Applying total interpretive structural modeling to study factors affecting construction labour productivity
Main Article Content
Abstract
Construction sector has always been dependent on manpower. Most of the activities carried out on any construction site are labour intensive. Since productivity of any project depends directly on productivity of labour, it is a prime responsibility of the employer to enhance labour productivity. Measures to improve the same depend on analysis of positive and negative factors affecting productivity. Major attention should be given to factors that decrease the productivity of labour. Factor analysis thus is an integral part of any study aiming to improve productivity. Interpretive structural modeling is a methodology for identifying and summarizing relationships among factors which define an issue or problem. It provides a means to arrange the factors in an order as per their complexity. This study attempts to use the latest version of interpretive structural modeling i.e. total interpretive structural modeling to analyze factors negatively affecting construction labour productivity. It establishes interpretive relationship among these factors facilitating improvement in the overall productivity of construction site.
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
a) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share and adapt the work with an acknowledgement of the work's authorship and initial publication in this journal.
b) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
c) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Open Access Citation Advantage Service). Where authors include such a work in an institutional repository or on their website (ie. a copy of a work which has been published in a UTS ePRESS journal, or a pre-print or post-print version of that work), we request that they include a statement that acknowledges the UTS ePRESS publication including the name of the journal, the volume number and a web-link to the journal item.
d) Authors should be aware that the Creative Commons Attribution (CC-BY) License permits readers to share (copy and redistribute the work in any medium or format) and adapt (remix, transform, and build upon the work) for any purpose, even commercially, provided they also give appropriate credit to the work, provide a link to the license, and indicate if changes were made. They may do these things in any reasonable manner, but not in any way that suggests you or your publisher endorses their use.
References
2. Arditi, D. (1985), Construction productivity improvement, Journal of Construction Engineering and Management, ASCE, Vol. 111 No. 1, pp. 1-14.
3. Thomas, H.R., Moloney, W.F., Horner, R.M.W., Smith, G.R., Handa, V.K. and Sanders, S.R. (1990), Modeling construction labour productivity, Journal of Construction Engineering and Management, ASCE, Vol. 116 No. 4, pp. 705-26.
4. Kaming, F.P., Olomolaiye, P.O., Holi, G.D., Kometa, S.T. and Harris, F.C. (1996), Project manager’s perceptions of production problems: an Indonesian case study, Building Research and Information, Vol. 24 No. 5, pp. 302-329.
5. Moselhi O.and Khan Z. (2012), Significance ranking of parameters impacting construction labour productivity, Construction Innovation, Vol. 12 No. 3, 2012,pp. 272-296.
6. Jarkas, A. and Bitar, C. (2012). ”Factors Affecting Construction Labour Productivity in Kuwait.” Journal of Construction Engineering and Management, 138(7), 811–820.
7. Warfield, J.N. (1974), “Towards interpretation of complex structural models”, IEEE Transactions: System, Man and Cybernetics, Vol. 4 No. 5, pp. 405-17.
8. Sage, A.P. (1977), Interpretive Structural Modelling: Methodology for Large Scale Systems, McGraw-Hill, New York, NY, 1977, pp. 91-164.
9. Farris D.R., Sage A.P. (1975), On the use of interpretive structural modeling for worth assessment , Computers & Electrical Engineering, Volume 2, Issues 2–3, June 1975, Pages 149–174.
10. Sushil (2012), “Interpreting the interpretive structural model: organization research methods”, Global Journal of Flexible Systems Management (June 2012) 13(2):87–106.
11. Nasim, S. (2011), “Total interpretive structural modeling of continuity and change forces in e-government”, Journal of Enterprise Transformation, Vol. 1 No. 2, pp. 147-68.
12. Watson R.H. (1978), Interpretive structural modeling- A useful tool for technology assessment, Technological Forecasting and Social Change, Volume 11, Issue 2, 1978, Pages 165–185.
13. Mandal, A. and Deshmukh, S.G. (1994), “Vendor selection using interpretive structural modelling (ISM)”, International Journal of Operations and Production Management, Vol. 14 No. 6, pp. 52-9.
14. Haleem, A., Sushil, Qadri, M.A. and Kumar, S. (2012), “Analysis of critical success factors of world class manufacturing practices: an application of interpretive structural modeling and interpretative ranking process”, Production Planning and Control: The Management of Operations, Vol. 23 Nos 10-11, pp. 722-34.
15. Singh, R.K. (2011), “Developing the framework for coordination in supply chain of SMEs”, Business Process Management Journal, Vol. 17 No. 4, pp. 619-38.
16. Qureshi, M.N., Kumar, D. and Kumar, P. (2008), “An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers”, Asia Pacific Journal of Marketing and Logistics, Vol. 20 No. 2, pp. 227-49.
17. Sahney S., Banwet D.K. and Karunes S. (2010), “Quality framework in education through the application of interpretive structural modeling: an administrative staff perspective in the Indian context”, The TQM Journal, Vol. 22 No. 1, pp. 56-71.
18. Soti A., Shankar R. and Kaushal O.P. (2010), “Modeling the enablers of Six Sigma using interpreting structural modeling”, Journal of Modelling in Management, Vol. 5 No. 2, pp. 124-41.
19. Singh A.K. and Sushil (2012), Modeling enablers of TQM to improve airline performance, International Journal of Productivity and Performance Management Vol. 62 No. 3, 2013 pp. 250-275.
20. Kumar S., Luthra S, Haleem A.(2013), Customer involvement in greening the supply chain: an interpretive structural modeling methodology. Journal of Industrial Engineering International, 9:6.
21. Sushil (2005a), “Interpretive matrix: a tool to aid interpretation of management in social research”, Global Journal of Flexible System Management, Vol. 6 No. 2, pp. 27-30.
22. Sushil, (2005b), “A flexible strategy framework for managing continuity and change”, International Journal of Global Business and Competitiveness, Vol. 1 No. 1, pp. 22-32.
23. Prasad U.C. and Suri R.K. (2011), “Modelling of continuity and change forces in private higher technical education using total interpretive structural modeling (TISM)”, Global Journal of Flexible Systems Management, Vol. 12 No. 3 & 4, pp. 31-40.
24. Wasuja S., Sagar M., Sushil (2012), Cognitive bias in salespersons in specialty drug selling of pharmaceutical industry, International Journal of Pharmaceutical and Healthcare Marketing Vol. 6 No. 4, 2012 pp. 310-335.
25. Srivastava A.K. and Sushil (2013), Modeling strategic performance factors for effective strategy execution, International Journal of Productivity and Performance Management, Vol. 62 No. 6, 2013, pp. 554-582.
26. Sonmez, R., and Rowings, J. (1998). Construction labour productivity modeling with neural network, Journal of Construction Engineering and Management, 124(6), 498–504.
27. Hanna, A. S., Chang, C., Sullivan, K., and Lackney, J. A. (2008). Impact of shift work on labour productivity for labour intensive contractor, Journal of Construction Engineering and Management, 134(3), 197–204.
28. Eastman C. M., and Sacks R. (2008). Relative productivity in the AEC industries in the United States for on-site and off-site activities, J. Constr. Eng. Manage., 134(7), 517–526.
29. Zhigang Shen, Wayne Jensen, Charles Berryman, Yimin Zhu (2011), Comparative Study of Activity-Based Construction Labour Productivity in the United States and China, Journal Of Management in Engineering © ASCE / APRIL 2011.
30. Ibbs W. (2012), Construction Change: Likelihood, Severity, and Impact on Productivity, Journal of Legal Affairs and Dispute Resolution in Engineering And Construction, ASCE, Vol 4, No. 3, /2012/ pp 67–73.
31. Shehata M.E. and El-Gohary K.M. (2011), Towards improving construction labour productivity and projects’ performance, Alexandria Engineering Journal (2011) 50, 321–330.
32. Enshassi A., Mohamed S., Mustafa Z.A., Mayer P.E. (2007), Factors affecting labour productivity in building projects in the gaza strip, Journal of civil engineering and management, 2007, Vol XIII, No 4, 245–254.
33. Attri R., Dev N. and Sharma V. (2013), Interpretive Structural Modelling (ISM) approach: An Overview, Research Journal of Management Sciences, Vol. 2(2), 3-8, February (2013).