behavioral intention except for the variable habit (H). Of six
variables (i.e. PE, EE, FC, SI, HM, and H), FC provides the
most significant contribution to instructors’ behavioral
intention followed by PE and HM. Thus, while considering
the adoption of LMS, FC, PE, and HM are the critical factors
and form the basis of any successful adoption of LMS
endeavor in the Middle Eastern region.
8.8 Summary
In this chapter, we addressed many research questions about
the adoption of LMS technology. We analyzed both direct
and indirect factors responsible for adoption LMS in higher
educational institutions. Then we presented our tested model,
Table 8.1 Summary of the
findings
RQ
Statement
Findings
1
To what extent (if any) is ‘behavioral intention’
the predictor of ‘use behavior’ of LMS
technology at HEIs of Saudi Arabia?
The quantitative findings support that
behavioral intention is a strong predictor of the
actual use of the LMS
2
To what extent (if any) do independent
variables (PE, EE, SI, FC, HM, and H) impact
instructors’ behavioral intention to adopt LMS
technology at HEIs?
The constructs PE, EE, SI, FC, and HM were
found to be the significant predictors of the
behavioral intention, except the construct H
3
This research question was framed to explore
which out of the six independent variables (EE,
PE, FC, SI, HM, and H) delivers the most
significant contribution to instructors’
behavioral intention to use an LMS
Of the six UTAUT2’s independent variables,
FC provides the highest significant contribution
to the instructor’s BI to use the LMS, while PE
provides the second-highest significant
contribution to the instructor’s BI to use the
LMS
4
To what extent (if any) do moderating variables
moderate the relationship between the
independent and dependent variables?
Age as a Moderator of LMS adoption: Age
was not found to be the moderator in
quantitative analysis
Experience as a moderator of LMS
adoption: Experience was found to be a
moderator on the overall model and it
moderates the effect of FC to BI and FC to UB
Technology awareness as a moderator of
LMS adoption: TA was found to be a
moderator on the overall model and moderates
the effect of EE on BI, SI on BI, and M on BI
Power distance as a moderator of LMS
adoption: In the quantitative strand, the power
distance was found to be a moderator on the
overall model, and it moderates the effect of SI
to BI, M to BI, and FC to BI
Uncertainty avoidance as a moderator of
LMS adoption: In the quantitative strand, it
was found that UA moderates the model. It
moderates the effect of EE on BI, and FC on BI
Individualism as a moderator of LMS
adoption: In the quantitative strand, no
moderating impact was found on the model
level
Masculinity as a moderator of LMS
adoption: In the quantitative study, no
moderating impact was observed on the model
level or path level
8.7 UTAUT2 Tested Model
79
variables (i.e. PE, EE, FC, SI, HM, and H), FC provides the
most significant contribution to instructors’ behavioral
intention followed by PE and HM. Thus, while considering
the adoption of LMS, FC, PE, and HM are the critical factors
and form the basis of any successful adoption of LMS
endeavor in the Middle Eastern region.
8.8 Summary
In this chapter, we addressed many research questions about
the adoption of LMS technology. We analyzed both direct
and indirect factors responsible for adoption LMS in higher
educational institutions. Then we presented our tested model,
Table 8.1 Summary of the
findings
RQ
Statement
Findings
1
To what extent (if any) is ‘behavioral intention’
the predictor of ‘use behavior’ of LMS
technology at HEIs of Saudi Arabia?
The quantitative findings support that
behavioral intention is a strong predictor of the
actual use of the LMS
2
To what extent (if any) do independent
variables (PE, EE, SI, FC, HM, and H) impact
instructors’ behavioral intention to adopt LMS
technology at HEIs?
The constructs PE, EE, SI, FC, and HM were
found to be the significant predictors of the
behavioral intention, except the construct H
3
This research question was framed to explore
which out of the six independent variables (EE,
PE, FC, SI, HM, and H) delivers the most
significant contribution to instructors’
behavioral intention to use an LMS
Of the six UTAUT2’s independent variables,
FC provides the highest significant contribution
to the instructor’s BI to use the LMS, while PE
provides the second-highest significant
contribution to the instructor’s BI to use the
LMS
4
To what extent (if any) do moderating variables
moderate the relationship between the
independent and dependent variables?
Age as a Moderator of LMS adoption: Age
was not found to be the moderator in
quantitative analysis
Experience as a moderator of LMS
adoption: Experience was found to be a
moderator on the overall model and it
moderates the effect of FC to BI and FC to UB
Technology awareness as a moderator of
LMS adoption: TA was found to be a
moderator on the overall model and moderates
the effect of EE on BI, SI on BI, and M on BI
Power distance as a moderator of LMS
adoption: In the quantitative strand, the power
distance was found to be a moderator on the
overall model, and it moderates the effect of SI
to BI, M to BI, and FC to BI
Uncertainty avoidance as a moderator of
LMS adoption: In the quantitative strand, it
was found that UA moderates the model. It
moderates the effect of EE on BI, and FC on BI
Individualism as a moderator of LMS
adoption: In the quantitative strand, no
moderating impact was found on the model
level
Masculinity as a moderator of LMS
adoption: In the quantitative study, no
moderating impact was observed on the model
level or path level
8.7 UTAUT2 Tested Model
79