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An Empirical Study on Performance Measurement Factors for Construction Organizations

  • Construction Management
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KSCE Journal of Civil Engineering Aims and scope Submit manuscript

Abstract

Like any other organization, it has become essential for the organizations in the construction industry to measure their performance effectively for long-term survival in today’s competitive business environment. Therefore, it is imperative for a construction organization to know about various performance measurement factors to evaluate its performance. However, most of the previous studies have focused on identification of factors for measuring performance at the level of projects only. Moreover, the majority of these studies have been undertaken in context to the developed construction markets. The present study addresses these gaps in the literature by identifying critical factors for examining the performance of construction firms at the organizational level. A total of 20 organizational performance attributes were identified and analyzed using a questionnaire survey conducted on 106 respondents among 90 different organizations operating in the National Capital Region (NCR) of India. It was found that attributes such as timely completion, relationship with the client, and satisfaction (in terms of both product and services) carry more weight than the cost performance of a construction organization. In addition to this, factor analysis conducted on the performance attributes of high importance has resulted in six performance factors: (1) profitability and asset management, (2) satisfaction of key stakeholders, (3) predictability of time and cost, (4) environment, health, and safety (EHS), (5) quality consciousness, and (6) low staff turnover. The performance factors obtained from the study may provide useful guidelines to the construction organizations enabling them to examine and improve their performance.

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References

  • Ali, H. A. E. M., Al-Sulaihi, I. A., and Al-Gahtani, K. S. (2013). “Indicators for measuring performance of building construction companies in Kingdom of Saudi Arabia.” Journal of King Saud University-Engineering Science, Vol. 25, No. 2, pp. 125–134, DOI: 10.1016/j.jksues.2012.03.002.

    Article  Google Scholar 

  • Alarcon, L. F. and Mourgues, C. (2002). “Performance modeling for contractor selection.” Journal of Construction Engineering and Management, ASCE, Vol. 18, No. 2, pp. 52–60, DOI: 10.1061/(ASCE)0742-597X(2002)18:2(52).

    Google Scholar 

  • Balatbat, M. C. A., Lin, C., and Carmichael, D. G. (2010). “Comparative performance of publicly listed construction companies: Australian evidence.” Construction Management and Economics, Vol. 28, No. 9, pp. 919–932, DOI: 10.1080/01446193.2010.501805.

    Article  Google Scholar 

  • Bassioni, H. A., Price, A. D. F., and Hassan, T. M. (2004). “Performance measurement in construction.” Journal of Management in Engineering, ASCE, Vol. 20, No. 2, pp. 42–50, DOI: 10.1061/(ASCE)0742-597X (2004)20:2(42).

    Article  Google Scholar 

  • Bassioni, H. A., Price, A. D. F., and Hassan, T. M. (2005). “Building a conceptual framework Performance for measuring business performance in construction: An empirical evaluation.” Construction Management and Economics, Vol. 23, No. 5, pp. 495–507, DOI: 10.1080/0144619042000301401.

    Article  Google Scholar 

  • Castro-Lacouture, D. and Skibniewski, M. J. (2003). “Applicability of ework models for the automation of construction materials management systems.” Production Planning & Control, Vol. 14, No. 8, pp. 789–797, http://dx.doi.org/10.1080/09537280310001647869.

    Article  Google Scholar 

  • Chan, D. W. M., Chan, A. P. C., Lam, P. T. I., and Wong, J. M. W. (2010). “Identifying the critical success factors for target cost contracts in the construction industry.” Journal of Facilities Management, Vol. 8, No. 3, pp. 179–201, DOI: 10.1108/14725961011058820.

    Article  Google Scholar 

  • Chan, T. K. (2009). “Measuring performance of Malaysian construction industry.” Construction Management and Economics, Vol. 27, No. 12, pp. 1231–1244, DOI: 10.1080/01446190903233137.

    Article  Google Scholar 

  • Chau, K. W., Anson, M., and De Saram, D. D. (2005). “4D dynamic construction management and visualization software: 2. Site trial.” Automation in Construction, Vol. 14, No. 4, pp. 525–536. DOI: 10.1016/j.autcon.2004.11.005.

    Article  Google Scholar 

  • Chau, K. W., Cao, Y., Anson, M., and Zhang, J. (2003). “Application of data warehouse and Decision Support System in construction management.” Automation in Construction, Vol. 12, No. 2, pp. 213–214. DOI:10.1016/S0926-5805(02)00087-0.

    Article  Google Scholar 

  • Cox, R. F., Issa, R. R. A., and Ahrens, D. (2003). “Management’s perception of key performance indicators for construction.” Journal of Construction Engineering and Management, ASCE, Vol. 129, No. 2, pp. 142–151, DOI: 10.1061/(ASCE)0733-9364(2003)129:2(142).

    Article  Google Scholar 

  • Dawood, N., Sriprasert, E., Mallasi, Z., and Hobbs, B. (2003). “Development of an integrated information resource base for 4D/VR construction processes simulation.” Automation in Construction, Vol. 12, No. 2, pp. 123–131, DOI:10.1016/S0926-5805(02)00045-6.

    Article  Google Scholar 

  • Delgado-Hernandez, D. J., and Aspinwall, E. M. (2005). “Improvement tools in the UK construction industry.” Construction Management and Economics, Vol. 23, pp. 965–977, DOI: 10.1080/01446190500204705.

    Article  Google Scholar 

  • Doloi, H. (2009). “Analysis of pre-qualification criteria in contractor selection and their impacts on project success.” Construction Management and Economics, Vol. 27, No. 12, pp. 1245–1263, DOI: 10.1080/01446190903394541.

    Article  Google Scholar 

  • El-Mashaleh, M. S., Minchin, R. E., and O’Brien, W. J. (2007). “Management of construction firm performance using benchmarking.” Journal of Management in Engineering, ASCE, Vol. 23, No. 1, pp. 10–17, DOI: 10.1061/(ASCE)0742-597X(2007)23:1(10).

    Article  Google Scholar 

  • Elyamany, A., Basha, I., and Zayed, T. (2007). “Performance evaluating model for construction companies: Egyptian case study.” Journal of Construction Engineering and Management, ASCE, Vol. 133, No. 8, pp. 574–581, DOI: 10.1061/(ASCE)0733-9364(2007)133:8(574).

    Article  Google Scholar 

  • Fong, P. S. and Choi, S. K. (2000). “Final contractor selection using the analytical hierarchy process.” Construction Management and Economics, Vol. 18, pp. 557–557, DOI: 10.1080/014461900407356.

    Article  Google Scholar 

  • Giossi, S. (2012). “Reliability testing of a new scale for measuring attitude towards lifelong learning and human resource development.” American International Journal of Contemporary Research, Vol. 2, No. 2, pp. 183–193.

    Google Scholar 

  • Hassaan, H. S., Fors, M. N., and Shehata, M. S. (2013). “Fuzzy decision model for construction contractor’s selection in Egypt: Tender Phase.” Proceedings of the 2013 IEEE, DOI: 10.1109/IEEM.2013.6962446.

    Google Scholar 

  • Horta, I. M., Camanho, A. S. and Da Costa, J. M. (2010). “Performance assessment of construction companies integrating key performance indicators and data envelopment analysis.” Journal of Construction Engineering and Management, ASCE, Vol. 136, No. 5, pp. 581–594, DOI: 10.1061/(ASCE)CO.1943-7862.0000145.

    Article  Google Scholar 

  • Ibadov, N. (2015). “Contractor selection for construction project, with the use of fuzzy preference relation.” Procedia Engineering 111, pp. 317–323, DOI: 10.1016/j.proeng.2015.07.095.

    Article  Google Scholar 

  • Isik, Z. (2009). A Conceptual performance measurement framework for construction industry, Ph.D. Thesis, The Graduate School of Natural and Applied Science of Middle East Technical University.

    Google Scholar 

  • Kagioglou, M., Cooper, R., and Aouad, G. (2001). “Performance management in construction: A conceptual framework.” Construction Management and Economics, Vol. 19, No. 1, pp. 85–95, DOI: 10.1080/01446190010003425.

    Article  Google Scholar 

  • Kamat, V. R. and Martinez, J. C. (2001). “Visualizing simulated construction operations in 3D.” Journal of Computing in Civil Engineering, Vol. 15, No. 4, pp. 339–337, DOI: 10.1061/(ASCE)0887-3801 (2001)15:4(329).

    Article  Google Scholar 

  • Kamat, V. R. and Martinez, J. C. (2002). “Scene graph and frame update algorithms for smooth and scalable 3D visualization of simulated construction operations.” Computer-Aided Civil and Infrastructure Engineering, Vol. 17, No. 4, pp. 228–245, DOI: 10.1111/1467-8667.00272.

    Article  Google Scholar 

  • Kim, A. and Arditi, D. (2010). “Performance of minority firms providing construction management services in the US transportation sector.” Construction Management and Economics, Vol. 28, No. 8, pp. 839–851, http://dx.doi.org/10.1080/01446193.2010.483331

    Article  Google Scholar 

  • Leung, M., Chan, Y., and Chong, A. M. L. (2010). “Chinese values and stressors of Construction professionals in Hong Kong.” Journal of Construction Engineering and Management, ASCE, Vol. 136, No. 12, pp. 1289–1298, DOI: 10.1061/(ASCE)CO.1943-7862.0000234.

    Article  Google Scholar 

  • Li, Y. and Liu, C. (2010). “Malmquist indices of total factor productivity changes in the Australian construction industry.” Construction Management and Economics, Vol. 28, No. 9, pp. 933–945, DOI: 10.1080/01446191003762231.

    Article  Google Scholar 

  • Lin, G., Shen, G. Q., Sun, M., and Kelly, J. (2011). “Identification of key performance indicators for measuring the performance of value management studies in construction.” Journal of Construction Engineering and Management, ASCE, Vol. 137, No. 9, pp. 698–706, DOI: 10.1061/(ASCE)CO.1943-7862.0000348.

    Article  Google Scholar 

  • Love, P. E. D. and Holt, G. D. (2000). “Construction business performance measurement: The stakeholder perspective measurement alternative.” Business Process Management Journal, Vol. 6, No. 5, pp. 408–416, DOI:10.1108/14637150010352417.

    Article  Google Scholar 

  • Luu, T.-V., Kim, S.-Y., Cao, H.-L., and Park, Y.-M. (2008). “Performance measurement of construction firms in developing countries.” Construction Management and Economics, Vol. 26, No. 4, pp. 373–386, DOI: 10.1080/01446190801918706.

    Article  Google Scholar 

  • Markovic, L., Dutina, V., and Kovacevic, M. (2011). “Application of benchmarking method in the construction companies.” Facta Universitatis, Series: Architecture and Civil Engineering, Vol. 9, No. 2, pp. 301–314, DOI: 10.2298/FUACE1102301M.

    Google Scholar 

  • Mbugua, L. M., Harris, P., Holt, G. D., and Olomolaiye, P. O. (1999). “Framework for determining critical success factors influencing construction business performance.” 15th Annual ARCOM Conference of Association of Research in Construction Management, Liverpool, John Moores University.

    Google Scholar 

  • Morrison, M. (2009). “Key Performance Indicators.” RapidBI (Rapid Business Improvement UK. http://rapidbi.com/writing-key-performanceindicators-kpis accessed on 10th August, 2014.

    Google Scholar 

  • Nemcova-Zuzana (2009). Application of Key Performance Indicators in the Construction Industry: A comparison of the United Kingdom with the Czech Republic, The European Council of Construction Economists, 6pages.

  • Newing, H. (2011). A textbook on Conducting research in conservation: A social science perspective, Routledge, Taylor & Francis group.

    Google Scholar 

  • Pheng, L. S. and Teo, J. A. (2004). “Implementing total quality management in construction firms.” Journal of Management in Engineering, Vol. 20, No. 1, pp. 8–15, DOI: 10.1061/(ASCE)0742-597X (2004)20:1(8).

    Article  Google Scholar 

  • Pongpeng, J. and Liston, J. (2003). “Contractor ability criteria: A view point from the Thai construction industry.” Construction Management and Economics, Vol. 21, No. 3, pp. 267–282, DOI: 10.1080/0144619032000049647.

    Article  Google Scholar 

  • Rimbalova, J. and Vilcekova, S. (2013). “The proposal of key performance indicators in facility management and determination the Weights of Significance.” Journal of Civil Engineering, Vol. 8, No. 2, pp. 73–84, DOI: 10.2478/sspjce-2013-0020.

    Google Scholar 

  • Robinson, H. S., Carrillo, P. M., Anumba, C. J., and Al-Ghassani, A. M. (2001). “Perceptions and barriers in implementing knowledge management strategies in large construction organisations.” COBRA, Cutting Edge and ROOTS conferences, 11 pages. www.rics-foundation. org.

    Google Scholar 

  • Shen, L., Lu, W., and Yam, M. C. H. (2006). “Contractor key competitiveness indicators: A China study.” Journal of Construction Engineering and Management, ASCE, Vol. 132, No. 4, pp. 416–424, DOI: 10.1061/(ASCE)0733-9364(2006)132:4(416).

    Article  Google Scholar 

  • Singh, K. and Tiong, R. L. K. (2006). “Contractor selection criteria: Investigation of opinions in Singapore construction practitioners.” Journal of Construction Engineering and Management, ASCE, Vol. 132, No. 9, pp. 998–1008, DOI: 10.1061/(ASCE)0733-9364 (2006)132:9(998).

    Article  Google Scholar 

  • Skibniewski, M. J. and Ghosh, S. (2009). “Determination of key performance indicators with enterprise resource planning systems in engineering construction firms.” Journal of Construction Engineering and Management, ASCE, Vol. 135, No. 10, pp. 965–978, DOI: 10.1061/(ASCE)0733-9364(2009)135:10(965).

    Article  Google Scholar 

  • Tang, S. L., Lu, Ming, and Chan, Y. L. (2003). “Achieving client satisfaction for engineering consulting firm.” Journal of Construction Engineering and Management, ASCE, Vol. 19, No. 4, pp. 166–172, DOI: 10.1061/(ASCE)0742-597X (2003)19:4(166).

    Google Scholar 

  • Tsolas, I. E. (2011). “Modelling profitability and effectiveness of Greeklisted construction firms: An integrated DEA and ratio analysis.” Construction Management and Economics, Vol. 29, No. 8, pp. 795–807, DOI: 10.1080/01446193.2011.610330.

    Article  Google Scholar 

  • Wang, H. J., Zhang, J. P. Chau, K. W., and Anson, M. (2004). “4D dynamic management for construction planning and resource utilization.” Automation in Construction, Vol. 13, No. 5, pp. 575–589, DOI: 10.1016/j.autcon.2004.04.003.

    Article  Google Scholar 

  • Wong, C. H. (2004). “Contractor performance prediction model for the United Kingdom construction contractor: Study of logistic regression approach.” Journal of Construction Engineering and Management, ASCE, Vol. 130, No. 5, pp. 691–698, DOI: 10.1061/(ASCE)0733-9364(2004)130:5(691).

    Article  Google Scholar 

  • Yin, R. K. (2009). Case study research: Design and methods (4th ed), Sage Publications, Thousand Oaks.

    Google Scholar 

  • Yu, I., Kim, K., Jung, Y., and Chin, S. (2007). “Comparable performance measurement system for construction companies.” Journal of Management in Engineering, ASCE, Vol. 23, No. 3, pp. 131–139, DOI: 10.1061/(ASCE)0742-597X (2007)23:3(131).

    Article  Google Scholar 

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Authors and Affiliations

  1. Dept. of Civil Engineering, Indian Institute of Technology Delhi, Delhi, 110016, India

    K. K. Tripathi & K. N. Jha

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  1. K. K. Tripathi
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Tripathi, K.K., Jha, K.N. An Empirical Study on Performance Measurement Factors for Construction Organizations. KSCE J Civ Eng 22, 1052–1066 (2018). https://doi.org/10.1007/s12205-017-1892-z

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