight that the individual gave to

each variable. Therefore, respondents were asked to rate

each factor from the salient beliefs from 1 to 7 based on

how important it was in their acceptance or non-

acceptance decision.

The ITA e-Gov Model could help governments better

deploy and manage their ICT investments by better un-

derstanding their citizens. Extensive efforts are necessary

to increase citizens‟ awareness about the transition to the

online delivery of government services. Government

communication could incorporate the variables that influ-

ence e-service acceptance intention.

In this paper, we begin by reviewing the technology ac-

ceptance literature. Then, we review the public e-services

acceptance literature. Based on prior research, a start list

of beliefs related to public e-service acceptance has been

created. After having defined each variable, we compare

respondents‟ answers to the start list. Then, we summar-

ize the conceptual model of the public e-services accep-

tance intention by citizens. Finally, after testing the mod-

el, we describe in the conclusion the lessons learned from

this study. We also highlight the limitations and future

directions.



1. Constructs that shape the pub-

lic e-services acceptance process

One of the main reasons of difficulties that developing

countries face when implementing e-government, is the

low public e-services‟ acceptance and use by citizens

(Heeks 1999; Jaeger and Thompson 2003; Moon 2002;

Odedra-Straub 2003). Indeed, providing e-service access

and creating conditions for its usage (e-access and e-

skills) does not guarantee the acceptance and use of pub-

lic e-services by citizens. Research indicates that in de-

veloping countries, e-government offerings have failed to

capture the imagination of the citizens (Dwivedi et al.

2009). Understanding the reasons of such low acceptance

and use may allow opportunities to develop more effec-

tive e-government policies. Thus, success of e-

government will depend on how governments entice citi-

zens to accept and use online public services. Therefore,

governments need to better understand the factors that

influence the e-services acceptance/rejection by citizens.

According to Schwaerz and Chin (2007), ICT acceptance

“involves a holistic conjunction of a user‟s behavioral

interaction with the ICT over the time and his or her psy-

chological understanding/willingness or resis-

tance/acceptance that develops within a specific so-

cial/environmental/organizational setting. The accepta-

tion process may be conceptualized as a temporal se-

quence of activities that lead to initial acceptance and

subsequent adaptation and continued usage of an ICT by

the adopter.

1.1 Factors Influencing ICT Accep-

tance Key constructs that shape the ICT acceptance process are

numerous. Based on Schwaerz‟s and Chin‟s (2007) defi-

nition and on diffusion of innovation theory (Rogers

1983), these key constructs are divided to four categories:

(1) ICT's perceived attributes and characteristics; (2) so-

cial influences and communication concerning the ICT

innovation received by the individual from his social

environment (3) individual differences and psychological

processes, and finally (4) the environmental influences

such as contextual factors.

The IS community has deeply investigated the relation

between these constructs. Indeed, twenty years ago, Davis

presented the most influential and commonly employed

theory in this domain (Lee et al. 2003): the technology

acceptance model (TAM). This model, presented as a

simplified adaptation of the theory of reasoned action

(TRA) and the theory of planned behavior (TPB) in the

IS context, has became a dominant paradigm (Straub and

Burton-Jones 2007). Ten percent of the total journal ca-

pacity in the IS field has been occupied with TAM stu-

dies (Lee et al 2003). The Journal of the Association of

Information Systems has even devoted a Special Issue in

2007 for the TAM research entitled “Quo Vadis TAM”.

But, fourteen years after TAM, by presenting the unified

theory of acceptance and use of technology (UTAUT, e.g.

Venkatesh et al. 2003) that synthesizes a large number of

TAM research, Venkatesh et al., ironically conveyed us

back to the TAM‟s origin: the TPB (Benbasat and Barki

2007).

Therefore, in order to explain the reasons for acceptance

or rejection (non-acceptance) of e-services by Lebanese,

we have selected one of the most useful theories in the

ICT voluntary acceptance context: the model of accep-

tance of technology in households (MATH, e.g. Brown

and Venkatesh 2005; Venkatesh and Brown 2001). The

MATH model extends the theory of planned behavior

(TPB, e.g. Ajzen 1991) by decomposing the beliefs that

comprise the attitude which determine the behavior inten-

tion (IA).

We think that MATH is more relevant than TAM because

MATH introduces a large number of factors that may

influence the acceptance/rejection of ICTs and e-services.

MATH allows the integration of more contextual beliefs

in the acceptance process. Indeed, according to the

MATH, ICT “acceptance intention” (IA) is a weighted

function of attitudinal, normative, and the control beliefs

structure. Attitudinal, normative, and the control beliefs

are decomposed into multi-dimensional beliefs structures.

1.1.1 Attitudinal beliefs According to TRA, there are two kinds of attitudes: (1)

the individual‟s attitude towards the ICT (AICT) and

attitude concerning the ICT acceptance and use (Aacp).

Attitude toward the ICT represents a summary evaluation

of the ICT captured in such attribute dimensions as

good/bad, harmful/beneficial, likable/dislikable (Ajzen

and Fishbein 2000). Attitude toward ICT acceptance and

use (Aacp) is defined as a person's favorable/unfavorable

evaluation of the ICT acceptance and use. According to

Fishbein and Ajzen (1975), attitudes toward an innova-

tion (AICT) do not strongly predict innovation accep-

tance and use (IA). Only the individual‟s attitude con-

cerning the acceptance of an innovation (Aacp) deter-

mines its acceptance intention (IA). The individual‟s

attitude towards the innovation (AICT) influences the

ICT acceptance (IA) indirectly through influencing the

attitude concerning ICT acceptance and use (Aacp).

The individual‟s attitude toward accepting an ICT (Aacp)

is the function of the perceived consequences and out-

comes that result from the acceptance and the usage of

the ICT. According to the decomposed theory of planned

behavior (DTPB, e.g., Taylor and Todd 1995a; 1995b)

and to MATH, attitude belief can be decomposed to a set

of attitudinal beliefs that derive from the literature which

describe the perceived characteristics of an innovation.

Indeed, in the Diffusion of Innovation Theory (IDT,

summarized in Table 1), Rogers indicates five ICT

attributes that are associated with the ICT acceptance



process: relative advantage, compatibility, complexity (or

ease of use), observability, and trialability (Rogers 1995).

The diffusion of Innovation Theory

Theory Original measured ICT attributes Results

The IDT examines

the determinants of

ICT or the ICT at-

tributes which has

been translated in

Perceived Character-

istics of using an

Innovation (PCI).

- Rogers (1995): relative advantage, compatibility,

complexity, observability, and trialability.

- Tornatzky and Klein (1982): cost, communicability,

divisibility, profitability, and social approval.

Tornatzky and Klein (1982); Agrawal and Prasad

(1998); Cooper and Zmud (1990) results:

- compatibility and relative advantage are positively

related to acceptance

- complexity is negatively related to acceptance

- PCI factors: relative advantage, compatibility, com-

plexity, trialability, visibility, result demonstrability,

and image.

Moore and Benbasat (1991; 1996) results:

- all of the PCI factors, including voluntariness and

social norms influence the acceptance.

Table 1. This Table resumes the ICT attributes according to the diffusion of Innovation Theory

Tornatzky and Klein (1982), after analyzing 105 research

papers related to the IDT, identified five more characte-

ristics: cost, communicability, divisibility, profitability,

and social approval. But, in their conclusion, they argued

that communicability and divisibility are closely related

to observability and to trialability.

Based on the TRA‟s (e.g. Fishbein and Ajzen 1975) as-

sumption that users think about an ICT in terms of their

consequences not their attributes, Moore and Benbasat

(1991, p. 195; 1996) redefined the Rogers‟ five variables

in terms of usage consequences. They presented the full

set of perceived characteristics of using an innovation

(PCI) by adding image and willingness of use, and by

dividing observability into visibility and result demon-

strability (e.g. Moore and Benbasat 1991, p. 202). They

also argued that the relative cost of an innovation (or

perceived cost) has a great effect on acceptance behavior.

But they did not include it in their research because they

were studying the ICT acceptance by employees within

organizations. Table 2 summarizes the definition of these

concepts.

Perceived characteristics of using an Innovation (PCI)

Concepts Definition Sources Redefined by… as …

Relative advan-

tage

The degree to which an innovation is

perceived as being better than its pre-

cursor.

Rogers‟

IDT

(1995,

p.15).

Moore and Benbasat (1991) redefined “relative advan-

tage” as the degree to which using the ICT innovation is

perceived as being better than using its precursor.

Compatibility


perceived as being consistent with the

existing values, past experiences, and

needs of potential adopters.

Moore and Benbasat (1991) redefined “compatibility”

as the degree to which using this ICT is perceived as

being consistent with the existing values, needs, and

past experiences of potential adopters.

Complexity


perceived as being difficult to under-

stand and use.

Moore and Benbasat (1991) redefined “complexity” as

ease of use.

Trialability

The degree to which an innovation may

be experimented with on a limited basis

before acceptance.

Moore and Benbasat (1991) redefined “trialability” as

the degree to which an innovation ICT may be experi-

mented before acceptance.

Observability

The degree to which the results of an

innovation are visible and communica-

ble to others.

Moore and Benbasat (1991) redefined “observability”

as the degree to which the results of using the innova-

tion ICT are observable to others. They found that “ob-

servability” has construct ambiguity problems, so they

divided it into visibility and result demonstrability.

Cost Introduced by Tornatzky and Klein

(1982)

Tornatzky

and Klein

(1982)

Moore and Benbasat (1991) redefined it as relative cost

or perceived cost.

Communicabil-

ity

Closely related to observability. Moore and Benbasat (1991) redefined it as “observabil-

ity”.

Divisibility Closely related to trialability. Moore and Benbasat (1991) redefined it as “trialabil-

ity”.

Profitability Introduced by Tornatzky and Klein

(1982).

Moore and Benbasat did not include this characteristic.



Social Approval Closely related to Moore and Benbasat‟s

image (1991).

Moore and Benbasat (1991, p.195) redefined it as “im-

age” or the degree to which use of an innovation ICT is

perceived to enhance one‟s image or status in one‟s

social system.

Voluntariness to

use

The degree to which use of the innova-

tion is perceived as being voluntary, or

of free will.

Moore and

Benbasat‟

PCI (1991,

p.195;

203).

Moore and Benbasat (1991, p.195) redefined it as the

degree to which the use of the innovation is perceived

as being voluntary, or of free will.

Result Demon-

strability

Moore and Benbasat‟ PCI (1991, p.203)

found that observability has construct

ambiguity problems, so they divided it

into visibility and result demonstrability.

The degree to which the results of adopt-

ing/accepting/using the ICT innovation are observable

and communicable to others.

Visibility Moore and Benbasat‟ PCI (1991, p.203)

found that observability has construct

ambiguity problems, so they divided it

into visibility and result demonstrability.

The degree to which the ICT innovation is visible in the

environment of the adopter.

Table 2 Perceived characteristics of using an Innovation (PCI)

Perceived usefulness (PU) and perceived ease of use

(PEU), proposed by Davis (1989) and Davis et al. (1989)

in TAM, are attributed to the PCI (Davis 1993; Davis et

al. 1989).

Based on the decomposed theory of planned behavior

(DTPB), the model of acceptance of technology in

households (MATH, e.g. Brown and Venkatesh 2005;

Venkatesh and Brown 2001) decomposes the attitudinal

beliefs in: utilitarian outcome (UO), hedonic outcome

(HO), and social outcomes (SO).

2.1.1.1. Utilitarian outcomes

Utilitarian outcomes (UO) adapt the rational basis for

ICT acceptance which is usually characterized by the

perceived usefulness of the ICT (Davis 1989; Davis et al.

1989, p.320) to the households‟ context. As summarized

in Table 3, PU is equivalent to Rogers‟ “relative advan-

tage” (Rogers 1983; Moore and Benbasat 1991), to Com-

peau and Higgins‟ “outcome expectations” (1995b), to

Davis et al.‟s “extrinsic motivation” (1992), to Thompson

et al.‟s (1991) “job-fit”, and to Venkatesh et al.‟s (2003)

“performance expectancy”. The utilitarian outcomes

(UO) can be defined as the extent to which using an ICT

can enhances the effectiveness of user‟s activities.

Utilitarian outcomes

Constructs Similar

constructs

Model Authors Definition

Utilitarian out-

comes

(MATH, e.g.

Brown and

Venkatesh 2005;

Venkatesh and

Brown 2001)

Perceived use-

fulness (PU)

TAM Davis (1989); Davis

et al. (1989, p. 320)

The degree to which a person believes that using a

particular system would enhance his or her job per-

formance

Performance

expectancy

UTAUT Venkatesh et al.

(2003, p. 447)

The degree to which an individual believes that using

the system will help him or her to attain gains in job

performance.

Extrinsic Moti-

vation

MM

Davis et al. (1992, p.

1112)

The perception that users will want to perform an

activity because it is perceived to be instrumental in

achieving valued outcomes that are distinct from the

activity itself, such as improved job performance,

pay, or promotions.

Job Fit MPCU

Thompson et al.

(1991, p. 129)

The extent to which an individual believes that using

an ICT can enhance the performance of his or her job.

Relative advan-

tage

IDT Rogers (1983);

Moore and Benbasat

(1991, p. 195)

The degree to which an innovation is perceived as

being better than its precursor.

Outcome ex-

pectation

SCT Compeau and Hig-

gins (1995b)

The performance related consequences of the behav-

ior. Specifically, the performance expectations that

deal with job related outcomes.

Table 3. Utilitarian outcomes

2.1.1.2. Hedonic outcomes (HO) Hedonic outcomes can be defined as the pleasure derived

from the acceptance and usage of an ICT. Hedonic out-

comes (HO) adapt hedonic and affective ICT attributes.

Indeed, recently, IS scholars have included hedonic crite-

ria and affective ICT attributes like: perceived enjoyment

(PE, Van der Heijden 2004; Sun and Zhang 2006), per-

ceived affective quality of ICT (Zhang and Li 2005),

heightened enjoyment (Agarwal and Karahanna 2000),

and perceived playfulness (Sun and Zhang 2006). Table 4

summarizes these constructs that comprise the pleasure or



entertainment potential derived from the interaction with the ICT.

Hedonic outcomes

Constructs Similar con-

structs


Hedonic out-

comes

(MATH, e.g.

Brown and

Venkatesh 2005;

Venkatesh and

Brown 2001)

Perceived enjoy-

ment (PE)

User Acceptance of

Hedonic Informa-

tion Systems

Van der Heijden

(2004, p. 697); Lewis

et al. (2003, p. 163)

The extent to which fun can be derived

from using the system as such or the intrin-

sic enjoyment of the interaction with the

ICT

Perceived Affec-

tive Quality of

ICT (PAQ)

Extended TAM Zhang and Li (2005) Fun and enjoyment perceived when using

an ICT.

Perceived play-

fulness (PP)

Extended TAM Sun and Zhang

(2006)

The extent to which the activity of using

ICT is perceived to be enjoyable in its own

right, apart from any performance conse-

quences that may be anticipated

Heightened en-

joyment

Extended TAM Agarwal and Kara-

hanna (2000)

Defined and measured the same as per-

ceived enjoyment.

Table 4. Hedonic outcomes

2.1.1.3. Social outcomes Social outcomes (SO) refer to the image or the power that

the ICT acceptance gives to the user within his social

group. Social outcomes include the image or status gains,

result demonstrability and visibility (Venkatesh and

Brown 2001). These construct were defined in Table 2.

Karahana et al. (1999), in the decomposed theory of rea-

soned action (DTRA, e.g. Karahana et al. 1999) argued

that social outcomes play an important role in the accep-

tance process especially in the pre-acceptance period.

DTRA results show that visibility and result demonstra-

bility are strong predictors of ICT acceptance but only in

the first phase of the acceptance process, while percep-

tion of image enhancement can predict ICT continued

usage in the post acceptance phase. According to MATH,

status gains impact on the acceptance of ICT increases

with the user‟s age (Brown and Venkatesh 2005).

2.1.1.4. Control outcomes In addition to the MATH‟s utilitarian outcome (UO),

hedonic outcome (HO), and social outcomes (SO), we

include the control outcomes (CO). The control outcomes

(CO) refer to the perceived characteristics of an ICT re-

lated to the control, like compatibility, trialability, rela-

tive cost, declining cost, and complexity or PEU. Control

outcomes (C0) differ from the perceived behavioral con-

trol that refers to the internal personal variables and to the

external resources, contextual, and environmental va-

riables. Control outcomes are only related to ICT per-

ceived characteristics. The most measured and used vari-

able between the control outcomes is PEU. As summa-

rized in Table 5, PEU is similar to “perceived complexi-

ty” (Rogers 1983; Thompson et al. 1991), to “effort ex-

pectancy” (UTAUT, Venkatesh et al. 2003), and to

Thompson et al.‟s “complexity” (1991).

Perceived ease of use construct

Constructs Similar

constructs


Perceived ease of use

(PEU)

The degree to which an

individual believes that

performing the behavior of

interest would be free of

effort.

Effort expec-

tancy

UTAUT Venkatesh et al.

(2003, p. 450)

The degree of ease associated with the use of the

system.

Perceived

complexity

IDT Rogers 1983;

Thompson et al.

(1991)

The degree to which an innovation is perceived

as being difficult to understand and use.

Complexity MPCU Thompson et al.

(1991, p. 128)

The degree to which an innovation is perceived

as relatively difficult to understand and use.

Table 5. Perceived ease of use construct according to the TAM (Davis 1989; Davis et al. 1989, p.320).

1.1.2 Normative beliefs Prior research presented evidence that subjective norms

and social influences (friends and family influences FFI

and workplace referents‟ influences WRI) play a key role

in ICT acceptance, especially in the first stages of the

acceptance process (Karahana et al. 1999; Thompson et

al. 1991; Triandis 1971) and/or when users' knowledge

concerning the ICT are vague (Hartwick and Barki 1994).

As summarized in Table 6, the subjective norms construct

(TRA e.g. Ajzen 1991, TPB e.g. Fishbein and Azjen

1975; C-TAM-TPB e.g. Taylor and Todd 1995a; 1995b ;

and Matheison 1991) is equivalent to the social influ-

ences construct (Venkatesh et al. 2003), to the social

factors construct (Triandis 1980; Thompson et al. 1991),

and to societal norms (Warshaw 1980).

According to Fishbein and Azjen (1975), Ajzen (1991),

and Taylor and Todd (1995a; 1995b), a person's subjec-



tive norms (SN) may be influenced indirectly, for exam-

ple, when the person infers that others think he or she

should use a system, or directly by other individuals, for

example, when referents tell the person that they think he

or she should use a system. The direct compliance effect

of subjective norms (SN) on intention to accept ICT (IA)

was identified in TRA, TPB, C-TAM-TPB, and MPCU

theories. This has also been proven by Hartwick and Bar-

ki (1994) in a mandatory context, but not in voluntary

usage contexts. Venkatesh and Brown (2001) have also

proven that the acceptance intention (IA) is influenced by

messages and stimuli conveyed via mass media and sec-

ondary sources like News, Newspapers, TVs, and radios

(Secondary Sources‟ Influences, SSI). In addition, TAM2

reflects the impact of two additional theoretical mechan-

isms: internalization and identification. Subjective norms,

also, includes the personal network exposure (PNE, e.g.

Hsieh et al. 2008; Valente 1995, p. 70) of the potential

adopters. Indeed, individuals‟ ICT acceptance intention

(IA) can also be influenced by how other members in the

individual‟s personal network respond to this ICT inno-

vation. The personal network exposure (PNE) accounts

for the observed aggregate ICT acceptance behaviors in

an individual‟s personal network (Hsieh et al. 2008; Va-

lente 1995, p. 70).

Normative beliefs construct

Construct Authors Definition

Normative

beliefs

(MATH, e.g.;

Brown and

Venkatesh

2005;

Venkatesh and

Brown 2001)

Subjective Norms

(SN)

Ajzen and Fishbein

(1980), Taylor and

Todd (1995a ; 1995b)

Belief of the consumer concerning the expectations of significant

others about the behavior multiplied by the consumer‟s felt need to

comply with those expectations.

Societal Norms Warshaw (1980) Felt pressure from others.

Social Factors (SF) Triandis (1980),

Thompson et al.‟s

(1991)

The individual‟s internalization of the reference groups‟ subjective

culture, and specific interpersonal agreements that the individual

has made with others, in specific social situations.

Social Influences

(SI)

Venkatesh et al.

(2003)

The general social pressure (in an organizational cultural setting)

for an individual to perform a behavior.

Table 6. Normative beliefs construct.

Consequently, subjective norms (SN) include friends and

family influences (FFI), secondary sources‟ influences

(SSI), and workplace referents‟ influences (WRI), with

personal network exposure (PNE).

1.1.3 Control beliefs In general, perceived behavioral control (PBC) refers to

the user‟s ability to control the behavior. Ajzen (1991)

defined Perceived behaviour control (PBC) as both inter-

nal psychological determinant related to the target beha-

viour and to external resource and contextual constraints.

Consequently, PBC results only from the user‟s personal

variables and from the contextual factors.

2.1.3.1. Individual differences and psychologi-

cal determinants Researchers have studied a range of individual user cha-

racteristics that influence the acceptance or non-

acceptance of the ICT innovation. Between all the indi-

vidual characteristics, only one trait variable is specific to

ICT and refers to comparatively stable characteristics of

individuals which is invariant to situational stimuli:

Computer self efficacy (CSE). According to Compeau

and Higgins (1995a), computer self efficacy (CSE) can

predict ICT acceptance intention (IA). As defined in Ta-

ble 7, computer self efficacy (CSE) refers to the individu-

al's perceptions of his or her ability to use ICT in the ac-

complishment of a specific task (Compeau and Higgins

1995a; 1995b). Computer self efficacy (CSE) is equiva-

lent to computer plaSyfulness (CP, Webster and Martoc-

chio 1992; Moon and Kim 2001) and to personal innova-

tiveness in ICT (PIIT, Agrawal and Prasad 1998; Agraw-

al and Karahanna 2000).

2.1.3.2. Contextual factors External control factors vary from context to context (Aj-

zen 2001) and depend on the situation. External control

factors consist of a large number of constructs like:

MPCU‟s Thompson et al.‟s (1991) facilitating conditions

(Venkatesh et al. 2003), Igbaria et al.‟s (1996) end user

support. Hartwick and Barki (1994) proved that control

evaluation is also related to the resources available for the

individual (such as: money, time, and information) that

can be a barrier inhibiting acceptance (such as: low fi-

nancial resources, lack of time, or low experience).

Control beliefs are also related to trust. Indeed, according

to Gefen (2000), trust is a complex, multi-dimensional,

context-dependent construct. Trust is necessary for online

interactions where personal and financial information

exchange goes through the virtual channel of service de-

livery system characterized by high uncertainty (Hoffman

et al., 1999). It deals with the belief that the trusted party

will carry out its obligations (Gefen et al. 2003 a, 2003b).

This definition is rooted in Giddens‟ (1994) definition

which considers trust as a belief in someone‟s honesty

and credibility (Giddens 1994).



In a virtual context, trust refers to the user‟s ability to

control the actions of an e-service provider (Nah and

Davis 2002). This construct was defined as trust in the

privacy of the e-services and trust in the security aspects

of e-services (Chen and Barnes 2007; Hernandez and

Mazzon 2007; Nah and Davis 2002). The trust in privacy

reflects the user‟s confidence in the service provider‟s

ability to respect the user's privacy. Trust reveals the citi-

zens confidence in the fact that his private information

will not be used by the service provider, or sold to others.

The trust in the security aspects of the e-services (Chen

and Barnes 2007; Hernandez and Mazzon 2007; Nah and

Davis 2002) refers to user‟s confidence in the e-service

provider‟s ability to protect and prevent the information

from being hacked (Nah and Davis 2002). Table 7 re-

sumes the control beliefs.

Control beliefs

Construct Authors Definition

Computer self effi-

cacy (Compeau and

Higgins 1995a)

Computer playfulness

(CP)

Webster and Martocchio (1992);

Moon and Kim (2001).

The degree of cognitive spontaneity in

microcomputer interactions.

Personal innovativeness

in IT (PIIT)

Agrawal and Prasad (1998);

Agrawal and Karahanna (2000).

An individual trait reflecting a willingness

to try out any new ICT;

End user support

(Igbaria et al. 1996)

Facilitating conditions

MPCU‟s Thompson et al. (1991);

Venkatesh et al. (2003); Taylor

and Todd (1995b).

The control beliefs relating to resource

factors such as time and money and ICT

compatibility issues that may constrain

usage.

End User Support Igbaria et al. (1996).

High levels of support that promotes more

favorable beliefs about the system among

users as well as MIS staffs.

Trust (Gefen et al.

2003 a, 2003b)

Trust in the security

Chen and Barnes (2007); Hernan-

dez and Mazzon (2007); Nah and

Davis (2002).

The user‟ confidence over the security

aspects of the e-services.

The user‟s confidence in the e-service

provider‟s ability to protect the information

by preventing it from being hacked.

Trust in the privacy

Chen and Barnes (2007); Hernan-

dez and Mazzon (2007); Nah and

Davis (2002).

The user‟s confidence in the service pro-

vider‟s ability to respect user's privacy.

User‟s confidence that his private informa-

tion will not be used or sold to others.

Table 7. Control beliefs

1.2 Factors Influencing Public E-

services Acceptance After reviewing large number of beliefs related to ICT

acceptance, we will compare it to literature in the e-

government research. Our aim is to detect the antecedents

that are unique to the e-service context as compared to

other voluntary ICT acceptance (e.g., PC acceptance,

Internet acceptance).

1.2.1 Attitudinal beliefs and public e-

services In adapting items from Van Slyke et al. (2004), Bélanger

and Carter (2006) measured the impact of the UO (rela-

tive advantage), SO (image), and CO (compatibility and

ease of use) on the intention to use e-government servic-

es. They found that higher level of UO (perceived relative

advantage) increases citizens‟ intentions to accept the

public e-services. Bretschneider et al. (2003) also demon-

strated that the perceived benefit factor (UO) is a major

predictor of government e-services. Gilbert et al (2004),

Phang et al. (2005) reconfirmed it when they found that

perceived usefulness (or UO) of websites was the most

significant predictor of senior citizens‟ intention to accept

e-government.

Gilbert et al. (2004) confirmed that control outcomes

(CO), such as cost and time available, significantly influ-

ence willingness to use e-services. Bélanger and Carter

(2006) also found that higher levels of perceived image

enhancing value of e-government (SO) and higher levels

of perceived compatibility (CO) increase citizens‟ inten-

tions to accept state e-government services. But contrary

to relative advantage, compatibility, and image, they

found that higher levels of perceived ease of use are not

significantly associated with increased use intentions of

e-government services (Bélanger and Carter 2006). By

doing so, they confirmed Phang et al. (2005) and Gilbert

et al. (2004) results. But we think that Bélanger and Cart-

er (2006) had this result concerning ease of use because

they tested their model with college students that had an

average of nine years of experience with computers.

Therefore, we consider that the ease of use is still an im-

portant predictor of the acceptance especially when it is

the case of a population of normal citizens.

1.2.2 Normative beliefs and public e-

services Researchers in the IS field have studied the social influ-

ences impact on public e-services acceptance. For exam-

ple Gefen et al. (2002) found that social influence has a



significant impact on intention to accept public e-

services.

In Government to Citizen (G2C), the government plays

an important role in facilitating the acceptance of public

e-services by citizens (Hsieh et al. 2008). But until today,

few studies have examined the governmental direct influ-

ences on public e-services acceptance/rejection. Hsieh et

al. (2008) have showed that governments may use syste-

matic approaches to raise awareness and interest among

citizens about public e-services. They can use different

media channels, including communicating directly with

citizens, to explain the benefits of using ICT and to offer

training and technical support (e.g., Kvasny 2002; Van

der Meer and Van Winden 2003). From the citizen‟s

perspective, these institutional efforts to encourage and

facilitate ICT use convey the message that the govern-

ment is committed to their interests and has taken their

needs and requirements into consideration (Kvasny and

Keil 2002). In fact, prior research has revealed that gov-

ernment agencies may serve as important referents whose

expectation affects individual innovation acceptance

(Lynne et al. 1995). Therefore, the government may in-

fluence individual‟s acceptance of public e-services. In

this research, the governmental influences are represented

by the construct: perceived governmental influences

(PGI).

1.2.3 Control beliefs and public e-services Several researchers have studied the impact of control

beliefs on the public e-services acceptance by citizens

(Warkentin et al. 2002). Results found that between all

the control beliefs, trust has the most significant predictor

on the intention to accept public e-services (Gefen et al.

2002; Lee et al. 2005). Indeed, Bélanger and Carter

(2005) demonstrated that trust has a significant influence

on intention. Lee et al. (2005) also confirmed that trust-

ing beliefs in government e-services have a significant

effect on intention to use public e-services. But they also

found that citizen‟s trust in their government have only a

marginal effects on trusting beliefs in public e-services

(Lee et al 2005). Indeed, other researches also show that

only trust related to the public e-services can have an

effect on the acceptance intention.

Dubauskas‟ (2005) results illustrate that governments are

not considering citizen privacy concerns. They also assert

that citizen‟s expectations are not being taken into con-

sideration by the government when implementing confi-

dentiality policies. Therefore, by adapting trust in privacy

(Chen and Barnes 2007; Hernandez and Mazzon 2007;

Nah and Davis 2002) definition to the G2C context, we

define trust as “the citizen‟s perception of the government

ability to respect his privacy by preventing the usage of

his personal information for other purposes after the

transaction has taken place.”

Gefen et al (2002) found that trust was significantly in-

fluenced by the security guarantees. Grundén (2009) as-

serts that from a citizen perspective, disadvantages with

e-government were related to the increased vulnerability

due to security problems that could occur.

Therefore, trust in the security is an important behavior

belief in the G2C context. Therefore, we adapt trust in

security (Chen and Barnes 2007; Gefen and al. 2002;

Hernandez and Mazzon 2007; Nah and Davis 2002) defi-

nition to the public e-services context as “the citizen‟s

perception of the government ability to protect his per-

sonal information from being hacked.”

Other researchers assert that many citizens see in the e-

government a way to impose more control. Therefore,

they fear from the obvious dangers of abuse of power

(Davies 2005). In his much cited book “the Future of

democracy” Bobbio (1987) asserts that the new question

today is “who controls the controllers? He argues that

today, governments can see every gesture and listen to

every word or their subjects. According to Grundén

(2009) and Griffin et al. (2007), citizen‟s also developed

a kind of a fear from the government potential control.

Therefore, we define fear from government control as

“the worrying from the fact that the government can use

the personnel data gathered through e-services in order to

increase the control over citizens activities or salaries.”

There is also the citizen‟s computer self efficacy which

can influence the acceptance. Indeed, according to Lee et

al. (2005) computer self efficacy is an important predictor

of the public e-services acceptance.

1.3 The e-services acceptance bounded

in the Lebanese context Unfortunately, there has been no scientific research link-

ing Lebanese culture with ICT acceptance. There is only

little research that compares Lebanon to other Arab coun-

tries. Rose and Straub (1998), Straub, Lock, and Hill

(2001), for example, compared ICT acceptance in four

Arab countries: Jordan, Saudi Arabia, Lebanon, and Su-

dan. They considered these four Arab countries as one

unique culture. However, Lebanese culture cannot be

considered as a pure Arab culture. Lebanon has a hetero-

geneous society characterized by the existing of 18 reli-

gious subgroups. Many civil wars in the 19th and 20th

centuries have plagued the Lebanese citizens. The diffi-

cult history of cohabitation between these different com-

munities has created a highly risky and hostile environ-

ment (Yahchouchi 2009). Therefore, Lebanese developed

tools such the wasta (or connections) as methods that can

assure trust in their daily transactions (Colli 2003). To-

day, the public services are accessed either through local

political leaders or through religious organization. This is

a deeply rooted practice among all Lebanese communi-

ties. Citizens‟ rights are re-packaged as favours (UNDP

2009).

Lebanese is a religious person who considers his life, at

any moment as whatever the Lord wills it to become

(Yahchouchi 2009). Religious social norms are deeply

embedded in everyday life.



Factors Influencing Public E-services Acceptance

Belief

structure

Core construct Definition References in ICT

acceptance

References in public

e-services accep-

tance

Attitudinal

Beliefs

Utilitarian Out-

comes (UO)

The degree to which a person

believes that using public e-

services would be useful.

Davis et al. (1989; 1992);

Rogers (1995, p.15-16);

Moore and Benbasat (1991,

p.195); Compeau and Hig-

gins (1995b); Thompson et

al. (1991); Venkatesh et al.

(2003).

Bélanger and Carter

(2006); Bretschneider et al.

(2003); Van Slyke et al.

(2004); Phang et al.

(2005).

Hedonic outcomes

(HO)

The extent to which using

public e-services is perceived

to be enjoyable in its own

right, apart from any per-

formance consequences that

may be anticipated.

Van der Heijden (2004); Sun

and Zhang (2006)

Social Outcomes

(SO)

The power that the public e-

services acceptance gives to

the user within his social

group.

Karahana et al. (1999);

Venkatesh and Brown

(2001); Brown and

Venkatesh (2005).



(2004).

Control Outcomes

(CO)

Refers to the perceived char-

acteristics of the public e-

services related to the con-

trol, such as compatibility,

trialability, relative cost,

declining cost, and complex-

ity or PEU.

Rogers (1983); Moore and

Benbasat (1991); Compeau

and Higgins (1995b); Davis

et al. (1992), Thompson et

al.‟s (1991); Venkatesh et

al.‟s (2003).



(2004).

Normative

Beliefs

Perceived social

influences to use e-

services (PSI)

The general social pressure

on individual to use e-

services.

Perceived social influences combine Secondary Sources

Influences like Media, News, News papers, TVs, etc. (SSI),

Direct Influences from Family and Friends (FFI), Workplace

Referents‟ Influences (WRI, e.g. Venkatesh and Brown

2001), and Personal Network Exposure (PNE, e.g. Valente

1995, p. 70, Hsieh et al. 2008).

Perceived govern-

ment influences

(PGI)

The perceived expectation

from the government institu-

tions for individuals to ac-

cept e-services.

Construct related only to the

e-government context.

Hsieh et al. (2008); Kvasny

(2002); Keil et al. (2003);

Kvasny and Keil (2002);

Lynne et al. (1995), Van

der Meer and Van Winden

(2003).

Control Beliefs

Trust

Trust in

the e-

services

security

(TSEC)

The citizen‟s perception of

the government‟s ability to

protect his personal informa-

tion from being hacked.

Adapted from Hernandez

and Mazzon (2007), Coyle

(2001), Chen and Barnes

(2007).

Gefen and al. (2002), Nah

and Davis (2002).

Trust in

the pri-

vacy

(TPRI)

The citizen‟s perception of

the government ability to

respect his privacy by pre-

venting the usage of his per-

sonal information for other

purposes after the transaction

has taken place.

Adapted from Chen and

Barnes (2007), Coyle (2001),

Hernandez and Mazzon

(2007).

Dubauskas (2005), Nah

and Davis (2002).

Fear from govern-

ment control (FGC)

The worrying from the fact

that the government can use

the personnel data gathered

through e-services in order to

increase the control over

citizens activities or salaries.


e-government context.

Adapted from Davies

(2005), Griffin et al.

(2007), Grundén (2009).

Computer self effi-

cacy (CSE)

The individual's perceptions

of his or her ability to use

ICT in the accomplishment

of a task

Compeau and Higgins

(1995). Lee et al. (2005).

Perceived govern-

ment support (PGS)

The help from the govern-

ment in using e-services.


e-government context but

originally adapted from the

end user support.

Grundén (2009), Tan and

Teo (2000).

Table 8. Factors Influencing Public E-services Acceptance



These norms have an impact on the citizen‟s beliefs.

Therefore, we will present the potential impact of the

Lebanese culture on the attitudinal, normative, and con-

trol beliefs.

1.3.1 Utilitarian, hedonic, social, and con-

trol outcomes in Lebanon The Lebanese culture can impact the acceptance or rejec-

tion of e-services. This influence goes through the rela-

tive importance that a person gives to utilitarian, hedonic,

social, and control outcomes in Lebanon.

For example, according to some researchers, Lebanese

actions are guided more by his emotional feeling than by

calculating reasoning (Weir 2002). This makes his beha-

vior unpredictable and little rational. Therefore, the pub-

lic e-services acceptance/rejection can results from the

hedonic outcomes which reflect the impacts of the affect

or feeling in the acceptance process;

In Lebanon, like in most of Arab World, human beha-

viour is mostly directed towards the long-term of accu-

mulation of prestige, standing, relationship, and respect

(Al Omian and Weir 2005). The reason behind this long

term objectives is that the status of the individual is de-

termined primarily by his image, his family position, and

his social contacts. Typical Lebanese statements are “My

father knows the minister” or “do you know with whom

you are talking?” The individual considers his personal

contacts as distinguishing himself from the rest of the

society. Therefore, Lebanese, like the other Arab seek

membership in those groups that offer them potential for

elevating their social standing (Ali 1990; 1995). Because

of this, Lebanese social image is considered as very im-

portant. Some researchers proved that the Lebanese is

ready to adopt a certain behaviors just in order to impress

his social group (Neal et al. 2005). Therefore, social out-

comes can have an important role in the individual beha-

viour. Acceptance or rejection of public e-services can be

the result of a

According to Ali (1995), in the Arab culture, the cost

may sometimes be the last and least important aspect of

the behavior process. There is always the question of the

appropriate behaviour that comes first. So in high context

culture, if the behaviour is seen as an inappropriate beha-

viour or incompatible with the individual cultural values,

the behavior is likely to be rejected (Ali 1995).

1.3.2 The role of normative beliefs in Leb-

anon In Lebanon, relationships are perceived as an important

factor in human behaviour. There are also, the relatives,

the friends, and the formal and informal groups who have

a decisive influence on the individual behaviour in Leba-

non (Yahchouchi 2009).

2.3.2.1. Family Influences In Lebanon, family has a significant influence on the

individual behaviour (Fahed-Sreih and Djoundourian

2006). Indeed, Lebanese spend most of his time within

his family. It is even common to find several generations

of the same family living next door to each other.

As the family is considered as an economic unit, family

norms and rules play a significant role in the individual

behaviour. The family structure is patriarchal. The most

influential person is the father. His role is to protect and

to provide resources for the entire family. All the family

members have to respect the father‟s wishes. Family tra-

ditions sanction consultation in the conduct of all aspect

of life.

The centrality of the father figure stems from the role of

the family as an economic unit, in which the father is the

property owner and producer on whom the rest of the

family depend.

2.3.2.2. Social influences: Informal Groups, formal

groups, and peers

In Lebanon, the individual is influenced mainly by one‟s

informal and formal groups. The Lebanese is also influ-

enced by his peers, by personal network exposure, and by

his workplace referents (Fahed-Sreih and Djoundourian

2006. In the ICT acceptance, Loch et al. (2003) found

that social norm was an important factor in explaining the

Internet adoption by Arabs. They affirmed that Arab in-

dividuals are influenced by whether others are also using

Internet.

1.3.3 The role of control beliefs in the Le-

banese context Between the control beliefs, trust is one of the most in-

fluential beliefs in the Lebanese context. Studies show

that the Lebanese consumers do not trust the online envi-

ronment. This fact is demonstrated by their fear of giving

away their personal and financial information because of

privacy concern (Jarvenpaa et al. 1999). But studies also

show that Lebanese do not trust their government. In-

deed, the last UNDP 2009 report shows that more then

62.7 percent of the total population does not trust the

Council of Ministers. Only 11.7 percent of the Chiite,

26.5 percent of Orthodox, 30.5 percent of Catholic, 34.5

percent of Maronite, 50.3 percent of Druze, and 64 per-

cent of Sunni trusts the Lebanese Council of Ministers

(UNDP 2009). Only 52 percent of the Lebanese trust

their parliament (UNDP 2009). But according to Lee et al

(2005), citizen‟s trust in their government have only a

marginal effects on trusting beliefs in public e-services.



2. Research methods and results

2.1 Discussions of the First Phase me-

thodology and instrument devel-

opment In the first part of this research we extracted a large num-

ber of beliefs from previous research in ICT acceptance

and public e-services acceptance. But none of these re-

search projects specify which beliefs are operative for the

public e-services context in Lebanon. According to TRA,

from this large number of beliefs, only a relative small

number serves as determinant of the citizen‟s behavioural

attitude in a specific context. Depending on the context,

researchers need to elicit the salient beliefs from the po-

tential adopters (Ajzen 1991; Ajzen and Fishbein 1980;

Fishbein and Ajzen 1975).

Taylor and Todd (1995a) did not use this method. They

developed the decomposed belief structure for technology

acceptance by drawing from previous research in tech-

nology acceptance. Their approach was justified on the

basis that there is a wealth of existing research on tech-

nology acceptance, thus minimizing the need to elicit

beliefs afresh for each new technology acceptance setting.

We think that the Taylor and Todd (1995a) method does

not take into consideration the specificity of the context

in which the acceptance process is embedded. Therefore,

we prefer Ajzen‟s (1991), Ajzen‟s and Fishbein‟s (1980),

and Fishbein‟s and Ajzen‟s (1975) method that better

detect the salient beliefs. From Taylor and Todd (1995a),

we choose the way they selected their items, measures, or

questions related to each belief.

Consequently, we will combine these two methods by

proposing a third way: the decomposed salient belief

structure for public e-services acceptance. The decom-

posed salient belief structure can reflect more the salient

beliefs in the e-government context in Lebanon and can

help the researcher in finding good measures that have

already been tested and retested for internal consistency

and reliability. Therefore, based on Fishbein‟s paradigm

(Fishbein 1968) and in order to obtain a correct specifica-

tion of the causal determinants of the public e-services

acceptance intention, we used, in Phase 1, a qualitative

method: interviews with open- ended questions. Ques-

tions were asked about government e-services acceptance

intention or about reasons for non-acceptance intention.

Therefore, after explaining the government online servic-

es to the interviewees, the respondents were asked if they

would agree or accept to use government e-services and

about influencing factors in their e-services intention

acceptance or non-acceptance decision. Regardless of

their answer, they were further questioned as to the rea-

sons for their choice. Therefore, respondents who ac-

cepted government e-services were asked to identify the

factors that led to the acceptance of e-services. Similarly,

respondents who refused to use e-services were asked to

identify the factors that led to this non-acceptance deci-

sion.

Then, open-ended responses were double coded based on

a start list of beliefs that included their definitions from

prior research (Miles and Huberman 1984, p. 58). The

salient beliefs were specified by the respondents. The

intercoder reliability was 81 percent, which is well above

the minimum of 70 percent identified by Miles and Hu-

berman (1984). Through the qualitative data anchored in

the trichotomous classification of TPB and MATH, our

first study identified the attitudinal, normative, and con-

trol salient beliefs related to the public e-services accep-

tance in the Lebanese context. These beliefs are salient

only in the government to citizen (G2C) context in the

Lebanese environment.

Therefore, the decomposed salient belief structure for

public e-services acceptance in Lebanon that we devel-

oped at the end of the phase one served as reference in

the development of the research model and questionnaire.

Items or measures were selected from prior research.

2.2 Salient beliefs that influence the

acceptance or rejection of public e-

services in Lebanon To understand the Lebanese citizen‟s intention decision

regarding acceptance or rejection of public e-services, the

qualitative data were divided into three categories based

on the citizens intentions expressed in the first stage: (1)

citizens who intended to accept e-services (intenders), (2)

citizens who intended not to accept (non-intenders), and

(3) those who were uncertain about their choice.

2.2.1 Salient Beliefs Affecting the Accep-

tance Intention As presented in Table 9, results show that among 188

citizens, only 33 (17.55 percent) intended to accept gov-

ernment e-services. For these 33 intenders, attitudinal

beliefs such as utilitarian outcomes (UO, frequency =

96.96 percent of the intenders or 17 percent of the res-

pondents), social outcomes (SO, frequency = 21.21 per-

cent of the intenders or 3.72 percent of the respondents),

and control outcomes represented by the perceived ease

of use (CO, frequency = 42.42 percent of the intenders or

7.44 percent of the respondents), normative beliefs such

as perceived government influences (PGI, frequency =

57.57 percent of the intenders or 10.10 percent of the

respondents), and control beliefs such as perceived gov-

ernment support (PGS, frequency = 57.57 percent of the

intenders or 10.10 percent of the respondents) and com-

puter self-efficacy (CSE, frequency = 60.60 percent of

the intenders or 10.63 percent of the respondents) were

the most cited key drivers of the e-services acceptance

intention (AI).

The behavioral beliefs related to the hedonic outcomes

(HO, frequency = 6.06 percent or 1 percent of the res-



pondents), the normative beliefs such as the social influ-

ences (SI, frequency = 12.12 percent or 2.12 percent from

the respondents) were also cited but by a few number of

citizens.

As expected, utilitarian outcomes (UO) were the most

salient behavioral beliefs, followed by computer self effi-

cacy (CSE), perceived government influences (PGI) and

support (PGS).

2.2.2 Salient Beliefs Affecting the Non-

Acceptance Intention For the citizens who intended to not-accept or reject the

government e-services, barriers like fear of the govern-

ment control (FGC), lack of trust in the public e-services

security (TSEC), lack of trust related to privacy (TPRI),

lack of support (PGS), and lack of knowledge (CSE)

were most significant. Perceived usefulless (PU) of the e-

service is also an important factor (frequency=54) that

impacts the non-intenders‟ decision (frequently men-

tioned as perceived privacy and computer self-efficacy).

But the fear of government control was the most impor-

tant determinant, both in terms of frequency and in terms

of importance.

Results also indicated that some factors (fear of govern-

ment control, Lack of trust in the privacy, and lack per-

ceived security) may act to uniquely impede acceptance

of government e-services. According to Cenfetelli (2004),

these acceptance inhibitors are beliefs held by a citizen

that act solely to impede acceptance intention when

present (and perceived) but which have no effect when

absent (or not perceived). These acceptance inhibitors are

distinguished from acceptance enablers, as being a per-

ception for which there is no clear, positively valanced

antipole that is psychologically meaningful.

Salient beliefs related to government e-services acceptance/rejection in Lebanon and their indicators

Salient

beliefs

Intenders

33 citizens (17.55 %)

Non-Intenders

146 citizens (77.65 %)

Uncertain

9 citizens Frequency % /Intenders % /188 Frequency % / Intenders % / 188

Attitu-

dinal

Beliefs

UO 32 96.96 % 17 % 54 36.98% 28.72 %

HO 2 6 % 1 % 19 13.01 % 10.10 %

SO 7 21.21% 3.72 % 1 0.6 % 0.5 %

CO 14 42.42 % 7.44 % 28 19.17 % 14.89 %

Norma-

tive

Beliefs

PSI 4 12.12 % 2.12 % 1 0.6 % 0.5 %

PGI 19 57.57 % 10.10 % 0 0 0

Control

Beliefs

TSEC 0 0 0 97 66.43 % 51.59 %

TPRI 0 0 0 54 36.98 % 28.72 %

FGC 0 0 0 119 81.50 % 63.29 %

CSE 20 60.60 % 10.63 % 53 36.30 % 28.19 %

PGS 19 57.57 % 10.10 % 63 43.15 % 33.51 %

Table 9. Salient beliefs related to government e-services acceptance/rejection in Lebanon and their indica-

tors

2.3 The e-government services accep-

tance intention model (ITA e-Gov

Model) Based on the above literature review and discussions, and

based on the phase one results, we developed the research

model that reflects the various elements involved in the

mental processes of Lebanese citizens‟ accep-

tance/rejection of public e-services. We developed the

ITA e-Gov Model based on MATH (Venkatesh and

Brown 2001, Brown and Venkatesh 2005). According to

MATH, ICT acceptance intention (IA) is a weighted

function of behavioral attitudinal beliefs (utilitarian, he-

donic, social, and control outcomes), normative beliefs,

and the control beliefs structure.

In the ITA e-Gov Model, the government e-services ac-

ceptance divide (ACD) is a weighted function of three

multidimensional formative constructs (or index): (1)

attitudinal beliefs toward accepting public e-services, (2)

normative beliefs, and (3) control beliefs.



Figure 1 The research model

As shown in Figure 1, there are several planes

represented in our model. The top plane represents the

conceptual plan. The middle planes represent first and

second-order empirical abstractions. The bottom plane

represents the observational plane.

The multidimensional constructs that constitute the mid-

dle plane (attitudinal beliefs, normative beliefs, and con-

trol beliefs) are usually operationalized by means of ref-

lective indicators. These constructs can be better captured

if approach from a formative perspective. Indeed, inspec-

tion of the items constituting these indexes reveals that

the causal priority runs from the indicators to the con-

struct. Attitudinal beliefs are formed as a combination of

utilitarian (UO), hedonic (HO), social (SO), and control

outcomes (CO) of public e-services acceptance. Norma-

tive beliefs are formed as perceived government influ-

ences (PGI) and subjective norms (SN). Control beliefs

are composed from perceived government support/lack of

support (PGS), computer self efficacy/non efficacy

(CSE), and inhibitors such as: lack of trust in the security

(LTSEC), lack of trust in the privacy (LTPRI), and fear

of government control (FGC).

2.4 Discussions of the Second Phase

methodology and instrument de-

velopment The ITA e-Gov Model was designed to capture the inhi-

bitors and enablers behavioral beliefs of public e-

services. In order to test the model, data was collected in

the Phase 2 from a sample of 210 randomly chosen po-

tential Lebanese public e-service users. The sample cha-

racteristics are presented in the Table 10.

To achieve a representative sample of the Lebanese popu-

lation above 18 years of age, a fixed percentage of

man/women, young adults/adults/young-seniors/seniors

were required. Interviewees were randomly approached

by personal interviews according to their region.

This approach by interviews was motivated by the wish

to be representative and to fully include the so-called

“have-nots” and “knows-nots”. We wanted to know their

reasons for non-acceptance and their intension for future

use of public e-services. A triple language instrument was

used. Respondents were randomly selected in the streets

from all the Lebanese regions.

The aim of this questionnaire was to measure the weight

or the importance that citizens give to each belief. There-

fore, we asked respondents to rate each factor on how

important it was in his acceptance or non-acceptance

decision, using a scale ranging from 1 (Not Important) to

7 (Very Important).



Population

Population Characteris-

tics

Sample Character-

istics

Nb % nb %

4017095 100 210 100

Gender Male 1952672 49% 103 49%

Female 2064423 51% 107 45%

Religion Christian 1566678 39% 81 39%

Muslim 2450417 61% 129 61%

Region Urban 2691453 67% 140 67%

Rural 1325641 33% 70 33%

Education

Less than secondary 1044445 26% 54 26%

Between secondary and high school

(baccalauréat II)

1205128 30% 75 36%

University (1-3 years) 1044445 26% 54 26%

University (more than 3 years) 723077 18% 27 12%

Monthly In-

come

Less than 500 USD

There is no accurate statistics on

this subject

10 5%

500-900 63 30%

900-1500 73 35%

1500-2000 28 13%

2000-2800 23 11%

> 2800 13 6%

Age

< 18 years 1084615 27% 0 0%

18-28 1004273 25% 73 35%

29-42 956068 23.8% 79 37%

43-64 682906 17% 50 24%

> 64 289232 7.2% 8 4%

Table 10. Population and sample characteristics

2.4.1 The dependent variable The main dependent variables to be explained were the

actual acceptance/rejection, intended accep-

tance/rejection, and potential acceptance/rejection of the

main public e-services of the Lebanese national adminis-

tration in 2008. Therefore, a part of this questionnaire

was about the actual, the intended, or the potential accep-

tance/rejection of a long list of public e-services proposed

by the Lebanese national administration, such as national

government information services at www.e-

gateway.gov.lb, or downloadable forms at

www.informs.gov.lb, websites of ministries presented in

Appendix A, tax transaction services or e-taxes, health

care e-services, social services and benefits, application

for a building permit, appointment to apply for a pass-

port, request for a certificate of birth, national identifica-

tion card, or citizenship, notification of a situation change

(marriage, address change, etc). In these questions we did

not distinguish one service from another. E-services were

presented in bulk or as a set of services proposed by the

government.

The interviewees were asked if they intended to use pub-

lic e-service when this is possible and when they need it.

Based on Van Dijk et al. (2008), we added the expression

“possible” because all public services are not yet availa-

ble in electronic version. We added also the expression

“when you need it” because many services/e-services are

only needed incidentally (such as a passport renewal).

The first three items that measures the intention accep-

tance divide: “I‟m very likely to (…) to use public e-

services when this is possible and when I need it”; “I

intend to (…) to use public e-services in future when this

is possible and when I need it”; “I will probably (…) to

use public e-services” on a scale going from extremely

reject to extremely accept (-3, -2, -1, 0, 1, 2, 3).

2.4.2 The independent variables In each of three multidimensional independent con-

structs, we need to consider all facets of the construct.

http://www.e-gateway.gov.lb/

http://www.e-gateway.gov.lb/

http://www.informs.gov.lb/



Indeed according to Nunnally and Bernstein (1994, p.

484) failure to consider all facets will lead to an exclu-

sion of relevant indicators and that will exclude a part of

the construct itself. For a parsimony reasons, the different

dimensions of beliefs will be measured by single-item

scales. Attitudinal beliefs (AB) will be measured with

four reflective measures: perceived utilitarian outcomes

(UO), perceived hedonic outcomes (HO), perceived so-

cial outcomes (SO), and perceived control outcomes of

public e-services acceptance (CO). Normative beliefs

(NB) will be formed with two single-items scales: per-

ceived government influences (PGI), subjective norms

(SN). Control beliefs (CB) will be composed from five

single-items scales: perceived government support/lack of

support (PGS), computer self efficacy/non efficacy

(CSE), and the three inhibitors: lack of trust in the securi-

ty (LTSEC), lack of trust in the privacy (LTPRI), and fear

of government control (FGC).

Attitudinal beliefs were measured in this way: “I think

public e-services are (…): Extremely worth-

less/Extremely useful” (on a scale going from -3 to 3); “I

really (…) using public e-services” (Extremely dislike/

Extremely like on a scale -3, -2, -1, 0, 1, 2, 3); “I think

that people who use public e-services have more prestige

than those who do not” (Strongly disagree/Strongly agree

on a scale -3, -2, -1, 0, 1, 2, 3); and “I find public e-

services to be (….)” (Difficult to use/easy to use on a

scale -3, -2, -1, 0, 1, 2, 3).

Normative beliefs were measured by these two measures:

“I think that the government wants me to use public e-

services” and “I Think that people who are important to

me, want me to use public e-services” on a scale going

from extremely reject to extremely accept.

Control beliefs were also measured with these factors: “I

feel comfortable using the public e-services on my own”

and “I find that the government is supporting the public

e-services usage on a scale going from strongly disagree

to strongly agree on a scale; “I do trust the security of the

public e-services in Lebanon” and “I do trust that the

government will respect privacy when using the public e-

services in Lebanon” on a scale going from extremely

reject to extremely accept; and finally, “I fear that the

government will use public e-services to control my ac-

tivities” on a scale from strongly disagree to strongly

agree. Table 11 resumes the questionnaire.

Subsequently, we used the Structural Equation Modeling

(SEM), Partial Least Squares (PLS) techniques to analyze

data with the purpose of relating the dependent variable

(e-service non-acceptance intention or acceptance inten-

tion) to the set of independent variables. PLS was most

appropriate given the large number of constructs that

resulted when all these salient beliefs were combined.

SmartPLS (Chin and Frye 1996) was used for the analy-

sis. The bootstrap resampling method (200 resamples)

was used to determine the significance of the paths within

the structural model.

Operationalization of the constructs

Belief

structure

Core con-

struct

Questions References in public e-

services acceptance

Attitudinal

Beliefs

Utilitarian

Outcomes

(UO)

Q1. I think public e-services are:

Extremely worthless -3 -2 -1 0 1 2 3 Extremely useful


(2006); Bretschneider et

al. (2003); Van Slyke et

al. (2004); Phang et al.

(2005).

Hedonic

outcomes

(HO)

Q2. I really … using public e-services

Extremely dislike -3 - 2 -1 0 1 2 3 Extremely like

Van der Heijden

(2004); Sun and Zhang

(2006)

Social Out-

comes (SO)

Q3. I think that people who use public e-services have

more prestige than those who do not

Strongly disagree -3 -2 -1 0 1 2 3 Strongly agree



(2004).

Control

Outcomes

(CO)

Q4. I find public e-services to be:

Difficult to use 3 -2 -1 0 1 2 3 easy to use



(2004).

Normative

Beliefs

Perceived

social influ-

ences to use

e-services

(PSI)

Q5. I Think that people who are important to me, want me

to use public e-services

Extremely reject -3 -2 -1 0 1 2 3 Extremely accept

Hsieh et al. (2008)



Perceived

government

influences

(PGI)

Q6. I think that the government wants me to use public e-

services


Hsieh et al. (2008);

Kvasny (2002); Keil et

al. (2003); Kvasny and

Keil (2002); Lynne et

al. (1995), Van der

Meer and Van Winden

(2003).

Control

Beliefs

Trust in the

e-services

security

(TSEC)

Q7. I do trust the security of the public e-services in Leba-

non


Gefen and al. (2002),

Nah and Davis (2002).

Trust in the

privacy

(TPRI)

Q8. I do trust that the government will respect privacy

when using the public e-services in Lebanon


Dubauskas (2005), Nah

and Davis (2002).

Fear from

government

control

(FGC)

Q9. I fear that the government will use public e-services to

control my activities

Strongly agree -3 -2 -1 0 1 2 3 Strongly disagree

Adapted from Davies

(2005), Griffin et al.

(2007), Grundén

(2009).

Computer

self efficacy

(CSE)

Q10. I feel comfortable using the public e-services on my

own


Lee et al. (2005).

Perceived

government

support

(PGS)

Q11. I find that the government is supporting the public e-

services usage


Grundén (2009), Tan

and Teo (2000).

Public

e-services

accep-

tance/rejec

tion

Actual ac-

cep-

tance/reject

ion (INT1)

Q12. I‟m very likely to (…) to use public e-services when

this is possible and when I need it.


van Dijk et al. (2008)

Intended

accep-

tance/reject

ion (INT2)

Q13. I intend to (…) to use public e-services in future

when this is possible and when I need it.

Extremely reject -3 -2 -1 0 1 2 3 Extremely accept van Dijk et al. (2008)

Potential

accep-

tance/reject

ion (INT3)

Q14. I will probably (…) to use public e-services.

Extremely reject -3 -2 -1 0 1 2 3 Extremely accept van Dijk et al. (2008)

Table 11 Operationalization of the constructs

3. Research Results According to our survey, only 15 % of respondents ac-

cepted or had the intention to accept public e-services. In

order to understand which behavioral beliefs explain the

current level of actual and intended rejection/acceptance

of the e-services in Lebanon, we begin by testing the

instrumentation of the study as recommended by Straub

(1989).

All our multidimensional constructs are formatives.

Therefore, Cronbach is not the appropriate test for these

kinds of constructs. In PLS, the weights represent the

influence of individual scale items on the formative con-

struct. Like in Loch et al. (2003), and because all data

were a seven point scale constructs, we multiplied values

by their individual PLS weights and summed them up for

each construct. Then we created a weighted score for

each measure and a composite score for each formative

construct. We used these values to run inter-item correla-

tions as well as item-to-construct correlations (Lock et al.

2003). Results are presented in the Table 12.



The Inter-item correlations

AB SN PBC AI

CO 0.7795 0.4806 0.1556 0.2354

SO 0.7801 0.0409 -0.1007 -0.0453

UO 0.8687 0.2699 0.3270 0.3294

HO 0.6163 -0.3028 -0.0910 -0.0374

PSI 0.0245 0.5472 0.0054 0.0261

PGI 0.4559 0.9899 0.1864 0.1779

PGS 0.0551 0.2483 0.7926 -0.1308

TPRI 0.3257 0.2634 0.8035 0.6542

TSEC 0.2995 0.1489 0.9004 0.7514

CSE -0.0302 0.0924 0.8948 -0.0994

FGC 0.2564 0.1488 0.9197 0.8092

INT1 0.3138 0.1540 0.6851 0.9414

INT2 0.2556 0.1661 0.5693 0.9160

INT3 0.4175 0.1780 0.5226 0.9573

Table 12. The Inter-item correlations

Table 12 shows that all items correlate significantly to

their constructs. All items load more highly on their re-

spective constructs than on other constructs. Table 13

resumes the outer weights of these constructs.

The outer weights of the formative constructs

AB SN PBC AI

CO 0.3462

SO 0.1370

UO 0.3404

HO 0.6041

PSI 0.6665

PGI 0.2186

PGS 0.1600

TPRI 0.2315

TSEC 0.3055

CSE 0.2738

FGC 0.5397

INT1 0.1614

INT2 0.6051

INT3 0.3655

Table 13. The outer weights of the formative constructs

3.1 Discussion Today, there is a large literature that explores the overall

beliefs about ICT acceptance/rejection.

Some researches concentrate their efforts on the object

based beliefs (DeLone and McLean 2003 with system,

information, and service qualities), others on the beha-

vioral beliefs (Davis et al. 1989 with PU and PEU).



Table 14. The results of the quantitative study.

A large number of researches developed models that

resume enablers of the ICT acceptance. Others much

fewer presented some object-based beliefs as inhibitors of

the usage (Cenfetelli 2004). But very few of the IS re-

search determined the inhibitors and enablers behavioral

beliefs that can predict the public e-services accep-

tance/rejection.

The aim of this paper was to identify behavioral beliefs

that enable or inhibit the public e-services acceptance in

the Lebanese context. According to Cenfetelli (2004), the

inhibitors are not only the opposite of the enablers. In

this study, we show that some behavioral beliefs can act

positively or negatively on acceptance. Others can lead

solely to discourage the public e-services acceptance. The

existence of these inhibitors may explain why Lebanese

citizens reject the public online services (77.75 percent of

the qualitative sample and 85 percent of the quantitative

sample). Therefore, we developed the ITA e-Gov Model

which considers inhibitors and enablers of the public e-

services in Lebanon.

After testing the model, the results show a substantial R2

of 0.482 for the intention to accept the public e-services.

Table 14 resumes the research results.

In our first qualitative research, 63 percent of respondents

refused e-services because they feared from the control of

the government. In the quantitative research the fear of

government control had also a positive strong relation

with public e-services acceptance intention (weight =

0.413). These results can be explained by the fact that

Lebanese are used to live with minimum or even without

any government control. Indeed, according to Antoun

(2009, p. 9), Lebanese are used to violate laws and regu-

lations: they avoid tax payments, they bribe officials into

accepting incomplete or illegal applications; and they

regularly abuse public services for personal interest. In

her 2009 report supported by the UNDP, Antoun conclu-

ded that, in Lebanon, avoiding paying taxes has become

more of a culture than a practice (Antoun 2009).

For other citizens, barriers like lack of trust in the public

e-services security (TSEC), lack of trust related to priva-

cy (TPRI), lack of government support (PGS), and lack

of computer self efficacy (CSE) were also significant.

Results of the quantitative research showed that the mul-

tidimensional construct known as the control beliefs (CB)

which combine all these control variables has a strong

prediction power on the acceptance intention ( weight =

0.608). This result can be explained by the fact that the

lack of trust in the security and the lack of trust in the

privacy are highly related to the acceptance intention

(weights = 0.300 and 0.223).

The lack of trust in the security can be explained by the

fact that when we conducted the open ended questions,

Lebanon was shocked about a discovery made by the

police concerning a tower transmission that belonged to a

private Lebanese television station in the Barouk moun-

tains which it seems was been used by Israel to spy on

anyone using the Internet in Lebanon. During the three

weeks of the interviews the country was living through

statements and counter statements. Indeed, a large num-

ber of respondents mentioned this fact in their answers.

“How can we trust that the public e-services are secured

if the government is not capable of securing the Internet

network” said a young male from the North. We think

that this event has also influenced the interviewee‟s an-

swers in the second quantitative research.

The lack of privacy is related to the fact that Lebanese are

sensitive to the issues of eavesdropping. During years of

Syrian occupation, telephone monitoring was an integral

part of the repressive regime. Some local unofficial re-

ports showed that even the former Prime Minister who

was murder was and during a long period monitored by

the secret services. Recently, the Lebanese telecommuni-

cations Minister had set up a new telephone monitoring

department. This event has created a lot of reactions. That

can explain why citizens do not trust the privacy of the

ICTs usage in Lebanon.

We can also say that the climate of fear, of lack of trust in

the security and in the privacy can be seen as a result of



the complete political chaos that characterizes the Leba-

nese political system.

3.2 Contribution to theory This paper adds to the IS literature in many ways:

The first contribution of this paper is that it integrates the

appropriate ICT and public e-services acceptance litera-

ture in order to propose a model that can capture the ac-

ceptance enablers and inhibitors of the public e-services

in the pre-acceptance stage.

The second contribution is that it assimilates previous

research findings in order to develop a coherent and

comprehensive picture of the public e-services accep-

tance in Lebanon.

Third, this paper introduces the ITA e-Gov Model that

integrates salient behavioural beliefs that can explain and

predict citizen‟s acceptance or rejection of public e-

services.

Fourth, it introduces a new method to determine the sa-

lient behaviour beliefs by combining the TRA‟s method

of the salient beliefs structure with the DTPB‟s method of

the decomposed belief structure for ICT acceptance or

rejection. From the TRA, we took the method that aims to

extract the salient behavioural beliefs from the potential

adopters (Ajzen 1985; 1991; Ajzen and Fishbein 1980;

Fishbein and Ajzen 1975) depending on their context.

From Taylor‟s and Todd‟s (1995a) we took their decom-

posed method of selecting items, measures, or questions

related to each behavioural belief by drawing from pre-

vious research. Consequently, we combined these two

methods and we proposed a third way: the decomposed

salient belief structure for public e-services acceptance.

We think that this new method can reflect more the sa-

lient beliefs in a specific context and it can help the re-

searcher in finding good measures that have already been

tested and retested for internal consistency and reliability.

Finally, its fifth contribution is that like the two-factors

theory or motivation-hygiene theory of Herzberg (Herz-

berg 1964), this article identifies the existence of two

categories of salient behavioural beliefs: (1) salient enab-

lers beliefs and (2) salient inhibitors beliefs. These two

salient behavioral beliefs act independent of each other.

Salient enabler beliefs can impact positively or negatively

the ICT acceptance. The inhibitors salient beliefs act only

as unique negative effects on usage. These results are

important because the majority of research on ICT accep-

tance and usage has assumed that the behavioural beliefs

which impede the usage are simply the opposite of the

positive beliefs. Like Cenfetelli‟s research, this study

adds a new proof that can help to counter this paradigm

and establish that there exist enablers and inhibitors be-

liefs.

3.3 Implications for practice Success of the e-services implementation projects will

depend on how the government will encourage all the

citizens to accept using online public services. Indeed,

implementing e-government and providing e-services

does not guarantee the success of the e-government

project. Heeks (2003a; 2003b) estimated that the failure

rate of e-government projects in developing countries

may be as high as 85 percent. One of the main reasons of

difficulties that developing countries face, when imple-

menting e-government, is the low rate of e-services‟ ac-

ceptance and use by citizens (Heeks 1999; Jaeger and

Thompson 2003; Moon 2002; Odedra-Straub 2003). De-

spite incentives and media campaigns that encourage

them to go online for government transactions, citizens of

developing countries still hesitate and sometimes reject

the usage of the public e-services (Dwivedi et al. 2009).

Understanding the inhibitors and enablers of e-services

acceptance may provide opportunities for developing

more effective e-government policies by creating condi-

tions for improved/ enhanced e-service usage.

The article results can help governments in persuading

their citizens to accept online public services. Based on

these results, the strategic aim is to develop a trust rela-

tionship with citizens, giving assurances that their data

(both personal and financial) will be secured, and that the

information contained on the website would be both cur-

rent and accurate.

3.4 Limitations As with any scientific research, this study has limitations.

First, it is important to recognize that the primary limita-

tion of this study is the potential for response bias. In

order to avoid cognitive dissonance, some people seek to

maintain some coherence and consistency in their an-

swers.

A second limitation concerns the way the e-services were

introduced by the interviewers. In other words, the way

the open-ended questionnaire was formulated might have

focused the citizen's attention on some advantages or

disadvantages of the government e-services.

A third limitation is related to the time chosen for the

field research. When we conducted the qualitative re-

search, Lebanon was living a political chaos that created

a climate of fear between citizens and a lack of trust in

the security and in the privacy. Therefore, this empirical

study need to be replicate and tested in other political

context.

The major challenge of this study was to collect empirical

data from enough participants. Since public e-services are

still in phase II, it was difficult to know who will accept

to use it in the stage III and IV. Therefore, in the future,

we consider it necessary to send survey questionnaire to a

larger number of participants to gather sufficient data in

order to validate the conceptual model.



In terms of comparisons, this study is limited due to the

lack of similar previous studies from Lebanon.

In this paper, we performed the first step in exploring the

public e-services inhibitors and enablers in Lebanon.

More empirical tests are needed to extend the model by

adding key demographic characteristics that can also ex-

plain the e-services acceptance or non-acceptance inten-

tion.

4. CONCLUSION In order to examine the inhibitors and enablers of the

citizens‟ intention to accept e-services, we combined

technological, normative, individual and psychological

factors that are related to citizens‟ subjective perception

in a unified model: ITA e-Gov Model.

A key finding in our study was the relationship between

utilitarian outcomes and e-government acceptance and

non-acceptance intention. First, the importance of UO

(perceived usefulness) of government e-services was sup-

ported by our qualitative study (open-ended questions).

The qualitative study also revealed that non-intenders

believe that online services do not offer anything relevant

for them: “No need or no reason for me to use govern-

ment e-services”. Then, results of the quantitative study

show that choosing to accept e-services is also rooted in

the perceived usefulness of these e-services. The impor-

tance of this behavioral belief has been confirmed by the

weight of the variable UO in the quantitative research

(weight = 0.127). In order to increase public e-services

take-up we suggest targeting citizens who believe that

they may benefit from the online services (businessmen

and travelers). Consequently, perceived usefulness may

serve as a motivation to encourage these citizens to start

using online government services. Proving to citizens that

public e-services in Lebanon are useful can also be used

to convince those who think that it is worthless.

This study also identified skeptics concerned about gov-

ernment control, perceived security and perceived privacy

of government online services.

To increase the public e-services acceptance, we recom-

mend that the Lebanese government increases the privacy

and security of their e-services. The Lebanese govern-

ment must publicly promise not to use the personal data

gathered through e-services in order to control citizens‟

income or activities

We also identified a relation between the computer self-

efficacy and the acceptance and non-acceptance inten-

tion. According to Dimitrova and Chen (2006), self-

efficacy refers to the potential adopter‟s confidence in his

or her own ability to utilize the government e-service.

The results show that lower confidence is likely to lead to

a non-acceptance decision. The lack of confidence in

one‟s ability to use government e-services will negatively

affect the intention to accept government online service.

The Lebanese government‟s communication can promote

the usefulness and the ease of use of the government e-

services.

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