definition of technology awareness understanding by Rogers
(1995), this study builds and uses a new variable ‘technology
awareness’, and defines it as the instructor’s knowledge
about the existence, features, benefit, and using the LMS in
his teaching (Faruq & Ahmad, 2013). Some researchers
(Faruq & Ahmad, 2013; Rehman et al., 2012; Sun, & Fang,
2016) used awareness as a moderator. In this study ‘technology awareness’ is considered as a moderator between
independent and dependent variables of technology adoption
model (i.e., UTAUT2).
5.8.5 Culture (As a Moderating Variable)
In a study, Baptista and Oliveira (2015) used Hofstede’s
cultural dimensions as moderator of the technology adoption and found that individualism/collectivism, power distance, and uncertainty avoidance have a strong influence on
user’s intention to use the technology. Baptista and Oliveira,
(2015) found that Venkatesh et al. (2012) combined Hofstede’s cultural dimensions as moderators with the
UTAUT2 model in the acceptance of mobile banking to
achieve the strengths of two theories. Culture is a substantial
moderator of technology adoption (Gallivan & Srite, 2005;
Im, Hong, & Kang, 2011; Martins, Oliveira, & Popovič,
2014) in the context of mobile banking (Abdullah et al.,
2016). Rajapakse (2011) proved that “culture is a direct
construct of behavioral intention”. It was proposed by
Rajapakse (2011) to extend the UTAUT model by including
culture as the fifth determinant, whereas much other
research on the adoption of technology has considered
culture as one of the possible moderating variables (Venkatesh & Zhang, 2010) that can accelerate or slow down the
adoption process (Robey & Rodriguez, 1989). In addition to
the validation of UTAUT, Al-Gahtani et al. (2007) also
examined the cultural differences and similarities between
Saudi Arabia and North America. Al-Gahtani et al. (2007)
and Venkatesh et al. (2003) determined that performance
expectancy (PE) has a significant effect on behavioral
intention, but they found no moderation of age or gender
between intention and behavioral intention. Al-Gahtani
et al. (2007) also discovered that effort expectancy
(EE) has insignificant impact on behavior intention (BI) in
the presence of moderating variables. In UTAUT2, cultural
moderators were integrated to evaluate the impact of culture
in a mobile banking context (Baptista & Oliveira, 2015).
They discovered that power distance, uncertainty avoidance,
and collectivism were the most significant cultural moderators of variables of UTAUT2. Saudi culture was added to
the list of moderating variables by Alzahrani and Goodwin,
(2012). In an African country, Mozambique, Hofstede’s
dimensions as moderators were combined with the
UTAUT2 model in a mobile banking context. Baptista and
Oliveira (2015) supported the integration of more theories to
achieve the strengths of different theories. Alzahrani and
Goodwin (2012) also supported that cultural construct for
KSA can be added as moderating variables. In order to
assess the UTAUT model in relation to Hofstede’s (1980)
cultural dimensions in non-Western countries, AbuShanab,
Pearson and Setterstrom (2009) conducted a quantitative
study of over 500 Internet banking customers in three Jordanian banks. The results of their study confirmed the cultural influence on technology adoption. Their study
indicated that effort expectancy (EE), performance expectancy (PE), and social influence (SI) were moderated by
gender. The results also showed that the impact of performance expectancy (PE) is stronger for males, and the impact
of social influence (SI) and effort expectancy (EE) is
stronger for females, confirming the results of Al-Gahtani
et al. (2007). Al-Gahtani et al. (2007) argued that the low
individualism index for KSA indicates a strong relationship
between behavioral intentions and subjective norms in the
Arab world. Although Al-Gahtani et al. (2007) conducted
their study on Saudi culture and examined the UTAUT
model. Their study was not a longitudinal study of design
and needed further validation (Hew et al., 2016). The above
review of the literature supports the fact that cultural aspects
are missing from most of the technology adoption models,
especially from the UTAUT2 model.
5.9 Research Questions
The overall objective of this study was to assess the predictors and moderators of the proposed model that influence
the adoption and the use of LMS technology among
instructors in the HEIs. Based on the above discussion, the
following research questions (RQ) and theoretical framework are derived.
Main Research Question: To what extent do independent variables (such as EE, PE, FC, SI HM, and H) and
moderating variables (such as age, experience, gender,
technology awareness, and cultural dimensions) of the proposed model influence instructors’ behavioral intentions to
use an LMS in the HEIs? The main RQ has the following
sub-research questions:
RQ 1: To what extent (if any) is behavioral intention (BI) a
predictor of use behavior (UB) of LMS technology at HEIs?
RQ 2: To what extent (if any) do independent variables (EE,
PE, SI, FC, HM, and H) impact instructors’ behavioral
intentions to adopt an LMS at HEIs?
42
5 Technology Adoption Theories and Models
(1995), this study builds and uses a new variable ‘technology
awareness’, and defines it as the instructor’s knowledge
about the existence, features, benefit, and using the LMS in
his teaching (Faruq & Ahmad, 2013). Some researchers
(Faruq & Ahmad, 2013; Rehman et al., 2012; Sun, & Fang,
2016) used awareness as a moderator. In this study ‘technology awareness’ is considered as a moderator between
independent and dependent variables of technology adoption
model (i.e., UTAUT2).
5.8.5 Culture (As a Moderating Variable)
In a study, Baptista and Oliveira (2015) used Hofstede’s
cultural dimensions as moderator of the technology adoption and found that individualism/collectivism, power distance, and uncertainty avoidance have a strong influence on
user’s intention to use the technology. Baptista and Oliveira,
(2015) found that Venkatesh et al. (2012) combined Hofstede’s cultural dimensions as moderators with the
UTAUT2 model in the acceptance of mobile banking to
achieve the strengths of two theories. Culture is a substantial
moderator of technology adoption (Gallivan & Srite, 2005;
Im, Hong, & Kang, 2011; Martins, Oliveira, & Popovič,
2014) in the context of mobile banking (Abdullah et al.,
2016). Rajapakse (2011) proved that “culture is a direct
construct of behavioral intention”. It was proposed by
Rajapakse (2011) to extend the UTAUT model by including
culture as the fifth determinant, whereas much other
research on the adoption of technology has considered
culture as one of the possible moderating variables (Venkatesh & Zhang, 2010) that can accelerate or slow down the
adoption process (Robey & Rodriguez, 1989). In addition to
the validation of UTAUT, Al-Gahtani et al. (2007) also
examined the cultural differences and similarities between
Saudi Arabia and North America. Al-Gahtani et al. (2007)
and Venkatesh et al. (2003) determined that performance
expectancy (PE) has a significant effect on behavioral
intention, but they found no moderation of age or gender
between intention and behavioral intention. Al-Gahtani
et al. (2007) also discovered that effort expectancy
(EE) has insignificant impact on behavior intention (BI) in
the presence of moderating variables. In UTAUT2, cultural
moderators were integrated to evaluate the impact of culture
in a mobile banking context (Baptista & Oliveira, 2015).
They discovered that power distance, uncertainty avoidance,
and collectivism were the most significant cultural moderators of variables of UTAUT2. Saudi culture was added to
the list of moderating variables by Alzahrani and Goodwin,
(2012). In an African country, Mozambique, Hofstede’s
dimensions as moderators were combined with the
UTAUT2 model in a mobile banking context. Baptista and
Oliveira (2015) supported the integration of more theories to
achieve the strengths of different theories. Alzahrani and
Goodwin (2012) also supported that cultural construct for
KSA can be added as moderating variables. In order to
assess the UTAUT model in relation to Hofstede’s (1980)
cultural dimensions in non-Western countries, AbuShanab,
Pearson and Setterstrom (2009) conducted a quantitative
study of over 500 Internet banking customers in three Jordanian banks. The results of their study confirmed the cultural influence on technology adoption. Their study
indicated that effort expectancy (EE), performance expectancy (PE), and social influence (SI) were moderated by
gender. The results also showed that the impact of performance expectancy (PE) is stronger for males, and the impact
of social influence (SI) and effort expectancy (EE) is
stronger for females, confirming the results of Al-Gahtani
et al. (2007). Al-Gahtani et al. (2007) argued that the low
individualism index for KSA indicates a strong relationship
between behavioral intentions and subjective norms in the
Arab world. Although Al-Gahtani et al. (2007) conducted
their study on Saudi culture and examined the UTAUT
model. Their study was not a longitudinal study of design
and needed further validation (Hew et al., 2016). The above
review of the literature supports the fact that cultural aspects
are missing from most of the technology adoption models,
especially from the UTAUT2 model.
5.9 Research Questions
The overall objective of this study was to assess the predictors and moderators of the proposed model that influence
the adoption and the use of LMS technology among
instructors in the HEIs. Based on the above discussion, the
following research questions (RQ) and theoretical framework are derived.
Main Research Question: To what extent do independent variables (such as EE, PE, FC, SI HM, and H) and
moderating variables (such as age, experience, gender,
technology awareness, and cultural dimensions) of the proposed model influence instructors’ behavioral intentions to
use an LMS in the HEIs? The main RQ has the following
sub-research questions:
RQ 1: To what extent (if any) is behavioral intention (BI) a
predictor of use behavior (UB) of LMS technology at HEIs?
RQ 2: To what extent (if any) do independent variables (EE,
PE, SI, FC, HM, and H) impact instructors’ behavioral
intentions to adopt an LMS at HEIs?
42
5 Technology Adoption Theories and Models