8.3.1 Performance Expectancy (PE)
Performance expectancy (PE) is “the degree to which an
individual believes that using the system will help him or her
to attain gains in job performance” (Venkatesh, Morris,
Davis, & Davis, 2003, p. 447). These results reveal that PE
is a significant determinant (p < 0.01) of BI to adopt the
LMS. Of the six independent variables (PE, EE, SI, FC, HM,
and H), PE provided the second highest contribution to
instructors’ behavioral intention to use the LMS. Aside from
the above quantitative findings, the literature also supports
the concept that PE (i.e. usefulness) has a significant positive
correlation with BI. For instance, in a study on computers as
a learning tool, Nistor, Lerche, Weinberger, Ceobanu, and
Heymann (2014) found that the PE has a positive influence
on BI and attitude toward its use (p. E144). In a similar
study, using the UTAUT2 model in the teacher’s acceptance
of LMS, Raman and Don (2013) found that PE has a positive impact on BI. The findings of other researchers
(Abdullah & Khanam, 2016; Alalwan & Williams, 2014;
Boateng, Mbrokoh, Boateng, & Ansong, 2016; Martins,
Oliveira, & Popovič, 2014; Sung, Jeong, Jeong, & Shin,
2015; Wang & Wang, 2010; Wong, Teo, & Russo, 2012;
Zhou, Lu, & Wang, 2010) also support this study that PE is
strongly correlated to BI. Similarly, in the cultural context of
the Middle East, the findings of many researchers such as
(Al-Gahtani, Hubona, & Wang, 2007; Al-Somali, Gholami,
& Clegg, 2009; Oshlyansky, Park, Cairns, & Thimbleby,
2007) are also similar to this study. Hence, the finding of the
above discussion shows that PE (usefulness) of the LMS is
highly related to its adoption. This strong determinant predicts that with the increase of benefits, convenience, and
advantages (e.g. saving time) of a technology, the acceptance of the technology will also increase. This implies that
if the technology is helpful and beneficial, then there are
more chances of its adoption. In other words, the more
useful LMS is perceived to be, the more the intention to
adopt it. Hence the usefulness factor should be considered as
an important variable when developing and implementing
the LMS at HEIs.
8.3.2 Effort Expectancy (EE)
Effort expectancy (EE) is “the degree of ease associated with
the use of the system” (Venkatesh et al., 2003, p. 450). This
construct is related to the relationship between instructors’
perception of how easy it is to learn and how easy to use
LMS, and how their perceptions affect their behavioral
intention to use LMS in teaching.
The results of this study showed that effort expectancy
(EE) is a significant predictor of BI (p < 0.01), which
indicates a positive and significant value. This implies that
the intention of using technology will increase if users perceive that particular technology is easy to use (Carlsson,
Carlsson, Hyvönen, Puhakainen, & Walden, 2006). The
finding of this research is consistent with prior research in
some cases but inconsistent in other cases. For instance, the
finding of this study is not consistent with the findings of
Abdullah and Khanam (2016), Carter and Belanger (2004),
Kang, Liew, Lim, Jang, and Lee (2015), and Yang (2013).
On the other hand, the finding of this research is consistent
with (Gawande, 2015; Raman & Don, 2013; Tosunta et al.,
2015; Wong et al., 2012; Yun, Han, & Lee, 2013). The
results of this research lead to the inference that ease of use of
an LMS is an important factor that influences the behavioral
intentions of instructors to adopt an LMS. This suggests that
instructors having highly positive perceptions of the ease of
use of the LMS or the instructors who feel comfortable using
LMS would have strong intentions to adopt an LMS system.
Hence, if the technology is easy and straightforward to
understand, then there are more chances of its adoption.
8.3.3 Social Influence (SI)
Social influence (SI) is based on the supposition that user
behavior is influenced by his/her perception of how his/her
usage of technology is viewed by other people (Venkatesh,
Thong, & Xu, 2016). The results show that SI has a positive
and significant link with BI (p < 0.01) showing that SI is a
strong predictor of BI. Some researchers (such as Anderson,
Schwager & Kerns, 2006; George, 2004) found an
insignificant or weak relation of SI with BI. However, the
finding of this study is supported by some researchers.
Similarly, the findings of this study are also supported by the
research conducted in non-Western countries by researchers
such as (Al-Gahtani et al., 2007; Al-Somali et al., 2009;
Tosunta et al., 2015).
This research and the previous research on intentions
toward technology have revealed that the influence of society is an important and critical aspect that influences personal beliefs to make decisions about technology adoption
(Anderson, Al-Gahtani, & Hubona, 2011). In this context, it
is important to consider the role of peers, teams, and groups
when implementing LMS. The social influence includes
what other people think of the use of technologies as well as
the support provided by the top management about the use
of an LMS. However, the peer social influence was developed from colleagues or peers who used the LMS. This
implies that the influence of other instructors may help in the
adoption of LMS. Thus, peer pressure was a key factor that
influenced instructors to integrate LMS into their teaching.
This shows that if the usage of the LMS is mandatory and
74
8 Adoption of LMS: Evidence from the Middle East
Performance expectancy (PE) is “the degree to which an
individual believes that using the system will help him or her
to attain gains in job performance” (Venkatesh, Morris,
Davis, & Davis, 2003, p. 447). These results reveal that PE
is a significant determinant (p < 0.01) of BI to adopt the
LMS. Of the six independent variables (PE, EE, SI, FC, HM,
and H), PE provided the second highest contribution to
instructors’ behavioral intention to use the LMS. Aside from
the above quantitative findings, the literature also supports
the concept that PE (i.e. usefulness) has a significant positive
correlation with BI. For instance, in a study on computers as
a learning tool, Nistor, Lerche, Weinberger, Ceobanu, and
Heymann (2014) found that the PE has a positive influence
on BI and attitude toward its use (p. E144). In a similar
study, using the UTAUT2 model in the teacher’s acceptance
of LMS, Raman and Don (2013) found that PE has a positive impact on BI. The findings of other researchers
(Abdullah & Khanam, 2016; Alalwan & Williams, 2014;
Boateng, Mbrokoh, Boateng, & Ansong, 2016; Martins,
Oliveira, & Popovič, 2014; Sung, Jeong, Jeong, & Shin,
2015; Wang & Wang, 2010; Wong, Teo, & Russo, 2012;
Zhou, Lu, & Wang, 2010) also support this study that PE is
strongly correlated to BI. Similarly, in the cultural context of
the Middle East, the findings of many researchers such as
(Al-Gahtani, Hubona, & Wang, 2007; Al-Somali, Gholami,
& Clegg, 2009; Oshlyansky, Park, Cairns, & Thimbleby,
2007) are also similar to this study. Hence, the finding of the
above discussion shows that PE (usefulness) of the LMS is
highly related to its adoption. This strong determinant predicts that with the increase of benefits, convenience, and
advantages (e.g. saving time) of a technology, the acceptance of the technology will also increase. This implies that
if the technology is helpful and beneficial, then there are
more chances of its adoption. In other words, the more
useful LMS is perceived to be, the more the intention to
adopt it. Hence the usefulness factor should be considered as
an important variable when developing and implementing
the LMS at HEIs.
8.3.2 Effort Expectancy (EE)
Effort expectancy (EE) is “the degree of ease associated with
the use of the system” (Venkatesh et al., 2003, p. 450). This
construct is related to the relationship between instructors’
perception of how easy it is to learn and how easy to use
LMS, and how their perceptions affect their behavioral
intention to use LMS in teaching.
The results of this study showed that effort expectancy
(EE) is a significant predictor of BI (p < 0.01), which
indicates a positive and significant value. This implies that
the intention of using technology will increase if users perceive that particular technology is easy to use (Carlsson,
Carlsson, Hyvönen, Puhakainen, & Walden, 2006). The
finding of this research is consistent with prior research in
some cases but inconsistent in other cases. For instance, the
finding of this study is not consistent with the findings of
Abdullah and Khanam (2016), Carter and Belanger (2004),
Kang, Liew, Lim, Jang, and Lee (2015), and Yang (2013).
On the other hand, the finding of this research is consistent
with (Gawande, 2015; Raman & Don, 2013; Tosunta et al.,
2015; Wong et al., 2012; Yun, Han, & Lee, 2013). The
results of this research lead to the inference that ease of use of
an LMS is an important factor that influences the behavioral
intentions of instructors to adopt an LMS. This suggests that
instructors having highly positive perceptions of the ease of
use of the LMS or the instructors who feel comfortable using
LMS would have strong intentions to adopt an LMS system.
Hence, if the technology is easy and straightforward to
understand, then there are more chances of its adoption.
8.3.3 Social Influence (SI)
Social influence (SI) is based on the supposition that user
behavior is influenced by his/her perception of how his/her
usage of technology is viewed by other people (Venkatesh,
Thong, & Xu, 2016). The results show that SI has a positive
and significant link with BI (p < 0.01) showing that SI is a
strong predictor of BI. Some researchers (such as Anderson,
Schwager & Kerns, 2006; George, 2004) found an
insignificant or weak relation of SI with BI. However, the
finding of this study is supported by some researchers.
Similarly, the findings of this study are also supported by the
research conducted in non-Western countries by researchers
such as (Al-Gahtani et al., 2007; Al-Somali et al., 2009;
Tosunta et al., 2015).
This research and the previous research on intentions
toward technology have revealed that the influence of society is an important and critical aspect that influences personal beliefs to make decisions about technology adoption
(Anderson, Al-Gahtani, & Hubona, 2011). In this context, it
is important to consider the role of peers, teams, and groups
when implementing LMS. The social influence includes
what other people think of the use of technologies as well as
the support provided by the top management about the use
of an LMS. However, the peer social influence was developed from colleagues or peers who used the LMS. This
implies that the influence of other instructors may help in the
adoption of LMS. Thus, peer pressure was a key factor that
influenced instructors to integrate LMS into their teaching.
This shows that if the usage of the LMS is mandatory and
74
8 Adoption of LMS: Evidence from the Middle East