KSCE Journal of Civil Engineering

, Volume 20, Issue 2, pp 564–570 | Cite as

Construction professionals’ perceived benefits of PMIS: The effects of PMIS quality and computer self-efficacy

  • Hyojoo Son
  • Nahyae Hwang
  • Changwan KimEmail author
  • Yong Cho
Construction Management


A Project Management Information System (PMIS) is a key Information System (IS) tool used for the successful completion of construction projects and the achievement of organizational goals. This study investigated the effects of computer self-efficacy and IS quality (information, system, and service quality) on perceived net benefits of PMIS. The study used the updated DeLone and McLean Information System Success Model (D&M ISSM) as a theoretical foundation. The proposed model was tested empirically by using survey data collected from 379 construction professionals. The empirical results suggest that construction professionals’ perceived benefits are determined by behavioral intention to use and user satisfaction, and that these are in turn influenced by computer self-efficacy and PMIS quality.


computer literacy construction professional DeLone and McLean IS success model (ISSM) project management information system (PMIS) structural equation model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ali, A. S. B., Anbari, F. T., and Money, W. H. (2008). “Impact of organizational and project factors on acceptance and usage of project management software and perceived project success.” Project Management Journal, Vol. 39, No. 2, pp. 5–33, DOI:  10.1002/pmj.20041.CrossRefGoogle Scholar
  2. Anderson, J. C. and Gerbing, D. W. (1988). “Structural equation modeling in practice: A review and recommended two-step approach.” Psychological Bulletin, Vol. 103, No. 3, pp. 411, DOI:  10.1037/0033-2909.103.3.411.CrossRefGoogle Scholar
  3. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory, Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
  4. Barclay, D., Higgins, C., and Thompson, R. (1995). “The Partial Least Squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration.” Technology Studies, Vol. 2, No. 2, pp. 285–309.Google Scholar
  5. Bhattacherjee, A. (2001). “Understanding information systems continuance:An expectation-confirmation model.” MIS Quarterly, Vol. 25, No. 3, pp. 351–370, DOI:  10.2307/3250921.CrossRefGoogle Scholar
  6. Chatterjee, S., Chakraborty, S., Sarker, S., Sarker, S., and Lau, F. Y. (2009). “Examining the success factors for mobile work in healthcare: A deductive study.” Decision Support Systems, Vol. 46, No. 3, pp. 620–633, DOI:  10.1016/j.dss.2008.11.003.CrossRefGoogle Scholar
  7. Chien, S. W. and Tsaur, S. M. (2007). “Investigating the success of ERP systems: Case studies in three Taiwanese high-tech industries.” Computers in Industry, Vol. 58, No. 8, pp. 783–793, DOI:  10.1016/j.compind.2007.02.001.CrossRefGoogle Scholar
  8. Compeau, D. R. and Higgins, C. A. (1995). “Computer self-efficacy: Development of a measure and initial test.” MIS Quarterly, Vol. 19, No. 2, pp. 189–211, DOI:  10.2307/249688.CrossRefGoogle Scholar
  9. Cronin, J. J., Brady, M. K., and Hult, G. T. M. (2000). “Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intention to uses in service environments.” Journal of Retailing, Vol. 76, No. 2, pp. 193–218, DOI:  10.1016/S0022-4359(00)00028-2.CrossRefGoogle Scholar
  10. Davis, F. (1989). “Perceived usefulness, perceived ease of use, and user acceptance.” MIS Quarterly, Vol. 13, No. 3, pp 319–340, DOI:  10.2307/249008.CrossRefGoogle Scholar
  11. Davis, K.A. and Songer, A.D. (2009). “Resistance to IT change in the AEC industry: Are the stereotypes true?.” Journal of Construction Engineering and Management, Vol. 135, No. 12, pp. 1324–1333, DOI:  10.1061/(ASCE)CO.1943-7862.0000108.CrossRefGoogle Scholar
  12. DeLone, W. H. and McLean, E. R. (1992). “Information systems success: The quest for the dependent variable.” Information Systems Research, Vol. 3, No. 1, pp. 60–95, DOI:  10.1287/isre.3.1.60.CrossRefGoogle Scholar
  13. DeLone, W. H. and McLean, E. R. (2003). “The DeLone and McLean model of information systems success: A ten-year update.” Journal of Management Information Systems, Vol. 19, No. 4, pp. 9–30.Google Scholar
  14. Etezadi-Amoli, J. and Farhoomand, A. F. (1996). “A structural model of end user computing satisfaction and user performance.” Information & Management, Vol. 30, No. 2, pp. 65–73, DOI:  10.1016/0378-7206(95)00052-6.CrossRefGoogle Scholar
  15. Fishbein, M. and Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research, Reading, MA: Addison-WesleyGoogle Scholar
  16. Fornell, C. and Larcker, D. F. (1981). “Evaluating structural equation models with unobservable variables and measurement error.” Journal of Marketing Research, Vol. 18, No. 1, pp. 39–50, DOI:  10.2307/3151312.CrossRefGoogle Scholar
  17. Grover, V., Cheon, M. J., and Teng, J. T. (1996). “The effect of service quality and partnership on the outsourcing of information systems functions.” Journal of Management Information Systems, Vol. 12, No. 4, pp. 89–116CrossRefGoogle Scholar
  18. Hair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C. (1998). Multivariate data analysis with readings, Prentice-Hall, Upper Saddle River, NJ.Google Scholar
  19. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., and Tatham, R. L. (2006). Multivariate data analysis, 6th Ed., Prentice-Hall, Upper Saddle River, NJ.Google Scholar
  20. Halawi, L. A., McCarthy, R. V., and Aronson, J. E. (2007). “An empirical investigation of knowledge management system success.” Journal of Computer Information Systems, Vol. 48, No. 2, pp. 121–135.Google Scholar
  21. Han, S. H., Chin, K. H., and Chae, M. J. (2007). “Evaluation of CITIS as a collaborative virtual organization for construction project management.” Automation in Construction, Vol. 16, No. 2, pp. 199–211, DOI:  10.1016/j.autcon.2006.04.002.CrossRefGoogle Scholar
  22. Hewage, K. N., Ruwanpura, J. Y., and Jergeas, G. F. (2008). “IT usage in Alberta's building construction projects: Current status and challenges.” Automation in Construction, Vol. 17, No. 8, pp. 940–947, DOI:  10.1016/j.autcon.2008.03.002.CrossRefGoogle Scholar
  23. Hu, L. T. and Bentler, P. M. (1999). “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives.” Structural Equation Modeling: A Multidisciplinary Journal, Vol. 6, No. 1, pp. 1–55, DOI:  10.1080/10705519909540118.CrossRefGoogle Scholar
  24. Igbaria, M. (1993). “User acceptance of microcomputer technology: An empirical test.” Omega, Vol. 21, No. 1, pp. 73–90, DOI:  10.1016/0305-0483(93)90040-R.CrossRefGoogle Scholar
  25. Igbaria, M. and Tan, M. (1997). “The consequences of information technology acceptance on subsequent individual performance.” Information & Management, Vol. 32, No. 3, pp. 113–121, DOI:  10.1016/S0378-7206(97)00006-2.CrossRefGoogle Scholar
  26. Jaafari, A. and Manivong, K. (1998). “Towards a smart project management information system.” International Journal of Project Management, Vol. 16, No. 4, pp. 249–265, DOI:  10.1016/S0263-7863(97)00037-9.CrossRefGoogle Scholar
  27. Lee, S.-K. and Yu, J.-H. (2012). “Success model of project management information system in construction.” Automation in Construction, Vol. 25, pp. 82–93, DOI:  10.1016/j.autcon.2012.04.015.CrossRefGoogle Scholar
  28. Lin, H. F. (2007). “Measuring online learning systems success: Applying the updated DeLone and McLean model.” Cyberpsychology & Behavior, Vol. 10, No. 6, pp. 817–820, DOI:  10.1089/cpb.2007.9948.CrossRefGoogle Scholar
  29. Mason, R. O. (1978). “Measuring information output: A communication systems approach.” Information & Management, Vol. 1, No. 5, pp. 219–234, DOI:  10.1016/0378-7206(78)90028-9.CrossRefGoogle Scholar
  30. Mathieson, K. (1991). “Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior.” Information Systems Research, Vol. 2, No. 3, pp. 173–191, DOI:  10.1287/isre.2.3.173.CrossRefGoogle Scholar
  31. Nitithamyong, P. and Skibniewski, M. J. (2006). “Success/failure factors and performance measures of web-based construction project management systems: Professionals' viewpoint.” Journal of Construction Engineering and Management, Vol. 132, No. 1, pp. 80–87, DOI:  10.1061/(ASCE)0733-9364(2006)132:1(80).CrossRefGoogle Scholar
  32. Nunnally, J. C. (1978). Psychometric theory, 2nd Ed., McGraw–Hill, New York, NY.Google Scholar
  33. Padilla-Meléndez, A., Garrido-Moreno, A., and Del Aguila-Obra, A. R. (2008). “Factors affecting e-collaboration technology use among management students.” Computers & Education, Vol. 51, No. 2, pp. 609–623, DOI:  10.1016/j.compedu.2007.06.013.CrossRefGoogle Scholar
  34. Parasuraman, A., Zeithaml, V. A., and Berry, L. L. (1985). “A conceptual model of service quality and its implications for future research.” The Journal of Marketing, Vol. 49, No. 4, pp. 41–50, DOI:  10.2307/1251430.CrossRefGoogle Scholar
  35. Peansupap, V. and Walker, D. H. T. (2005). “Factors enabling information and communication technology diffusion and actual implementation in construction organizations.” Electronic Journal of Information Technology in Construction, Vol. 10, No. 14, pp. 193–218.Google Scholar
  36. Petter, S. and McLean, E. R. (2009). “A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level.” Information & Management, Vol. 46, No. 3, pp. 159–166, DOI:  10.1016/ Scholar
  37. Rai, A., Lang, S. S., and Welker, R. B. (2002). “Assessing the validity of IS success models: An empirical test and theoretical information analysis.” Information System Research, Vol. 13, No. 1, pp. 50–69, DOI:  10.1287/isre. Scholar
  38. Raymond, L. and Bergeron, F. (2008). “Project management information systems: An empirical study of their impact on project managers and project success.” International Journal of Project Management, Vol. 26, No. 2, pp 213–220, DOI:  10.1016/j.ijproman.2007.06.002.CrossRefGoogle Scholar
  39. Roca, J. C., Chiu, C. M., and Martínez, F. J. (2006). “Understanding elearning continuance intention: An extension of the Technology Acceptance Model.” International Journal of Human–Computer Studies, Vol. 64, No. 8, pp. 683–696, DOI:  10.1016/j.ijhcs.2006.01.003.CrossRefGoogle Scholar
  40. Seddon, P. and Kiew, M. Y. (1996). “A partial test and development of DeLone and McLean's model of IS success.” Australasian Journal of Information Systems, Vol. 4, No. 1, pp. 90–109.CrossRefGoogle Scholar
  41. Shannon, C. E. and Weaver, W. (1949). The mathematical theory of communication, University of Illinois Press, Urbana, IL.zbMATHGoogle Scholar
  42. Shaw, N. C., DeLone, W. H., and Niederman, F. (2002). “Sources of dissatisfaction in end-user support: An empirical study.” ACM SIGMIS Database, Vol. 33, No. 2, pp. 41–56, DOI:  10.1145/513264.513272.CrossRefGoogle Scholar
  43. Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications, American Psychological Association, Washington, DC.CrossRefGoogle Scholar
  44. Wang, S. M. and Chuan-Chuan Lin, J. (2011). “The effect of social influence on bloggers' usage intention.” Online Information Review, Vol. 35, No. 1, pp. 50–65, DOI:  10.1108/14684521111113588.CrossRefGoogle Scholar
  45. Wang, Y. and Liao, Y. (2008). “Assessing eGovernment systems success: A validation of the DeLone and McLean model of information systems success.” Government Information Quarterly, Vol. 25, No. 4, pp. 717–733, DOI:  10.1016/j.giq.2007.06.002.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Hyojoo Son
    • 1
  • Nahyae Hwang
    • 1
  • Changwan Kim
    • 1
    Email author
  • Yong Cho
    • 2
  1. 1.Dept. of Architectural EngineeringChung-Ang UniversitySeoulKorea
  2. 2.The School of Civil and Environmental EngineeringGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations