A Study on the Key Factors Affecting Big Data Use Intention of Agriculture Ventures in Terms of Technology, Organization and Environment: Focusing on Moderating Effect of Technical Field

농업벤처기업의 빅데이터 활용의도에 영향을 미치는 기술·조직·환경 관점의 핵심요인 연구: 기술분야의 조절효과를 중심으로

  • Received : 2021.10.22
  • Accepted : 2021.12.20
  • Published : 2021.12.31

Abstract

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.

디지털화의 진전과 함께 축적된 빅데이터의 활용은 글로벌 농산업계에 파괴적 혁신을 가져오고 있다. 최근 정부는 농업 빅데이터 플랫폼 구축 및 지원조직 신설 등의 조치를 취하고 있으나 국내 농산업계는 재배생육 분야의 일부기업 외에는 빅데이터 활용이 미흡한 실정이다. 이러한 배경에서 본 연구는 빅데이터를 선도적으로 활용하여 혁신을 창출하는 주체가 되어야 할 농업벤처를 중심으로 기술, 조직, 환경의 맥락에서 빅데이터 활용의도에 영향을 미치는 요인을 규명하고 기술분야에 따른 조절효과를 확인하고자 하였다. 이에 농업기술실용화재단 A+센터의 지원을 받는 농업벤처 309개로부터 연구 데이터를 확보하여 SPSS 22.0을 이용하여 분석하였다. 연구결과, 기술적 요인 중에서는 상대적 이점과 호환성이 유의한 정(+)의 영향을 미치고, 조직적 요인 중에서는 경영층 지원이 정(+)의 영향을, 비용이 부(-)의 영향을 미치며, 환경적 요인 중에서는 정책적 지원이 정(+)의 영향을 미치는 것으로 나타났다. 기술분야의 조절효과 검증 결과, 재배생육 외 기업일수록 상대적 이점, 호환성, 경쟁자 압력 외의 모든 변수와 빅데이터 활용의도와의 관계를 완화하는 조절효과가 있는 것으로 나타났다. 이러한 결과를 통해 다음과 같은 시사점을 제시하였다. 첫째, 빅데이터 활용을 통해 농업벤처에 새로운 수익창출 및 운영효율성 제고 기회를 제공할 핵심사업을 선정하여 정책적으로 협업기회를 늘릴 필요가 있다. 둘째, 농산업 특성으로 인한 분석의 어려움을 극복할 수 있는 빅데이터 분석 솔루션 제공이 필요하다. 셋째, 농업벤처와 같은 소규모 조직에서는 최고경영층의 빅데이터 활용에 대한 높은 이해수준으로부터 출발한 조직문화 재편 의지가 선행되어야 한다. 넷째, 중소·벤처기업 수준에서 벤치마킹할 수 있는 성공사례를 발굴하고 홍보하는 것이 중요하다. 다섯째, 농업벤처 기술분야별로 핵심사업 추진과 지원사업의 우선순위를 나누어 추진하는 것이 보다 효과적일 것으로 판단된다. 마지막으로 본 연구의 한계점과 후속 연구과제를 제시하였다.

Keywords

References

  1. AgFunder(2021). 2021 AgriFoodTech Investment Report. San Francisco: AgFunder.
  2. Agrawal, D., Sudipto, D., & Abbabi, A. E.(2011). Big data and cloud computing: Current state and future opportunities. Proceedings of the 14th International Conference on Extending Database Technology, EDBT/ICDT '11 joint conference, Uppsala: Association for Computing Machinery.
  3. Ahn, D. K., Shin, C. H., & Choi, J. W.(2019). A Study on Efficient Decision-Making Using Big Data by Managers of Small and Medium Size Enterprises. Korean Review of Corporation Management, 10(3), 311-326.
  4. Baker, J.(2011). The Technology-Organization-Environment Framework. Berlin Heidelberg: Springer.
  5. Choi, M. J., Lee, D. H., Kim, S. H., Park, H. S., & Ahn, H. S.(2015). Impacts of Innovative Performance Through Adoption of Technology Convergence Intelligent Robot Among Medium-Sized Manufacturing Firms. Journal of Digital Convergence, 13(8), 301-313. https://doi.org/10.14400/JDC.2015.13.8.301
  6. DeLone, W. H., & Mclean, E. R.(2003). The DeLone and Mclean model of Information Systems Success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
  7. Depietro, R., Wiarda, E., & Fleischer, M.(1990). The context for change: Organization, technology and environment. The Processes of Technological Innovation, 199(0), 151-175.
  8. Dobbs, R., Manyika, J., & Woetzel, J.(2016). No Ordinary Disruption. NY: Perseus Books.
  9. Gil, H. C.(2019). An empirical study on adoption factor and performance analysis of smart factory through technical acceptance model: focusing on TOE and IS success model. Doctoral dissertation, Hansung University.
  10. Heo, C. M., & Ahn, M. H.(2021). Smart Farm Management Strategy. Seoul: Cheongram.
  11. Hsu, P. F., Kraemer, K. L., & Dunkle, D.(2006). Determinants of e-Business Use in US Firms. International Journal of Electronic Commerce, 10(4), 9-45. https://doi.org/10.2753/JEC1086-4415100401
  12. Hu, D., Chen, Y., & Hu, M.(2021). Industrial value chain model and big data application for developing green agriculture in China. Journal of Physics: Conference Series, 1883, 1-5.
  13. Ka, H. K., & Kim, J. S.(2014). An Empirical Study on the Influencing Factors for Big Data Intented Adoption: Focusing on the Strategic Value Recognition and TOE Framework. Asia Pacific Journal of Information Systems, 24(4), 443-472. https://doi.org/10.14329/apjis.2014.24.4.443
  14. Kim, B. C.(2015). A study on the intention to adopt omni-channel shopping and expected effects: focusing on innovation diffusion theory and TOE framework. Doctoral dissertation, Dankook University.
  15. Kim, S. H.(2019). A study on factors affecting the introduction intention of smart city platform based on data collaboration. Doctoral dissertation, Soongsil University.
  16. Lai, Y., Sun, H., & Ren, J.(2018). Understanding the determinants of big data analytics(BDA) adoption in logistics and supply chain management. The International Journal of Logistics Management, 29(2), 676-703. https://doi.org/10.1108/IJLM-06-2017-0153
  17. Lee, H. Y.(2012). Research Methodology, Seoul: Cheongram.
  18. Lee, J. P., & Chang, M. H.(2018). A Study on the Intention to Use Big Data Based on the Technology Organization Environment and Innovation Diffusion Theory in Shipping and Port Organization. Journal of Korea Port Economic Association, 34(3), 159-182. https://doi.org/10.38121/kpea.2018.09.34.3.159
  19. Lee, M. W.(2019). A Study on the Acceptance Factors of BEMS for Small and Medium Buildings in Smart Grid Environment. Doctoral dissertation, Soongsil University.
  20. Lee, N. S.(2020). A Study on the Factors Influencing the Intention to Use Autonomous Vehicles Continuously. Doctoral dissertation, Soongsil University.
  21. Lee, S. W., & Lee, H. S.(2014). A study on an integrative model for big data system adoption: based on TOE, DOI and UTAUT. Journal of Information Technology Applications & Management, 21(4), 463-483.
  22. Ministry of Agriculture, Food and Rural Affairs(2020a). Act Implementation Plan on Provision and Promotion of Use of Public Data. Retrieved(2021.06.24.) from https://www.mafra.go.kr/mafra/324/subview.do.
  23. Ministry of Agriculture, Food and Rural Affairs(2020b). Established "Big Data Strategy Officer", a venture-type organization. Retrieved(2021.06.18.) from https://www.mafra.go.kr/mafra/293/subview.do?enc=Zm5jdDF8QEB8JTJGYmJzJTJGbWFmcmElMkY2OCUyRjMyNDA5MiUyRmFydGNsVmlldy5kbyUzRg%3D%3D.
  24. Oliveira, T., Thomas, M., & Espadanal, M.(2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497-510. https://doi.org/10.1016/j.im.2014.03.006
  25. Park, D. H.(2020). A Study on the Factors that Affect the Perceived Intention to the Use of Robotic Process Automation. Doctoral dissertation, Dankook University.
  26. Park, D. I., & Park, C. H.(2018). Enterprise Competitiveness and Corporate Performance Creation. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 13(6), 177-189. https://doi.org/10.16972/APJBVE.13.6.201812.177
  27. Park, J. Y., Seo, D. S., & Lee, J. M.(2021). 2021 Agricultural Prospects: The future of agriculture, Digital agriculture(E04-2021). Naju: KREI.
  28. Park, K. A., & Lee, K. K.(2019). 2019 Agricultural Prospects: A plan to utilize big data to lead changes in agriculture and rural areas and the future(E04-2019). Naju: KREI.
  29. Park, S. K., Han, K. S., Hong, S. H., Yoo, H. J., & Seol, S. J.(2020). A study on the factors influencing the intention to adopt network streaming connection system characteristics in a smart city environment-focusing on IT industry workers. Journal of Digital Contents Society, 21(6), 1131-1141. https://doi.org/10.9728/dcs.2020.21.6.1131
  30. Robinson, L.(2009). A Summary of Diffusion of Innovations. Retrieved(2021.01.25.) from http://www.enablingchange.com.au/Summary_Diffusion_Theory.pdf
  31. Rogers, E. M.(2003). Diffusion of Innovation. 5th Edition, New York: Free Press.
  32. Shin, S. H., & Lee, S. J.(2019). The Key Factors of Big Data Utilization for Improvement of Management Quality of Companies in terms of Technology, Organization and Environment. Journal of Information Technology Services, 18(1), 91-112. https://doi.org/10.9716/KITS.2019.18.1.091
  33. Sila, I.(2010). Do Organizational and Environmental Factors Moderate the Effects of Internet-based Inter-Organizational Systems on Firm Performance? European Journal of Information Systems, 19. 581-600. https://doi.org/10.1057/ejis.2010.28
  34. Sourav, A. I., & Emanuel, A. W. R.(2021). Recent trends of big data in precision agriculture: A review. Materials Science and Engineering, 1096(1), 1-10.
  35. Sun, S., Cegielski, C. G., Jia. L., & Hall, D. J.(2016). Understanding the Factors Affecting the Organizational Adoption of Big Data. Journal of Computer Information Systems, 58(3), 193-203. https://doi.org/10.1080/08874417.2016.1222891
  36. Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K.(1990). The Process of Technological Innovation. MA: Lexington Books.
  37. Venkatesh, V., Thong, J., & Xu, X.(2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
  38. Verma, S., & Bhattacharyya, S. S.(2017). Perceived strategic value-based adoption of Big Data Analytics in emerging economy: A qualitative approach for Indian firms. Journal of Enterprise Information Management, 30(3), 354-382. https://doi.org/10.1108/JEIM-10-2015-0099
  39. Vong, S., Zo, H., & Ciganek, A. P.(2016). Knowledge Sharing in the Public Sector: Empirical Evidence from Cambodia. Information Development, 32(3), 409-423. https://doi.org/10.1177/0266666914553604
  40. Walker, R. S., & Brown, I.(2019). Big data analytics adoption: A case study in a large South African telecommunications organization. South African Journal of Information Management, 21(1), 1079-1089.
  41. Woo, S. K., Cho, S. I., & Yoon, S. Y.(2018). A Study on the Use of Big Data-based Personal Information De-identification Measures in the Financial Industry: Focused on TOE Framework. The Journal of Internet Electronic Commerce Resarch, 18(3), 71-90.
  42. Yoo, J. H., & Park, C.(2010). A Comprehensive Review of Technology Acceptance Model Research. Entrue Journal of Information Technology, 9(2), 31-50.
  43. Zhu, K., Kraemer, K., & Xu, S.(2003). Electronic Business Adoption by European Firms: A Cross-country Assessment of the Facilitators and Inhibitors. European Journal of Information Systems, 12(4), 251-268. https://doi.org/10.1057/palgrave.ejis.3000475
  44. Zhu, K., Dong, S., Xu, S. X.. & Kraemer, K. L.(2006). Innovation diffusion in global contexts: Determinants of post-adoption digital transformation of European companies. European Journal of Information Systems, 15(6), 601-616. https://doi.org/10.1057/palgrave.ejis.300065