A Study on Factors Affecting BigData Acceptance Intention of Agricultural Enterprises

농업 관련 기업의 빅데이터 수용 의도에 미치는 영향요인 연구

  • Ryu, GaHyun (Dept. of Management Information, Graduate School of Venture, Hoseo University) ;
  • Heo, Chul-Moo (Dept. of Management Information, Graduate School of Venture, Hoseo University)
  • 류가현 (호서대학교 벤처대학원 정보경영학과) ;
  • 허철무 (호서대학교 벤처대학원 정보경영학과)
  • Received : 2022.01.10
  • Accepted : 2022.02.20
  • Published : 2022.02.28

Abstract

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.

디지털 경제로의 전환을 위해 사회 전 분야에 걸쳐 패러다임의 대전환이 이루어지고 있다. 현재 시점에서 농업도 4차산업혁명의 핵심자원인 빅데이터를 활용하여 혁신 성장을 이루고자 글로벌 농산업계는 다양한 움직임이 일어나고 있다. 국내도 정부 차원으로 빅데이터 활용 촉진을 위해 다양한 시도를 시행하고 있으나, 빅데이터 활용 핵심 주체인 농산업계의 움직임은 아직 미흡한 실정이다. 이에 본 연구에서는 농업 관련 기업 종사자를 대상으로 빅데이터 활용 시 얻을 수 있는 경제적 혜택과 실용적 혜택을 조절변수로 하여 통합기술수용이론에 근거한 성과기대, 노력 기대, 사회적 영향, 촉진조건과 혁신성향이 빅데이터 수용 의도에 미치는 영향을 분석하였다. 농업 관련 기업 종사자를 대상으로 수집한 설문지 333부를 실증분석에 사용하였다. SPSS v22.0과 Process macro v3.4를 사용한 분석결과는 첫째, 노력 기대, 사회적 영향, 촉진조건 및 혁신성향은 빅데이터 수용 의도에 정(+)의 방향으로 유의한 영향을 미치는 것으로 나타났고, 성과기대가 수용 의도에 미치는 영향은 유의하지 않은 것으로 나타났다. 수용 의도에 사회적 영향이 가장 크게 영향을 미치고 혁신성향이 가장 작게 영향을 미치는 것으로 나타났다. 노력 기대와 수용 의도 간의 경제적 혜택과 실용적 혜택의 조절 효과, 사회적 영향과 수용 의도 간의 실용적 혜택의 조절 효과, 촉진조건과 수용 의도 간의 경제적 혜택과 실용적 혜택의 조절 효과는 유의한 것으로 나타났다. 반면에 경제적 혜택과 실용적 혜택은 성과기대와 혁신성향이 수용 의도에 미치는 영향력의 크기를 조절하지 않는 것으로 나타났다. 이러한 결과를 통해 다음과 같은 시사점을 제시하였다. 첫째, 기업의 빅데이터 활용 촉진을 위해 정부는 기업 맞춤형 정책 수립을 준비할 필요가 있다. 맞춤형 지원을 통해 기업 구성원들이 빅데이터 활용에 대한 올바른 이해와 공감대를 형성해야 유의미한 성과를 만들어 낼 수 있기 때문이다. 둘째, 농업 데이터 특화된 플랫폼 구축, 표준화 방식 기반으로 데이터 연계, 데이터 접근에 대한 단일화 창구 지원을 마련해야 한다. 이러한 플랫폼 구축은 기업 간 주체적인 협력 관계를 형성하여 산업을 고도화시킬 수 있을 것이다. 마지막으로 본 연구의 한계점과 후속 과제를 제시하였다.

Keywords

References

  1. AgFunder(2021). 2021 AgriFoodTech Investment Report. San Francisco: AgFunder.
  2. Agarwal, R., & Prasad, J.(1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information System Research, 9(2), 204-215. https://doi.org/10.1287/isre.9.2.204
  3. Ahn, M. H.(2021). 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. Asia-Pacific Journal of Business Venturing and Enterpreneurship, 16(6), 249-267.
  4. Alawan, A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R.(2018). Examing factors influencing Jordanian customers' intensions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Customer Services. 40, 125-138. https://doi.org/10.1016/j.jretconser.2017.08.026
  5. Chandon, P., Wansink, B., & Laurent, G.(2000). A Benefit Congruency Framework of Sales Promotion Effectiveness. Journal of Marketing, 64(4), 65-81. https://doi.org/10.1509/jmkg.64.4.65.18071
  6. Choi, E. K.(2019). Effects of Perceived Benefit, Risk, Value, Attitude and Usage Attitude on Continuous Usage Intention toward Mobile Coupon. The Journal of Internet Electronic Commerce Research, 19(3), 173-199. https://doi.org/10.37272/JIECR.2019.06.19.3.173
  7. Cho, S. I.(2018). A Study on Factors Affecting the Intention to Use of New Access Media in e-Financial Transactions. Doctoral dissertation, Soongsil University.
  8. Choi, S. S.(2018). A Study on the Factors Affecting the Intention to Use Drones Delivery Service. Doctoral dissertation, Soongsil University.
  9. Escobar-Rodriguez, T., & Carvajal-Trujillo, E.(2013). Perceived usefulness easy of use, and the user acceptance of information technology. Journal of Air Transport Management, 32, 58-64. https://doi.org/10.1016/j.jairtraman.2013.06.018
  10. Fishbein, M., & Ajzen, I.(1975). Belief, Attitude, Intension and Behavior: An Introduction to Theory and Research. MA: Addison-Wesley, Reading.
  11. Fosso, W. S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D.(2015). How Big Data can make impact: findings from a systematic review and a longitudinal case study. International Journal of Production Economics. 165(3), 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031
  12. Fu, Li., & Choi, K. H.(2021). An Effect of Mobile Travel Application Characteristics on Experience Value, Perceived Value, and Continuous Usage Intention: A Moderating Effect of Relational Benefits. The Korea Academic Society of Tourism and Leisure, 33(3), 179-199.
  13. GIIN(2020). Understanding Impact Performance: Agriculture Investments. Retrieved(2020.10.22.) from https://thegiin.org/research/publication/understanding-impact-performance.
  14. Goldsmith, R. E., & Hofacker, C. F.(1991). Measuring consumer innovationess. Journal of the Academy of Marketing Science, 19(3), 209-221. https://doi.org/10.1007/BF02726497
  15. Gupta, S.(1988). Impact of Sales Promotion on When, What, and How much to Buy. Journal of Marketing Research, 25(11), 342-355. https://doi.org/10.1177/002224378802500402
  16. Haley, R. I.(1968). Benefit segmentation, A decision-oriented research tool. Journal of Marketing, 32(3), 30-35. https://doi.org/10.2307/1249759
  17. Hayes, A. F.(2018). Introduction to Mediation, Moderation and Conditional Process Analysis(2nd ed.). NY: Guilford.
  18. Ham, S. Y.(2017). A Study on Factors Affecting to the Acceptance Intention of Fintech Service. Doctoral dissertation, Soongsil University.
  19. Heo, C. M., & Ahn, M. H.(2021). Smart Farm Management Strategy. Seoul: Cheongram.
  20. Hirschman, E. C.(1980). Innovativeness, Novelty Seeking and Consumer Creativity, Journal of Consumer Research, 7(3), 1980.
  21. Hirschman, E. C., & Holbrook, M. B.(1982). Hedonic consumption: emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92-101. https://doi.org/10.2307/1251707
  22. Hwang, J. S., & Lee, H. J.(2017). A Study on Unified Theory of Acceptance and Use of Technology(UTAUT) Improvement using Meta-Analysis: Focused on Analysis of Korea Citation Index(KCI)-Listed Researches. The Korean Journal of BigData, 2(2), 47-56. https://doi.org/10.36498/kbigdt.2017.2.2.47
  23. Hyun, H. W., Park, J. K., & Kim, D. Y.(2019). The Effect of Extended Brand Equity on Willingness to Pay Premium Price. Journal of channel and retailing, 24(4), 131-151. https://doi.org/10.17657/jcr.2019.10.31.6
  24. Jeong, C. H., & Nam, S. H.(2014). Cloud Computing Acceptance at Individual Level Based on Extended UTAUT. Journal of Digital Convergence, 12(1), 287-294. https://doi.org/10.14400/JDPM.2014.12.1.287
  25. Jeong, D. J., Moon, S. M., & Choi, S. M.(2020). A Study on the Effect of Perception on Technology Acceptance Attitudes: Focusing on the Moderating Effect of Government Capacity. GRI, 22(2), 225-251.
  26. Jeong, K. J.(2020). A Study on the Big Data Utilization of Small and Medium-sized Manufacturing Companies. Doctoral dissertation, Soongsil University.
  27. Kang, D. B., Chang, K. J., Lee, Y. K., & Jung, M. U.(2020). A study on the effects of changes in smart farm introduction Conditions on Willingness to Accept Agricultural Application of extended UTAUT Model, Korean Journal of Organic agriculture, 28(2), 119-138. https://doi.org/10.11625/KJOA.2020.28.2.119
  28. Kang, S. H.(2016). A Study on the User's Acceptance and Use of Easy Payment Service based on UTAUT: Focused on the Moderating Effect of Innovation Resistance. Doctoral dissertation, Pukung University.
  29. Kim, H. Y., & Kim, T. S.(2016). Why Do You Use A Podcast Service?: A UTAUT Model, Journal of Information Technology Applications & Management. 23(2), 153-176. https://doi.org/10.21219/jitam.2016.23.2.153
  30. Kim, J. S.(2016). A Study on Factors Affecting the Intention to Accept Blockchain Technology. Doctoral dissertation, Soongsil University.
  31. Kim, J. R., & Lee, S. J.(2020). Factors Affecting Technology Acceptance of Smart Factory. Journal of Information Technology Applications & Management, 27(1), 75-95. https://doi.org/10.21219/jitam.2020.27.1.075
  32. Kim, J. S., & Gim. G. Y.(2017). A Study on Factors Affecting the Intention to Accept Blockchain Technology. Korea Society of IT Serviecs, 16(2), 1-20.
  33. Kim, K. B.(2018). (A) Study on Factors Affecting Intention to Use Drone Technology Applying Extended Integrated Technology(UTAUT2). Doctoral dissertation, Hoseo University.
  34. Klemper, E. D.(1987). Markets with consumer switching costs. Quartery. Journal of Economics. 102(5), 375-394. https://doi.org/10.2307/1885068
  35. Ko, H. S.(2019). A Study on Factors Affecting the Intention to Use Big Data in Businesses. Doctoral dissertation, Soongsil University.
  36. Korea Data Agency(2021). 2020 Data Industry Report(No. 127004). Seoul: Korea Data Agency.
  37. Kyung, J. I., & Lee, J. W.(2020). The Fourth Industrial Revolution Factors Affecting the Vitalization of Smart Information Technology in Urban Regeneration. Journal of Real Estate Analysis, 12(1), 1-22.
  38. Lee, H. G., & Han, M. S.(2019). An Empirical Study on the Consumer Acceptance of Internet Primary Bank: The Application of UTAUT Model. The Korean Research Association for the Business Education, 33(1), 59-87.
  39. Lee, H. J.(2018). A Study on Factors Affecting the Investment Intention of Information Security. Doctoral dissertation, Soongsil University.
  40. Lee, H. J.(2016). A Study of Continuance Use for Hotel Booking Mobile App : Assessing the Moderating Role of Online Review Credibility and Membership Benefit. International Journal of Tourism Management and Sciences, 31(3), 135-155.
  41. Lee, H. Y.(2012). Research Methodology. Seoul: Cheongram.
  42. Lee, J. D.(2020). The Effect of Technology Acceptance Factors on Behavioral Intention for Agricultural Drone Service by Mediating Effect of Perceived Benefits. Journal of Digital Convergence, 18(8), 151-167. https://doi.org/10.14400/JDC.2020.18.8.151
  43. Lee, J. R.(2017). The Fourth Industrial Revolution and Agriculture(Global Agriculture 200th). Sejong: Office for Government Policy Coordination.
  44. Lee, J. W.(2014). Impacts of Small and Medium Enterprises' Recognition of Social Media on Their Behavioral Intention and Use Behavior. Doctoral dissertation, Kookmin University.
  45. Lee, M. B.(2012). Influence of the Innovativeness on the Use Intention in SNS: Focused on UTAUT. Journal of Korea Industry System Resource, 17(7), 177-186.
  46. Lee, T. Y., & Heo, C. M.(2019). A Study on the Influence of Acceptance Factors of ICT Convergence Technology on the Intention of Acceptance in Agriculture: Focusing on the Moderating Effect of Innovation Resistance. Journal of Digital Convergence, 17(9), 115-126. https://doi.org/10.14400/JDC.2019.17.9.115
  47. Lee, W. S., Son, K. J., Jun, D., & Shin, Y.(2020). Big Data Activation Plan for Digital Transformation of Agriculture and Rural. Korea Information Processing Society, 9(8), 235-242.
  48. Lee, Y. R.(2017). A Study on O2O Platform Factor and Intention of Use: Focusing on Mediating Effect of Preference and Moderating Effect of Innovation Propensity. Doctoral dissertation, Hoseo University.
  49. Min, S. R.(2021). The effect of individual innovation tendency on omni-channel continuous use intention. Academic Society of Global Business Administration, 18(3), 35-54. https://doi.org/10.38115/asgba.2021.18.3.35
  50. Midgley, D. F., & Dowling, G. R(1978). Innovativeness: The concept and its measurement. Journal of Consumer Research, 4(4), 229-242. https://doi.org/10.1086/208701
  51. Min, S. J., Kim, H. J., & Song, G. H.(2017). An exploratory study on the determinants of chatbot acceptance using the UTAUT. Korea Technology Innovation Society, 2017.5, 623-643.
  52. Moran, M., Hawkes, M., & Gayer, O.(2010). Tablet personal computer integration in higher education:Applying the unified theory of acceptance and use technology model to understand and supporting factors. Journal of Educational and Computing Research, 42(1), 79-101. https://doi.org/10.2190/EC.42.1.d
  53. Nho, K. S.(2019). The proper methods of statistical analysis for dissertation. Seoul: Hanbit Academy, Inc.
  54. Park, G. H.(2017). The Determinant for the Usage of Big Data in Administrative Service: mainly on the Quality Control of Data. Doctoral dissertation. Keimyeong University.
  55. Park, J. Y., Seo, D. S., & Lee, J. M.(2021). Chapter 6. The future of Agriculture. Digital Agriculture(Agricultural Outlook 2021). Najoo: KREI.
  56. Petty, R. E., Cacioppo, J. T., & Haugtvedt, C. P(1992). Ego-involvement and persuasion: An appreciative look at the Sherifs contribution. Chapter in D. Granberg and G.Sarup(Eds.) Social Judgment and Intergroup Relations: Essay in Honor of Muzafeherif. New York: Springer-Verlag.
  57. Queiroz, M., & Fosso-Wamba, S.(2019). Blockchain adoption Challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, vol 46, 70-82. https://doi.org/10.1016/j.ijinfomgt.2018.11.021
  58. Raman, A., & Don, Y.(2013). Preservice teachers' acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157-164.
  59. Roger, E. M.(2003). Diffusion of Innovation, 5th Edition. NY: Free Press.
  60. Shin, S. H.(2019). A Study on the Key Factors Affecting Big Data Utilization Intention of Companies in Organizational, Individual and Management Quality Perspective. Doctoral dissertation, Chonnam National University.
  61. Shim, Y. J.(2018). A Study on Factors Affecting to FinTech Service Adoption Using the UTAUT Model. Doctoral dissertation, Konkuk University.
  62. Shin, Y. D.(2011). Relationships among Perceived Value of Service, Relational Benefits, Relationship Factors, and Behavioral Intentions of Customers in Upscale Hotels. Doctoral dissertation. KyeongWon University.
  63. Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D.(2015). Modeling consumers's adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk, and trust. Psychology and Marketing. 32(8), 860-873. https://doi.org/10.1002/mar.20823
  64. Son, K. J.(2021). A study on the Factors Affecting Utilization Intention of Agricultural Management Data on the Agricultural Big Data Platform. Doctoral dissertation, Soongsil University.
  65. Son, H. J., Lee, S. W., & Cho, M. H.(2014). Influential Factors of College Students' Intention to Use Wearable Device An Application of the UTAUT2 Model. Korean Association For Communication And Information Studies, 68(4), 7-33.
  66. Stubbs, E.(2014). Big data, big innovation: Enabling competitive differentiation through business analytic. NY: John Wiley and Sons.
  67. Venkatesh, V., Thong, J. Y., & Xu, X.(2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 158-178.
  68. Yun, O. J.(2017). A Study on the Key Factors Affecting the Diffusion of Cybersecurity Threat Information Sharing System. Doctoral dissertation, SoongSil University.
  69. Yun, S. Y., & Yeo, J. S.(2021). A Study on the Expanded Consumer Data Sovereignty and Consumer Data Right in the Data Economy. Journal of Consumer studies, 32(5), 169-195. https://doi.org/10.35736/JCS.32.5.8