enforced by the top management, then everyone will use it.
It was also revealed that some instructors use LMS due to
the fear of bad evaluation by their students.
8.3.4 Facilitating Conditions (FC)
A facilitating condition (FC) is “the degree to which an
individual believes that an organizational and technical
infrastructure exists to support the use of the system”
(Venkatesh et al., 2003, p. 453). In the quantitative analysis,
two links (FC!BI and FC!UB) of the model were
examined. The relationship between FC to use behavior
(UB) was a non-significant link. This is an unexpected
finding and is inconsistent with the findings of Venkatesh,
Thong, and Xu (2012) who argued that FC influences both
BI and UB. Similarly, many researchers such as (Baptista &
Oliveira, 2015; Gawande, 2015; Im, Hong, & Kang, 2011)
have found that FC does not influence UB. However, other
researchers (Abdullah & Khanam, 2016; Al-Gahtani et al.,
2007; Al-Somali et al., 2009; Alalwan & Williams, 2014;
Kang et al., 2015; Oshlyansky et al., 2007; Tosunta et al.,
2015; Wu, Hsu, & Hwang, 2007) have concluded that the
facilitating conditions had positive effects on the usage
(UB) of the technology. Hence, there is somewhat of a
contradiction in previous studies concerning the relationship
between FC and UB. Many researchers (e.g. Bandyopadhyay & Fraccastoro, 2007) even excluded FC from their
research study. However, this study found that FC is a
positive and significant predictor of BI (p < 0.05). Of the six
UTAUT2’s independent variables (i.e. PE, EE, SI, FC, HM,
and H), FC provides the highest (0.272) contribution to
instructors’ BI to adopt the LMS. The majority of the past
studies (AbuShanab, Pearson, & Setterstrom, 2010; Eckhardt, Laumer, & Weitzel, 2009; Kang et al., 2015; San
Martin, & Herrero, 2012; Tosunta et al., 2015) support these
findings that there is a significant association between FC
and BI. Thus, in the quantitative study, facilitating conditions found to have the strongest relationship with BI and act
as enablers or motivators of LMS adoption. The technical
support is a significant factor in the adoption of new technology (Porter & Graham, 2016). The significant results of
facilitating conditions reflect that the existence of technical
support and supportive infrastructure is of prime importance
to help and support instructors to use LMS in their teaching.
8.3.5 Hedonic Motivation (HM)
Hedonic motivation (HM) is “the fun or pleasure derived
from using technology” and “plays an important role in
determining technology acceptance and use” (Venkatesh
et al., 2012, p. 161). The quantitative results show a strong
and positive relationship between hedonic motivation
(HM) and behavioral intention (p < 0.01). These results
show a strong significant positive relationship between
hedonic motivation and behavioral intention to use an LMS.
The finding of this study is supported by previous
researchers, such as (Alalwan & Williams, 2014; Baptista &
Oliveira, 2015; Kang et al., 2015; Raman & Don, 2013;
Vinodh & Mathew, 2012; Yang, 2013; Venkatesh et al.,
2012). The reason for enjoyment could be due to the usefulness and novel features of the LMS such as chatting,
interaction, and instant feedback of LMS, which leads
instructors and students to experience pleasure and to consider using this technology to be enjoyable. In sum, it can be
concluded that the hedonic motivation has a strong relationship with BI and is one of the core constructs of LMS
adoption. This implies that the possibility of LMS adoption
will increase among instructors who believe that using LMS
is enjoyable, pleasurable, and entertaining.
8.3.6 Habit (H)
Habit is the automaticity of behavior connected with the use
of technology over time. The previous use of technology
becomes a habit, and habit becomes a strong predictor of
future use (Kim & Malhotra, 2005). Previous studies show
that an individual’s habit is a major predictor of intention to
use a technology (Baptista & Oliveira, 2015). In the quantitative analysis, construct H (habit) showed poor values of
reliability and convergent validity. This might be because
there is no comparison of the functionality of an LMS with a
smartphone that is used extensively in our everyday life.
This reflects that unlike smartphones, LMS is a teaching tool
used only for teaching. Hence, it can be stated that the
construct habit might be a valid construct in the context of
mobile phones or smart devices but not in the case of LMS.
Perhaps, this reason could be the cause of poor reliability
and low significance of the habit construct in the quantitative
analysis. Hence, this construct should not be part of the LMS
adoption model. Therefore, the removal of the ‘habit’ constructs from the proposed UTAUT2 model would be an
appropriate decision in the context of this study.
8.4 The Most Significant Variable(s)
for the Adoption of LMS at HEIs
Here we address the question: 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?
Of the six independent variables, ‘facilitating conditions’
(FC) provides the most significant contribution to
8.3 Influence of Independent Variables on Behavioral Intention (BI)
75
It was also revealed that some instructors use LMS due to
the fear of bad evaluation by their students.
8.3.4 Facilitating Conditions (FC)
A facilitating condition (FC) is “the degree to which an
individual believes that an organizational and technical
infrastructure exists to support the use of the system”
(Venkatesh et al., 2003, p. 453). In the quantitative analysis,
two links (FC!BI and FC!UB) of the model were
examined. The relationship between FC to use behavior
(UB) was a non-significant link. This is an unexpected
finding and is inconsistent with the findings of Venkatesh,
Thong, and Xu (2012) who argued that FC influences both
BI and UB. Similarly, many researchers such as (Baptista &
Oliveira, 2015; Gawande, 2015; Im, Hong, & Kang, 2011)
have found that FC does not influence UB. However, other
researchers (Abdullah & Khanam, 2016; Al-Gahtani et al.,
2007; Al-Somali et al., 2009; Alalwan & Williams, 2014;
Kang et al., 2015; Oshlyansky et al., 2007; Tosunta et al.,
2015; Wu, Hsu, & Hwang, 2007) have concluded that the
facilitating conditions had positive effects on the usage
(UB) of the technology. Hence, there is somewhat of a
contradiction in previous studies concerning the relationship
between FC and UB. Many researchers (e.g. Bandyopadhyay & Fraccastoro, 2007) even excluded FC from their
research study. However, this study found that FC is a
positive and significant predictor of BI (p < 0.05). Of the six
UTAUT2’s independent variables (i.e. PE, EE, SI, FC, HM,
and H), FC provides the highest (0.272) contribution to
instructors’ BI to adopt the LMS. The majority of the past
studies (AbuShanab, Pearson, & Setterstrom, 2010; Eckhardt, Laumer, & Weitzel, 2009; Kang et al., 2015; San
Martin, & Herrero, 2012; Tosunta et al., 2015) support these
findings that there is a significant association between FC
and BI. Thus, in the quantitative study, facilitating conditions found to have the strongest relationship with BI and act
as enablers or motivators of LMS adoption. The technical
support is a significant factor in the adoption of new technology (Porter & Graham, 2016). The significant results of
facilitating conditions reflect that the existence of technical
support and supportive infrastructure is of prime importance
to help and support instructors to use LMS in their teaching.
8.3.5 Hedonic Motivation (HM)
Hedonic motivation (HM) is “the fun or pleasure derived
from using technology” and “plays an important role in
determining technology acceptance and use” (Venkatesh
et al., 2012, p. 161). The quantitative results show a strong
and positive relationship between hedonic motivation
(HM) and behavioral intention (p < 0.01). These results
show a strong significant positive relationship between
hedonic motivation and behavioral intention to use an LMS.
The finding of this study is supported by previous
researchers, such as (Alalwan & Williams, 2014; Baptista &
Oliveira, 2015; Kang et al., 2015; Raman & Don, 2013;
Vinodh & Mathew, 2012; Yang, 2013; Venkatesh et al.,
2012). The reason for enjoyment could be due to the usefulness and novel features of the LMS such as chatting,
interaction, and instant feedback of LMS, which leads
instructors and students to experience pleasure and to consider using this technology to be enjoyable. In sum, it can be
concluded that the hedonic motivation has a strong relationship with BI and is one of the core constructs of LMS
adoption. This implies that the possibility of LMS adoption
will increase among instructors who believe that using LMS
is enjoyable, pleasurable, and entertaining.
8.3.6 Habit (H)
Habit is the automaticity of behavior connected with the use
of technology over time. The previous use of technology
becomes a habit, and habit becomes a strong predictor of
future use (Kim & Malhotra, 2005). Previous studies show
that an individual’s habit is a major predictor of intention to
use a technology (Baptista & Oliveira, 2015). In the quantitative analysis, construct H (habit) showed poor values of
reliability and convergent validity. This might be because
there is no comparison of the functionality of an LMS with a
smartphone that is used extensively in our everyday life.
This reflects that unlike smartphones, LMS is a teaching tool
used only for teaching. Hence, it can be stated that the
construct habit might be a valid construct in the context of
mobile phones or smart devices but not in the case of LMS.
Perhaps, this reason could be the cause of poor reliability
and low significance of the habit construct in the quantitative
analysis. Hence, this construct should not be part of the LMS
adoption model. Therefore, the removal of the ‘habit’ constructs from the proposed UTAUT2 model would be an
appropriate decision in the context of this study.
8.4 The Most Significant Variable(s)
for the Adoption of LMS at HEIs
Here we address the question: 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?
Of the six independent variables, ‘facilitating conditions’
(FC) provides the most significant contribution to
8.3 Influence of Independent Variables on Behavioral Intention (BI)
75