La teoría de la estabilidad hegemónica

The Unified Theory of Acceptance and Use of Technology (UTAUT) is a unified model that was developed by Vankatesh et al. (2003), based on social cognitive theory with a combination of eight prominent IT acceptance research models.These theories include the TAM, TRA, Motivational Model (MM), combined Theory of Planned/Technology Acceptance Model (TPB-TAM), Social Cognitive Theory (SCT), Innovative Diffusion Theory (IDT), and the Model of PC Utilization. According to Vankatesh et al. (2003), the unified model was more effective than the other theories because it explained 70 percent of variation in usage and acceptance of technology. The UTAUT model uses four essential constructs that determine technology use and acceptance – effort expectance, performance expectancy, facilitating conditions, and social influence. These constructs were moderated by variables such as experience, gender, age, and voluntariness of use.

The model has been cited in a number of studies since its publication in 2003; however, few of these studies probe its constructs fully. Al-Awadhi and Morris (2008) conducted a study with the UTAUT framework to establish whether peer impact, exertion anticipation, and presentation expectation affected the behavioral intention of students. The study was conducted on 880 participants to investigate the acceptance of e-government services in Qatar. Another research study was conducted by Biemans et al. (2005) to investigate the

selected empirical studies. The authors showed that effort expectation and performance expectancy are essential predictors of behaviour intention.

UTAUT was tested in different organizational and cultural contexts, including cross- cultural validation. Šumak et al. (2010) noted that communal effect is a powerful predictor of students’ behaviour on the intent to use the Internet. The study identified students’ behavior about their intention to use the Internet as an essential predictor of an e-learning system. In their research, Cheng et al. (2008) established that communal effect and presentation expectation provided the strongest indications of behavioral intention of use among Internet banking users in China. The researchers conducted another study establishing that social influence and performance expectancy are essential predictors of behavioural intention of users of Internet banking services. Fang et al. (2016) established that effort expectation, performance expectation, and communal impact are critical predictors of intention of use by managers when sharing knowledge about the Web 2.0. Maldonado et al. (2009) conducted a study in Peru with 240 participants from secondary school to investigate the acceptance of e-learning technology. They found that social influence plays a role in predicting behavioural intention, and that behavioural intention affects the behaviour to predict use. Bhatti (2015) also conducted research exploring the acceptance of mobile banking and found that exertion expectation, presentation expectancy, and communal effect are essential predictors of behavioural intention.

In the telecommunication industry, Wu (2003) investigated the acceptance of 3G services in Taiwan and found performance expectancy and social influence to be predictors of behavioural intention. Interestingly, the authors also found performance expectancy, effort expectation, social influence and facilitating conditions to be predictors of use behaviour. He and Lu (2007) proposed that social influence and presentation expectation are essential analysts of behavioural intention of use of consumers. In mobile advertising, they claim, behavioural intention and facilitating conditions play a significant role in predicting behavioural use. Cheng et al. (2008) investigated Internet banking acceptance, and revealed that communal impact and presentation expectation influence behavioural intention of use.

intention behaviours. Chen and Li (2006) conducted a study establishing that exertion expectation, presentation expectation, easing situations, and communal effect have invariant scores. Thus, the researchers called for caution when interpreting the UTAUT model. Heerink et al. (2009) found that exertion expectation, presentation expectation, and communal impact have an insignificant role in predicting behavioural intention of use. Heerink et al. (2009) conducted a study to establish acceptance of a screen agent and robot interface by elderly users. Šumak et al. (2010) established that effort expectancy and performance expectancy have low power in predicting the behavioural intention of use. In a similar study, Cheng et al. (2008) found that effort expectancy plays no role in predicting behavioural intention. He and Lu (2007), Cheng et al. (2008) and Wu (2003) agree that effort expectancy does not predict behavioural intention to use. Maldonado et al. (2009) conducted research to establish the motivational role of adopting e-learning technology. They found that facilitating conditions do not impact use behaviour among users. Cheng et al. (2008) said that intention to use Internet banking is not affected by effort expectancy. They focused on customers’ use of Internet banking to establish the role of UTAUT constructs in intention to use behaviours. Schaupp and Carter (2009) conducted research to examine tax payers’ acceptance of e-filing. The study showed that effort expectancy does not affect behavioural intention of use. Therefore, inconsistencies in the studies on the UTAUT model showed inconclusive relationships with the model.

Figure 3-4. UTAUT, Source: Venkatesh et al., 2003

Table 3.1 reflects the development of OSAM over time. It started in the form of the TRA, which holds that an individual’s behavioural intention depends on their attitudes; however, it was found that a person’s real behaviour is not always influenced by the person’s intentions. Thus, as discussed in Section 3.2.2, Ajzen (1985) introduced the TPB by coming up with another factor referred to as ‘perceived behavioural control’, which distinguishes the TPB from the TRA. The TAM was developed, and the components that affect and determined it are perceived usefulness, perceived ease, and behavioural intentions. The last stage of development is OSAM, where the focus is on consumer’s satisfaction.

Table 3-1. Summary of history of OSAM

Study Subject

Fishbein (1975) Theory of Reasoned Action (TRA)

Ajzen (1985) Theory of Planned Behaviour (TPB)

Davis (1989) Technology Acceptance Model (TAM)

Venkatesh et al. (2003) Unified Theory of Acceptance and Use of Technology (UTAUT)

Zhou et al. (2007) Online Shopping Acceptance Model