• Korean Journal of Organic Agriculture
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• v.28 no.3
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• pp.315-334
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• 2020

This study was conducted to analyze factors affecting acceptance of smart farm technology. Smart farm technology is rapidly being introduced to agriculture in accordance with the progress of the 4th Industrial Revolution, but research on this is still little. Therefore, in this study, based on the unified theory of acceptance and use of technology (UTAUT), a research model reflecting the characteristics of smart farm technology was constructed. To test this, empirical analysis was performed. A survey was conducted for students in smart farm technology education and adult male and female farmers who are currently planning to operate smart farms. Valid 204 sample were used for analysis. The hypothesis test was based on multiple regression analysis using SPSS 24 statistical package. For the mediating effect and moderating effect, Process Macro 3.4 based on the regression equation was used. The results of testing the hypothesis are as follows. First, in the causal hypothesis test, it was shown that performance expectancy, social influence and price value have a significant positive effect on the intention to use smart farm technology. On the other hand, effort expectancy, facilitating conditions were not tested for a significant influence on the use of smart farm technology. As a result of analyzing the mediating effect of trust, it was found that trust plays a mediating role between performance expectancy, effort expectancy, social influence, facilitating conditions, price value and intention to use smart farm technology. In particular, the effort expectancy has not been tested for a direct significant effect on intention to use smart farm technology, but it has been shown to have an impact through trust. Trust was found to be a full mediating between the effort expectancy and the intention to use the smart farm technology. The current IT level of prospective users has been shown to play a moderating role between performance expectancy, facilitating conditions and intention to use smart farm technology. In particular, the IT level was found to strengthen the relationship between performance expectancy and intention to use smart farm technology. Based on the results of these studies, academic and practical implications were suggested.

• The Journal of Bigdata
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• v.9 no.1
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• pp.17-30
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• 2024

This study categorizes the factors that affect the intention to continue using ChatGPT into positive motivational factors (personalization, social influence) and negative motivational factors (privacy concern, perceived risk) and investigates whether they affect the intention to continue using ChatGPT through information trust, performance expectancy, and effort expectancy. To this end, a survey was conducted among 265 adults in their 20s and above who have used ChatGPT service. The results showed that personalization and social influence had a defining effect on information trust, performance expectancy, and effort expectancy. For privacy concern, we found a negative effect of wealth on performance expectations, but not on effort expectations. Perceived risk had a negative effect on performance expectancy and effort expectancy. In addition, information trust, performance expectancy, and effort expectancy have a defining effect on continuance intention. This study extends the scope of existing research that focuses on positive factors, deepens our the understanding of ChatGPT. It also provides useful suggestions for continued use of ChatGPT.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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• pp.151-162
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• 2011

This study defined acceptance of high-tech new product as user's active using the product such as utilizing application software rather than purchasing it. As exploring factors to effect on customer's acceptance, usefulness expectancy was examined from product side and application side. When investigating the exogenous variables to influence on usefulness expectancy from the product side, customer's product knowledge and social influences are put forward to support the hypothesis. From the application side, customer's knowledge about the application and its trust are put to explain usefulness expectancy of the application. Smart phone users were good subjects for this study and most hypotheses were tested using structural equation model and the results are followings. First usefulness expectancy of the product and of application significantly effect on customer's intention to use high-tech new product and also usefulness expectancy of the product positively effects on which of the application. Second customer's perceived knowledge about the product and social influences impact usefulness expectancy of the product. But third customer's perceived trust toward application didn't any positive impact usefulness expectancy of the application. Through the result, there will be several implications. First, from both of side; product and application, customer's usefulness expectancy should be satisfied to be successful in high-tech products market. Second, customer should be Ieant about advantages of the product and knowledge about the application as well, and then trigger their usefulness expectancy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.801-808
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• 2021

The issue of the relationship between environmental degradation and human health has been widely addressed by medical doctors. However, economists have sparsely debated it. The release of carbon dioxide (CO2) into the air can cause several environmental problems and, thus, it can affect human health. Therefore, it is imperative to examine the effect of CO2 emissions on life expectancy in the D-8 countries (Malaysia, Indonesia, Bangladesh, Nigeria, Egypt, Iran, Pakistan, and Turkey) from 1992 to 2017. The panel ARDL method is employed and, then, the PMG estimator is selected. The results show that economic growth, population growth and health expenditure can significantly and positively affect life expectancy, but CO2 emissions can have a significant and negative effect on life expectancy. Since, the major findings reveal that life expectancy can be explained by CO2 emissions. Hence, it is important to formulate policies on reducing CO2 emissions so that life expectancy will not be affected. Energy diversification policies should be formulated or improved in some countries. This is to ensure that the countries are not highly dependent on non-renewable energy that can harm the environment. The government should increase its expenditure on the health sector to save more lives by extend human lifespan.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.181-189
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• 2017

This study examines whether the variables used in the Unified Theory of Acceptance and Use of Technology(performance expectancy, effort expectancy, social influence, and facilitating conditions) is applicable to continuous usage of uTradeHub at a time of expansion in the use of uTradeHub. In addition, the role of user satisfaction(mediating effect) and CEO support(interaction effect) in the relationship is identified attempting to provide basic data to help uTradeHub management strategy establishment. A total of 101 valid responses collected through questionnaires were used for empirical analysis(using SPSS 24.0), and the results are as follows. First, for the effect of the integration technology acceptance factor on user satisfaction(H1), only performance expectancy, effort expectancy, and social influence were significant, but facilitating conditions was not significant. Second, for the effect of user satisfaction on the continued use of uTradeHub(H2), there was a significant result. Third, the mediation effect on verification of user satisfaction(H3) was full where performance expectancy, effort expectancy, and social influence prompted continuous usage through user satisfaction. Fourth, for interactive effect verification of CEO support(H4), an interaction effect was shown only in the influence relationship of performance expectancy and social influence on user satisfaction.

• 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.

• YAKHAK HOEJI
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• v.56 no.1
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• pp.66-69
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• 2012

This study aims to assess the aggregate contribution of new drugs to the increase in life expectancy. We constructed a panel data combining mortality data in KOSIS and a drug dataset generated by assigning new drugs listed in 2000~2009 to their respective ICD codes. We found that 10% increase in stock of new drug led to 0.13~0.27% increase in the probability of survival to age 65. Due to lack of disease-specific life table, we used indirect approach to estimate the effect of new drugs on longevity. Using ordinary least squares, the estimate of the probability of survival to age 65 (logarithm) on life expectancy for all ages was 24.92. In conclusion, the increase in life expectancy of the entire population in Korea between 2000 and 2009 resulting from NMEs is 1.95 years, which explains 46.6% of real increase in life expectancy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.4162-4179
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• 2017

The main purpose of this research is to investigate relationships between the significant control factors on acceptance intention to User Experience (UX) sports smart wearable devices by applying Technology Readiness (TR) and Unified Theory of Technology (UTAUT). Research survey targeted on users of golf smart devices in Seoul. A total 534 questionnaires were collected and used for testing hypotheses. Methods to analyze the data included frequency analysis, reliability analysis, confirmatory factor analysis, correlation analysis, and structural equation modeling in accordance with the purpose of the study by using SPSS and AMOS. The results are as follows; First, positive TR had a significantly positive effect on social influence, effort expectancy, facilitating conditions, perceived enjoyment, performance expectancy. Second, negative TR had a significant negative effect on performance expectancy, social influence, facilitating conditions, perceived enjoyment. Third, TR had a no significantly effect on behavioral intention. Fourth, performance expectancy, perceived enjoyment and facilitating conditions had a significantly positive effect on behavioral intention. Fifth, behavioral intention had a significantly positive effect on use behavior. Thus it became crucial to identify the difference in acceptance intention models per each products are as follows. Positive TR of golf-related mobile application users has a positive effect on both technology acceptance belief and acceptance intention, whereas negative TR has no statistically significant effect on technology acceptance belief nor acceptance intention.

• Korea journal of population studies
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• v.16 no.2
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• pp.65-83
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• 1993

The purpose of this study is estimate limits of Korean life expectancy at birth by 'Gompertz growth curse Model', 'Cause-Elimination Model' and Multidimensional models of Senescencee and Mortality'. Data used in Gompertz curve were obtained from all life tables published from 1905 to 1990 in Korea, and life expectancies at birth of eighteen groups were selected at five-year interval in consideration of time-series changes. Data used in Cause-Elimination Model are 'Cause of Death statistics in 1991' published in 1992 by National Bureau of Statistics of Korea and 'life table of 1989' published in 1990 by National Bureau of Statistics, Economic Planning Board of Korea. The materials are all classifiable death data, 119, 253 cases of male and 82, 420 cases of female, which is from 1991 Causes of Death statistics. The cases of death analyzed belong to one of 8 categories; i.e., Infectious and Parasitic Diseases(001-139; with notation of Infectious Diseases), Malignant Neoplasms(140-208), Hypertensive Diseases(401-405), Ischemic Heart Dieases and Diseases of Pulmonary Circulation and Other Forms of Heart Diseases(410-429;with notation of Heart Disease), Cerebrovascular Diseases(430-438), Chronic Liver Diseases and Cirrhosis(571; with notation of Liver Diseases), Injury and Poisoning(800-999) and all other disease. Data used in 'Multidimensional models of senescence and mortality' were life table of 1989 published by National Bureau of statistics, Economic Planning Board of Korea and life table of 1970, 1978-79, 1983, 1985 and 1987. The major findings may be summarised as follows: 1. Estimate equations of Gompertz growth curve using life expectancy at birth during the 1905-1990 period are as the following. Male : y = 88.047697 $\times$ $0.199690^{0.903381x}$ Female : y = 95.632828 $\times$ $0.199690^{0.903381x}$ Limits of life expectancy at birth, which were estimated by Gompertz growth curve, are 88.05 for male and 95.63 for female. 2. The effect on life expectancy at birth eliminationg all causes death is 14.04 years(for male) and 10.86 years(for female). Astonishingly, eliminating the malignant neoplasms increase life expectancy at birth by 2.85 years for male 2.03 years for female in 1991. In table 8 we show the effect on life expectancy at birth of separately eliminating each of the 8 categorical causes of death. The theoretical limit to life expectancy by Cause-Elimination Model is 80.96 for male and 85.82 for female. 3. If the same rate of delay [0.376 year(male), 0.435 year(femable) per calendar year] continued, then life expectancy at birth would reach 74.82(male) years and 84, 10(female) years in 2010. With 14.04-years(male) and 10.86-years(female) effect attributable in 2010 would be 88.86 years(male) and 94.96(femable) years. 4. 'Multidimensional models of senescence and death' permits calculations of the value of the attribution coefficient (B), percent of loss per year of physiologic function. The results of Ro and B during the 1970-1989 period are listed in table 9. Estimate of limit to Korean life expectancy at birth by 'Multidimensional models of senescence and death' is 99.47 years for male and 104.74 years for female in 1989.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.41-76
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• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.