• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.157-175
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• 2022

At this moment, a paradigm shift is taking place across all sectors of society for the transition movements to the digital economy. Various movements are taking place in the global agricultural industry to achieve innovative growth using big data which is a key resource of the 4th industrial revolution. Although the government is making various attempts to promote the use of big data, the movement of the agricultural industry as a key player in the use of big data, is still insufficient. Therefore, in this study, effects of performance expectations, effort expectations, social impact, facilitation conditions, based on the Unified Theory of Acceptance and Use of Technology(UTAUT), and innovation tendencies on the acceptance intention of big data were analyzed using the economic and practical benefits that can be obtained from the use of big data for agricultural-related companies as moderating variables. 333 questionnaires collected from agricultural-related companies were used for empirical analysis. The analysis results using SPSS v22.0 and Process macro v3.4 were found to have a significant positive (+) effect on the intention to accept big data by effort expectations, social impact, facilitation conditions, and innovation tendencies. However, it was found that the effect of performance expectations on acceptance intention was insignificant, with social impact having the greatest influence on acceptance intention and innovation tendency the least. Moderating effects of economic benefit and practical benefit between effort expectation and acceptance intention, moderating effect of practical benefit between social impact and acceptance intention, and moderating effect of economic benefit and practical benefit between facilitation condition and acceptance intention were found to be significant. On the other hand, it was found that economic benefits and practical benefits did not moderate the magnitude of the influence of performance expectations and innovation tendency on acceptance intention. These results suggest the following implications. First, in order to promote the use of big data by companies, the government needs to establish a policy to support the use of big data tailored to companies. Significant results can only be achieved when corporate members form a correct understanding and consensus on the use of big data. Second, it is necessary to establish and implement a platform specialized for agricultural data which can support standardized linkage between diverse agricultural big data, and support for a unified path for data access. Building such a platform will be able to advance the industry by forming an independent cooperative relationship between companies. Finally, the limitations of this study and follow-up tasks are presented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.1
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• pp.74-82
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• 2019

The revolution of industry 4.0 is enabling us to build an intelligent connection society called smart cities. The use of renewable energy in particular solar energy is extremely important for modern society due to the growing power demand in smart cities, but its difficult to monitor and manage in each buildings since need to be deploy low energy sensors and information need to be transfer via wireless sensor network (WSN). The Internet of Things (IoT) / low-power wide-area (LPWA) is an emerging WSN technology, to collect and monitor data about environmental and physical electrical / electronics devices conditions in real time. However, providing power to IoT sensor end devices and other public electrical loads such as street lights, etc is an important challenging role because the sensor are usually battery powered and have a limited life time. In this paper, we proposes an efficient solar energy-based power management scheme for smart city based on IoT technology using LoRa wide-area network (LoRaWAN). This approach facilitates to maintain and prevent errors of solar panel based energy systems. The proposed solution maximizing output the power generated from solar panels system to distribute the power to the load and the grid. In this paper, we proved the efficiency of the proposed system with Simulink based system modeling and real-time emulation.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.1-21
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• 2020

Recently, the emergence of new technologies caused by the Fourth Industrial Revolution caused a great change not only in the overall society but also in the retail industry. In the retail industry, unmanned stores based on new technologies have emerged, changing the consumption behavior of consumers. In particular, the global pandemic caused by COVID-19, which appeared in December 2019, raised social risks, and as a result of this, the beginning of the non-face-to-face era, interest in unmanned stores is increasing. In this study, the effects of benefits factors (perceived usefulness, perceived economics, perceived enjoyment, relative advantages) and sacrifice factors (perceived risk, technicality) perceived by unmanned store users on continuous use intention through perceived value. In addition, it is a study to test through empirical analysis what role the social risk from COVID-19 plays in the process of consumption through unmanned stores. The purpose of this study is to provide strategic implications for the activation of unmanned stores in the non-face-to-face era. In this study, a total of 293 copies of data were collected for users of unmanned stores for hypothesis testing. In addition, the collected data was analyzed using SPSS 21.0 and AMOS 21.0 statistical programs. The results of the study are summarized as follows. First, it was found that the perceived benefits (perceived usefulness, perceived economics, perceived playfulness, and relative advantages) of unmanned stores all had a significant positive effect on perceived value. Second, it was found that all perceived sacrifices (perceived risk, technicality) of unmanned stores had a significant negative effect on perceived value. Third, it was found that the perceived value of unmanned stores had a significant positive effect on the intention to continue use. Finally, the social risk from COVID-19 has been shown to play a moderating role when the perceived sacrifice of unmanned stores affects the perceived value.

• 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.

• Proceedings of the Korean Society of Crop Science Conference
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• 2019.09a
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• pp.5-5
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• 2019

The objectives of this paper are (i) to analyze past trends and future directions of rice production, consumption and trade across the world and (ii) to discuss emerging challenges and future directions in the global rice industry. Rice is a staple food of over half of the world's 7.7 billion people. It is an important economic, social, political, and cultural commodity in most Asian countries. Rice is the $1^{st}$ most widely consumed, $2^{nd}$ largely produced, and $3^{rd}$ most widely grown food crop in the world. It was cultivated by 144 million farms in over 100 countries with harvested area of over 163 million ha producing about 745 million tons paddy in 2018. About 90% of the total rice is produced in Asia. China and India, the biggest rice producers, account for over half of the world's rice production. Between 1960 and 2018, world rice production increased over threefold from 221 to 745 million tons (2.1% per year) due to area expansion from 120 to 163 million ha (0.5% per year) and paddy yield increase from 1.8 to 4.6 t/ha (1.6% per year). The Green Revolution led massive increase in rice production prevented famines, provided food for millions of people, reduced poverty and hunger, and improved livelihoods of millions of Asians. The future increase in rice production must come from yield increase as the scope for area expansion is limited. Rice is the most widely consumed food crop. The world's average per capita milled rice consumption is 64 kilograms providing 19% of daily calories. Asia accounted for 84% of global consumption followed by Africa (7%), South America (3%), and the Middle East (2%). Asia's per capita rice consumption is 100 kilograms per year providing 28% of daily calories. The global and Asian per capita consumption increased from the 1960s to the 1990s but stable afterward. The per capita rice consumption is expected to decline in Asia but increase outside Asia especially in Africa in the future. The total milled rice consumption was about 490 million tons in 2018 and projected to reach 550 million tons by 2030 and 590 million tons by 2040. Rice is thinly traded in international market because it is a highly protected commodity. Only about 9% of the total production is traded in global rice market. However, the volume of global rice trade has increased over six-fold from 7.5 to 46.5 million tons between the 1960s and 2018. A relatively small number of exporting countries interact with a large number of importing countries. The top five rice exporting countries are India, Thailand, Vietnam, Pakistan, and China accounting for 74% of the global rice export. The top five rice importing countries are China, Philippines, Nigeria, European Union and Saudi Arabia accounting for 26% of the global rice import. Within rice varieties, Japonica rice accounts for the highest share of the global rice trade (about 12%) followed by Basmati rice (about 10%). The high concentration of exports to a few countries makes international rice market vulnerable to supply disruptions in exporting countries, leading to higher world prices of rice. The export price of Thai 5% broken rice increased from 198 US$/ton in 2000 to 421 US$/ton in 2018. The volumes of trade and rice prices in the global market are expected to increase in the future. The major future challenges of the rice industry are increasing demand due to population growth, rising demand in Africa, economic growth and diet diversification, competition for natural resources (land and water), labor scarcity, climate change and natural hazards, poverty and inequality, hunger and malnutrition, urbanization, low income in rice farming, yield saturation, aging of farmers, feminization of agriculture, health and environmental concerns, improving value chains, and shifting donor priorities away from agriculture. At the same time, new opportunities are available due to access to new technologies, increased investment by the private sector, and increased global partnership. More investment in rice research and development is needed to develop and disseminate innovative technologies and practices to overcome problems and ensure food and nutrition security of the future population.