instructors’ behavioral intention. The findings show that
technical support and facilitating the environment are given
importance by all instructors. This implies that the instructors appear to realize the efforts of management in providing
all resources and support. The availability of resources and
technical support will encourage the instructors to use the
LMS. This, in turn, will improve the instructors’ perceptions
about using the LMS in their teaching. The second most
influential variable is PE. This reflects that instructors realized the LMS to be useful in teaching and learning and have
more inclination to use it.
8.5 Demographics and Cultural Dimensions
Here we address the question: To what extent (if any) do
moderating variables, moderate the relationship between the
independent and dependent variables?
A moderator is a quantitative or qualitative variable that
influences the strength and direction of the relationship
between two other variables (Kripanont, 2007; Lakhal,
Khechine, & Pascot, 2013; Schaper, & Pervan, 2007). The
moderators of the original UTAUT2 model are age, gender,
and experience (Baptista & Oliveira, 2015; Venkatesh et al.,
2012). However, for this study, the UTAUT2 model is
extended with ‘technology awareness’ (Hall & Hord, 2011)
and ‘cultural dimensions’ (Bandyopadhyay & Fraccastoro,
2007; Al Gahtani et al., 2007) as moderating variables
(Kripanont, 2007; Tibenderana, & Ogao, 2009).
Here we will discuss the findings of age, experience,
awareness, and cultural dimensions as the moderators of the
UTAUT2 model.
8.5.1 Age as a Moderator of LMS Adoption
In the overall moderation test using SPSS/AMOS, it was
found that at the model level, there is no moderating impact
of age (insignificant p = 0.169) on behavioral intention to
adopt LMS. The literature also shows conflicting findings of
the age as a moderating variable. Burton-Jones and Hubona
(2006) found that age is a significant moderator (Al-Gahtani
et al., 2007; Tosunta et al., 2015; Venkatesh et al., 2012) in
one technology but insignificant (Hsbollah & Idris, 2009) in
another technology. Therefore, there is a need to investigate
qualitatively whether age plays an important role in technology adoption (Gibson, Harris, & Colaric, 2008). However, in this study, age is not one of the moderators of BI
showing that there is no impact of age on the adoption of
LMS.
8.5.2 Experience as a Moderator of LMS
Adoption
In an overall moderation test at the model level, it was found
that experience moderates (significant p = 0.01) the effect of
independent variables on the behavioral intention. This
shows that there is a difference between two groups of
instructors (of low-experience and high-experience) in the
adoption of an LMS. The path-by-path test discovered that
experience moderates the effect of FC on BI and FC on UB.
Experience moderates the effect of FC on UB (t = 2.8***,
p < 0.01). It was found that the relationship between FC and
UB is significantly moderated both by high experience
(0.259 at p < 0.05) and low experience (0.187 at p < 0.01).
Experience moderates the effect of FC on BI (t = 1.661*,
p < 0.1). These results show that the relationship between
FC and BI is significantly moderated by high experience
(0.374 at p < 0.01) as well as low experience (0.155 at
p < 0.05).
The above finding shows that experience affects both
paths of FC (i.e. FC to BI and FC to UB). This implied that
better technical support and facilitating environments
encourage instructors to adopt LMS in their teaching. The
finding of this study overlaps with the results of the original
model by Venkatesh et al., (2012) and Taylor and Todd
(1995). The finding of this study is also supported by the
findings of Al-Gahtani et al. (2007), Tosunta et al. (2015),
Taylor, and Todd (1995). “It is quite likely that as FC deals
with broader infrastructure and support issues, it will always
be important to those who value it even if they have significant experience with the target technology” (Venkatesh
et al., 2012). Therefore, the experience should be considered
to predict the behavioral intention of instructors to use LMS.
In sum, the level of experience moderates the overall model
(i.e. UTAUT2). It moderates the relationship between FC
and BI as well as FC and UB to use the LMS.
8.5.3 Technology Awareness as a Moderator
of LMS Adoption
This study uses technology awareness as a moderator of
technology adoption as suggested by Faruq and Ahmad
(2013), Rehman, Esichaikul, and Kamal (2012), Sun and
Fang (2016). In the overall moderation test, the significant pvalue (= 0.099) shows that awareness has a moderating
impact on the behavioral intention at the model level. Furthermore, the path-by-path moderation test showed moderation by three paths: First, the awareness moderates the
effect of EE on BI (t = 2.04**, p < 0.05). It may be noted
76
8 Adoption of LMS: Evidence from the Middle East
technical support and facilitating the environment are given
importance by all instructors. This implies that the instructors appear to realize the efforts of management in providing
all resources and support. The availability of resources and
technical support will encourage the instructors to use the
LMS. This, in turn, will improve the instructors’ perceptions
about using the LMS in their teaching. The second most
influential variable is PE. This reflects that instructors realized the LMS to be useful in teaching and learning and have
more inclination to use it.
8.5 Demographics and Cultural Dimensions
Here we address the question: To what extent (if any) do
moderating variables, moderate the relationship between the
independent and dependent variables?
A moderator is a quantitative or qualitative variable that
influences the strength and direction of the relationship
between two other variables (Kripanont, 2007; Lakhal,
Khechine, & Pascot, 2013; Schaper, & Pervan, 2007). The
moderators of the original UTAUT2 model are age, gender,
and experience (Baptista & Oliveira, 2015; Venkatesh et al.,
2012). However, for this study, the UTAUT2 model is
extended with ‘technology awareness’ (Hall & Hord, 2011)
and ‘cultural dimensions’ (Bandyopadhyay & Fraccastoro,
2007; Al Gahtani et al., 2007) as moderating variables
(Kripanont, 2007; Tibenderana, & Ogao, 2009).
Here we will discuss the findings of age, experience,
awareness, and cultural dimensions as the moderators of the
UTAUT2 model.
8.5.1 Age as a Moderator of LMS Adoption
In the overall moderation test using SPSS/AMOS, it was
found that at the model level, there is no moderating impact
of age (insignificant p = 0.169) on behavioral intention to
adopt LMS. The literature also shows conflicting findings of
the age as a moderating variable. Burton-Jones and Hubona
(2006) found that age is a significant moderator (Al-Gahtani
et al., 2007; Tosunta et al., 2015; Venkatesh et al., 2012) in
one technology but insignificant (Hsbollah & Idris, 2009) in
another technology. Therefore, there is a need to investigate
qualitatively whether age plays an important role in technology adoption (Gibson, Harris, & Colaric, 2008). However, in this study, age is not one of the moderators of BI
showing that there is no impact of age on the adoption of
LMS.
8.5.2 Experience as a Moderator of LMS
Adoption
In an overall moderation test at the model level, it was found
that experience moderates (significant p = 0.01) the effect of
independent variables on the behavioral intention. This
shows that there is a difference between two groups of
instructors (of low-experience and high-experience) in the
adoption of an LMS. The path-by-path test discovered that
experience moderates the effect of FC on BI and FC on UB.
Experience moderates the effect of FC on UB (t = 2.8***,
p < 0.01). It was found that the relationship between FC and
UB is significantly moderated both by high experience
(0.259 at p < 0.05) and low experience (0.187 at p < 0.01).
Experience moderates the effect of FC on BI (t = 1.661*,
p < 0.1). These results show that the relationship between
FC and BI is significantly moderated by high experience
(0.374 at p < 0.01) as well as low experience (0.155 at
p < 0.05).
The above finding shows that experience affects both
paths of FC (i.e. FC to BI and FC to UB). This implied that
better technical support and facilitating environments
encourage instructors to adopt LMS in their teaching. The
finding of this study overlaps with the results of the original
model by Venkatesh et al., (2012) and Taylor and Todd
(1995). The finding of this study is also supported by the
findings of Al-Gahtani et al. (2007), Tosunta et al. (2015),
Taylor, and Todd (1995). “It is quite likely that as FC deals
with broader infrastructure and support issues, it will always
be important to those who value it even if they have significant experience with the target technology” (Venkatesh
et al., 2012). Therefore, the experience should be considered
to predict the behavioral intention of instructors to use LMS.
In sum, the level of experience moderates the overall model
(i.e. UTAUT2). It moderates the relationship between FC
and BI as well as FC and UB to use the LMS.
8.5.3 Technology Awareness as a Moderator
of LMS Adoption
This study uses technology awareness as a moderator of
technology adoption as suggested by Faruq and Ahmad
(2013), Rehman, Esichaikul, and Kamal (2012), Sun and
Fang (2016). In the overall moderation test, the significant pvalue (= 0.099) shows that awareness has a moderating
impact on the behavioral intention at the model level. Furthermore, the path-by-path moderation test showed moderation by three paths: First, the awareness moderates the
effect of EE on BI (t = 2.04**, p < 0.05). It may be noted
76
8 Adoption of LMS: Evidence from the Middle East