• The Journal of Korean Association of Computer Education
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• v.17 no.5
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• pp.35-42
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• 2014

This study utilized an analysis of teachers' use of smart devices in the class, which highlighted the impact factors, and elucidated the relationship between in fluencing factors. The Unified theory of Acceptance and Use of Technology (UTAUT) model is based on the study of extension and modification of the model proposed. For the study, a survey of 1,016 elementary school teachers in the Gangwon and Metropolitan(Seoul, Gyeonggi, Incheon) area was conducted. Was applied using the AMOS structural equation modeling analysis. The analysis examines the utilization of smart devices in class as factors affecting performance expectancy, effort expectancy of the main variables showed, Instant connectivity and Openness that has a direct impact on the performance expectations and effort expectancy.

• 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.