have a direct influence on the intention to use the system.
However, age, experience, gender, and voluntariness act as
moderating variables (Venkatesh et al., 2003). This means
that if the values of these variables are higher, the value of
behavioral intention to use the technology is higher, and so
is the individual’s acceptance of the technology (Venkatesh
et al., 2003).
• Performance Expectancy (PE): Performance expectancy is the degree to which a user perceives that using
technology will assist him or her to achieve benefits from
job performance. Venkatesh et al. (2003) stated that
“performance expectancy is the strongest predictor of
intention” (p. 447). It is an independent variable of the
UTAUT model and is the most significant determinant of
an individual’s behavioral intentions to use a technology
(Al-Gahtani et al., 2007; Al-sobhi, Weerakkody, &
El-haddadeh, 2011; AlAwadhi & Morris, 2008; Chen,
Lai, & Ho, 2015; Venkatesh et al., 2003). Bandyopadhyay and Fraccastoro (2007) found that performance
expectancy (PE) is strongly related to behavioral intention
(BI) among consumer prepayment metering systems
(p. 535). With the use of Internet banking software in
Jordan, AbuShanab et al. (2010) determined that performance expectancy (PE) is strongly associated with the
behavioral intention (BI) among bank customers (p. 511).
The performance expectancy (PE) in UTAUT, perceived
usefulness in (TAM/TAM2 and C-TAM-TPB), extrinsic
motivation (MM), outcome expectations (SCT), relative
advantages in DOI, or job-fit (MPCU) are important
constructs in measuring the advantages achieved by using
the technology (Shareef et al., 2011).
• Effort Expectancy (EE): Venkatesh et al. (2003) defined
that effort expectancy is “the degree of ease associated
with the use of the system” (Venkatesh et al., 2003,
p. 450). Effort expectancy refers to the ease of use a user
linked with the use of technology, as perceived by the
user. Venkatesh and Zhang (2010) emphasized that effort
expectancy (EE) is a very strong predictor of technology
adoption. Rogers (2003) commented that if a technology
is perceived to be difficult to understand or to use, then it
would be regarded as complexity. In the UTAUT model,
Venkatesh et al. (2003) considered the equivalent factors
from other models that capture the concept of EE are
complexity (DOI, MPCU) and perceived ease of use
(TAM/TAM2).
• Social Influence (SI): Social influence (SI) includes the
social pressure exercised on a person by the beliefs of
other individuals or groups. The social influence is “the
degree to which an individual perceives that important
others believe he or she should use the new system”
(Venkatesh et al., 2003, p. 451). This determinant 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 et al., 2012).
According to Rogers (2003), the decisions of adoption are
socially influenced by the role available to a person or a
group of people. Empirical outcomes show that social
influence employs a positive influence on intention to use
the technology (Venkatesh & Davis, 2000; Venkatesh,
et al., 2003; Wong, Teo, & Russo, 2012). The construct
SI is known as the subjective norm in other models such
as TAM2, TRA, TAM/TPB, and TPB/DTPB models. It is
known as social factors in the MPCU model and image in
the DOI model (Yeow & Loo, 2009). Based on the
concepts related to social influence defined by authors
such as Ajzen (1991), Davis et al., (1989), Fishbein and
Ajzen, (1975), and Taylor and Todd, (1995), the SI is
categorized into two sub-variables: peer social influence
and general social influence. The general social influence,
in general, means what other people think of the use of
technologies and the support offered by the management
about the use of the technology. Peer social influence was
developed from colleagues or peers who used the technology. Previous research on attitudes and intention
toward technology have revealed that the influence of
society is an important and critical aspect that influences
personal belief to make decisions about technology
adoption (Anderson, Al-Gahtani, & Hubona, 2011).
Venkatesh et al. (2003) suggest a positive and direct link
between social influence (both peer social and general
influence) and intentional behavior to use technology.
• Facilitating Conditions (FC): Venkatesh et al. (2003)
defined that facilitating conditions are “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 UTAUT
model, Venkatesh et al. (2003) considered other models
having constructs similar to facilitating conditions. The
researchers found that FC has a direct impact on the
actual use of technology (Venkatesh et al., 2003). There
is somewhat of a contradiction in previous studies concerning the relationship between ‘actual use’ of technology and facilitating conditions. Some authors argue that
facilitating conditions are directly linked with behavioral
intentions (Eckhardt et al., 2009; San Martín, & Herrero,
2012). On the other hand, some authors excluded FC
from their research study (Bandyopadhyay & Fraccastoro
2007). According to Wu, Tao, & Yang (2007), the
facilitating conditions have a strong effect on the intention
to adopt the technology. The equivalent variables from
other models having a similar concept of facilitating
36
5 Technology Adoption Theories and Models
However, age, experience, gender, and voluntariness act as
moderating variables (Venkatesh et al., 2003). This means
that if the values of these variables are higher, the value of
behavioral intention to use the technology is higher, and so
is the individual’s acceptance of the technology (Venkatesh
et al., 2003).
• Performance Expectancy (PE): Performance expectancy is the degree to which a user perceives that using
technology will assist him or her to achieve benefits from
job performance. Venkatesh et al. (2003) stated that
“performance expectancy is the strongest predictor of
intention” (p. 447). It is an independent variable of the
UTAUT model and is the most significant determinant of
an individual’s behavioral intentions to use a technology
(Al-Gahtani et al., 2007; Al-sobhi, Weerakkody, &
El-haddadeh, 2011; AlAwadhi & Morris, 2008; Chen,
Lai, & Ho, 2015; Venkatesh et al., 2003). Bandyopadhyay and Fraccastoro (2007) found that performance
expectancy (PE) is strongly related to behavioral intention
(BI) among consumer prepayment metering systems
(p. 535). With the use of Internet banking software in
Jordan, AbuShanab et al. (2010) determined that performance expectancy (PE) is strongly associated with the
behavioral intention (BI) among bank customers (p. 511).
The performance expectancy (PE) in UTAUT, perceived
usefulness in (TAM/TAM2 and C-TAM-TPB), extrinsic
motivation (MM), outcome expectations (SCT), relative
advantages in DOI, or job-fit (MPCU) are important
constructs in measuring the advantages achieved by using
the technology (Shareef et al., 2011).
• Effort Expectancy (EE): Venkatesh et al. (2003) defined
that effort expectancy is “the degree of ease associated
with the use of the system” (Venkatesh et al., 2003,
p. 450). Effort expectancy refers to the ease of use a user
linked with the use of technology, as perceived by the
user. Venkatesh and Zhang (2010) emphasized that effort
expectancy (EE) is a very strong predictor of technology
adoption. Rogers (2003) commented that if a technology
is perceived to be difficult to understand or to use, then it
would be regarded as complexity. In the UTAUT model,
Venkatesh et al. (2003) considered the equivalent factors
from other models that capture the concept of EE are
complexity (DOI, MPCU) and perceived ease of use
(TAM/TAM2).
• Social Influence (SI): Social influence (SI) includes the
social pressure exercised on a person by the beliefs of
other individuals or groups. The social influence is “the
degree to which an individual perceives that important
others believe he or she should use the new system”
(Venkatesh et al., 2003, p. 451). This determinant 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 et al., 2012).
According to Rogers (2003), the decisions of adoption are
socially influenced by the role available to a person or a
group of people. Empirical outcomes show that social
influence employs a positive influence on intention to use
the technology (Venkatesh & Davis, 2000; Venkatesh,
et al., 2003; Wong, Teo, & Russo, 2012). The construct
SI is known as the subjective norm in other models such
as TAM2, TRA, TAM/TPB, and TPB/DTPB models. It is
known as social factors in the MPCU model and image in
the DOI model (Yeow & Loo, 2009). Based on the
concepts related to social influence defined by authors
such as Ajzen (1991), Davis et al., (1989), Fishbein and
Ajzen, (1975), and Taylor and Todd, (1995), the SI is
categorized into two sub-variables: peer social influence
and general social influence. The general social influence,
in general, means what other people think of the use of
technologies and the support offered by the management
about the use of the technology. Peer social influence was
developed from colleagues or peers who used the technology. Previous research on attitudes and intention
toward technology have revealed that the influence of
society is an important and critical aspect that influences
personal belief to make decisions about technology
adoption (Anderson, Al-Gahtani, & Hubona, 2011).
Venkatesh et al. (2003) suggest a positive and direct link
between social influence (both peer social and general
influence) and intentional behavior to use technology.
• Facilitating Conditions (FC): Venkatesh et al. (2003)
defined that facilitating conditions are “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 UTAUT
model, Venkatesh et al. (2003) considered other models
having constructs similar to facilitating conditions. The
researchers found that FC has a direct impact on the
actual use of technology (Venkatesh et al., 2003). There
is somewhat of a contradiction in previous studies concerning the relationship between ‘actual use’ of technology and facilitating conditions. Some authors argue that
facilitating conditions are directly linked with behavioral
intentions (Eckhardt et al., 2009; San Martín, & Herrero,
2012). On the other hand, some authors excluded FC
from their research study (Bandyopadhyay & Fraccastoro
2007). According to Wu, Tao, & Yang (2007), the
facilitating conditions have a strong effect on the intention
to adopt the technology. The equivalent variables from
other models having a similar concept of facilitating
36
5 Technology Adoption Theories and Models