해운항만기업의 빅데이터 사용의도에 영향을 미치는 요인에 관한 연구

Abstract

As global competition is intensified, more and more companies have tried to enhance their competitiveness by gaining insights through big data analysis. In particular, shipping and port companies which traditionally have focused on hardware expansion, are now actively driving themselves to introduce the new technology related to big data analysis. With the emergence of the fourth industrial revolution, it is for sure that these companies are now faced with unprecedented opportunities which ICT could create. Nowadays, it is evident that success heavily depends on how strategically and effectively the companies utilize and analyze this massive volume of data. As an effort to adopt big data system in the field of shipping and port companies increases, ways of enhancing intention to use big data are absolutely needed for the companies which have just employed this new technology or consider to do so.

In this respect, the purpose of this study is as follows.

Firstly, through prior research, the study identifies definition and characteristics of big data, its related technology, as well as the current status of the port and shipping companies. Secondly, based on the theory of TOE (Technology Organization Environment) and IDT (Innovation Diffusion Theory) this study is designed to indicate the factors which affect the companies' intention of using big data. Thirdly, this study is designed to empirically verify how those factors affect expected performance by and intention of using big data. To achieve the objective of this study, an empirical analysis has been conducted targeting for staff involved in the department of strategic planning and information technology in the related field. However, since the recognition of big data is relatively low in general and rarely applied to works, the analysis of factors affecting intention of using big data is heavily reliant upon staff having higher recognition on this area.

In designing model of research, TOE is chosen considering that big data changes work process of the organization, support from top management level is critical in its adoption, and sometimes big data can be used as means of responding to outside pressure (not by company's voluntary will), while IDT is chosen because big data can lead innovation of companies. The main reason of verifying an intention to use big data by individual level, is to take into account his or her separate intention, even though big data is adopted companywide. Variables of DOI (Diffusion of Innovation)'s are used instead of technology characteristic among technology characteristic, organization characteristic and environment characteristic of TOE model in this study. A relative advantage, complexity and compatibility are adopted as variables of the technology characteristic while a firm size and support from top management are adopted as variables of organization characteristic and a competitor pressure and regulatory support are adopted as environment characteristic.

Eight hypotheses were set up in verifying relevance between variables of the above mentioned three characteristics, and expected performance and intention of using big data. A survey for hypothesis test had been conducted and collected for two weeks from 30th of October to 15th of November in 2017 by mail, e-mail and visit. Likert 5-pont scale was used to develop measurement instrument and fomulate questionnaire, 155 effective questionnaires out of 200 were gathered. SPSS 21.0 was used to analyze the demographic characteristic and frequency while Smart-PLS 3.0 was used to conduct hypothesis test, analysis of reliability and validity.

The summarized result of this study is as follows.

Firstly, from Technology Characteristic, relative advantage, complexity and compatibility reacted positively to the expected performance. Secondly, from Organization Characteristic, support from top management reacted positively but the firm size reacted not positively to the expected performance. Thirdly, from Environment Characteristic, competitive pressure reacted positively while the regulatory support reacted not positively to the expected performance. Uniqueness of this study can be found in empirically verifying various factors which could promote big data analytics for shipping and port companies, based on models of TOE and IDT.

In addition, following practical implications are presented as result of this study.

Firstly, if the big data has technical convenience and advantage, it has a positive effect on both expected performance and intention to use big data. In addition, if technology of big data analytics is as convenient as existing technology, can be easily employed in current works, and there is no extra burden in application, companies are positively likely to adopt this new technology. Therefore, to increase the intention to use big data technology, shipping and port companies have to make plans for enhancing relative advantage and compatibility while decreasing complexity.

Secondly, in a view of organization characteristic, support from top management will have positive effect on intention of using big data because extra expenditure is required for operation and training both internally and externally. Size of the firms is found not to have statistically meaningful relevance with the expected performance. Therefore, support from top management is definitely required to develop the intention to use big data for the shipping and port companies. For future research it is suggested to clarify how the firm size is related to expected performance and intention to use big data conducted.

Lastly, from environment characteristic, use of big data will increase if there is high level of competitor pressure and as a result company's effort not to fall behind in the competition is followed. When it comes to regulatory support rejection, it might be explained that the respondent may do not know their company's actual regulation well or even if they knew, due to limited awareness it might be difficult for them to respond with clarity.