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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 openended 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 security (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 government control (FGC) was the most important determinant, both in terms of frequency and in terms of
importance. The willingness to use the public eservices 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.
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, Laboratoire CERMAT
Directeur du M2 Marketing des Services
Tel : + 33 (0)2 47 36 10 42
Key-words:
E-government, e-services acceptance, MATH, TPB,
ICT acceptance intention, ITA e-Gov Model.
[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 acceptance 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 technologies (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 acceptance of online public services by citizens. Government eservice 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 government transactions, most citizens of developing countries 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 virtual channel of service delivery system (VCSDS). This
multichannel of service delivery system will allow the
government to offer two types of public service: traditional services and online services. As a result, the Lebanese government is appreciating the need to increase citizens‟ awareness regarding the transition to online delivery of public services. Therefore, the Lebanese government 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 acceptance 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 special 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 inhibitors) 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 association 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 reasons 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‟ intention to accept e-services, data was collected in a first
stage from 188 randomly chosen potential Lebanese public e-service users. In Phase 1, open-ended questions
were asked about government e-services acceptance/nonacceptance intention, and about reasons for such acceptance 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 selected from prior research. After identifying salient beliefs of public e-services acceptance in Lebanon, the research 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 nonacceptance decision.
The ITA e-Gov Model could help governments better
deploy and manage their ICT investments by better understanding 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 influence e-service acceptance intention.
In this paper, we begin by reviewing the technology acceptance 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 summarize the conceptual model of the public e-services acceptance intention by citizens. Finally, after testing the model, we describe in the conclusion the lessons learned from
this study. We also highlight the limitations and future
directions.
1. Constructs that shape the public 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 eskills) does not guarantee the acceptance and use of public e-services by citizens. Research indicates that in developing 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 effective e-government policies. Thus, success of egovernment will depend on how governments entice citizens 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 psychological
understanding/willingness
or
resistance/acceptance that develops within a specific social/environmental/organizational setting. The acceptation process may be conceptualized as a temporal sequence of activities that lead to initial acceptance and
subsequent adaptation and continued usage of an ICT by
the adopter.
1.1
Factors Influencing ICT Acceptance
Key constructs that shape the ICT acceptance process are
numerous. Based on Schwaerz‟s and Chin‟s (2007) definition and on diffusion of innovation theory (Rogers
1983), these key constructs are divided to four categories:
(1) ICT's perceived attributes and characteristics; (2) social 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 capacity in the IS field has been occupied with TAM studies (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 acceptance 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 intention (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 innovation (AICT) do not strongly predict innovation acceptance and use (IA). Only the individual‟s attitude concerning the acceptance of an innovation (Aacp) determines 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 outcomes 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 attributes which has
been translated in
Perceived Characteristics 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, complexity, 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 characteristics: 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) assumption 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 demonstrability (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)
Definition
Relative advantage
The degree to which an innovation is
perceived as being better than its precursor.
Moore and Benbasat (1991) redefined “relative advantage” 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 understand and use.
Trialability
The degree to which an innovation may
be experimented with on a limited basis
before acceptance.
The degree to which the results of an
innovation are visible and communicable to others.
Observability
Cost
Communicability
Divisibility
Profitability
Sources
Redefined by… as …
Concepts
Rogers‟
IDT
(1995,
p.15).
Introduced by Tornatzky and Klein
(1982)
Closely related to observability.
Closely related to trialability.
Introduced by Tornatzky and Klein
(1982).
Tornatzky
and Klein
(1982)
Moore and Benbasat (1991) redefined “complexity” as
ease of use.
Moore and Benbasat (1991) redefined “trialability” as
the degree to which an innovation ICT may be experimented before acceptance.
Moore and Benbasat (1991) redefined “observability”
as the degree to which the results of using the innovation ICT are observable to others. They found that “observability” has construct ambiguity problems, so they
divided it into visibility and result demonstrability.
Moore and Benbasat (1991) redefined it as relative cost
or perceived cost.
Moore and Benbasat (1991) redefined it as “observability”.
Moore and Benbasat (1991) redefined it as “trialability”.
Moore and Benbasat did not include this characteristic.
Social Approval
Closely related to Moore and Benbasat‟s
image (1991).
Voluntariness to
use
The degree to which use of the innovation is perceived as being voluntary, or
of free will.
Moore and Benbasat‟ PCI (1991, p.203)
found that observability has construct
ambiguity problems, so they divided it
into visibility and result demonstrability.
Moore and Benbasat‟ PCI (1991, p.203)
found that observability has construct
ambiguity problems, so they divided it
into visibility and result demonstrability.
Result Demonstrability
Visibility
Table 2
Moore and
Benbasat‟
PCI (1991,
p.195;
203).
Moore and Benbasat (1991, p.195) redefined it as “image” or the degree to which use of an innovation ICT is
perceived to enhance one‟s image or status in one‟s
social system.
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.
The degree to which the results of adopting/accepting/using the ICT innovation are observable
and communicable to others.
The degree to which the ICT innovation is visible in the
environment of the adopter.
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 advantage” (Rogers 1983; Moore and Benbasat 1991), to Compeau 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
Utilitarian
comes
Similar
constructs
Model
Authors
Definition
Perceived usefulness (PU)
TAM
Davis (1989); Davis
et al. (1989, p. 320)
Performance
expectancy
UTAUT
Venkatesh et
(2003, p. 447)
Extrinsic Motivation
MM
Davis et al. (1992, p.
1112)
Job Fit
MPCU
Thompson et
(1991, p. 129)
The degree to which a person believes that using a
particular system would enhance his or her job performance
The degree to which an individual believes that using
the system will help him or her to attain gains in job
performance.
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.
The extent to which an individual believes that using
an ICT can enhance the performance of his or her job.
Relative advantage
IDT
Outcome
pectation
SCT
Rogers
(1983);
Moore and Benbasat
(1991, p. 195)
Compeau and Higgins (1995b)
out-
(MATH,
e.g.
Brown
and
Venkatesh 2005;
Venkatesh
and
Brown 2001)
ex-
Table 3.
al.
al.
The degree to which an innovation is perceived as
being better than its precursor.
The performance related consequences of the behavior. Specifically, the performance expectations that
deal with job related outcomes.
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 outcomes (HO) adapt hedonic and affective ICT attributes.
Indeed, recently, IS scholars have included hedonic criteria and affective ICT attributes like: perceived enjoyment
(PE, Van der Heijden 2004; Sun and Zhang 2006), perceived 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
Hedonic
outcomes
(MATH,
e.g.
Brown
and
Venkatesh 2005;
Venkatesh
and
Brown 2001)
Similar constructs
Model
Authors
Definition
Perceived enjoyment (PE)
User Acceptance of
Hedonic Information Systems
Van der Heijden
(2004, p. 697); Lewis
et al. (2003, p. 163)
Perceived Affective Quality of
ICT (PAQ)
Perceived playfulness (PP)
Extended TAM
Zhang and Li (2005)
The extent to which fun can be derived
from using the system as such or the intrinsic enjoyment of the interaction with the
ICT
Fun and enjoyment perceived when using
an ICT.
Extended TAM
Sun
and
(2006)
Zhang
The extent to which the activity of using
ICT is perceived to be enjoyable in its own
right, apart from any performance consequences that may be anticipated
Heightened
joyment
Extended TAM
Agarwal and Karahanna (2000)
Defined and measured the same as perceived enjoyment.
en-
Table 4. Hedonic outcomes
2.1.1.3. Social outcomes
2.1.1.4. Control 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 reasoned action (DTRA, e.g. Karahana et al. 1999) argued
that social outcomes play an important role in the acceptance process especially in the pre-acceptance period.
DTRA results show that visibility and result demonstrability are strong predictors of ICT acceptance but only in
the first phase of the acceptance process, while perception 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).
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 related to the control, like compatibility, trialability, relative cost, declining cost, and complexity or PEU. Control
outcomes (C0) differ from the perceived behavioral control that refers to the internal personal variables and to the
external resources, contextual, and environmental variables. Control outcomes are only related to ICT perceived characteristics. The most measured and used variable between the control outcomes is PEU. As summarized in Table 5, PEU is similar to “perceived complexity” (Rogers 1983; Thompson et al. 1991), to “effort expectancy” (UTAUT, Venkatesh et al. 2003), and to
Thompson et al.‟s “complexity” (1991).
Perceived ease of use construct
Constructs
Similar
constructs
Model
Authors
Definition
Perceived ease of use
(PEU)
The degree to which an
individual believes that
performing the behavior of
interest would be free of
effort.
Effort expectancy
Perceived
complexity
UTAUT
The degree of ease associated with the use of the
system.
The degree to which an innovation is perceived
as being difficult to understand and use.
Complexity
MPCU
Venkatesh et al.
(2003, p. 450)
Rogers
1983;
Thompson et al.
(1991)
Thompson et al.
(1991, p. 128)
IDT
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 influences 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 example, 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 Barki (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 secondary sources like News, Newspapers, TVs, and radios
(Secondary Sources‟ Influences, SSI). In addition, TAM2
reflects the impact of two additional theoretical mechanisms: 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 innovation. The personal network exposure (PNE) accounts
for the observed aggregate ICT acceptance behaviors in
an individual‟s personal network (Hsieh et al. 2008; Valente 1995, p. 70).
Normative beliefs construct
Construct
Normative
beliefs
(MATH, e.g.;
Brown
and
Venkatesh
2005;
Venkatesh and
Brown 2001)
Authors
Definition
Ajzen and Fishbein
(1980), Taylor and
Todd (1995a ; 1995b)
Warshaw (1980)
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.
Felt pressure from others.
Social Factors (SF)
Triandis
Thompson
(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
(SI)
Venkatesh
(2003)
Subjective
(SN)
Norms
Societal Norms
Influences
(1980),
et al.‟s
et
al.
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).
lent to computer plaSyfulness (CP, Webster and Martocchio 1992; Moon and Kim 2001) and to personal innovativeness in ICT (PIIT, Agrawal and Prasad 1998; Agrawal and Karahanna 2000).
1.1.3
2.1.3.2. Contextual factors
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 internal psychological determinant related to the target behaviour 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 psychological determinants
Researchers have studied a range of individual user characteristics that influence the acceptance or nonacceptance of the ICT innovation. Between all the individual 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 Table 7, computer self efficacy (CSE) refers to the individual's perceptions of his or her ability to use ICT in the accomplishment of a specific task (Compeau and Higgins
1995a; 1995b). Computer self efficacy (CSE) is equiva-
External control factors vary from context to context (Ajzen 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 financial 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 delivery 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 citizens 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 resumes the control beliefs.
Control beliefs
Construct
Computer self efficacy (Compeau and
Higgins 1995a)
Authors
Definition
playfulness
Webster and Martocchio (1992);
Moon and Kim (2001).
Personal innovativeness
in IT (PIIT)
Agrawal and Prasad (1998);
Agrawal and Karahanna (2000).
The degree of cognitive spontaneity in
microcomputer interactions.
An individual trait reflecting a willingness
to try out any new ICT;
Facilitating conditions
MPCU‟s Thompson et al. (1991);
Venkatesh et al. (2003); Taylor
and Todd (1995b).
End User Support
Igbaria et al. (1996).
Trust in the security
Chen and Barnes (2007); Hernandez and Mazzon (2007); Nah and
Davis (2002).
Trust in the privacy
Chen and Barnes (2007); Hernandez and Mazzon (2007); Nah and
Davis (2002).
Computer
(CP)
End user support
(Igbaria et al. 1996)
Trust (Gefen et al.
2003 a, 2003b)
The control beliefs relating to resource
factors such as time and money and ICT
compatibility issues that may constrain
usage.
High levels of support that promotes more
favorable beliefs about the system among
users as well as MIS staffs.
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.
The user‟s confidence in the service provider‟s ability to respect user's privacy.
User‟s confidence that his private information will not be used or sold to others.
Table 7. Control beliefs
1.2
Factors Influencing Public Eservices Acceptance
After reviewing large number of beliefs related to ICT
acceptance, we will compare it to literature in the egovernment 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 eservices
In adapting items from Van Slyke et al. (2004), Bélanger
and Carter (2006) measured the impact of the UO (relative advantage), SO (image), and CO (compatibility and
ease of use) on the intention to use e-government services. They found that higher level of UO (perceived relative
advantage) increases citizens‟ intentions to accept the
public e-services. Bretschneider et al. (2003) also demonstrated 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 influence 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‟ intentions 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 Carter (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 important predictor of the acceptance especially when it is
the case of a population of normal citizens.
1.2.2
Normative beliefs and public eservices
Researchers in the IS field have studied the social influences impact on public e-services acceptance. For example Gefen et al. (2002) found that social influence has a
significant impact on intention to accept public eservices.
e-government were related to the increased vulnerability
due to security problems that could occur.
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 influences on public e-services acceptance/rejection. Hsieh et
al. (2008) have showed that governments may use systematic 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 government 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 government agencies may serve as important referents whose
expectation affects individual innovation acceptance
(Lynne et al. 1995). Therefore, the government may influence individual‟s acceptance of public e-services. In
this research, the governmental influences are represented
by the construct: perceived governmental influences
(PGI).
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) definition to the public e-services context as “the citizen‟s
perception of the government ability to protect his personal information from being hacked.”
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 trusting 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 consideration by the government when implementing confidentiality 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 influenced by the security guarantees. Grundén (2009) asserts that from a citizen perspective, disadvantages with
Other researchers assert that many citizens see in the egovernment 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 linking Lebanese culture with ICT acceptance. There is only
little research that compares Lebanon to other Arab countries. Rose and Straub (1998), Straub, Lock, and Hill
(2001), for example, compared ICT acceptance in four
Arab countries: Jordan, Saudi Arabia, Lebanon, and Sudan. They considered these four Arab countries as one
unique culture. However, Lebanese culture cannot be
considered as a pure Arab culture. Lebanon has a heterogeneous society characterized by the existing of 18 religious subgroups. Many civil wars in the 19th and 20th
centuries have plagued the Lebanese citizens. The difficult history of cohabitation between these different communities has created a highly risky and hostile environment (Yahchouchi 2009). Therefore, Lebanese developed
tools such the wasta (or connections) as methods that can
assure trust in their daily transactions (Colli 2003). Today, the public services are accessed either through local
political leaders or through religious organization. This is
a deeply rooted practice among all Lebanese communities. 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
Utilitarian
comes (UO)
Out-
Hedonic
(HO)
outcomes
Social
(SO)
Outcomes
Attitudinal
Beliefs
Control
(CO)
Outcomes
Definition
The degree to which a person
believes that using public eservices would be useful.
The extent to which using
public e-services is perceived
to be enjoyable in its own
right, apart from any performance consequences that
may be anticipated.
The power that the public eservices acceptance gives to
the user within his social
group.
Refers to the perceived characteristics of the public eservices related to the control, such as compatibility,
trialability, relative cost,
declining cost, and complexity or PEU.
Perceived
social
influences to use eservices (PSI)
The general social pressure
on individual to use eservices.
Perceived government
influences
(PGI)
The perceived expectation
from the government institutions for individuals to accept e-services.
Trust in
the
eservices
security
(TSEC)
The citizen‟s perception of
the government‟s ability to
protect his personal information from being hacked.
Normative
Beliefs
Trust
Trust in
the privacy
(TPRI)
Control Beliefs
Fear from government control (FGC)
Computer self efficacy (CSE)
Perceived government support (PGS)
Table 8.
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.
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.
The individual's perceptions
of his or her ability to use
ICT in the accomplishment
of a task
The help from the government in using e-services.
References in ICT
acceptance
References in public
e-services acceptance
Davis et al. (1989; 1992);
Rogers (1995, p.15-16);
Moore and Benbasat (1991,
p.195); Compeau and Higgins (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).
Van der Heijden (2004); Sun
and Zhang (2006)
Karahana et al. (1999);
Venkatesh
and
Brown
(2001);
Brown
and
Venkatesh (2005).
Rogers (1983); Moore and
Benbasat (1991); Compeau
and Higgins (1995b); Davis
et al. (1992), Thompson et
al.‟s (1991); Venkatesh et
al.‟s (2003).
Bélanger
and
Carter
(2004).
Bélanger
and
Carter
(2004).
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).
Hsieh et al. (2008); Kvasny
(2002); Keil et al. (2003);
Construct related only to the Kvasny and Keil (2002);
e-government context.
Lynne et al. (1995), Van
der Meer and Van Winden
(2003).
Adapted from Hernandez
and Mazzon (2007), Coyle
(2001), Chen and Barnes
(2007).
Gefen and al. (2002), Nah
and Davis (2002).
Adapted from Chen and
Barnes (2007), Coyle (2001),
Hernandez and Mazzon
(2007).
Dubauskas (2005),
and Davis (2002).
Construct related only to the
e-government context.
Adapted
from
Davies
(2005), Griffin et al.
(2007), Grundén (2009).
Compeau
(1995).
Lee et al. (2005).
and
Higgins
Construct related only to the
e-government context but
originally adapted from the
end user support.
Factors Influencing Public E-services Acceptance
Nah
Grundén (2009), Tan and
Teo (2000).
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 control beliefs.
1.3.1
Utilitarian, hedonic, social, and control outcomes in Lebanon
The Lebanese culture can impact the acceptance or rejection of e-services. This influence goes through the relative 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 behavior unpredictable and little rational. Therefore, the public 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 behaviour is mostly directed towards the long-term of accumulation 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 determined 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 important. 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 outcomes can have an important role in the individual behaviour. 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 behaviour 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 Lebanon
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 Lebanon (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 traditions 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 influenced 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 individuals are influenced by whether others are also using
Internet.
1.3.3
The role of control beliefs in the Lebanese context
Between the control beliefs, trust is one of the most influential beliefs in the Lebanese context. Studies show
that the Lebanese consumers do not trust the online environment. 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. Indeed, 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 percent 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.
2.1
Research methods and results
Discussions of the First Phase methodology and instrument development
In the first part of this research we extracted a large number of beliefs from previous research in ICT acceptance
and public e-services acceptance. But none of these research 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 potential 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 technology acceptance. Their approach was justified on the
basis that there is a wealth of existing research on technology 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 decomposed 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 specification of the causal determinants of the public e-services
acceptance intention, we used, in Phase 1, a qualitative
method: interviews with open- ended questions. Questions were asked about government e-services acceptance
intention or about reasons for non-acceptance intention.
Therefore, after explaining the government online services 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 reasons for their choice. Therefore, respondents who accepted 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 decision.
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 Huberman (1984). Through the qualitative data anchored in
the trichotomous classification of TPB and MATH, our
first study identified the attitudinal, normative, and control salient beliefs related to the public e-services acceptance 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 developed 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 eservices 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 Acceptance Intention
As presented in Table 9, results show that among 188
citizens, only 33 (17.55 percent) intended to accept government 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 respondents), social outcomes (SO, frequency = 21.21 percent 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 government support (PGS, frequency = 57.57 percent of the
intenders or 10.10 percent of the respondents) and computer 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 influences (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 efficacy (CSE), perceived government influences (PGI) and
support (PGS).
2.2.2
Salient Beliefs Affecting the NonAcceptance Intention
For the citizens who intended to not-accept or reject the
government e-services, barriers like fear of the government 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 mentioned as perceived privacy and computer self-efficacy).
But the fear of government control was the most important determinant, both in terms of frequency and in terms
of importance.
Results also indicated that some factors (fear of government control, Lack of trust in the privacy, and lack perceived 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 perception 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
Attitudinal
Beliefs
Salient
beliefs
Intenders
33 citizens (17.55 %)
Frequency
% /Intenders
% /188
Non-Intenders
146 citizens (77.65 %)
Frequency
% / Intenders
% / 188
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 %
14
42.42 %
7.44 %
28
19.17 %
14.89 %
PSI
4
12.12 %
2.12 %
1
0.6 %
0.5 %
PGI
19
57.57 %
10.10 %
0
0
0
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 %
CO
Normative
Beliefs
Control
Beliefs
Uncertain
9 citizens
Table 9. Salient beliefs related to government e-services acceptance/rejection in Lebanon and their indicators
2.3
The e-government services acceptance 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‟ acceptance/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, hedonic, social, and control outcomes), normative beliefs,
and the control beliefs structure.
In the ITA e-Gov Model, the government e-services acceptance 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 middle plane (attitudinal beliefs, normative beliefs, and control beliefs) are usually operationalized by means of reflective indicators. These constructs can be better captured
if approach from a formative perspective. Indeed, inspection of the items constituting these indexes reveals that
the causal priority runs from the indicators to the construct. Attitudinal beliefs are formed as a combination of
utilitarian (UO), hedonic (HO), social (SO), and control
outcomes (CO) of public e-services acceptance. Normative beliefs are formed as perceived government influences (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 development
The ITA e-Gov Model was designed to capture the inhibitors and enablers behavioral beliefs of public eservices. In order to test the model, data was collected in
the Phase 2 from a sample of 210 randomly chosen potential Lebanese public e-service users. The sample characteristics are presented in the Table 10.
To achieve a representative sample of the Lebanese population 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. Therefore, 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
Gender
Religion
Region
Education
Monthly Income
Age
Male
Female
Christian
Muslim
Urban
Rural
Less than secondary
Between secondary and high school
(baccalauréat II)
University (1-3 years)
University (more than 3 years)
Less than 500 USD
500-900
900-1500
1500-2000
2000-2800
> 2800
< 18 years
18-28
29-42
43-64
> 64
Population Characteristics
Sample Characteristics
Nb
%
nb
%
4017095
100
210
100
1952672
49%
103
49%
2064423
51%
107
45%
1566678
39%
81
39%
2450417
61%
129
61%
2691453
67%
140
67%
1325641
33%
70
33%
1044445
26%
54
26%
1205128
30%
75
36%
1044445
26%
54
26%
723077
18%
27
12%
10
5%
63
30%
73
35%
28
13%
23
11%
13
6%
There is no accurate statistics on
this subject
1084615
27%
0
0%
1004273
25%
73
35%
956068
23.8%
79
37%
682906
17%
50
24%
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
acceptance/rejection, and potential acceptance/rejection of the
main public e-services of the Lebanese national administration in 2008. Therefore, a part of this questionnaire
was about the actual, the intended, or the potential acceptance/rejection of a long list of public e-services proposed
by the Lebanese national administration, such as national
government
information
services
at
www.egateway.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 passport, request for a certificate of birth, national identification 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 public 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 available 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 acceptance divide: “I‟m very likely to (…) to use public eservices 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 constructs, we need to consider all facets of the construct.
Indeed according to Nunnally and Bernstein (1994, p.
484) failure to consider all facets will lead to an exclusion 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 social outcomes (SO), and perceived control outcomes of
public e-services acceptance (CO). Normative beliefs
(NB) will be formed with two single-items scales: perceived 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 security (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 worthless/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 eservices 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 eservices” 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 eservices 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 activities” 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 intention) 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 analysis. 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 construct
Utilitarian
Outcomes
(UO)
Attitudinal
Beliefs
Hedonic
outcomes
(HO)
Social Outcomes (SO)
Control
Outcomes
(CO)
Normative
Beliefs
Perceived
social influences to use
e-services
(PSI)
Questions
Q1. I think public e-services are:
Extremely worthless -3 -2 -1 0 1 2 3 Extremely useful
Q2. I really … using public e-services
Extremely dislike -3 - 2 -1 0 1 2 3 Extremely like
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
Q4. I find public e-services to be:
Difficult to use 3 -2 -1 0 1 2 3 easy to use
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
References in public eservices acceptance
Bélanger and Carter
(2006); Bretschneider et
al. (2003); Van Slyke et
al. (2004); Phang et al.
(2005).
Van
der
Heijden
(2004); Sun and Zhang
(2006)
(2004).
(2004).
Hsieh et al. (2008)
Control
Beliefs
Public
e-services
acceptance/rejec
tion
Perceived
government
influences
(PGI)
Q6. I think that the government wants me to use public eservices
Trust in the
e-services
security
(TSEC)
Q7. I do trust the security of the public e-services in Lebanon
Trust in the
privacy
(TPRI)
Q8. I do trust that the government will respect privacy
when using the public e-services in Lebanon
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 eservices usage
Actual acceptance/reject
ion (INT1)
Q12. I‟m very likely to (…) to use public e-services when
this is possible and when I need it.
Intended
acceptance/reject
ion (INT2)
Q13. I intend to (…) to use public e-services in future
when this is possible and when I need it.
Potential
acceptance/reject
ion (INT3)
Q14. I will probably (…) to use public e-services.
Gefen and al. (2002),
Nah and Davis (2002).
Dubauskas (2005), Nah
and Davis (2002).
Grundén (2009), Tan
and Teo (2000).
van Dijk et al. (2008)
Table 11
3.
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).
Operationalization of the constructs
Research Results
According to our survey, only 15 % of respondents accepted 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 construct. 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 correlations as well as item-to-construct correlations (Lock et al.
2003). Results are presented in the Table 12.
The Inter-item correlations
AB
CO
SO
UO
HO
PSI
PGI
PGS
TPRI
TSEC
CSE
FGC
INT1
INT2
INT3
0.7795
0.7801
0.8687
0.6163
0.0245
0.4559
0.0551
0.3257
0.2995
-0.0302
0.2564
0.3138
0.2556
0.4175
SN
0.4806
0.0409
0.2699
-0.3028
0.5472
0.9899
0.2483
0.2634
0.1489
0.0924
0.1488
0.1540
0.1661
0.1780
PBC
0.1556
-0.1007
0.3270
-0.0910
0.0054
0.1864
0.7926
0.8035
0.9004
0.8948
0.9197
0.6851
0.5693
0.5226
AI
0.2354
-0.0453
0.3294
-0.0374
0.0261
0.1779
-0.1308
0.6542
0.7514
-0.0994
0.8092
0.9414
0.9160
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
CO
SO
UO
HO
PSI
PGI
PGS
TPRI
TSEC
CSE
FGC
INT1
INT2
INT3
SN
PBC
AI
0.3462
0.1370
0.3404
0.6041
0.6665
0.2186
0.1600
0.2315
0.3055
0.2738
0.5397
0.1614
0.6051
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 behavioral 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 research determined the inhibitors and enablers behavioral
beliefs that can predict the public e-services acceptance/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 eservices 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 regulations: 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 privacy (TPRI), lack of government support (PGS), and lack
of computer self efficacy (CSE) were also significant.
Results of the quantitative research showed that the multidimensional 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 mountains 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 number 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 answers 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 reports showed that even the former Prime Minister who
was murder was and during a long period monitored by
the secret services. Recently, the Lebanese telecommunications 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 Lebanese 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 literature in order to propose a model that can capture the acceptance 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 acceptance 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 eservices.
Fourth, it introduces a new method to determine the salient 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 decomposed method of selecting items, measures, or questions
related to each behavioural belief by drawing from previous 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 salient beliefs in a specific context and it can help the researcher 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 (Herzberg 1964), this article identifies the existence of two
categories of salient behavioural beliefs: (1) salient enablers 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 acceptance 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 beliefs.
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 implementing e-government, is the low rate of e-services‟ acceptance and use by citizens (Heeks 1999; Jaeger and
Thompson 2003; Moon 2002; Odedra-Straub 2003). Despite 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 conditions 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 relationship 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 current and accurate.
3.4
Limitations
As with any scientific research, this study has limitations.
First, it is important to recognize that the primary limitation 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 answers.
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 research, 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 explain the e-services acceptance or non-acceptance intention.
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 supported 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 government 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 importance 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 government control, perceived security and perceived privacy
of government online services.
To increase the public e-services acceptance, we recommend that the Lebanese government increases the privacy
and security of their e-services. The Lebanese government 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 selfefficacy and the acceptance and non-acceptance intention. According to Dimitrova and Chen (2006), selfefficacy 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 eservices.
References
Agarwal, R, Karahanna, E. (2000), “Time Flies When
You're Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage,” MIS
Quarterly, (24:4), pp. 665-694.
Agarwal. R., Prasad, J. (1998), “A Conceptual and Operational Definition of Personal innovativeness in the
Domain of Information Technology,” Information
Systems Research, (9:2), pp. 204-215.
Ajzen I, Fishbein M. (2000). Attitudes and the attitudebehavior relation: reasoned and automatic processes.
In European Review of Social Psychology, ed. W
Stroebe, M Hewstone. Chichester, England: Wiley. In
press
Ajzen, I. (1991), “The Theory of Planned Behavior,”
Organizational Behavior and Human Decision
Processes, (50), pp.179-211.
Ajzen, I. (2001), “Nature and Operation of Attitudes”,
Annual Review of Psychology, (52:1), pp. 27- 32.
Ajzen, I., Fishbein, M. (1980), Understanding Attitudes
and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ.
Al Omian, M., & Weir, D. (2005). Leadership in the
Arab World. University of Jordan.
Ali, A. (1990), “Management theory in a transitional
society: the Arab‟s experience,” International Studies
of Management & Organisation (20:3), pp.7-35.
Ali, A. (1995), “Cultural discontinuity and Arab management thought,” International Studies of Management & Organisation (25:3), pp.7-30.
Bagozzi, R. (2007), “The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift,”
Journal of the association for information systems,
(8:4), pp. 244-254.
Bélanger, F., Carter, L. (2005). Trust and risk in egovernment adoption. In Proceedings of the Eleventh
Americas Conference on Information Systems, Omaha, Nebraska, USA, pp. 1955- 1964).
Bélanger, F., Carter, L. (2006). “The effects of the digital
divide on EGovernment: An empirical evaluation.”
Proceedings of the 39th Hawaii International Conference on System Sciences, HICSS, 2006. Hawaii,
USA
Benbasat I., Brki. H. (2007) “Quo vadis, TAM?”, Journal
of the association for information systems, (8:4/3),
pp. 211-218.
Bobbio, N. (1987), The Future of Democracy: a Defense
of the rules of the game, University of Minnesota
Press.
Bretschneider, S., Gant, J., and Ahn, M. (2003), A general model of e-government adoption and diffusion,
Paper presented at Public Management Research
Conference, Georgetown Public Policy Institute,Washington, DC.
Brown, S., Venkatesh, V. (2005), Model of Adoption of
Technology in Households: A Baseline Model Test
and Extension Incorporating Household Life Cycle,
MIS Quartely, (29:3), pp. 339-446.
Cenfetelli R. (2004), Inhibitors and Enablers as Dual
Factor Concepts in Technology Usage, Journal of the
Association for Information Systems, (5:11-12), pp.
472-492.
Chen, YH, Barnes, S (2007), Initial trust and online buyer behaviour. Ind. Manage. Data Syst, (107:1), pp.
21-36.
Colli, A. (2003). The history of family business 18502000. Cambridge, England: Cambridge University
Press.
Compeau, D.R. Higgins, C.A. (1995b) Application of
Social Cognitive Theory to Training for Computer
Skills. Information Systems Research, (6:2), pp. 118143.
Cooper, R.B., Zmud, R.W. (1990), “Information Technology Implementation Research: A Technological
Diffusion Approach,” Management Science, vol. 36,
N°2, pp. 123-139.
Coyle, F.P. (2001), Wireless Web: A Manager‟s Guide.
Addison Wesley, NJ.
Davies, W. (2005), The age of surveillance: a new dotcom boom, available at: www.opendemocracy.cet .
web site visited the 30-08-2008.
Davis F.D. (1993), “User acceptance of information technology: system characteristics, user perception and
behavioural impacts,” International Journal of ManMachine Studies, Vol. 38, N°3, pp. 475-487.
Davis, F.D. (1989), “Perceived Usefulness, perceived
ease of use and user acceptance of information technology,” MIS Quarterly, vol. 13, N° 3, pp.319-340.
Davis, F.D., Bagozzi, R.P., Warshaw, P.R. (1989), “User
Acceptance of Computer technology: A Comparison
of two Theoretical Models,” Management Science,
vol. 35, N° 8, pp. 982-1003.
Davis, F.D., Bagozzi, R.P., Warshaw, P.R. (1992), “Extrinsic and Intrinsic Motivation to Use Computers in
the Workplace,” Journal of Applied Social Psychology, Vol. 22, pp. 1111-1132.
Dubauskas, N. (2005). Business compliance to changing
privacy protections. In Proceedings of the 38th Hawaii International Conference on System Sciences.
Dwivedi Y.K., Weerakkody V., and Williams M. (2009)
Guest editorial: From implementation to adoption:
Challenges to successful E-Government diffusion,
Government Information Quarterly, 26, 3–4.
Fahed-Sreih, J., Djoundourian, S. (2006). Determinants
of longevity and success in Lebanese Family Businesses: An Exploratory Study. Family Business Review, 19(3), 225-234.
Fishbein, M (1968), An investigation of relationships
between beliefs about an object and the attitude towards that object, Human Relationships, 16, 233-240.
Fishbein, M., Ajzen, I. (1975), Belief, attitude, intention,
and behavior: An introduction to theory and research,
Reading Mass, Don Mills, Ontario: Addison-Wesley
Pub. Co. 1975.
Gefen, D. (2000), “E-commerce: the Role of Familiarity
and Trust,” Omega, Vol. 28, No. 6:725-737.
Gefen, D., Karahanna, E., and Straub, D.W. “Inexperience and Experience with Online Stores: The Importance of TAM and Trust,” IEEE Transactions on
Engineering Management (50:3), 2003a, pp. 307321.
Gefen, D., Karahanna, E., and Straub, D.W. “Trust and
TAM in Online Shopping: An Integrated Model,”
MIS Quarterly (27:1), 2003b, pp. 51-90
Gefen, D., Pavlou, P.A., Warkentin, M., Gregory, M.R.
(2002). E-government adoption. In Proceedings of
the Eighth Americas Conference on Information Systems, pp. 569-576).
Giddens A., (1994), Les conséquences de la modernité,
Paris, L‟Harmattan.
Gilbert, D., Balestrini, P., & Littleboy, D. (2004). Barriers and benefits in the acceptance of e-government.
The International Journal of Public Sector Management, 14(4), pp. 286-301.
Griffin, D., Trevorrow, P. & Halpin, E. (2007) Introduction e-Government: A welcome Guest or Uninvited
Stranger? In Developments in e-Government. A critical Analysis, Griffin, D., Trevorrow, P., & Halpin, E.
Amsterdam: IOS Press.
Grundén, K. (2009), A social perspective on Implementation of e-Government – A longitudinal study at the
country administration of Sweden, Electronic Journal
of e-Government, Volume 7, Issue 1, 2009. pp. 6576.
Hartwick, J., Barki H. (1994), “Explaining the Role of
User Participation in Information System Use,” Management Science, Vol. 40, N° 4, pp. 440-465.
Heeks, R. (1999) Reinventing government in the information age. International practice in IT-enabled public sector reform, New York: Routledge.
Hernandez, JMC, Mazzon, JA (2007) Adoption of internet banking: proposition and implementation of an
integrated methodology approach, International J.
Bank Mark, 25 (2): 72-88.
and the Theory of Planned Behavior, Journal of Economic Psychology, 16, (4), 581-598.
Hoffman, D.L., Novak, T.P., Peralta, M. (1999), “Building Consumer Trust Online,” Communications of the
ACM, Vol. 42, No. 4: pp. 80-85.
Mathieson, K. (1991), “Predicting User Intentions: Comparing the Technology Acceptance Model with the
Theory of Planned Behavior,” Information Systems
Research (2:3), pp. 173-191.
Hsieh J. J. Po-An, Rai A., Keil M. (2008), “Understanding Digital Inequality: Comparing Continued Use
Behavior Models of The Socio-Economically Advantaged and Disadvantaged,” MIS Quarterly, March,
Vol. 32, No. 1, pp. 97-126.
Jaeger, P.T., Thompson, K.M. (2003) E-Government
around the World: Lessons, Challenges, and Future
Directions, Government Information Quarterly, 20,
(40), 389-394.
Jarvenpaa, S.L., Tractinsky, N., Saarinen, L., Vitale, M.
“Consumer trust in an Internet store: A cross-cultural
validation“. Journal of Computer-Mediated Communications, (5 : 2). 1999.
Karahanna, E., Straub, D.W., Chervany, N.L. (1999),
“Information Technology Adoption Across Time: A
Cross-Sectional Comparison of Pre-Adoption and
Post-Adoption Beliefs,” MIS Quarterly, Vol. 23, N°2,
183-214.
Keil, M., Meader, G. W. and Kvasny, L. (2003) Bridging
the Digital Divide: The Story of the Free Internet Initiative in LaGrange, Georgia, in Proceedings of the
36th Annual Hawaii International Conference on System Sciences, Los Alamitos, CA: IEEE Computer
Society Press, Vol. 5, 140.2
Kvasny, L. (2002), Problematizing the Digital Divide:
Cultural and Social Reproduction in a Community
Technology Initiative, Unpublished Doctoral Dissertation, Georgia State University, Atlanta, GA.
Kvasny, L., Keil, M. (2002), “The Challenges of Redressing the Digital Divide: A Tale of Two Cities,” in Proceedings of the 23rd International Conference on Information Systems, L. Applegate, R. Galliers, and J.
I. DeGross (eds.), Barcelona, Spain, December 15-18,
pp. 817-828.
Lee, J.K., Kim, D.J., Rao, H.R. (2005), An examination
of trust effects and pre-existing relational risks in egovernment services. In Proceedings of the Eleventh
Americas Conference on Information Systems, pp.
1949- 1954).
Miles, M., Huberman, A. M. (1984) Qualitative data
analysis. Beverly Hills, CA: Sage Publications.
Moon, J., Kim., Y. (2001), “Extending the TAM for a
World-Wide-Web Context,” Information and Management, (38:4), pp. 217-230.
Moon, M.J. (2002), “The evolution of e-Government
among Municipalities: Rhetoric or Reality,” Public
Administration Review, (62:4), pp. 424-433.
Moore, G.C, Benbasat, I. (1996), “Integrating Diffusion
of Innovations and Theory of Reasoned Action Models to Predict Utiiization of Information Technology
by End-Users,” In Diffusion and Adoption of Information Technology, K. Kautz and J. Pries-Heje
(eds.), Chapman and Hall, London, pp. 132-146.
Moore, G.C., Benbasat, I. (1991). “Development of an
instrument to measure the perceptions of adopting an
information technology innovation,” Information
Systems Research, (2,3), pp.192-222.
Nah, F.F.H., Davis, S. (2002), HCI Research Issues in
Electronic Commerce, Journal of Electronic Commerce Research, (3:3), pp. 98-113.
Odedra-Straub, M. (2003) E-commerce and Development: Whose Development? The Electronic Journal
of information Systems in Developing Countries, 11,
(2), 1-5.
Phang, C.W., Li, Y., Sutanto, J. and Kankanhalli, A.
(2005). Senior citizens‟ acceptance of e-government:
In quest of the antecedents of perceived usefulness. In
Proceedings of the 38th Hawaii International Conference on System Sciences (HICSS) 38.
Rogers, E.M. (1983), Diffusion of Innovations, 3d Ed.,
Free Press, New York.
Rose, G. and Straub, D. (1998) “Predicting General IT
Use: Applying TAM to the Arab World”, Journal of
Global Information Management, Vol 6 No 3, pp. 3946.
Lewis W., Agarwal R., Sambamurthy V. (2003),
“Sources of Influence on Beliefs about Information
Technology Use: An Empirical Study of Knowledge
Workers”, MISQ, Vol. 27, N 4, pp. 657-678.
Schwarz, A., & W. Chin, Looking Forward: Toward an
Understanding of the Nature and Definition of IT Acceptance. Journal of the association for information
systems, Volume 8, Issue 4, Article 6, pp. 230-243,
April 2007.
Loch K., Straub DW., Kamel S. “Diffusing Internet in the
Arab World: The role of social norms and technological culturation “, IEEE Transactions on Engineering Management, (50 : 1), 2003, pp.43-63
Straub, D., Burton-Jones, A. (2007), Veni, Vidi, Vici:
Breaking the TAM Logjam, Journal of the association
for information systems, Volume 8, Issue 4, Article 5,
pp. 223-229.
Lynne, G. D., Casey, C. F., Hodges, A., and Rahmani, M.
(1995) Conservation Technology Adoption Decisions
Straub, D., Lock, K., and Hill, C. (2001) Transfer of Information Technology to the Arab World: A test of
Cultural Influence Modeling, Journal of Global Information Management,.
Van der Heijden, H. (2004), “User Acceptance of Hedonic Information Systems,” MISQ, (28:4), pp. 695-704.
Sun, H. & Zhang, P. (2006) The role of affect in IS research: A critical Survey and a research Model. In: P.
Zhang and Galletta, Humain-Computer Interaction
and Management Information Systems-Foundations,
ME Sharpe, Inc., Armonk, NY.
Van der Meer, A., Van Winden, W. (2003), “EGovernance in Cities: A Comparison of Urban Information and Communication Technology Policies,”
Regional Studies (37:4), pp. 407-419
Tan, M. & Teo, T. (2000) Factor influencing the adoption
of Internet Banking, J.A.I.S., 1, 5, 173-191.
Taylor, S., Todd P.A. (1995b), “Understanding Information Technology Usage: A Test of Competing Models,” Information Systems Research, Vol. 6, N° 2, pp.
144-176.
Taylor, S., Todd, P.A. (1995a), Assessing IT Usage: The
Role of Prior Experience, MIS Quarterly (19:2), pp.
561-570.
Thompson, R. L., Higgins, C. A., Howell, J. M. (1991),
“Personal Computing: Toward a Conceptual Model
of Utilization,” MIS Quarterly, Vol.15, N°1, pp. 124143.
Tornatzky, L. G., Klein, K. J. (1982). “Innovation characteristics and innovation adoption-implementation: A
meta-analysis of findings,” IEEE Transactions on
Engineering Management, vol. 29, N°1, pp. 28-45.
Triandis, H.C. (1971), Attitude and Attitude Change,
John Wiley Sons Inc., New York.
Triandis, H.C. (1980), "Values, Attitudes, and Interpersonal Behavior," In H.E. Howe (ed.), Nebraska Symposium on Motivation: Beliefs, Attitudes, and Values, University of Nebraska P ress, Lincoln, NE, pp.
195-259.
Triandis, H.C. (1980), "Values, Attitudes, and Interpersonal Behavior," In H.E. Howe (ed.), Nebraska Symposium on Motivation: Beliefs, Attitudes, and Values, University of Nebraska P ress, Lincoln, NE, pp.
195-259.
UNDESA (2008) UN e-government surveys: from egovernment to connected governance, Department of
Economic and Social Affairs, Division for Public
Administration and Development Management, Report: ST/ESA/PAD/SER.E/11, ISBN 978-92-1123174-8.
UNDP (2009), Lebanon 2008 – 2009. The National Human Development Report: toward a citizen‟s state.
UNDP Publications.
UNPAN (2005), UN Global E-government Readiness
Report 2005: From E-government to E-inclusion,
Department of Economic and Social Affairs Division
for Public Administration and Development Management, UNPAN/2005/14.
Valente, T. (1995), Network Models of the Diffusion of
Innovations, New York: Hampton Press.
van Dijk, J., Peters, O., Ebbers, W. (2008), Explaining
the acceptance and use of government Internet services: A multivariate analysis of 2006 survey data in
the Netherlands, Government Information Quarterly
25 (2008), pp. 379–399.
Van Slyke, C.F, Bélanger, CL., Comunale (2004), „Factors Influencing the Acceptance of Web-Based Shopping: The Impact of Trust‟ The Data Base for Advances in Information Systems, (35:2).
Venkatesh, V., Brown, S. (2001), “A Longitudinal Investigation of Personal Computers in Homes: Adoption
Determinants and Emerging Challenges,” MIS Quarterly, (25:1), p.p. 71-102.
Venkatesh, V., Morri, M.G., Davis, G.B., Davis, F.D.
“User Acceptance of Information Technology: Toward a Unified View,” MIS Quarterly (27:3), 2003,
pp. 425-478.
Warkentin, M, Gefen, D. Pavlou, P.A., Rose, G.M.
(2002). Encouraging citizen adoption of egovernement by building trust. Electronic Markets,
12(3), pp. 157-162.
Warshaw, P.R. (1980), “A New Model for Predicting
Behavioral Intentions: An Alternative to Fishbein,”
Journal of Marketing Research. Vol. 17, N° 2, pp.
153-172.
Webster, J, Martochhio. J. (1992), “Microcomputer playfulness: Development of measure with workplace implications,” MIS Quartirly. Vol. 16, N° 2, pp. 201226.
Weir, D. (2002), “Management in the Arab World: A
fourth paradigm?,” The European Academy of Management, (EURAM 2002), Stockholm, Sweden.
Yahchouchi, G. (2009), Employees‟ Perceptions of Lebanese Managers‟ Leadership Styles and Organizational Commitment, International Journal of Leadership Studies, (4:2), pp. 127-140.

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