TECHNICAL PAPERS
Dec 4, 2010

Model to Predict the Impact of a Technology on Construction Productivity

Publication: Journal of Construction Engineering and Management
Volume 137, Issue 9

Abstract

Although some new technologies promise to improve construction productivity, their ability to deliver is not always realized. Building on a great deal of prior research, a four-stage predictive model was developed and validated to estimate the potential for a technology to have a positive impact on construction productivity. The four stages examine the costs, feasibility, usage history, and technical impact of a technology. The predictive model combines results from historical analyses to formalize how selected technologies with improved construction productivity can be used as a predictor of how future technologies might do the same. Each of the stages of a predictive model was subdivided into a series of categories and questions, which were weighted by importance by using the analytic hierarchy process and historical analysis to generate a performance score for the analyzed technology. The predictive model was then validated by using 74 previous and existing construction technologies. Statistical analysis confirmed that average performance scores produced by the model were significantly different across the categories of successful, inconclusive, and unsuccessful in the actual implementation experience of technologies.

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Acknowledgments

The authors thank the CII and Fully Integrated and Automated Technologies (FIATECH) for funding this research (Project number CII RT-240), along with the numerous affiliated companies and individuals who participated in the research.

References

Construction Industry Institute (CII). (1986). “Construction Industry Institute model plant.” RS2-1, Cockrell School of Engineering at the Univ. of Texas at Austin, Austin, TX.
Cooper, R. G., and Kleinschmidt, E. J. (1987). “New products: What separates winners from losers?” J. Prod. Innovat. Manag., 4(3), 169–184.
Davis, F., Bagozzi, R. P., and Warshaw, P. R. (1989). “User acceptance of computer technology: A comparison of two theoretical models.” Manage. Sci., 35(8), 982–1003.
Goodrum, P., and Haas, C. (2004). “Long-term impact of equipment technology on labor productivity in the U.S. construction industry at the activity level.” J. Constr. Eng. Manage., 130(1), 124–133.
Goodrum, P., Wang, Y., Haas, C., Vaziri, S., and Glover, R. (2007). “Construction industry craft training in the United States and Canada.” RS231-1, Cockrell School of Engineering at the Univ. of Texas at Austin, Austin, TX.
Goodrum, P., Zhai, D., and Yasin, M. (2009). “The relationship between changes in material technology and construction productivity.” J. Constr. Eng. Manage., 135(4), 278–287.
Haas, C., Goodrum, P., and Caldas, C. (2008). “Leveraging technology to improve construction productivity.” RS240-1, Construction Industry Institute, Univ. of Texas at Austin, Austin, TX.
Jang, H., Russell, J. S., and Yi, J. S. (2003). “A project manager’s level of satisfaction in construction logistics.” Can. J. Civ. Eng., 30(6), 1133–1142.
Mankins, J. (1995). “Technology readiness levels: A white paper.” 〈http://www.hq.nasa.gov/office/codeq/trl/trl.pdf〉 (Sep. 1, 2008).
Mohanty, R. P. (1993). “Analysis of justification problems in CIMS: Review and projection.” Prod. Plan. Control, 4(3), 260–272.
Nam, C. H., and Tatum, C. B. (1992). “Strategies for technology push: Lessons from construction innovations.” J. Constr. Eng. Manage., 118(3), 507–524.
Committee on Advancing the Competitiveness and Productivity of the U.S. Construction Industry, National Research Council. (2009). Advancing the competitiveness and efficiency of the U.S. construction industry, National Academies, Washington, DC.
Rosefielde, S., and Quinn Mills, D. (1979). “Is construction technologically stagnant?” The construction industry: Balance wheel of the economy, J. Lange and D. Mills, eds., Lexington Books, Lexington, MA.
Svavarsson, D., Ekstrom, M., Bergendahl, G., and Bjornson, H. (2002). “Evaluating IT investments in the AEC industry.” Proc., 9th European Conf. on Information Technology Evaluation, A. Brown, ed., Management Centre Int., Reading, UK, 415–424.
Saaty, T. L. (1980). The Analytic Hierarchy Process.” McGraw-Hill, New York.
Szajna, B. (1996). “Empirical evaluation of the revised technology acceptance model.” Manage. Sci., 42(1), 85–92.
Zhai, D., Goodrum, P., Haas, C., and Caldas, C. (2009). “Relationship between the automation and integration of construction information systems and productivity.” J. Constr. Eng. Manage., 135(8), 746–753.

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Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 137Issue 9September 2011
Pages: 678 - 688

History

Received: Nov 27, 2009
Accepted: Nov 22, 2010
Published online: Dec 4, 2010
Published in print: Sep 1, 2011

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Authors

Affiliations

Paul M. Goodrum, M.ASCE [email protected]
Professor, Dept. of Civil Engineering, Univ. of Kentucky, C151C Raymond Building, Lexington, KY 40506 (corresponding author). E-mail: [email protected]
Carl T. Haas, M.ASCE
Professor, Dept. of Civil and Environmental Engineering, Univ. of Waterloo, ON, Canada N2L 3G1.
Carlos Caldas, A.M.ASCE
Associate Professor, Dept. of Civil, Architectural and Environmental Engineering, Univ. of Texas at Austin, Austin, TX.
Dong Zhai
Doctoral Candidate, Dept. of Civil Engineering, Univ. of Kentucky, Lexington, KY.
Jordan Yeiser
Graduate Research Assistant, Dept. of Civil Engineering, Univ. of Kentucky, Lexington, KY.
Daniel Homm
Graduate Research Assistant, Dept. of Civil Engineering, Univ. of Kentucky, Lexington, KY.

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