Journal of the Architectural Institute of Korea Planning & Design (대한건축학회논문집:계획계)

Architectural Institute of Korea (대한건축학회)
권호 표지
DOI QR코드

DOI QR Code

Key Factors Affecting BIM Acceptance in Construction

BIM 수용에 영향을 미치는 요인 분석

  • Published : 2013.08.25
⟨ Previous Next ⟩

Abstract

Although many researchers and practitioners are in agreement about the potential applicability and benefit of BIM in construction, it is still unclear why BIM is adopted, and what factors enhance adoption and implementation of BIM. Thus, the mechanism of BIM acceptance and use remains in question. Therefore, this paper aims to identify the key factors affecting the acceptance of BIM in construction organizations, and analyze the effect of intrinsic and extrinsic motivation factor on BIM acceptance. The key factors for BIM acceptance are identified through a literature review in TAM (Davis 1989) and related theories, and consolidated by interviews and pilot studies with professionals in construction industry. Based on the factors, a questionnaire was designed and sent out to construction organizations such as contractors, architects, and engineers in Korea. Using factor analysis, key factors were grouped into five dimensions. And using multi regression analysis, we analyzed relationship between key factors and BIM acceptance. These findings will clarify what the highly prioritized factors are, and can also be used in an assessment tool for the performance of BIM utilization.

Keywords

References

  1. 김언용, 지능형 디지털 아키텍처 도구와 BIM 패러다임, 대한건축학회지, 48(11), p.p. 56-59, 2004
  2. 박영진, 원서결, 한충희, 이준복, 공동주택 골조공사의 3D BIM기반 개산견적 모델 연구, 대한건축학회, 27(6), p.p. 123-130, 2011
  3. 안승준, 이현수, 박문서, 김우영, 공정 원가 통합 관리를 위한 BIM 기반 객체지향형 공정 모델링, 대한건축학회, 25(12), p.p. 165-174, 2009
  4. 이상효, 안병주, 김주형, 김경환, 이윤선, 김재준. 계층분석법을 이용한 3D CAD 활용 저조에 대한 영향 요인 분석 연구, 한국건설관리학회, 8(6), p.p. 188-196, 2007
  5. 조달청, BIM 가이드라인 개발, 2010
  6. 최희선, 건설산업 BIM 활성화 저해 영향요인 도출에 관한 연구, 한양대학교 석사학위논문, 2010
  7. 홍두승, 사회조사분석사, 3판, 다산출판사, 2000
  8. Agarwal, R. and Prasad, J.. The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Science, the Decision Sciences Institute, 28(3), p.p. 557-582. 1998
  9. Aksorn, T. and Hadikusumo, B. H. W., Critical Success factors influencing safety program performance in construction projects, Safety Science, 46(4), p.p. 709-727, 2008 https://doi.org/10.1016/j.ssci.2007.06.006
  10. Bandura, A., Social Learning Theory. Prentice-Hall, Englewood Cliffs.1977
  11. Bartlett, M. S., A note on the multiplying factors for various chi square approximations, Journal of the Royal Statistical Society, 16, p.p. 296-298, 1954
  12. Belsley, D. A., Kuh, E. and Welsch, R. E., Regression Diagnostics: Identifying Influential Data And Source of collinearity, John Wiley and Sons, 1980
  13. Cohen, J., Statistical Power Analysis for the Behavioral Sciences(2nd Ed.), Lawrence Erlbaum Associates, Inc.1988
  14. Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technologies, MIS Quarterly, 13(3), p.p. 319-40, 1989 https://doi.org/10.2307/249008
  15. Eastman, C., Teicholz, P., Rafael, S. and Kathleen, L.. BIM handbook : a guide to building information modeling for owners, managers, designers, engineers, and contractor John Wiley & Sons Inc: New Jersey, 2008
  16. Froese, T. and Yu, K., Industry Foundation Class Modelling For Estimating And Scheduling, Durability Of Building Materials And Components 8. Vancouver, p.p. 2825-2835, May 1999
  17. Fu, J. R., Farn, C. K. and Chao, W. P., Acceptance of electronic tax filing-A study of taxpayer intentions, Information & Management, 43(1), p.p. 109-126, 2006 https://doi.org/10.1016/j.im.2005.04.001
  18. Featherman, S. M. and Pavlou, A. P., Predicting E-Service Adoption a Perceived Risk Facets Perspective, Eighth Americas Conference on Information Systems, p.p. 1034-1046, 2002
  19. Gefen, D. and Keil, M., The impact of developer responsiveness on perceptions of usefulness and ease of use: an extension of the technology acceptance model, The DATA BASE for Advances in Information Systems, 29(2), p.p. 35-49, 1998 https://doi.org/10.1145/298752.298757
  20. Hair, J. F., Ronald, L., Tatham, R. E., and Anderson, W. B., Multivariate Data Analysis, Multivariate Data Analysis, Prentice-Hall Int, 1998
  21. Hong, W. U., Thong, J. Y. L., Wong, W. M. and Tam, K. Y., Determinants of user acceptance of digital libraries-An empirical examination of individual differences and systems characteristics, Journal of Management Information System, 18(3), p.p. 97-124, 2001
  22. Igbaria, M., Zinatelli, N., Cragg, P. and Cavaye, A., Personal computing acceptance factors in small firms: a structural equation model, MIS Quarterly, September, p.p. 279-302, 1997
  23. Jackson, C. M., Chow, S. and Leitch, R. A., Toward an understanding of the behavioral intention to use an information system, Decision Sciences, 28(2), p.p. 357-389, 1997. https://doi.org/10.1111/j.1540-5915.1997.tb01315.x
  24. Kaiser, H. F., A second generation little jiffy, Psychometrika, 35(4), p.p. 401-415, 1970 https://doi.org/10.1007/BF02291817
  25. Kwasi, A. G. and Salam, A. F., An extension of the technology acceptance model in an ERP implementation environment, Information & Management, 41(6), p.p. 731-745, 2004 https://doi.org/10.1016/j.im.2003.08.010
  26. Liu, L. and Ma, Q., The impact of service level on the acceptance of application service oriented medical records, Information & Management, 42(8), pp.1-15, 2005
  27. Lucas, H. C. and Spitler, V. K., Technology use and performance: a field study of broker workstations, Decisions Sciences, 30(2), p.p. 291-311, 1999 https://doi.org/10.1111/j.1540-5915.1999.tb01611.x
  28. Nunnalyy, J. O., Psychometric Theory. New York, McGraw-Hill, 1978
  29. Pallant, J., SPSS Survival Manual Open University Press, Buckingham and Philadelphia, 2001
  30. Raafat, S. and Bouchaib, B., The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptancemodel, Information & Management,42(2), p.p. 317-327, 2005 https://doi.org/10.1016/j.im.2003.12.013
  31. Riemenschneider, K. C., Harrison, A. D. and Mykytyn, P. P., Understanding it adoption decisions in small business-integrating current theories, Information & Management, 40 (4), p.p. 269-285, 2003 https://doi.org/10.1016/S0378-7206(02)00010-1
  32. SmartMarket Report, The Business Value of BIM for Infrastructure, McGrawHill Constructon, 2012
  33. Tabachnik, B. G. and Fidell, L. S., Using Multivariate Statistics 3rd edition, New York, Harper Collins, 1996
  34. Shih, H. P., An empirical study on predicting user acceptance of e-shopping on the Web, Information & Management, 41(3), p.p. 351-368, 2004 https://doi.org/10.1016/S0378-7206(03)00079-X
  35. Taylor, S. and Todd, P. A., Understanding information technology usage: a test of competing models, Information Systems Research, 6(2), p.p. 144-176, 1995 https://doi.org/10.1287/isre.6.2.144
  36. Venkatesh, V. and Davis F. D., A Theoretical Extension of the Technology Acceptance Model : Four Longitudinal Field Studies, Management Science, 45(2), pp.186-204, 2000
  37. Venkatesh, V. and Bala, H., TAM 3: Advancing the Technology Acceptance Model with a Focus on Interventions, Decision Sciences, 39(2), p.p. 273-315, 2008 https://doi.org/10.1111/j.1540-5915.2008.00192.x