2.4 Technology acceptance-related theories
2.4.9 Unified Theory of Acceptance and Use of Technology (UTAUT)
the technology acceptance models, namely, the unified theory of acceptance and use of technology (UTAUT), capturing the essential elements of the different models.
In fact, this theory is accepted as the most comprehensive individual acceptance model for overcoming the limitations of the technology acceptance models (Tan et al., 2013). The UTAUT model is formulated by comparing and conceptualizing empirical similarities across eight models (summarized in Table 2.3) of the
determinants of intention and usage of information technology (IT) models consisting of innovation diffusion theory (IDT), theory of reasoned action (TRA), technology acceptance model (TAM), theory of planned behavior (TPB), combined TAM and TPB (C-TAM-TPB), model of PC utilization (MPCU), motivational model (MM), and social cognitive theory (SCT) (Tan, 2013; Wang & Wang, 2010). The UTAUT consists of four key constructs (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions) that determine the user’s behavioral intention to use technology and/or his/her actual use, and four moderators in the acceptance of technology (i.e., gender, age, experience, and voluntariness of use) (Wang & Wang, 2010) as depicted below in Figure 2.11. Therefore, in the UTAUT, it is assumed that these variables will capture the motivational factors and influencing behavior, and
“will indicate the efforts that an individual is willing to undertake in order to develop an action” (Ajzen, 1991).
Figure 2.11: Unified theory of acceptance and use of technology (UTAUT) Source: Venkatesh et al. (2003)
Performance Expectancy
Effort Expectancy
Behavioral Intention Social
Influence
Use Behavior
Facilitating Conditions
Voluntariness of Use Experience
Age Gender
Table 2.3: Four core determinants of UTAUT
Source: Wu et al. (2007)
The first variable ‘performance expectancy’ is defined as “the degree to which an individual believes that using the system will help him/her to attain gains in job performance” (Venkatesh et al., 2003, p. 447). To be concise, performance expectancy is the degree to which the individual thinks that new technology can help him/her to more easily and efficiently perform jobs or tasks (Alhilali, 2013). According to Venkatesh et al. (2003) this construct was developed after being derived from five different models, consisting of PU (TAM1/TAM2 and C-TAM-TPB); extrinsic motivation (MM); job fit (MPCU); relative advantage (IDT); and outcome expectations (SCT). This construct has therefore been justified as a determinant for predicting the behavioral intention to use technology (Wang & Wang, 2010). Interestingly, the relationship between performance expectancy and the behavioral intention to use technology is moderated by age and gender. The reason is that men are more ‘task-
UTAUT Determinant Sub-Determinant Source of Integrated Model Performance Expectancy Perceived Usefulness TAM/TAM2/C-TAM-TPB
Extrinsic Motivation MM
Job Fit MPCU
Relative Advantage IDT Outcome Expectations SCT
Effort Expectancy Perceived Ease of Use TAM/TAM2
Complexity MPCU
Ease of Use IDT
Social Influence Subjective Norm TRA/TAM2/TPB/DPTB/
C-TAM/TPB
Social Factors MPCU
Image IDT
Facilitating Conditions Perceived Behavioral Control
TPB/DTPB/C-TAM-TPB
Facilitating Conditions MPCU
Compatibility IDT
oriented’ than women which is similar to performance expectancy’s focus on ‘task accomplishment,’ and that those of a younger age will change work expectations, thus also adapting to the technology adoption context (Venkatesh et al., 2003, pp. 449-450).
The second variable ‘effort expectancy’ is defined as “the degree of ease associated with the use of the system” (Venkatesh et al., 2003, p. 450). To elaborate further, this variable expresses the degree to which the individual believes the new technology to be easy, or effortless, to use (Alhilali, 2013). According to Venkatesh et al. (2003), this construct embodies three constructs from the different models, consisting of PEOU (TAM1/TAM2), complexity (MPCU), and ease of use (IDT), and is able to directly determine the behavioral intention to use technology. Furthermore, this effort-oriented construct seems to be more significant in the early stage of new behavior. It is also suggested that, for women, older people, and those less experienced in technology usage, it is a strong determinant of the intention to use technology as these groups of people seem to relate to the difficulty of using a complex system (Venkatesh et al., 2003). Consequently, gender, age, and experience are moderators of the relationship of effort expectancy and the behavioral intention to use technology.
The third variable ‘social influence’ is defined as “the degree to which an individual perceives that important others believe he/she should use the new system”
(Venkatesh et al., 2003 p. 451). To be more concise, the opinions of others are a significant factor in the decision to use technology. This construct can be compared with the “Subjective Norm, introduced in TRA, TAM2, TPB/DTPB and C-TAM-TPB, Social factors in MCPU and Image in IDT” (Venkatesh et al., 2003, p. 451). Venkatesh et al. (2003) emphasized that social influence on the behavioral intention to use technology is moderated by gender, age, voluntariness, and experience which have a stronger effect for women, for older women, in a mandatory setting, and in the early stage of experience.
The fourth variable ‘facilitating conditions’ is defined as “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). This means that
“an individual is the direct determinant of use of the technology, as they reflect the environmental factors that limit or incentivize their acceptance” (Venkatesh
et al., 2003, cited in Martín & Herrero, 2012, pp. 342-343). To be more concise, facilitation conditions are environmental factors that make an action easy (or easier) (Im, Hong, & Kang, 2011). This construct was derived from the combination of three different constructs, namely, perceived behavioral control (TPB/DTPB, C-TAM-TPB);
facilitating conditions (MCPU); and compatibility (IDT) (Venkatesh et al., 2003).
Facilitating conditions are very much related to performance expectancy and effort expectancy: when these constructs are presented in the model, this diminishes the influence of facilitating conditions. As a result, no significant effect is found on the behavioral intention to use technology, but use behavior is directly affected instead (Venkatesh et al., 2003; Abubakar & Ahmad, 2013). In addition, the empirical evidence suggests a stronger effect will be found when facilitating conditions are moderated by age and experience. This particularly applies to older people and experienced people as older users probably need more help and support than younger users, while experienced users would find more ways to obtain help and support.