A Study on the Intention to Use Big Data Based on the Technology Organization Environment and Innovation Diffusion Theory in Shipping and Port Organization

TOE와 혁신확산이론에 따른 해운항만조직의 빅데이터 사용의도에 관한 연구

  • Received : 2018.08.31
  • Accepted : 2018.09.28
  • Published : 2018.09.30

Abstract

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.

본 연구의 목적은 해운항만조직에서 새로운 ICT인 빅데이터를 도입하고 사용함에 있어 조직구성원들이 빅데이터 사용을 통해 기대하는 성과와 사용의도를 파악함으로써 경쟁력을 높일 수 있는 방안을 제시하는 것이다. 본 연구는 빅데이터가 조직의 프로세스를 변화시키고, 최고경영층의 지원이 필수적이고 때로는 자의보다는 기업이 처한 환경적 압박에 대처할 수 있는 수단인 점에서 기술 조직 환경(Technology Organization Environment)프레임워크와 기업의 혁신을 주도하는 혁신기술로 보고 혁신확산이론(Diffusion of Innovation Theory)모형을 기반으로 본 연구에 적합한 변수들을 도출하여 이들 변수간의 인과관계를 설정하여 연구모형을 구성하였다. 본 연구에서는 TOE모형의 기술적 요인, 조직적 요인, 환경적 요인 중에서 기술적 요인 대신에 혁신특성인 혁신확산모형 변수를 사용하였다. 기술적 요인에 관한 변수로는 혁신확산이론 변수들 중 상대적 이점, 복잡성, 호환성을 선택하였고, 조직적 요인에 관한 변수로 조직의 규모와 최고경영층의 지원, 환경적 요인에 속하는 변수로는 경쟁자 압력과 규정지원을 선택하였다. 이들 3가지 요인에 속한 변수들과 빅데이터 사용에 대한 기대성과와 사용의도 간의 관련성에 대한 8개의 가설을 설정하였다. 본 연구 결과를 정리하면 다음과 같다. 첫째, 기술적 요인에서는 상대적 이점, 복잡성, 호환성이 기대성과에 모두 유의한 영향을 미치는 것으로 나타났다. 둘째, 조직적 요인에서는 최고경영층의 지원은 기대성과에 유의한 영향을 미쳤으나, 조직 규모는 기대성과에 미치는 영향이 유의하지 않은 것으로 나타났다. 셋째, 환경적 요인에서 경쟁자의 압력은 기대성과에 유의한 영향을 미치는 것으로 나타났으나, 규정지원은 기대성과에 유의한 영향을 미치지 않는 것으로 나타났다. 마지막으로 빅데이터 사용에 대한 기대성과는 사용의도에 유의한 영향을 미치는 것으로 나타났다.

Keywords

References

  1. 강미주(2015), 특집기획 : 해운분야 ICT혁명, 새로운 해운시대 열리나, 해양한국, 500호, http://www.monthlymaritimekorea.com/news/article-View.html?idxno=16027.
  2. 고준철.이해욱.정지윤.강경식(2012), 빅데이터의 새로운 고객 가치와 비즈니스 창출을 위한 대응 전략, 대한안전경영과학회지, 14권 4호, 229-238.
  3. 고태형.김영택(2012), 중소기업의 이러닝 수용과 성과분석을 위한 통합연구모형, 대한경영학회지, 25권, 2509-2529.
  4. 김승섭(2015), 특집기획 : 항만 터미널분야-빅데이터, IoT, 드론, 로봇 활용해 생산.효율성, 안전.친환경성 높인다, 해양한국, 500호, http://www.monthlymaritimekorea.com/news/articleView.html?idxno=16048.
  5. 김은영.이정훈.서동욱(2013), 빅데이터 시스템의 수용의도에 영향을 미치는 수용조직의 환경요인에 관한 연구, Journal of Information Technology Applications & Management, 20(4), 1-18.
  6. 김이환(2015), 업무-기술적합에 따른 빅데이터 분석기술이 기대성과에 미치는 영향-혁신확산이론을 중심으로, 경희대학교 박사학위논문.
  7. 김정선(2015), 혁신기술로서의 빅데이터 국내 기술수용 초기 특성 연구, 이화여자대학교 박사학위논문.
  8. 김정환.박종석(2016), 정보기술(ICT) 경쟁우위가 공급사슬통합에 미치는 영향, 한국항만경제학회지, 제32권 1호, 151-163.
  9. 김태훈.김상열(2013), 효율적인 항만공사의 운영과 관리를 위한 데이터 웨어하우스 구현방안에 관한 연구, 한국항만경제학회지, 제29권 2호, 195-209.
  10. 박귀희(2016), 행정서비스에서 빅데이터 활용의 결정요인에 관한 연구-데이터 품질관리를 중심으로, 계명대학교 박사학위논문.
  11. 염수환(2015), 정보자산 빅데이터의 서비스기대가 이용의도에 미치는 영향- e Commerce 유용성의 조절효과를 중심으로, 단국대학교 석사학위논문.
  12. 윤수영(2016), 자원기반관점에서 빅데이터 사용의도에 영향을 미치는 요인에 관한 연구, 단국대학교 박사학위논문.
  13. 윤수영(2016), 자원기반관점에서 빅데이터 사용의도에 영향을 미치는 요인에 관한 연구, 단국대학교 박사학위논문.
  14. 이선우(2016), 조직에서의 빅데이터 시스템 도입을 위한 결정요인에 대한 연구, 성균관대학교 박사학위논문.
  15. 이재홍(2011), 항만 물류서비스의 기술수용모델(TAM) 적용에 관한 실증적 연구, 한국항만경제학회지, 제27권 4호, 13-35.
  16. 한국IDC(2016), 2017년 국내 IT 시장 10대 주요 예측.
  17. Agarwal, R., and Karahanna, E.(2000), Time Flies When You're Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage, MIS Quarterly, 24(4), 665-694.
  18. Bagozzi, R. and Yi, Y.(1988) On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Sciences, 16, 74-94.
  19. Bagozzi, R.(2011). Measurement and Meaning in Information Systems and Organizational Research: Methodological and Philosophical Foundations, MIS Quarterly, 35(2), 261-292.
  20. Barclay, D., Thompson, R. and Higgins, C. (1995), The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use an Illustration, Technology Studies, 2(2), 285-309.
  21. Borrero, J. D. Yousafzai, S. Y., Javed, U. and Page, K. L.(2014), Expressive Participation in Internet Social Movements: Testing the Moderating Effect of Technology Readiness and Sex on Student SNS Use, Computers in Human Behavior, 30, 39-49.
  22. Chan, F. T. S., Chong, A. Y. L. and Zhou, L.(2012), An Empirical Investigation of Factors Affecting e-Collaboration Diffusion in SMEs, International Journal of Production Economics, 138(2), 329-344.
  23. Ciganek, A., Haseman, W. D. and Ramamurthy, K. (2014). Time to Decision: The Drivers of Innovation Adoption Decisions, Enterprise Information Systems, 8(2), 279-308.
  24. Crump, G.(2012), Cloud Storage Infrastructures Raise Many Issues, Information Week.
  25. Dasgupta, S., Agarwal, D., Ioannidis, A. and Gopalakrishnan, S.(1999), Determinants of Information Technology Adoption: An Extension of Existing Models to Firms in a Developing Country, Journal of Global Information Management, 7(3), 30-40.
  26. Davis, F. D.(1989), Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology, MIS Quarterly, 13(3), 319-340.
  27. Delone, W. and McLean, E.(2004), The DeLone and McLean Model of Information Systems Success: A Ten-Year Update, Journal of Management Informations, 19(4), 9-30.
  28. Efron, B. and Tibshirani, R. J.(1993). An Introduction to the Bootstrap. New York: Chapman & Hall.
  29. Fornell, C, D. and Larcker, F.(1981) Evaluating Structural Equation Models with Unobserved Variables and Measurement Errors, Marketing Res., 18(1), 39-50.
  30. Fuksa, M.(2013), Mobile Technologies and Services Development Impact on Mobile Internet Usage in Latvia, Procedia Computer Science, 26, 41-50.
  31. Gartner(2011), Getting Value from Big Data.
  32. Gartner(2012), High-Tech Tuesday Webinar: Big Data Opportunities in Vertical Industries.
  33. Gefen, D. and Straub, D.(2005) A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example, Communications of the Association for Information Systems, 16(1), 91-109.
  34. Hair, J. F., Hult, G. T. M., Ringle, C. M. and Sarstedt, M.(2013), A Primer on Partial Least Squares Structural Equation Modeling(PLS-SEM), Sage.
  35. Hsiu-Fen L.(2013), Understanding the Determinants of Electronic Supply Chain Management System Adoption : Using the Technology-Organization-Environment Framework, Technological Forecasting and Social Change, 86, 80-92.
  36. Im, II, Hong, S. and Kang, M. S.(2011), An International Comparison of Technology Adoption: Testing the UTAUT Model, Information & Management, 48(1), 1-8.
  37. James, W. P., Yushan, Z. and John A. S.(2012), Technology Adoption by Small Business : An Exploratory Study of The Interrelationships of Owner and Environmental Factors, International Small Business Journal, 30, 406-431.
  38. Jeyaraj, A., Joseph, W. and Mary, C.(2006), A Review of the Predictors, Linkages, and Biases in IT Innovation Adoption Research, Journal of Information Technology, 21(1), 1-23.
  39. Jiunn-Woei L., David, C. Y. and Yen-Ting, W.(2004), An Exploratory Study to Understanding the Critical Factors Affecting the Decision to Adopt Cloud Computing in Taiwan Hospital, International Journal of Information Management, 34(1), 28-36.
  40. Lai, I. K. W. and Lai, D. C. F.(2014), User Acceptance of Mobile Commerce : An Empirical Study in Macau, International Journal of Systems Science, 45(6), 1321-1331.
  41. Lancaster, S., Yen, D. C. and Ku, C. Y.(2006) E-Supply Chain Management: An Evaluation of Current Web Initiatives, Information Management & Computer Security, 14(2), 167-184.
  42. Lin, H. F.(2014), Understanding the Determinants of Electronic Supply Chain Management System Adoption : Using the Technology-Organization-Environment Framework, Technological Forecasting & Social Change, 86, 80-92.
  43. Mansfield, E.(1997), Links between Academic Research and Industrial Innovations, in: David, P. & E. Steinmueller (Eds.), A Production Tension: University-Industry Collaboration in the Era of Knowledge- based Economic Development (Stanford University Press, Palo Alto).
  44. Mayer J. D. and Salovey P.(1997). What is Emotional intelligence?, in Emotional Development and Emotional Intelligence: Implications for Educators, eds Salovey P., Sluyter D., editors. (New York, NY: Basic Books;), 3-31.
  45. Moore, G. C. and Benbasat, I.(1991), Development of An Instrument to Measure the Perceptions of Adopting an Information Technology Innovation, Information Systems Research, 2(3), 192-222.
  46. Mumtaz, A. H., Steve, C. and Stephen, S.(2012), A Conceptual Model for the Process of IT Innovation Adoption in Organizations, Journal of Engineering and Technology Management, 29(3), 358-390.
  47. Nunnally, J. C. and Bernstein, I. H.(1994), Psychometric Theory, McGraw-Hill Series in Psychology, McGraw-Hill, New York.
  48. Oliveira, T., Thomas, M. and Espadanal, M. (2014), Assessing the Determinants of Cloud Computing Adoption: An Analysis of the Manufacturing and Services Sectors, Information and Management, 51, 497-510.
  49. Robinson, L.(2009), A Summary of Diffusion of Innovations, Available at: http://www.enablingchange.com.au/Summary_Diffusion_Theory.pdf
  50. Rogers, E. M.(2003), Diffusion of Innovations, Free Press, 5th ed.
  51. Schniederjans, D. G. and Yadav, S.(2013), Successful ERP Implementation : An Integrative Model, Business Process Management Journal, 19(2), 346-398.
  52. 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.
  53. Tarofder, A. K., Marthandan, G. and Haque, A. (2010), Critical Factors for Diffusion of Web Technologies for Supply Chain Management Functions: Malaysian Perspective, European Journal of Social Sciences, 12(3), 490-505.
  54. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y. and Lauro, C.(2005), PLS Path Modeling, Computational Statistics and Data Analysis, 48, 159-205.
  55. Tenenhaus, M., Mauger, E. and Guinot, C. (2010). Use of ULS-SEM and PLS-SEM to Measure a Group Effect in a Regression Model Relating Two Blocks of Binary Variables, Handbook of Partial Least Squares, Springer.
  56. Tiago, O., Manoj, T., and Mariana, E.(2014), Assessing the Determinants of Cloud Computing Adoption: An Analysis of the Manufacturing and Services Sectors, Information & Management. 51, 497-510.
  57. Tornatzky, L. G., Fleischer, M. and Chakrabarti, A. K.(1990) The Process of Technological Innovation, Lexington Books.
  58. Venkatesh, V. and Davis, F.(2000), A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science, 46(2), 186-204.
  59. Venkatesh, V., Morris, M., Davis, G. and Davis, F.(2003), User Acceptance of Information Technology: Toward a Unified View, MIS Quarterly, 27(3), 424-478.
  60. Vong, S., Zo, H. and Ciganek, A. P.(2016). Knowledge Sharing in the Public Sector: Empirical Evidence from Cambodia, Information Development, 32(3), 409- 423.
  61. Waller, M. A. and Fawcett, S. E.(2013), Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management, Journal of Business Logistics, 34(2), 77-84.
  62. Werts, C. E., Linn, R. L. and Joreskog, K. G. (1974), Intra Class Reliability Estimates: Testing Structural Assumptions, Educational and Psychological Measurement, 34, 25-33.
  63. Wold, S.(1997). Wold, Herman Ole Andreas". In Leading Personalities in Statistical Sciences. From the Seventeenth Century to the Present. Johnson, N. L. and Kotz, S. (eds.) Wiley, New York.
  64. Wu, I. L. and Wu, K. W.(2005), A Hybrid Acceptance Approach for Exploring e-CRM Adoption in Organizations, Behaviour & Information Technology, 24(4), 303-316.
  65. Zhu, K., Kraemer, K. L., Xu, S. and Dedrick, J. (2004), Information Technology Payoff in e-Business Environments: An International Perspective on Value Creation of e-Business in the Financial Services Industry, Management Inform. Systems, 21(1), 17-54.
  66. Zhu, K., and Kraemer, K. L.(2005), Post-Adoption Variations in Usage and Value of e-Business by Organizations: Cross-Country Evidence from the Retail Industry, Inform. Systems Res, 16(1), 61-84.
  67. Zhu, K., Kraemer, K. L. and Xu, S.(2006), The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business, Management Science, 52(10), 1557-1576.