• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.1-18
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• 2013

Big data has become a worldwide topic. Despite this, big data accurately understand and acquire the business to take advantage of companies that were only very few. The purpose of this study is to investigate the factors that effect Korean firm's adopting big data system. Empirical test was conducted to verify hypotheses using extended technology acceptance model and we analyzed factors which affect the behavioral intention of big data System. Based upon previous researches, we have selected organization innovation, organization slank, organization information system infra maturity, perceived benefits of big data system, perceived usefulness, perceived ease of use, behavioral intention as variables and proposed a research model based on survey questionnaires. From those, we drew that perceived usefulness and perceived ease of use influenced the behavioral intention. The results of this study will increase the users' awareness on big data system and contribute to develop a way to enable the introduction of new technologies.

• The Journal of Information Systems
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• v.30 no.3
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• pp.159-175
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• 2021

Purpose The purpose of this study was to find the effect of the determinants on the Big data utilization in industry. The determinants of Big data utilization were deduced by reviewing theoretical background and discussions on Big data related researches. Research model and proposed hypothesis were constructed from TOE framework and UTAUT model. Design/methodology/approach The research was conducted to collect a sample data from the experts involved in the Big data projects in industry. In addition, interviews and online survey were performed to get sample data. Exploratory factor analysis was conducted to verify the grouping of these questionnaire items and confirmatory factor analysis was done to verify the validity and reliability of the measurement model. Finally, research hypothesis was verified and theoretical and practical implications were proposed for further studies. Findings The results show that the technical factor have a significant effect on the expectancy factor and the behavioral factor. The organizational factor have a significant effect on the behavioral factor. In addition, the expectancy factor was significant on the behavioral factor and the intention-to-use of Big data system.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.478-485
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• 2019

This is an exploratory study to examine factors affecting South Korean companies' intentions to use big data technology and services based on whether the companies use public data or not. This study, using R, conducted chi-squared tests and logistic regression analysis. As a result of the logistic regression analysis, cost reduction had a positive effect on the big data-use intentions in companies that use public data, whereas with companies that do not use public data, customer satisfaction had a positive impact, and support for decision-making had a negative impact on the intention to use big data. Recently, the South Korean government has focused on improving the utilization of public data and big data. However, as a result of this study, the use of public data and big data in South Korea is still insufficient. Yet, considering that the data utilized for this study was created in 2017, additional study using public data and big data is also required.

• Informatization Policy
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• v.24 no.1
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• pp.79-99
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• 2017

Despite the early success story, the pan-industry diffusion of big data has been slow mostly due to lack of confidence of the value creation and privacy-related concerns. The problem leads us to the need to a stakeholder analysis on the adoption process of big data. The present study combines technology acceptance model, task-technology fit theory, and privacy calculus theory to integrate the positive and negative factors on the big data adoption. The empirical analysis was performed based on the survey from the current and potential big data users. Results revealed perceived usefulness, task-technology fit, and privacy concern are significant antecedents to the intention to use big data. Furthermore, there are significant differences in the perceptions of each constructs among groups divided by the types of big data use, with several exceptions. And the control effect was found in the magnitude of the relation between independent variables and dependent variable. The theoretical and politic implications of the analysis are discussed as to the promotion of big data industry.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.237-253
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• 2018

In a rapidly changing environment, dynamic resources have become important factors for companies, the use of Big Data come into focus new core value of business but researches on the major resources and capabilities of companies are insufficient. In this study, the effect of dynamic capability and entrepreneurial orientation in the SMEs on the intention of Big Data utilization are explored. For the purpose of empirical analysis, the survey condusted of 364 domestic SMEs to analyze the effect of dynamic capability on the intention of Big Data utilization through entrepreneurial orientation, performed a parallel multi-parameter analysis of using SPSS Win Ver.22.0 and PROCESS macro v3.0. The results of hypothesis testing showing that dynamic resources and entrepreneurial orientation had positive influence intention of big data utilization. For the determinants of Big Data utilization related to AI it provide suggestions thereby improving the understanding of dynamic capabilities and entrepreneurial orientation and helping to improve the management of SMEs.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1132-1139
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• 2019

Big data is prominently used as a prediction method in achieving a goal, because it can analyze the regularities to predict future results from a vast amount of past data. Furthermore, big data has huge influence in very diverse academic fields. On such awareness, this study analyzed the regular effect of e-Commerce usefulness from the effects which expectations on big-data service affect the usage purpose of e-Commerce usefulness. This study categorized e-Commerce usefulness into quality recognition, service, and ease, and studied how each category works between the relationship of big-data service expectation and the use intention.

• Journal of Information Technology Applications and Management
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• v.21 no.4_spc
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• pp.463-483
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• 2014

Data are dramatically increased and big data technology is spotlighted innovative technology among the latest information technologies. Organizations are interested in adoption of big data system to analyze various data format and to identify new business opportunity. The purpose of this study is to build a unified model for a system adoption through analysis of impact that affects behavioral intention and usage behavior of using big data. This study in addition to Technology-Organization-Environment (TOE), that is used the introduction of organizational studies, and Diffusion of Innovation (DOI) have implemented an extended unified model including the unified theory of acceptance and use of technology (UTAUT) that is usually used in personal level adoption study. The hypothesis was set up after implementing research model, and then got 411 effective survey data to target the member of organizations. As a result, all models (UTAUT, TOE, DOI) are affect to behavioral intention and usage behavior. It is verified that the suggested unified model was appropriate.

• Journal of Korea Port Economic Association
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• v.34 no.3
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• pp.159-182
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• 2018

The purpose of this study is to increase the competitiveness of big data in the maritime port organization, by understanding the expected performance and the intention to accept and use big data. In the empirical analysis of factors affecting the intention to use the big data technology for maritime port organizations, the variables employed are based on the Technology Organization Environment(TOE) and Diffusion of Innovations(DOI) theories, which are related to the acceptance of information and communication technologies. To achieve the objective of this study, an empirical analysis was conducted; this analysis targeted the personnel involved in the department of strategic planning and information technology in the related field. We set up eight hypotheses to examine the relevance between variables having three characteristics-technology, organization, and environmental characteristics. The empirical results are summarized as follows. First, it was seen that the technology characteristic, including relative advantage, complexity, and compatibility, has a significant effect on the expected performance. Second, the top management support of the organization characteristic has a significant effect, but the firm size of this characteristic has no significant effect on the expected performance. Third, the competitive pressure of the environment characteristic has a positive effect on the expected performance, while the regulatory support has no significant effect. Finally, the expected performance has a significant effect on the intention to use big data.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.249-267
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• 2021

The use of big data accumulated along with the progress of digitalization is bringing disruptive innovation to the global agricultural industry. Recently, the government is establishing an agricultural big data platform and a support organization. However, in the domestic agricultural industry, the use of big data is insufficient except for some companies in the field of cultivation and growth. In this context, this study identifies factors affecting the intention to use big data in terms of technology, organization and environment, and also confirm the moderating effect of technical field, focusing on agricultural ventures which should be the main entities in creating innovation by using big data. Research data was obtained from 309 agricultural ventures supported by the A+ Center of FACT(Foundation of AgTech Commercialization and Transfer), and was analyzed using IBM SPSS 22.0. As a result, Among technical factors, relative advantage and compatibility were found to have a significant positive (+) effect. Among organizational factors, it was found that management support had a positive (+) effect and cost had a negative (-) effect. Among environmental factors, policy support were found to have a positive (+) effect. As a result of the verification of the moderating effect of technology field, it was found that firms other than cultivation had a moderating effect that alleviated the relationship between all variables other than relative advantage, compatibility, and competitor pressure and the intention to use big data. These results suggest the following implications. First, it is necessary to select a core business that will provide opportunities to generate new profits and improve operational efficiency to agricultural ventures through the use of big data, and to increase collaboration opportunities through policy. Second, it is necessary to provide a big data analysis solution that can overcome the difficulties of analysis due to the characteristics of the agricultural industry. Third, in small organizations such as agricultural ventures, the will of the top management to reorganize the organizational culture should be preceded by a high level of understanding on the use of big data. Fourth, it is important to discover and promote successful cases that can be benchmarked at the level of SMEs and venture companies. Fifth, it will be more effective to divide the priorities of core business and support business by agricultural venture technology sector. Finally, the limitations of this study and follow-up research tasks are presented.