ABSTRACT With advances in internet technologies, innovative industrialization applying big data-based artificial intelligence is leading Industry 4.0. However, opportunities for the public to learn about virtual cryptocurrency or utilities based on blockchain technology are limited, except for Bitcoin. This lack of opportunities generates misunderstandings. This study ascertained and examined the determinants affecting the acceptance of new technologies on Jeju Island, Korea, where tourism is the dominant business. A total of 537 Jeju-do residents and potential visitors from the mainland were analyzed by conducting a partial least squares (PLS)- structural equation modeling (SEM) and one-group pretest–posttest design through a survey after observing a video clip on blockchain implementation on YouTube. The empirical analysis results showed the effect of blockchain trust transparency on the performance and effort expectancy. Facilitating conditions were found to be the most influential in blockchain technology acceptance. Following a video experiment introducing blockchain implementation as a currency for Jeju tourism, a significant positive change towards the implementation of blockchain was observed. This paper presents a concrete application of blockchain technology and shows the possibility of using social media (YouTube) to elicit user awareness and potential interest in the domestic tourism market.
The results of the analysis show that the chi-square value is statistically significant at 134.046 (p < 0.05). The revised residual of the recognition of the concept of the blockchain before watching the video and after watching the video has an average of zero and a standard deviation of one, which is an approximately normal distribution. Therefore, when the absolute value is greater than 1.96, the residual is judged to be significant, and Hypothesis 7 is supported.
id: e399c08f718d73b6a51cf2b1fbd5bf5e - page: 7
PLS-SEM is an analysis method that focuses on the explanation and prediction of the endogenous latent variable corresponding to the dependent variable rather than the structural characteristics of the model; thus, model fit is not a concern. The standardized root mean squared residual (SRMR) value is usually approximately 0.10 to 0.08 or less, and the normed fit index (NFI) value, which is closer to 1, without providing strict standards for model fit . Therefore, the fit of the overall research model presented in Table 7 was considered acceptable. The coefficient of determination R2 value for predictive suitability is generally classified as high if it is 0.26 or higher, medium if it is less than 0.130.26, and low if it is less than 0.020.13 . Therefore, the explanatory power of performance expectancy, which was more influenced by trust and transparency, was 0.605, and the explanatory power of behavioral intention, which was significantly influenced by performance expectanc
id: 3b28471aa0a8d9572cfccdcaf3342959 - page: 7
515.
id: 8c0220b63445e333cd85ce032944ead6 - page: 7
In addition, in the chi-square test, the adjusted residuals (AR) value contributes to statistical significance when its absolute value is greater than 1.96. In particular, the expected counts from those who kept the same perception among those answered as distributed ledger and global trust computing technology result in the top two AR values at 9.2 and 4.9, respectively, which are far higher than 1.96. Therefore, the overall changes from one concept to another were statistically significant. Notably, the case that showed the highest change, especially when looking only at the change from the expected count, was the transition from the perception of Bitcoin to distributed data technology and global trust computing technology. Although only 48 respondents initially recognized blockchain as a global trust computing technology, 102 participants changed their perception after the experiment. Moreover,
id: 21f69ac4962b23d99ba2a0938ebec758 - page: 7