ANALÍTICA EN LA INTERPRETACIÓN CONSTITUCIONAL DE LA LEY.

The UTAUT model was formulated by leading researchers in the field of technology acceptance study. The model is based on conceptual similarities among eight dominant models in the field. According to its authors, the UTAUT is a definitive model that synthesises what is known and advances cumulative theory while retaining a parsimonious structure. Although published studies adopting this model are still scarce, this does not diminish the power of this model compared to all other technology acceptance models.

Li and Kishore (2006) studied the invariance of the new measurement scale for the UTAUT instrument to examine whether the focal point of the UTAUT model was

116

consistently alike across various subgroups of Web log system users. Results showed that the difference in the respondents were highly based on the demographic profiles of the users, such as gender, general knowledge on the use of the computer, specific knowledge on the use of the Internet, experience with Web logs, and frequency of using Web logs.

Results showed that there was a similar interpretation of both performance and effort expectancies among the respondents despite their dissimilar experiences and knowledge in using computers and the Internet. The researchers recommended that there must be a careful evaluation and interpretation of the model’s results since they showed that the majority of the subgroups’ scores reflecting their acceptance of online community Web log systems such as social media and the Internet were similar and consistent. Another recommendation is that future studies must be more consistent and homogeneous in terms of variance in the formulation of UTAUT, which this study failed to achieve (Li & Kishore, 2006).

Wang and Yang (2005) extended the UTAUT to fit with their study on online stocking in the financial market by adding the personal trait construct to the model. They used this extension to explore the role personal traits play in the UTAUT model as indirect or intervening. The personal traits studied were the big five factors (or FFM), categorising personality traits into extraversion, conscientiousness, agreeableness, neuroticism, and openness. In their research design, personality traits were hypothesised to affect the participants’ intention to adopt online stocking indirectly through UTAUT constructs in the first design model and in the second model to moderate the effect of UTAUT constructs on the participants’ intention to adopt online stocking. For simplification purposes, the other moderators in the original UTAUT model were removed except for Internet experience.

The results showed that the variance explained in the intervention was very low compared to the moderating effect, which was 60%, suggesting that personality traits play more important roles as moderators than do external variables. For the first model design, results suggested that among the five different personality traits, the extraversion trait affected intention through the four key constructs of UTAUT. The openness trait, however, affected intention through the effort expectancy construct and facilitated the condition construct. As for the second model design, the results found that Internet

117

experience and the openness personality trait unexpectedly moderated the relationship between the PE construct and the participants’ intention to adopt online stocking, with negative effect. The trait of agreeableness with Internet experience moderates the SIintention relationship with positive effect, as does the trait of conscientiousness with Internet experience, which moderates the SIintention relationship but in a negative manner. Finally, neuroticism with Internet experience was found to significantly moderate the FCintention relationship with positive effect. The authors recommended that future research may reconsider the moderators in the original UTAUT model to supplement it (Wang & Yang, 2005).

The UTAUT model was adopted to explain mobile advanced services and device adoption on an individual level and within a mass use context. The Carlsson et al. (2006) objective was to examine the factors affecting the intention to use and factors affecting the use of mobile devices/services. The effects of attitude toward using mobile devices/services and mobile devices/services anxiety on behavioural intention and the use of mobile services were examined in addition to the original paths in the model.

The results showed that PE and EE had a strong direct effect on intention to use mobile devices and that such an effect was weakened when attitude was added to the model, which indicated that attitude explains part of the intention to use a mobile device. SI also had a significant, positive, crude effect on intention; however, the effect was not sustained in all models examined. Anxiety did not have a direct effect on intention, but rather the influence was mediated by other variables such as PE and SI. Attitude did not have a direct effect on intention, which confirms the original model assumption that with presence of EE and PE, attitude would not have a direct effect on intention.

Moreover, when analysing the actual use of three different mobile services, intention to use had a significant, positive, direct influence on the use of the studied services, but when the model was adjusted for the other variables (EE, PE, FC, anxiety, and attitude) the direct effect of intention disappeared. The authors argued that these results showed the central part played by these variables in the influence of behavioural intention on the use of mobile services. Using logistic regression models, the results showed, for all occurrences studied, that incorporating behavioural intention into the model would diminish the effect of independent variables on the use of mobile services

118

(with one exception when FC is the independent variable for one of the services studied, ringtones). Thus, the assumption that PE, EE, SI, FC, anxiety, and attitude affect usage through behavioural intention is partly correct. Likewise, FC did not have a direct influence on the use of mobile services nor an indirect effect through behavioural intention.

The authors acknowledge that the results obtained do not support in all cases the original UTAUT hypotheses. Thus, their earlier reservation on the use of the UTAUT for explaining both behaviours of intention/usage of mobile devices and mobile services in an asynchronous manner was fairly justified. The authors argued the need for modification or extension of the model used to account for the differences in the adoption behaviour of the mobile devices and services (Carlsson et al., 2006).

Knutsen (2005) used a subset of the UTAUT to explore the relationship among expectations related to performance of a new mobile service, efforts needed to utilise new mobile services, and how these constructs affect attitudes towards new mobile services. The research design consisted of PE and EE, age as an antecedent to the UTAUT constructs, and attitude as subsequent to the two constructs of UTAUT. Also, EE was hypothesised to affect PE.

Data were collected in two time periods: pre-launch of the trial service and 2 weeks after the m-service trial. The empirical results significantly verified the relationship between PEEE and attitude as well as between EEPE. Results also suggested that PE and EE are strong determinants of attitude toward new mobile services. Increased age was found to be connected to lower levels of anticipated ease with new mobile services. However, age appeared to have a positive effect on PE, indicating that older individuals have higher expectations of new mobile services (Knusten, 2005).

To sum up this chapter, Section 2.12 provides a summary of the common threads and identifies the similarities and differences found among the previously discussed models that are dominant in the area of technology acceptance literature.

119