Reporting moderator in PLSFormative Indication.pdf

Information technology (IT) in Saudi Arabia:
Culture and the acceptance and use of IT
Said S. Al-Gahtani a
, Geoffrey S. Hubona b,*, Jijie Wang b
a
King Khalid University, Abha, Saudi Arabia
b
Department of Computer Information Systems, J. Mack Robinson College of Business Administration,
Georgia State University, Atlanta, GA 30303, USA
Received 6 September 2006; received in revised form 10 May 2007; accepted 13 September 2007
Available online 26 October 2007
Abstract
The unified theory of acceptance and use of technology (UTAUT), a model of the user acceptance of IT, synthesizes elements
from several prevailing user acceptance models. It has been credited with explaining a larger proportion of the variance of ‘intention
to use’ and ‘usage behavior’ than do preceding models. However, it has not been validated in non-Western cultures. Using a survey
sample collected from 722 knowledge workers using desktop computer applications on a voluntary basis in Saudi Arabia, we
examined the relative power of a modified version of UTAUT in determining ‘intention to use’ and ‘usage behavior’. We found that
the model explained 39.1% of intention to use variance, and 42.1% of usage variance. In addition, drawing on the theory of cultural
dimensions, we hypothesized and tested the similarities and differences between the North American and Saudi validations of
UTAUT in terms of cultural differences that affected the organizational acceptance of IT in the two societies.
# 2007 Elsevier B.V. All rights reserved.
Keywords: Unified theory of acceptance and use of technology (UTAUT); Technology acceptance; IT adoption; Cultural differences; Technology
social factors; Saudi Arabia
1. Introduction
In mainstream MIS research, there are many studies
that have investigated user acceptance and usage of new
IT. Of these many have used TAM [9,10] or made
changes to it [4,13]. Other models, such as the theory of
planned behavior (TPB) [1,18], and social cognitive
theory (SCT) [8] are also well known.
Venkatesh and Davis [26] introduced an extension to
TAM, TAM2, which examined the influences of select
antecedent social influence and cognitive instrumental
constructs on perceived usefulness and usage intentions.
Subsequently, they [27] synthesized the various models
into the unified theory of acceptance and use of
technology (UTAUT).
However, it has been exclusively validated in the
North American contexts. Clearly, in contexts removed
from Western nations, the impact of subjective norms
on the individual and organizational acceptance of IT
could vary markedly. Accordingly, the objectives of our
research was to: (1) empirically validate a modified
UTAUT model in a non-Western cultural context,
specifically Saudi Arabia and (2) explain anomalies
between these validations in terms of cultural differ-
ences that affect the organizational acceptance of IT. To
achieve this second objective, we draw from research on
cultural dimensions [14,15] that explain international
differences in work-related values.
www.elsevier.com/locate/im
Available online at www.sciencedirect.com
Information & Management 44 (2007) 681–691
* Corresponding author. Tel.: +1 404 413 7360.
E-mail address: hubona@gsu.edu (G.S. Hubona).
0378-7206/$ – see front matter # 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.im.2007.09.002

2. Theory and background
2.1. Technology acceptance
TAM postulated that two belief constructs, perceived
usefulness and perceived ease of use, accounted for a
large proportion of the variance in behavioral intentions
and voluntary usage behaviors of new ITs. Empirical
validations of TAM have typically accounted for
between 15% and 45% of the variance in the ‘intention
to use’ and self-reported ‘usage’.
An extension examined the impact of social
influence and cognitive instrumental processes as
predictors of the perceived usefulness and behavioral
intention to use. The subjective norm, originally
developed from the theory of reasoned action (TRA)
predicts general behavioral intentions in strictly
voluntary contexts; it assumes that people’s perception
that others important to them think a behavior should or
should not be followed has an impact on their actions.
Additional models, including the theory of planned
behavior (TPB) [11] and the decomposed theory of
planned behavior (DTPB) [24], lead to the combined
model as illustrated in the UTAUT model of Fig. 1.
UTAUT postulates that four constructs act as
determinants of behavioral intentions and usage
behavior:
1. Performance expectancy: ‘‘The degree to which an
individual believes that using the system will help
him or her attain gains in job performance.’’
2. Effort expectancy: ‘‘The degree of ease associated
with the use of the system.’’
3. Social influence: ‘‘The degree to which an individual
perceives that important others believe he or she
should use the new system. Social influence is
system- or application-specific, whereas subjective
norm relates to non-system-specific behavior.’’
4. Facilitating conditions: ‘‘The degree to which an
individual believes that an organizational and
technical infrastructure exists to support use of the
system.’’
In addition, UTAUT also posits the role of four key
moderator variables: gender, age, experience, and
voluntariness of use.
2.2. Technology acceptance and culture
The globalization of business has highlighted the
need to understand the effectiveness of IS that span
different cultures. Multinational and trans-cultural
organizations use IT to achieve economies of scale,
coordinate operations, and facilitate collaborative work
across locations and cultures. Cultural differences have
become an important issue in the evaluation of
computer applications.
To make valid comparisons, models should be robust
across cultures. Therefore, determining whether similar
models are comparable across cultures is a first step
needed to: (1) enhance understanding of cultural effects
of IT acceptance and (2) improve the organizational
management of IT globally.
Rose and Straub [20] conducted a study of IT
adoption and use in the Arab world. Using a cross-
sectional survey of 274 knowledge workers in five Arab
nations (Egypt, Jordan, Saudi Arabia, Lebanon, and the
Sudan), they applied a modified TAM to assess the
diffusion of personal computing. Their model explained
40% of the variance of PC use in these nations.
Subsequently, Straub et al. [23] developed a cultural
influence model and suggested that Arab cultural beliefs
were a strong predictor of resistance to IT transfer. Loch
et al. [17] applied this model to examine culture-specific
enablers and impediments to the adoption and use of the
Internet in the Arab world. They showed that both social
norms and the degree of technological culturation can
impact the individual and organizational acceptance
and use of the Internet.
Different approaches have been used to study the
organizational effects of culture; one is to apply a
quantitative methodology to identify and measure
national cultural dimensions. Such studies include
Tiandis’s [25] and Hofstede’s national cultural dimen-
sions, and social identity theory [21]. Of these,
Hofstede’s are most commonly used. These facilitate
national-level analyses and allow multiple country
comparisons. Furthermore, Hofstede’s cultural dimen-
sions have been used to explore the impact of cultural
differences on technology acceptance [22]. We also
drew on Hofstede’s dimensions to describe select
cultural differences between Saudi Arabia and North
American Nations and to discuss cultural implications
of IT user acceptance. Hofstede’s dimensions are shown
in Table 1, which briefly describes his five cultural
dimensions.
Table 2 shows country scores of these dimensions for
the United States and Saudi Arabia. Thus, Saudi Arabia
ranks much higher than the US in uncertainty avoidance
and power distance; approximately the same in
masculinity; and much lower in individualism.
High uncertainty avoidance deals with tolerance for
uncertainty and ambiguity. It indicates to what extent a
person feels uncomfortable in unstructured situations.
S.S. Al-Gahtani et al. / Information & Management 44 (2007) 681–691
682

High power distance refers to the propensity to defer to
authority, and conform to the expectations of others in
superior social roles. Masculinity focuses on the extent
to which a society stresses achievement versus caring
and nurturing behaviors, and low individualism refers to
the extent to which individuals are integrated into
cohesive in-groups and value the protection it provides.
2.3. Research model and hypotheses
The model for our study is presented as Fig. 2. It was
derived from UTAUT, but was modified. First, as we
examined the factors that promoted the use of
computers only on a voluntary basis, we eliminated
voluntary use as a moderating construct. And second,
we substituted subjective norm for social influence; our
target behavior related to the use of desktop computers
in general, and not to any application or system. Two of
our four usage measures explicitly related to computer
usage in many different computer applications and with
respect to performing a variety of tasks. Thus, we used
the more general subjective norm construct.
We analyzed all of the path linkages in our research
model (Fig. 2), including all direct and moderating (or
interacting) effects, forming specific hypotheses for
every path except for that from behavioral intention to
use behavior. The theoretical basis for the research
model is UTAUT, which is assumed to justify the
indicated path linkages. However, we assumed a
number of specific effects inherent that we expected
to arise from cultural differences. Since the score for
long-term orientation dimension (sometimes termed a
S.S. Al-Gahtani et al. / Information & Management 44 (2007) 681–691 683
Fig. 1. Unified theory of acceptance and use of technology.
Table 1
Measures of cultural dimensions
Hofstede’s dimension Definition
Uncertainty avoidance (UA) Focuses on the level of tolerance for uncertainty and ambiguity within the society. High UA indicates
a structured, rule-oriented society that institutes rules, regulations, and controls in order to reduce the
amount of uncertainty
Power distance (PD) Focuses on the degree of equality, or inequality, between people in the country’s society. High PD
indicates that inequalities of power and wealth are accepted practices and have been allowed to grow
Masculinity (MAS) Masculinity measures the degree to which ‘‘masculine’’ values like assertiveness, performance, success
and competition prevail over ‘‘feminine’’ values like the quality of life, maintaining warm personal
relationships, service, caring, and solidarity
Individualism (IDV) Focuses on the degree the society reinforces individual or collective achievement and interpersonal
relationships. Low IDV typifies societies of a more collectivist nature with close ties between individuals.
These cultures reinforce collectives where everyone takes responsibility for fellow members of their group
Long-term orientation or
confucian dynamism (LTO)
Cultures typified by a long-term orientation are oriented towards future rewards, in particular perseverance
and thrift, while a short-term orientation is characterized by values relating to both the past and present,
in particular, the respect for tradition, preservation of ‘‘face’’ and the fulfillment of social obligations

Confucian effect) is not available for Saudi Arabia, we
only drew on the remaining four dimensions.
There are rigid boundaries in social roles and
expectations for women compared to men in Saudi
Arabiaandthustherearefarfewerwomeninprofessional
knowledge worker roles. Accordingly, we expected that
women in Saudi Arabia would be less inclined than men
to expect that the use of computers would enhance their
job performances and thus advance their professional
careers. Additionally, the majority of the Saudi work-
force is young,under theage of 40 [2], and well educated.
Consequently, we hypothesized:
H1: Performance expectancy will have a positive
influence on behavioral intentions to use computers.
H1a: Gender will positively moderate the influence of
performance expectancy on behavioral intentions to
use computers for men.
H1b: Age will not moderate the influence of
performance expectancy on behavioral intentions to
use computers.
Again, we expected a positive influence of effort
expectancy on behavioral intentions to use computers.
There is noreason to suspect that the effect of the ‘‘degree
of ease associated with the use of the system’’ should be
influenced by Hofstede’s cultural measures. In Saudi
Arabia, increased levels of ease of using computers
shouldbe associated with increased behavioral intentions
to use them. Furthermore, we hypothesized that men
would be more inclined than women to associate an
increased ease of use with increased intentions to use
computers. Furthermore, we expected no interaction of
age with this relationship. However, more experienced
users would tendto be less influenced by the ease of using
computers. Thus we hypothesized:
H2: Effort expectancy will have a positive influence
on behavioral intentions to use computers.
effort expectancy on behavioral intentions to use
computers for men.
H2b: Age will not moderate the influence of effort
expectancy on behavioral intentions to use computers.
H2c: Experience will negatively moderate the
influence of effort expectancy on behavioral inten-
tions to use computers for men.
In cultures characterized by high power distance,
individuals will acquiesce to the expectations of others
who are seen as important or influential. Consequently,
in an Arabic culture, employees should exhibit a
stronger association between social influence variables
and behavioral intention, than, for example, in the US.
Furthermore, the low individualism score for Saudi
Arabia is characteristic of a culture that values
collective achievements and interpersonal relationships.
A high regard for groups suggests that the opinions of
others would impact an individual’s behavioral inten-
S.S. Al-Gahtani et al. / Information Management 44 (2007) 681–691
684
Fig. 2. Research model.
Table 2
Hofstede country scores for the USA and Saudi Arabia
Cultural dimension United States Saudi Arabia
Uncertainty avoidance 46 68
Power distance 40 80
Masculinity 62 52
Individualism 91 38
Long-term orientation 29 N/A

tions. Consequently, the collective opinions of others
would strongly influence individual behavioral inten-
tions and result in a positive relationship between
subjective norm and behavioral influence.
In addition, younger people tend to occupy
subordinate roles and thus are likely to be influenced
by the subjective norm in a culture characterized by
high power distance. Consequently, we hypothesized:
H3: Subjective norm will have a positive influence on
behavioral intentions to use computers.
subjective norm on behavioral intentions to use
computers for men.
H3b: Age will negatively moderate the influence of
subjective norm on behavioral intentions to use
computers.
H3c: Experience will negatively moderate the
influence of subjective norm on behavioral intentions
to use computers.
In terms of UTAUT, we argued that the relationships
between facilitating conditions and use behavior should
be strong for cultures that score high on uncertainty
avoidance. We reasoned that increasing levels of
facilitating conditions should serve to reduce uncom-
forting levels of uncertainty or ambiguity with
computers. Therefore, we expected that a direct
relationship between facilitating conditions and use
behavior would hold true in Saudi Arabia. Additionally,
we reasoned that age and experience should negatively
interact with the influence of facilitating conditions on
computer usage. Specifically, we speculated that
increasing levels of age and experience would mute
the dependence on a facilitating infrastructure to utilize
computers. Consequently, we hypothesized:
H4: Facilitating conditions will have a positive
influence on computer usage behavior.
H4a: Age will negatively moderate the influence of
facilitating conditions on computer usage behavior.
H4b: Experience will negatively moderate the
influence of facilitating conditions on computer usage
behavior.
3. Method
The data used in our study were part of the material
collected in a project financed by the Saudi government
to build a comprehensive model of the antecedents and
of the mediating, moderating, and outcome factors, that
affect the acceptance and use of computers by
knowledge workers in Saudi Arabia. A list of the
major companies in the four main provinces of Saudi
Arabia was compiled with the assistance of the
chambers of commerce in each region. The general
managers of these organizations were asked to allow
participation of knowledge workers from their organi-
zation. Those that agreed to participate were asked to
nominate a contact person to help the researchers
distribute and collect the survey instruments. The
organizations participating included banking, merchan-
dising, manufacturing, and petroleum industries.
A total of 1190 usable survey responses were
collected. Of these, 468 responders indicated that their
use of computers was mandatory. The remaining 722
survey responders indicated volitional use of compu-
ters. These responses constituted our survey sample.
All survey items, originally published in English, as
discussed later, were converted into Arabic using
Brislin’s [3] back translation method. The items were
translated between English and Arabic by several
bilingual professors and repeated until both versions
converged.
Table 3 shows the items used to estimate the Saudi
predictor latent constructs. A seven point Likert scale
with anchors of strongly disagree to strongly agree was
S.S. Al-Gahtani et al. / Information Management 44 (2007) 681–691 685
Table 3
Saudi predictor latent construct items
Performance expectancy (PE)
PE1: I find computers useful in my job
PE2: Using computers in my job enables me to accomplish tasks
more quickly
PE3: Using computers in my job increases my productivity
PE4: Using computers enhances my effectiveness on the job
Effort expectancy (EE)
EE1: My interactions with computers are clear and
understandable
EE2: It is easy for me to become skillful using computers
EE3: I find computers easy to use
EE4: Learning to use computers is easy for me
Subjective norm (SN)
SN1: Most people who are important to me think I should use
computers
SN2: Most people who are important to me would want me to use
computers
SN3: People whose opinions I value would prefer me to use
computers
Facilitating conditions (FC)
FC1: I have the resources and the knowledge and the ability to make
use of the computer
FC2: A central support was available to help with computer
problems
FC3: Management provided most of the necessary help and
resources for computing

used to measure each item. The performance expec-
tancy and effort expectancy constructs were items used
by Venkatesh et al. with the references to ‘‘the system’’
changed to ‘‘computers.’’ The subjective norm items
originated from TRA/TPB. Facilitating conditions,
defined by Venkatesh et al. as: ‘‘the degree to which
an individual believes that an organizational and
technical infrastructure exists to support use of the
system,’’ included references to having resources,
knowledge, and technical and management support to
use computers.
Table 4 gives the Saudi items used to measure the
moderator variables. Similar to Venkatesh et al., we
used a binary dummy variable, 0 for female, and 1 for
male, to indicate gender. There was an imbalance of
male gender representation (82% of responses), but this
was inevitable due to the cultural preponderance of
working males in Saudi Arabia. As indicated, we
measured age using five ordinal categories in response
to the question: ‘‘For how many years have you been
using computers?’’
Table 5 indicates the items used to estimate the Saudi
predicted latent constructs, behavioral intention and use
behavior. For behavioral intention, we chose items that
reflected an individual’s self-assessment of his (or her)
likelihood to continue to use computers for an indefinite
period. Our measure differs from other measures in the
literature that have used time-specific measures. We
wanted to capture a self-assessment of likely continuing
computer usage.
We used a multi-item, self-reported latent usage
construct to provide four dimensions of computer
usage: (1) amount of time spent using computers
per day; (2) frequency of using computers; (3)
number of different software applications used; and
(4) number of different business tasks supported
through computer use. These four items had been
used in a study [16] investigating the acceptance of
desktop computing by 358 users in small private firms
in New Zealand.
4. Results
The research model of Fig. 2 was analyzed using
PLS-Graph (build 1126), a PLS structural equation
modeling tool [5]. It assesses the psychometric
properties of the measurement model, and estimates
the parameters of the structural model. This tool enables
the simultaneous analysis of up to 200 indicator
variables, allowing the examination of extensive
interactions among moderator and latent predictor
variable indicators.
4.1. The measurement model
Reliability results are given in Table 6. The data
indicates that the measures are robust in terms of their
internal consistency reliability as indexed by the
composite reliability. The composite reliabilities of
the different measures range from 0.76 to 0.95, which
exceed the recommended threshold value of 0.70 [19].
In addition, consistent with the guidelines of Fornell and
686
Table 4
Saudi moderating (interacting) variables
Gender Male (82%) or female (18%)
Age (1) Less than 20 years (1%); (2) 20–30 years (35%);
(3) 31–40 years (43%); (4) 41–50 years (19%);
and (5) above 50 years (2%)
Experience For how many years have you been using computers?
(1) Less than a year (8%); (2) 1–3 years (25%);
(3) 4–7 years (30%); (4) 8–10 years (13%);
(5) more than 10 years (24%)
Table 5
Saudi predicted latent construct items
Behavioral intention (BI)
BI1: I predict I will continue to use computers on a regular
basis (seven-point Likert scale anchored with strongly
disagree to strongly agree)
BI2: What are the chances in 100 that you will continue
as a computer user? (1) Zero; (2) 1–10%; (3) 11–30%;
(4) 31–50%; (5) 51–70%; (6) 71–90%;
or (7) more than 90%
BI3: To do my work, I would use computers rather than any
other means available (seven-point Likert scale anchored
with strongly disagree to strongly agree)
Use behavior (USE)
USE1: On an average working day, how much time do you
spend using computers? (1) Almost never; (2) less than
30 min; (3) from 30 min to 1 h; (4) from 1 to 2 h;
(5) from 2 to 3 h; and (6) more than 3 h
USE2: On average, how frequently do you use computers?
(1) Less than once a month; (2) once a month; (3) a few
times a month; (4) a few times a week; (5) about once a day;
and (6) several times a day
USE3: How many different computer applications have you
worked with or used in your job? (1) None; (2) one;
(3) two; (4) three to five applications; (5) six to ten
applications; and (6) more than 10 applications
USE4: According to your job requirements, please indicate
each task you use computers to perform (count of all that
apply)? (1) Letters and memos; (2) producing reports;
(3) data storage and retrieval; (4) making decisions;
(5) analyzing trends; (6) planning and forecasting;
(7) analyzing problems and alternatives; (8) budgeting;
(9) controlling and guiding activities; (10) electronic
communications with others; and (11) others (please indicate)

Larcker [12], the average variance extracted (AVE) for
each measure exceeded 0.50. Table 7 reports the results
of testing the discriminant validity of the measure
scales. The elements in the matrix diagonals, represent-
ing the square roots of the AVEs, are greater in all cases
than the off-diagonal elements in their corresponding
row and column, supporting the discriminant validity of
our scales.
We tested convergent validity using PLS-Graph by
extracting the factor and cross loadings of all indicator
items to their respective latent constructs. These results,
presented in Table 8, indicated that all items loaded: on
their respective construct from a lower bound of 0.70 to
an upper bound of 0.95; and more highly on their
respective construct than on any other. Furthermore,
each item’s factor loading on its respective construct
was highly significant ( p 0.0001) as indicated by the
T-statistics of the outer model loadings in the PLS-
Graph output. These values ranged from a low of 16 to a
high value of 121. The constructs’ items’ loadings and
cross loadings presented in Table 8, and the highly
significant T-statistic for each individual item loading
Table 6
Assessment of the measurement model
Variable constructs The composite
reliability (internal
consistency reliability)
Average variance
extracted/explained
Performance
expectancy
0.90 0.70
Effort expectancy 0.90 0.70
Subjective norm 0.95 0.87
Facilitating
conditions
0.77 0.53
Behavioral
intention
0.76 0.52
Use behavior 0.85 0.58
Table 7
Discriminant validity (intercorrelations) of variable constructs
Latent variables 1 2 3 4 5 6
1. Performance expectancy 0.84
2. Effort expectancy 0.43 0.84
3. Subjective norm 0.32 0.24 0.93
4. Facilitating conditions 0.32 0.48 0.24 0.73
5. Behavioral intention 0.43 0.50 0.36 0.47 0.72
6. Use behavior 0.20 0.29 0.04 0.38 0.47 0.76
Table 8
Factor loadings (bolded) and cross loadings
Performance
expectancy
Effort
expectancy
Subjective
norm
Facilitating
conditions
Behavioral
intention
Use
behavior
PE1 0.79 0.39 0.24 0.25 0.32 0.14
PE2 0.88 0.32 0.28 0.25 0.37 0.15
PE3 0.89 0.37 0.31 0.28 0.39 0.18
PE4 0.77 0.36 0.25 0.28 0.37 0.20
EE1 0.34 0.84 0.17 0.40 0.43 0.18
EE2 0.39 0.82 0.22 0.44 0.47 0.34
EE3 0.33 0.83 0.22 0.37 0.39 0.25
EE4 0.37 0.85 0.19 0.39 0.39 0.21
SN1 0.30 0.26 0.94 0.24 0.34 0.05
SN2 0.29 0.19 0.95 0.24 0.32 0.05
SN3 0.31 0.22 0.92 0.18 0.36 0.01
FC1 0.29 0.44 0.19 0.74 0.45 0.34
FC2 0.20 0.19 0.13 0.72 0.27 0.21
FC3 0.19 0.20 0.19 0.72 0.24 0.23
BI1 0.32 0.34 0.34 0.34 0.73 0.23
BI2 0.17 0.28 0.12 0.31 0.70 0.45
BI3 0.45 0.46 0.34 0.37 0.72 0.22
USE1 0.13 0.23 0.07 0.27 0.41 0.79
USE2 0.15 0.22 0.06 0.26 0.35 0.76
USE3 0.15 0.26 0.00 0.31 0.36 0.80
USE4 0.19 0.20 0.02 0.31 0.32 0.71

both confirm the convergent validity of these indicators
as representing distinct latent constructs.
4.2. The structural model
Fig. 3 shows the structural model results omitting the
influence of the interacting moderator variables. All
beta path coefficients are positive (i.e. in the expected
direction) and statistically significant (at p 0.05).
To model the interaction effects, we conformed to
Chin et al. [6,7]. Interaction terms were formulated by
multiplying the corresponding indicators of the predictor
and moderator constructs. Furthermore, we followed the
hierarchical process that they recommended to construct
and compare models with and without the respective
interacting constructs. Fig. 4 shows the results of the
structural model with interaction effects. It presents the
results of the structural model with moderator variables.
688
Fig. 3. Structural model results (without interacting variables).
Fig. 4. Structural model results.

For purposes of clarity, only statistically significant
moderator variables (e.g. age and experience) were
included. The beta values of all path coefficients are also
shown. Performance expectancy had a positive influence
(beta = 0.17, p 0.001) on intention. Effort expectancy
had a non-significant (beta = 0.12) influence on inten-
tion. Subjective norm had a positive influence
(beta = 0.45, p 0.001) on intention. The weak influ-
ence of facilitating conditions on use (beta = 0.03) was
not statistically significant. Behavioral intention had a
positive influence (beta = 0.31, p 0.001) on use.
For the moderator (interacting) variables, statisti-
cally significant beta path coefficients were indicated.
Surprisingly, gender did not exhibit significant inter-
actions with any predictor latent variables. Age had a
negative (beta = 0.09, p 0.05) interacting effect
with subjective norm upon behavioral intention. Age
also exhibited a negative (beta = 0.09, p 0.05)
interacting effect with facilitating conditions on use.
Experience exhibited three interacting effects: a
with effort expectancy on behavioral intention; a
with subjective norm on behavioral intention; and a
strongly positive (beta = 0.53, p 0.001) interacting
effect with facilitating conditions on use.
It is important to note that the strength and direction
(i.e. positive or negative) of main path coefficients cannot
be adequately interpreted without also considering the
influences of interacting variables. However, as a basis of
comparison, the (direct only) model explains 35.3% of
the variance in behavioral intention and 25.1% of the
variance in use behavior. In contrast, by including the
effects of the interacting variables, a larger proportion of
the respective variances in behavioral intention
(R2
= 0.391) and use (R2
= 0.421) are accounted for.
5. Discussion
Table 9 presents the hypotheses and outcomes. The
‘CONCLUSION’ column indicates whether that
hypothesis was: (1) supported; (2) refuted; or (3) not
Table 9
Hypotheses conclusions
Hypotheses Finding Conclusion Venkatesh finding
H1: Performance expectancy will have a positive
influence on behavioral intentions to use computers
Yes: (beta = 0.17, p 0.001) Supported Yes: (beta = 0.18, p 0.05)
H1a: Gender will positively moderate the influence
of performance expectancy on behavioral intentions
to use computers for men
No: not significant Not supported No: not significant
H1b: Age will not moderate the influence of performance
expectancy on behavioral intentions to use computers
Yes: not significant Supported Yes: not significant
H2: Effort expectancy will have a positive influence on
behavioral intentions to use computers
No: (beta = 0.12, n.s.) Not supported No: (beta = 0.04, n.s.)
effort expectancy on behavioral intentions to use
computers for men
H2b: Age will not moderate the influence of effort
expectancy on behavioral intentions to use computers
Yes: not significant Supported Yes: not significant
H2c: Experience will negatively moderate the influence of
effort expectancy on behavioral intentions to use computers
Yes: (beta = 0.56, p 0.001) Supported No: (beta = 0.02, n.s.)
H3: Subjective norm will have a positive influence on
behavioral intentions to use computers
subjective norm on behavioral intentions to use computers
for men
H3b: Age will negatively moderate the influence of
H3c: Experience will negatively moderate the influence of
H4: Facilitating conditions will have a positive influence on
computer usage behavior
No:(beta = 0.03, n.s.) Not supported No: (beta = 0.11, n.s.)
H4a: Age will negatively moderate the influence of
facilitating conditions on computer usage behavior
H4b: Experience will negatively moderate the influence
of facilitating conditions on computer usage behavior
No: (beta = 0.53, p 0.001) Refuted No: (beta = 0.00, n.s.)

supported. The ‘VENKATESH FINDING’ column
indicates the corresponding relationship presented in
the UTAUT study.
5.1. Findings
As suggested by Venkatesh et al., we found that
performance expectancy had a positive effect on
intention, but we found no interacting effect with
performance expectancy and either gender or age on
intention.
Also we found that effort expectancy did not have a
significant effect on intention in the presence of
interactions with the moderating variables. The negative
interaction between effort expectancy and experience
on intention indicated that, with increased years of
experience with computers, ease of use becomes less
important in predicting Saudi’s behavioral intentions.
In cultures characterized by a high power distance
dimension, we argued that individuals would be more
inclined to show deference to authority and conform to
the expectations of others in important or superior roles.
Consequently, we expected that higher power distance
cultures would exhibit a stronger association between
subjective norm and behavioral intention. We also
argued that the low individualism country score for
Saudi Arabia might indicate a strong relationship
between subjective norms and behavioral intentions in
the Arab world. In Fig. 4, subjective norm positively
influences intention, but negatively interacts with
increasing levels of age and experience on intention.
These results indicate that, among Saudi users,
subjective norm positively influences intention, but,
as expected, this influence is diminished by both
increasing age, and increasing years of experience using
computers.
The weak negative effect of facilitating conditions
on use was not significant in the presence of: the
negative interacting effect of increasing age with
facilitating conditions on use; and the strong positive
interacting effect of increasing experience with facil-
itating conditions on use.
5.2. Limitations
Our study was not longitudinal in design, and did not
target intentions and behaviors with respect to applica-
tion software use. Further, we substituted subjective
norm for social influence. We also hypothesized cultural
affects on the basis of Hofstede’s cultural dimensions.
Although this approach has been proven in general, it
would be more informative if measures of cultural-
specific work values were collected at the individual
level, commensurate with the survey data.
References
[1] I. Ajzen, The theory of planned behavior, Organizational Beha-
vior and Human Decision Processes 50, 1991, pp. 179–211.
[2] S.S. Al-Gahtani, Computer technology acceptance success fac-
tors in Saudi Arabia: an exploratory study, Journal of Global
Information Technology Management 7 (1), 2004, pp. 5–29.
[3] R. Brislin, The wording and translation of research instruments,
in: W. Lonner, J. Berry (Eds.), Field Methods in Cross-Cultural
Research, Sage, Beverly Hills, 1986.
[4] A. Burton-Jones, G.S. Hubona, The mediation of external vari-
ables in the technology acceptance model, Information Man-
agement 43 (6), 2006, pp. 706–717.
[5] W.W. Chin, PLS-Graph User’s Guide, Version 3.0, 2001.
[6] W.W. Chin, B.L. Marcolin, P.R. Newsted, A partial least squares
latent variable modeling approach for measuring interaction
effects: results from a Monte Carlo simulation study and voice
mail emotion/adoption study, in: J.I. DeGross, A. Srinivasan, S.
Jarvenpaa (Eds.), in: Proceedings of the International Confer-
ence on Information Systems, Cleveland, OH, 1996, pp. 21–41.
[7] W.W. Chin, B.L. Marcolin, P.R. Newsted, A partial least squares
latent variable modeling approach for measuring interaction
effects: results from a Monte Carlo simulation study and an
electronic-mail emotion/adoption study, Information Systems
Research 14 (2), 2003, pp. 189–217.
[8] D. Compeau, C.A. Higgins, S. Huff, Social cognitive theory and
individual reactions to computing technology: a longitudinal
study, MIS Quarterly 23 (2), 1999, pp. 145–158.
[9] F. Davis, Perceived usefulness, perceived ease of use, and end
user acceptance of information technology, MIS Quarterly 13
(3), 1989, pp. 318–339.
[10] F.D. Davis, R.P. Bagozzi, P.R. Warshaw, User acceptance of
computer technology: a comparison of two theoretical models,
Management Science 35, 1989, pp. 982–1003.
[11] M. Fishbein, I. Ajzen, Belief, Attitude, Intention and Behavior:
An Introduction to Theory and Research, Addison-Wesley,
Reading, MA, 1975.
[12] C. Fornell, D. Larcker, Evaluating structural equation models
with unobservable variables and measurement error, Journal of
Marketing Research 18, 1981, pp. 39–50.
[13] B. Hasan, Delineating the effects of general and system-specific
computer self-efficacy beliefs on IS acceptance, Information
Management 43 (5), 2006, pp. 565–571.
[14] G. Hofstede, Culture’s Consequences: International Differences
in Work-Related Values, Sage, Beverly Hills, CA, 1980.
[15] G. Hofstede, Culture’s Consequences: Comparing Values, Beha-
viors, Institutions and Organizations Across Nations, Sage,
Newbury Park, CA, 2001.
[16] M. Igbaria, N. Zinatelli, P. Cragg, A.L.M. Cavaye, Personal
computing acceptance factors in small firms: a structural equa-
tion model, MIS Quarterly 21 (3), 1997, pp. 279–305.
[17] K. Loch, D. Straub, S. Kamel, Diffusing the Internet in the Arab
world: the role of social norms and technological culturation,
IEEE Transactions on Engineering Management 50, 2003, pp.
45–63.
[18] K. Mathiesen, Predicting user intentions: comparing the tech-
nology acceptance model with the theory of planned behavior,
Information Systems Research 2, 1991, pp. 173–191.
690

[19] J.C. Nunnally, Psychometric Theory, McGraw Hill, New York,
1978.
[20] G. Rose, D. Straub, Predicting general IT use: applying TAM to
the Arab world, Journal of Global Information Management 6,
1998, pp. 39–46.
[21] D. Straub, Toward a theory-based measurement of culture,
Journal of Global Information Management 10 (1), 2002, pp.
24–32.
[22] D. Straub, M. Keil, W. Brenner, Testing the technology accep-
tance model across cultures: a three country study, Information
Management 33, 1997, pp. 1–11.
[23] D.W. Straub, K. Loch, C. Hill, Transfer of information technol-
ogy to the Arab world: a test of cultural influence modeling,
Journal of Global Information Management 9, 2001, pp. 6–28.
[24] S. Taylor, P.A. Todd, Understanding information technology
usage: a test of competing models, Information Systems
Research 6 (2), 1995, pp. 144–176.
[25] H. Triandis, Dimensions of cultural variation as parameters for
organizational theories, International Studies of Management
and Organization 12 (4), 1982, pp. 139–159.
[26] V. Venkatesh, F.D. Davis, A theoretical extension of the tech-
nology acceptance model: four longitudinal field studies, Man-
agement Science 46 (2), 2000, pp. 186–204.
[27] V. Venkatesh, M.G. Morris, G.B. Davis, F.D. Davis, User
acceptance of information technology: toward a unified view,
MIS Quarterly 27 (3), 2003, pp. 425–478.
Said S. Al-Gahtani is an associate profes-
sor of computer information systems in the
Department of Administrative Sciences at
King Khalid University, Abha, Saudi Ara-
bia. He has a BSc in systems engineering
from King Fahad University of Petroleum
Minerals, MSc in computer sciences
from Atlanta University, Atlanta, Georgia,
and a PhD in computer-based information
systems from Lougborough University,
Loughborough, UK. His research interests include the user acceptance
of information technologies, the modeling of IT acceptance, end-user
computing, and organizational cross-cultural research. He has pub-
lished journal research articles in Information Technology People,
Journal of Global Information Technology Management, Information
Technology for Development, Information Resources Management
Journal, and the Behaviour Information Technology.
Geoffrey S. Hubona is an associate pro-
fessor of computer information systems in
the J. Mack Robinson College of Business
at Georgia State University in Atlanta, GA.
He has a BA in psychology from the Uni-
versity of Virginia, an MBA from George
Mason University, and an MA in economics
and a PhD in MIS from the University of
South Florida. His research interests
include the user acceptance of information
technologies, the human perception of computer visualizations, and
technology usability issues. He has published journal research articles
in Information Management, Information Technology People,
ACM Transactions on Computer–Human Interaction, IEEE Transac-
tions on Systems, Man and Cybernetics, Part A: Systems and Humans,
International Journal of Human–Computer Studies, The DATA BASE
for Advances in Information Systems, International Journal of Tech-
nology and Human Interaction, and the Journal of Information
Technology Management.
Jijie Wang is a PhD candidate in the
Department of Computer Information Sys-
tems at Georgia State University. She got
her bachelors degree in accounting from
Beijing University, P.R. China, and master
degree in CIS from Georgia State Univer-
sity. Her work has been presented at Amer-
icas Conference on Information Systems
and Information Resources Management
Association International Conference.
She has published papers in Decision Sciences, Information Resource
Management Journal, and the Communications of the Association for
Information Systems, and book chapters in Encyclopedia of E-Com-
merce, E-Government, and Mobile Commerce and Human-Computer
Interaction and Management Information Systems: Applications. Her
dissertation focuses on organizational controls in mobile virtual work.
Her other research interests include IT project management, open
source software community, and research methodology in information
systems research.

Reporting moderator in PLSFormative Indication.pdf

More Related Content

Similar to Reporting moderator in PLSFormative Indication.pdf (20)

More from amitcms1 (7)

Recently uploaded (20)

Reporting moderator in PLSFormative Indication.pdf