• The Journal of Bigdata
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• v.2 no.2
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• pp.47-56
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• 2017

The UTAUT was presented as a comprehensive of eight existing theories to improve the limit of Technology Acceptance Model (TAM), and it has been also utilizing in various fields related to acceptance and diffusion of new technology. In this study, we analyzed factors utilized in UTAUT through meta-analysis, and confirms the consistency of the model. We presented the principal factors and the additional factors. Moreover, we presented differences and suggestions through comparative analysis with previous researches. The meta-analysis showed that satisfaction, hedonic motivation, attitude, perceived enjoyment showed a important factors as additional factors. Based on this result, we presented an extended UTAUT model. In the case of Korea studies, it was found that increasing the degree of behavior intention is the most important factor leading to use behavior. The results of this research will be able to support researchers who research the acceptance and diffusion of new technologies, and companies trying to launch new products.

• Journal of the Korea Fashion and Costume Design Association
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• v.25 no.1
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• pp.97-113
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• 2023

This study investigates the effect of consumers' trust in communication with online fashion mall avatars on performance expectancy and re-use intention. For the empirical study, 207 adults were surveyed using a 5-point Likert scale, and the results were analyzed with SPSS 21.0. The analysis reveals the following results. First, the factor analysis of trust in communication with the avatar, performance expectancy, and re-use intention revealed cognitive and affective trust as subfactors of the trust in communication with the avatar, while purchase choice expectations and performance expectancy were identified as subfactors of performance expectancy for the avatar. A total of five factors, including re-use intention, were recognized. Second, the trust in communication with online fashion mall avatars significantly positively affected performance expectancy for the avatar. Among the subfactors, cognitive trust was determined to have a greater influence on purchase choice expectations than affective trust. Third, the performance expectancy for the online fashion mall avatar significantly positively affected re-use intention. Notably, the subfactor performance expectancy had a greater influence than purchase choice expectations. Finally, trust in communication with online fashion mall avatars significantly positively affected re-use intention. Accordingly, the sub-factor cognitive trust had a greater influence on re-use intention than affective trust. The results of this study are academically significant in that they empirically test the influence relationship between trust in communication, performance expectancy, and re-use intention, considering the personal characteristics of online fashion mall avatars on consumers using the Meta-UTAUT model in the fashion field.

• Journal of Venture Innovation
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• v.4 no.3
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• pp.125-139
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• 2021

As the 4th industrial revolution progresses, new technologies and services are being born, growing, and maturing. Now, beyond the mobile era, the metaverse is being discussed as a new paradigm. Therefore, in this study, in preparation for the metaverse era, we tried to analyze what factors have an important influence when consumers want to use new technologies. In particular, the research was conducted focusing on how the context in which consumers use the technology changes depending on whether they are B2C or B2B. For this, augmented reality (AR) was selected in the B2C context by linking the research subject with the metaverse era, and the smart factory was selected in the B2B context. The research model for the analysis was established by deriving and setting common influence variables by reflecting the characteristics of the research target technology based on the modified extended unified theory of acceptance and use of technology. A survey was conducted for empirical analysis, and 150 AR and 150 smart factory subjects were analyzed. The empirical study results are as follows. The relationship between performance expectancy and intention to use, technology readiness and intention to use was found to have a significant positive (+) effect on both AR and smart factory. On the other hand, it was found that effort expectancy, social influence, and trust had a positive (+) effect on intention to use only in AR. Only in smart factory, facilitating conditions had a significant positive (+) effect on intention to use. It was also found that the perceived risk had a significant negative (-) effect on the intention to use only in the smart factory. The results of this study are academically significant in that we empirically test that influencing factors of technology use varies depending on the context in which it is used by consumers. In practice, it provided an implication of what to focus on first is being implemented.