5.7 Extension of the UTAUT2 Model
Many researchers have argued that the UTAUT model
ignored the variables that might be significant predictors of
technology acceptance and usage (Yeow & Loo, 2009).
Therefore, to capture the ignored variables, many critics have
highlighted the importance of the extension of this model
(Alazzam et al., 2016; Berthon, Pitt, Ewing, & Carr, 2002;
Venkatesh et al., 2003). Venkatesh et al. (2003) and Venkatesh, Speier and Morris (2002) suggested that the future
model should be supported with enough constructs such as
individual constructs and technology fit. Dwivedi, Rana,
Chen and Williams (2011) pointed out that the trend of using
external variables with UTAUT is increasing. In a study of
‘UTAUT external variables’, Dwivedi et al. (2011) found that
22 out of 43 researchers have used external constructs in their
research and the remaining 21 used the original variables of
UTAUT in their studies. Venkatesh et al. (2012) emphasized
the need for extension of the UTAUT/UTAUT2 models and
stated that the extension of UTAUT/UTAUT2 is valuable in
understanding and expanding technology acceptance
boundaries (Venkatesh et al., 2012, p. 158) as well as
expanding the scope and generalizability of the model
(Venkatesh et al., 2012, p. 160). Three types of extensions are
suggested by Venkatesh et al. (2012). The suggested extension of the model is a new technological extension, such as
the health information system (Chang, Hwang, Hung & Li,
2007) or new user populations such as educational users,
consumers, and new cultural settings (Gupta et al., 2008).
Another type of extension is the inclusion of new constructs
within UTAUT (Sun, Bhattacherjee, & Ma, 2009). The third
type of extension is the addition of exogenous predictors of
the UTAUT variables (Yi et al., 2006).
In the guidance of the literature review, the technological
extension includes the use of LMS technology by instructors
in the context of higher educational institutions (HEIs).
Another extension of the model was the inclusion of moderating variables (Al-Gahtani et al., 2007; Kripanont, 2007;
Tibenderana & Ogao, 2009; Venkatesh et al., 2012).
Table 5.2 provides a list of external variables used by different researchers. It shows that anxiety, trust, attitude,
self-efficacy, PU, PEOU, perceived credibility, and perceived risk are the most common external variables.
5.8 Moderating Variables (Age, Experience,
Gender, Technology Awareness,
and Culture)
A moderator is a quantitative or qualitative variable that
influences the direction of the relationship and strength
between two variables (Baron & Kenny, 1986; Kripanont,
2007; Lakhal et al., 2013; Schaper & Pervan, 2007; Serenko,
Turel, & Yol, 2006; Venkatesh et al., 2003). Literature
shows that demographic features such as age, gender, marital
status, and family structure influence the acceptance of
technology and therefore cannot be neglected. The concept
of moderators and core constructs is well documented in
their research by several researchers (Kripanont, 2007;
Schaper & Pervan, 2007; Venkatesh et al., 2003).
The moderators of UTAUT are gender, age, experience,
and voluntariness (Venkatesh et al., 2003), whereas those of
the UTAUT2 model are: age, gender, and experience
(Baptista & Oliveira, 2015; Venkatesh et al., 2012). The
moderators of this study include age, experience, technology
awareness, racial demography, and cultural dimensions.
5.8.1 Age (As a Moderating Variable)
Age is the key personal characteristic included in demographic variables. Research shows that the adoption of
technology is associated with age (Venkatesh et al. 2003;
Morris & Venkatesh, 2000). It was found that age moderates
the influence of all of the variables on behavioral intentions
(BI). Venkatesh et al. (2003) stated that age and gender play a
moderating role in UTAUT model for BI to adopt a technology. Venkatesh et al. (2003) found that age is the moderator of adoption for EE, PE, and SI in the UTAUT model.
Bandyopadhyay and Fraccastoro (2007) stated that age and
gender are the moderators between PE, EE, and SI, and
behavioral intentions to use the technology. According to
Morris and Venkatesh (2000), age has a moderating impact
on PE such that it has a greater impact on younger men than
in older. However, Cheng et al. (2012) reported that age and
gender mediate the relationship between SI and BI. Research
by Al-Gahtani (2003) in the Saudi context revealed more
chances of adoption of technology in older men, whereas
Oyelaran-Oyeyinka and Adeya (2003) identified that
younger men and women are more likely to use innovation.
Research reveals that older people have greater difficulties in
adopting new technology than younger people (Ellis &
Allaire, 1999). Previous research shows that variables connected to effort expectancy (EE) are good predictors of
behavioral intention for females (Venkatesh & Morris, 2000)
and older workers (Morris & Venkatesh, 2000). Facilitating
conditions are moderated by experience and age (Venkatesh
et al., 2003). Similarly, AbuShanab et al. (2009) found that
both age and gender significantly moderate both performance
expectancy (PE) and effort expectancy (EE).
5.8.2 Experience (As a Moderating Variable)
Experience is a key personal characteristic recognized as a
moderating variable in UTAUT2. Prior research shows
5.7 Extension of the UTAUT2 Model
39
Many researchers have argued that the UTAUT model
ignored the variables that might be significant predictors of
technology acceptance and usage (Yeow & Loo, 2009).
Therefore, to capture the ignored variables, many critics have
highlighted the importance of the extension of this model
(Alazzam et al., 2016; Berthon, Pitt, Ewing, & Carr, 2002;
Venkatesh et al., 2003). Venkatesh et al. (2003) and Venkatesh, Speier and Morris (2002) suggested that the future
model should be supported with enough constructs such as
individual constructs and technology fit. Dwivedi, Rana,
Chen and Williams (2011) pointed out that the trend of using
external variables with UTAUT is increasing. In a study of
‘UTAUT external variables’, Dwivedi et al. (2011) found that
22 out of 43 researchers have used external constructs in their
research and the remaining 21 used the original variables of
UTAUT in their studies. Venkatesh et al. (2012) emphasized
the need for extension of the UTAUT/UTAUT2 models and
stated that the extension of UTAUT/UTAUT2 is valuable in
understanding and expanding technology acceptance
boundaries (Venkatesh et al., 2012, p. 158) as well as
expanding the scope and generalizability of the model
(Venkatesh et al., 2012, p. 160). Three types of extensions are
suggested by Venkatesh et al. (2012). The suggested extension of the model is a new technological extension, such as
the health information system (Chang, Hwang, Hung & Li,
2007) or new user populations such as educational users,
consumers, and new cultural settings (Gupta et al., 2008).
Another type of extension is the inclusion of new constructs
within UTAUT (Sun, Bhattacherjee, & Ma, 2009). The third
type of extension is the addition of exogenous predictors of
the UTAUT variables (Yi et al., 2006).
In the guidance of the literature review, the technological
extension includes the use of LMS technology by instructors
in the context of higher educational institutions (HEIs).
Another extension of the model was the inclusion of moderating variables (Al-Gahtani et al., 2007; Kripanont, 2007;
Tibenderana & Ogao, 2009; Venkatesh et al., 2012).
Table 5.2 provides a list of external variables used by different researchers. It shows that anxiety, trust, attitude,
self-efficacy, PU, PEOU, perceived credibility, and perceived risk are the most common external variables.
5.8 Moderating Variables (Age, Experience,
Gender, Technology Awareness,
and Culture)
A moderator is a quantitative or qualitative variable that
influences the direction of the relationship and strength
between two variables (Baron & Kenny, 1986; Kripanont,
2007; Lakhal et al., 2013; Schaper & Pervan, 2007; Serenko,
Turel, & Yol, 2006; Venkatesh et al., 2003). Literature
shows that demographic features such as age, gender, marital
status, and family structure influence the acceptance of
technology and therefore cannot be neglected. The concept
of moderators and core constructs is well documented in
their research by several researchers (Kripanont, 2007;
Schaper & Pervan, 2007; Venkatesh et al., 2003).
The moderators of UTAUT are gender, age, experience,
and voluntariness (Venkatesh et al., 2003), whereas those of
the UTAUT2 model are: age, gender, and experience
(Baptista & Oliveira, 2015; Venkatesh et al., 2012). The
moderators of this study include age, experience, technology
awareness, racial demography, and cultural dimensions.
5.8.1 Age (As a Moderating Variable)
Age is the key personal characteristic included in demographic variables. Research shows that the adoption of
technology is associated with age (Venkatesh et al. 2003;
Morris & Venkatesh, 2000). It was found that age moderates
the influence of all of the variables on behavioral intentions
(BI). Venkatesh et al. (2003) stated that age and gender play a
moderating role in UTAUT model for BI to adopt a technology. Venkatesh et al. (2003) found that age is the moderator of adoption for EE, PE, and SI in the UTAUT model.
Bandyopadhyay and Fraccastoro (2007) stated that age and
gender are the moderators between PE, EE, and SI, and
behavioral intentions to use the technology. According to
Morris and Venkatesh (2000), age has a moderating impact
on PE such that it has a greater impact on younger men than
in older. However, Cheng et al. (2012) reported that age and
gender mediate the relationship between SI and BI. Research
by Al-Gahtani (2003) in the Saudi context revealed more
chances of adoption of technology in older men, whereas
Oyelaran-Oyeyinka and Adeya (2003) identified that
younger men and women are more likely to use innovation.
Research reveals that older people have greater difficulties in
adopting new technology than younger people (Ellis &
Allaire, 1999). Previous research shows that variables connected to effort expectancy (EE) are good predictors of
behavioral intention for females (Venkatesh & Morris, 2000)
and older workers (Morris & Venkatesh, 2000). Facilitating
conditions are moderated by experience and age (Venkatesh
et al., 2003). Similarly, AbuShanab et al. (2009) found that
both age and gender significantly moderate both performance
expectancy (PE) and effort expectancy (EE).
5.8.2 Experience (As a Moderating Variable)
Experience is a key personal characteristic recognized as a
moderating variable in UTAUT2. Prior research shows
5.7 Extension of the UTAUT2 Model
39