5
Technology Adoption Theories and Models
5.1 Introduction
This research compares different technology adoption
models, and then builds a model on the modified unified
theory of acceptance and use of technology (UTAUT2). This
study extends the UTAUT2 model with ‘technology
awareness’ and ‘Hofstede’s cultural dimensions’ as moderating variables of the UTAUT2 model. This study will also
examine the adequacy of the original UTAUT2 model in the
higher educational institutions (HEI) of Saudi Arabia.
Numerous theories and models have been presented to
examine the variables impacting the adoption of new technologies (Baptista & Oliveira, 2015; Van Biljon & Kotzé,
2008; Rodrigues, Sarabdeen & Balasubramanian, 2016).
According to Oliveira and Martins (2011), many technology
acceptance theories exist in information systems research.
Some popular technology adoption theories and models
consist of: theory of planned behavior, motivational model,
decomposed theory of planned behavior, theory of reasoned
action, technology acceptance model, combined TAM, and
TPB, diffusion of innovation, TAM2, TAM3, model of PC
utilization, social cognitive theory, unified theory of acceptance and use of technology (UTAUT), and extended
UTAUT (Alazzam, Basari, Sibghatullah, Ibrahim, Ramli, &
Naim, 2016; Brown & Venkatesh, 2005; Ghobakhloo,
Zulkifli, & Aziz, 2010; Jayasingh & Eze, 2010). The models
such as technology, organization, and environment
(TOE) and diffusion of innovation (DOI) deal with the
adoption of technology at the firm level. The technology
acceptance theories that deal with acceptance at the individual level include technology acceptance model (TAM),
the theory of planned behavior (TPB), and the unified theory
of acceptance and use of technology (Rahim, Lallmahomed,
Ibrahim, & Rahman, 2011). This research will explore and
adopt the model that deals with technology acceptance at the
individual level.
5.2 Behavioral Intention (BI) and Use
Behavior (UB)
This study reviews various models of technology adoption
and the constructs of the models. However, it is important to
understand the intentions and behaviors of the users for the
adoption of technology. A fundamental concept behind
adoption is that the ‘intention’ of a person to adopt new
technology is the prediction of its ‘actual usage’ (Ajzen,
1991; Davis, Bagozzi & Warshaw, 1989; Venkatesh &
Davis, 2000). Many researchers such as Compeau and
Higgins (1995), Taylor and Todd (1995), Davis et al. (1989),
and Venkatesh and Davis (2000) have been using ‘intention
to use’ and ‘actual usage’ as dependent variables of the
technology adoption. The use of behavioral intention
(BI) and actual use behavior (UB) have been used interchangeably by the researchers as dependent variables. Previous studies show that the ‘intention to use’ is a predictor of
‘use behavior’ of a technology (Ajzen, 1991; Sheppard,
Hartwick, & Warshaw, 1988). Venkatesh, Morris, Davis and
Davis (2003) also concluded that user’s acceptance of new
technology is dependent on ‘the behavioral intention’ and
‘actual use’ of technology. Literature shows that these two
determinants are considered to measure the degree of
acceptance of a technology (Keramati, Sharif, Azad, &
Soofifard, 2012). A review of the literature shows that the
‘intention to use’ a technology is highly correlated with the
‘actual use’ of the technology (Shiau & Chau, 2015;
Wakefield & Whitten, 2006). Figure 5.1 demonstrates the
most basic model of ‘information system acceptance’,
adapted from Venkatesh et al. (2003). It shows that an
individual’s reactions to use technology make the individual’s intention directly linked to its actual use. The dotted
line in the following figure is the feedback loop from usage
and reflects a user’s continued intention of using the system.
Hence, the ‘behavioral intention’ (BI) has a significant
© Springer Nature Switzerland AG 2021
R. A. Khan and H. Qudrat-Ullah, Adoption of LMS in Higher Educational Institutions of the Middle East,
Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-50112-9_5
27
Technology Adoption Theories and Models
5.1 Introduction
This research compares different technology adoption
models, and then builds a model on the modified unified
theory of acceptance and use of technology (UTAUT2). This
study extends the UTAUT2 model with ‘technology
awareness’ and ‘Hofstede’s cultural dimensions’ as moderating variables of the UTAUT2 model. This study will also
examine the adequacy of the original UTAUT2 model in the
higher educational institutions (HEI) of Saudi Arabia.
Numerous theories and models have been presented to
examine the variables impacting the adoption of new technologies (Baptista & Oliveira, 2015; Van Biljon & Kotzé,
2008; Rodrigues, Sarabdeen & Balasubramanian, 2016).
According to Oliveira and Martins (2011), many technology
acceptance theories exist in information systems research.
Some popular technology adoption theories and models
consist of: theory of planned behavior, motivational model,
decomposed theory of planned behavior, theory of reasoned
action, technology acceptance model, combined TAM, and
TPB, diffusion of innovation, TAM2, TAM3, model of PC
utilization, social cognitive theory, unified theory of acceptance and use of technology (UTAUT), and extended
UTAUT (Alazzam, Basari, Sibghatullah, Ibrahim, Ramli, &
Naim, 2016; Brown & Venkatesh, 2005; Ghobakhloo,
Zulkifli, & Aziz, 2010; Jayasingh & Eze, 2010). The models
such as technology, organization, and environment
(TOE) and diffusion of innovation (DOI) deal with the
adoption of technology at the firm level. The technology
acceptance theories that deal with acceptance at the individual level include technology acceptance model (TAM),
the theory of planned behavior (TPB), and the unified theory
of acceptance and use of technology (Rahim, Lallmahomed,
Ibrahim, & Rahman, 2011). This research will explore and
adopt the model that deals with technology acceptance at the
individual level.
5.2 Behavioral Intention (BI) and Use
Behavior (UB)
This study reviews various models of technology adoption
and the constructs of the models. However, it is important to
understand the intentions and behaviors of the users for the
adoption of technology. A fundamental concept behind
adoption is that the ‘intention’ of a person to adopt new
technology is the prediction of its ‘actual usage’ (Ajzen,
1991; Davis, Bagozzi & Warshaw, 1989; Venkatesh &
Davis, 2000). Many researchers such as Compeau and
Higgins (1995), Taylor and Todd (1995), Davis et al. (1989),
and Venkatesh and Davis (2000) have been using ‘intention
to use’ and ‘actual usage’ as dependent variables of the
technology adoption. The use of behavioral intention
(BI) and actual use behavior (UB) have been used interchangeably by the researchers as dependent variables. Previous studies show that the ‘intention to use’ is a predictor of
‘use behavior’ of a technology (Ajzen, 1991; Sheppard,
Hartwick, & Warshaw, 1988). Venkatesh, Morris, Davis and
Davis (2003) also concluded that user’s acceptance of new
technology is dependent on ‘the behavioral intention’ and
‘actual use’ of technology. Literature shows that these two
determinants are considered to measure the degree of
acceptance of a technology (Keramati, Sharif, Azad, &
Soofifard, 2012). A review of the literature shows that the
‘intention to use’ a technology is highly correlated with the
‘actual use’ of the technology (Shiau & Chau, 2015;
Wakefield & Whitten, 2006). Figure 5.1 demonstrates the
most basic model of ‘information system acceptance’,
adapted from Venkatesh et al. (2003). It shows that an
individual’s reactions to use technology make the individual’s intention directly linked to its actual use. The dotted
line in the following figure is the feedback loop from usage
and reflects a user’s continued intention of using the system.
Hence, the ‘behavioral intention’ (BI) has a significant
© Springer Nature Switzerland AG 2021
R. A. Khan and H. Qudrat-Ullah, Adoption of LMS in Higher Educational Institutions of the Middle East,
Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-50112-9_5
27