8
Adoption of LMS: Evidence from the Middle
East
8.1 Introduction
This chapter provides empirical evidence regarding instructors’ adoption of LMS in higher education institutions
(HEIs) in the context of the Middle East in general and Saudi
Arabia in particular. The key research questions that have
been addressed in this chapter are: (i) To what extent (if any)
is behavioral intention (BI) a predictor of use behavior
(UB) of LMS technology at Saudi higher educational institutions (SHEIs)?, (ii) To what extent (if any) do independent
variables (effort expectancy (EE), performance expectancy
(PE), social influence (SI), facilitating condition (FC),
hedonic motivation (HM), and habit (H)) impact instructors’
behavioral intentions to adopt an LMS at SHEIs?,
(iii) Which out of the six independent variables (EE, PE, SI,
FC, HM, and H) delivers the most significant contribution to
instructors’ behavioral intentions to adopt an LMS at
SHEIs?, and (iv) To what extent (if any) do moderating
variables moderate the relationship between dependent and
independent variables? Overall, both direct and indirect
critical factors responsible for users’ adoption of LMS
technology in SHEIs are presented.
8.2 Behavioral Intention and Use Behavior
of LMS Technology
Here we address the question: To what extent (if any) is
‘behavioral intention’ the predictor of ‘use behavior’ of
LMS technology at HEIs of Saudi Arabia?
A fundamental concept behind the ‘adoption’ or ‘actual
usage’ of technology is the ‘intention’ of an individual
(Ajzen, 1991; Taylor & Todd, 1995; Venkatesh & Davis,
2000). The relationship between ‘behavioral intention’
(BI) and ‘use behavior’ (UB) has been extensively used in
information system (IS) research as a success factor of
adoption of the technology (Taylor & Todd, 1995).
UTAUT2 and other models such as UTAUT and TAM
hypothesized that the behavioral intention (BI) to use a
particular technology is determined by the actual use (UB) of
a particular technology. Based on this argument in the literature (see Chap. 2), this study assumes that behavioral
intention is a good predictor of user behavior. Accordingly,
the research question was designed to validate the relationship between behavioral intention and user behavior. The
quantitative results showed that behavioral intention is a
significant predictor of the adoption of the LMS. The standardized regression coefficient (SRC) for the path between
behavioral intention (BI) and use behavior (UB) was found
to be 0.459 (critical ratio = 5.68, p-value < 0.01) showing
that BI is a strong predictor of the UB. The finding of this
research is also supported by the findings of Alalwan and
Williams (2014), Oliveira (2015), and Tosunta, Karada, and
Orhan (2015).
In sum, the literature, and the qualitative results show that
BI is a strong predictor of UB. This research model explains
58% of the variation in BI and 23.5% of the variation in the
user behavior of LMS.
8.3 Influence of Independent Variables
on Behavioral Intention (BI)
Here we address the question: To what extent (if any) do
independent variables (PE, EE, SI, FC, HM, and H) impact
instructors’ behavioral intention to adopt LMS technology at
higher educational institutions?
The purpose of this research question was to examine the
influence of independent variables (PE, EE, SI, FC, HM, and
H) on the dependent variable (instructors’ behavioral intention) to use LMS in their teaching. The influence of independent variables (PE, EE, SI, FC, HM, and H) on the
dependent variable (BI) is discussed in the following
segment.
© Springer Nature Switzerland AG 2021
R. A. Khan and H. Qudrat-Ullah, Adoption of LMS in Higher Educational Institutions of the Middle East,
Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-50112-9_8
73
Adoption of LMS: Evidence from the Middle
East
8.1 Introduction
This chapter provides empirical evidence regarding instructors’ adoption of LMS in higher education institutions
(HEIs) in the context of the Middle East in general and Saudi
Arabia in particular. The key research questions that have
been addressed in this chapter are: (i) To what extent (if any)
is behavioral intention (BI) a predictor of use behavior
(UB) of LMS technology at Saudi higher educational institutions (SHEIs)?, (ii) To what extent (if any) do independent
variables (effort expectancy (EE), performance expectancy
(PE), social influence (SI), facilitating condition (FC),
hedonic motivation (HM), and habit (H)) impact instructors’
behavioral intentions to adopt an LMS at SHEIs?,
(iii) Which out of the six independent variables (EE, PE, SI,
FC, HM, and H) delivers the most significant contribution to
instructors’ behavioral intentions to adopt an LMS at
SHEIs?, and (iv) To what extent (if any) do moderating
variables moderate the relationship between dependent and
independent variables? Overall, both direct and indirect
critical factors responsible for users’ adoption of LMS
technology in SHEIs are presented.
8.2 Behavioral Intention and Use Behavior
of LMS Technology
Here we address the question: To what extent (if any) is
‘behavioral intention’ the predictor of ‘use behavior’ of
LMS technology at HEIs of Saudi Arabia?
A fundamental concept behind the ‘adoption’ or ‘actual
usage’ of technology is the ‘intention’ of an individual
(Ajzen, 1991; Taylor & Todd, 1995; Venkatesh & Davis,
2000). The relationship between ‘behavioral intention’
(BI) and ‘use behavior’ (UB) has been extensively used in
information system (IS) research as a success factor of
adoption of the technology (Taylor & Todd, 1995).
UTAUT2 and other models such as UTAUT and TAM
hypothesized that the behavioral intention (BI) to use a
particular technology is determined by the actual use (UB) of
a particular technology. Based on this argument in the literature (see Chap. 2), this study assumes that behavioral
intention is a good predictor of user behavior. Accordingly,
the research question was designed to validate the relationship between behavioral intention and user behavior. The
quantitative results showed that behavioral intention is a
significant predictor of the adoption of the LMS. The standardized regression coefficient (SRC) for the path between
behavioral intention (BI) and use behavior (UB) was found
to be 0.459 (critical ratio = 5.68, p-value < 0.01) showing
that BI is a strong predictor of the UB. The finding of this
research is also supported by the findings of Alalwan and
Williams (2014), Oliveira (2015), and Tosunta, Karada, and
Orhan (2015).
In sum, the literature, and the qualitative results show that
BI is a strong predictor of UB. This research model explains
58% of the variation in BI and 23.5% of the variation in the
user behavior of LMS.
8.3 Influence of Independent Variables
on Behavioral Intention (BI)
Here we address the question: To what extent (if any) do
independent variables (PE, EE, SI, FC, HM, and H) impact
instructors’ behavioral intention to adopt LMS technology at
higher educational institutions?
The purpose of this research question was to examine the
influence of independent variables (PE, EE, SI, FC, HM, and
H) on the dependent variable (instructors’ behavioral intention) to use LMS in their teaching. The influence of independent variables (PE, EE, SI, FC, HM, and H) on the
dependent variable (BI) is discussed in the following
segment.
© Springer Nature Switzerland AG 2021
R. A. Khan and H. Qudrat-Ullah, Adoption of LMS in Higher Educational Institutions of the Middle East,
Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-50112-9_8
73