Human Resource Allocation to Multiple Projects Based on Members’ Expertise, Group Heterogeneity, and Social Cohesion
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 2
Abstract
Project managers regularly allocate human resources to construction projects. This critical task is usually executed by fulfilling the minimum project staffing requirements, normally based around the quantity and competence of project members. However, research has shown that team performance can increase by up to 10% and 18%, respectively, as a consequence of the group members’ heterogeneity and social cohesion. There is currently no practical quantitative tool that incorporates these aspects, allowing project managers to achieve this task efficiently and objectively. A new quantitative model for the effective allocation of human resources to multiple projects, which takes group heterogeneity and social cohesion into account, is proposed. This model is easy to build, update, and use in real project environments with the use of a spreadsheet and a basic optimization engine (e.g., Excel Solver). A case study is proposed and solved with a genetic algorithm to illustrate the implementation of the model. Finally, a validation example is provided to exemplify how group heterogeneity and social cohesion condition academic achievement in an academic setting.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data generated or analyzed during the study are included in the published paper. Information about the Journal’s data-sharing policy can be found here: https://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
References
Agrawal, R., B. Golshan, and E. Terzi. 2014. “Grouping students in educational settings.” In Proc., 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining-KDD ‘14, 1017–1026. New York: ACM Press.
Ahmadian Fard Fini, A., A. Akbarnezhad, T. H. Rashidi, and S. T. Waller. 2017. “Job assignment based on brain demands and human resource strategies.” J. Constr. Eng. Manage. 143 (5): 4016123. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001273.
Ahmadian Fard Fini, A., T. H. Rashidi, A. Akbarnezhad, and S. Travis Waller. 2016. “Incorporating multiskilling and learning in the optimization of crew composition.” J. Constr. Eng. Manage. 142 (5): 4015106. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001085.
Al-Bayati, A. J., O. Abudayyeh, T. Fredericks, and S. E. Butt. 2017. “Managing cultural diversity at U.S. construction sites: Hispanic workers’ perspectives.” J. Constr. Eng. Manage. 143 (9): 4017064. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001359.
Allison, P. D. 1978. “Measures of inequality.” Am. Sociol. Rev. 43 (6): 865–880.
Anvuur, A. M., and M. M. Kumaraswamy. 2016. “Effects of teamwork climate on cooperation in crossfunctional temporary multi-organization workgroups.” J. Constr. Eng. Manage. 142 (1): 4015054. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001029.
Awal, G. K., and K. K. Bharadwaj. 2014. “Team formation in social networks based on collective intelligence: An evolutionary approach.” Appl. Intelligence 41 (2): 627–648. https://doi.org/10.1007/s10489-014-0528-y.
Ballesteros-Pérez, P., M. C. González-Cruz, and M. Fernández-Diego. 2012. “Human resource allocation management in multiple projects using sociometric techniques.” Int. J. Project Manage. 30 (8): 901–913. https://doi.org/10.1016/j.ijproman.2012.02.005.
Beal, D. J., R. R. Cohen, M. J. Burke, and C. L. McLendon. 2003. “Cohesion and performance in groups: A meta-analytic clarification of construct relations.” J. Appl. Psychol. 88 (6): 989–1004. https://doi.org/10.1037/0021-9010.88.6.989.
Blau, P. M. 1977. Inequality and heterogeneity. New York: Free press.
Castaño, N., T. Watts, and A. G. Tekleab. 2013. “A reexamination of the cohesion-performance relationship meta-analyses: A comprehensive approach.” Group Dyn. Theory Res. Pract. 17 (4): 207–231. https://doi.org/10.1037/a0034142.
Chang, A., and P. Bordia. 2001. “A multidimensional approach to the group cohesion-group performance relationship.” Small Group Res. 32 (4): 379–405. https://doi.org/10.1177/104649640103200401.
Chen, S., D. Wang, Y. Zhou, Z. Chen, and D. Wu. 2017a. “When too little or too much hurts: Evidence for a curvilinear relationship between team faultlines and performance.” Asia Pacific J. Manage. 34 (4): 931–950. https://doi.org/10.1007/s10490-017-9510-7.
Chen, Y., B. McCabe, and D. Hyatt. 2017b. “Relationship between individual resilience, interpersonal conflicts at work, and safety outcomes of construction workers.” J. Constr. Eng. Manage. 143 (8): 4017042. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001338.
Chiocchio, F., and H. Essiembre. 2009. “Cohesion and performance: A meta-analytic review of disparities between project teams, production teams, and service teams.” Small Group Res. 40 (4): 382–420. https://doi.org/10.1177/1046496409335103.
Dion, K. L. 2000. “Group cohesion: From ‘field of forces’ to multidimensional construct.” Group Dyn. Theory Res. Pract. 4 (1): 7–26. https://doi.org/10.1037/1089-2699.4.1.7.
Dorn, C., and S. Dustdar. 2010. “On the move to meaningful internet systems: OTM 2010.” In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), edited by R. Meersman, T. Dillon, and P. Herrero. Berlin: Springer.
Earley, P. C., and C. B. Gibson. 2002. Multinational work teams: A new perspective. Mahwah, NJ: Lawrence Erlbaum Associates.
Evans, C. R., and K. L. Dion. 2012. “Group cohesion and performance: A meta-analysis.” Small Group Res. 43 (6): 690–701. https://doi.org/10.1177/1046496412468074.
Farhadi, F., E. Hoseini, S. Hashemi, and A. Hamzeh. 2012a. “TeamFinder: A co-clustering based framework for finding an effective team of experts in social networks.” In Proc., 2012 IEEE 12th Int. Conf. on Data Mining Workshops, 107–114. Piscataway, NJ: IEEE.
Farhadi, F., M. Sorkhi, S. Hashemi, and A. Hamzeh. 2011. “An effective expert team formation in social networks based on skill grading.” In Proc., 2011 IEEE 11th Int. Conf. on Data Mining Workshops, 366–372. Piscataway, NJ: IEEE.
Farhadi, F., M. Sorkhi, S. Hashemi, and A. Hamzeh. 2012b. “An effective framework for fast expert mining in collaboration networks: A group-oriented and cost-based method.” J. Computer Sci. Technol. 27 (3): 577–590. https://doi.org/10.1007/s11390-012-1245-9.
Festinger, L., S. Schachter, and K. Back. 1950. Social pressures in informal groups; a study of human factors in housing. Oxford, UK: Harper.
Gajewar, A., and A. Sarma Das. 2012. “Multi-skill collaborative teams based on densest subgraphs.” In Proc., 12th SIAM Int. Conf. on Data Mining, SDM 2012, 165–176. Philadelphia: Society for Industrial and Applied Mathematics.
Gibson, C., and F. Vermeulen. 2003. “A healthy divide: Subgroups as a stimulus for team learning behavior.” Administrative Sci. Q. 48 (2): 202–239. https://doi.org/10.2307/3556657.
Gutiérrez, J. H., C. A. Astudillo, P. Ballesteros-Pérez, D. Mora-Melià, and A. Candia-Véjar. 2016. “The multiple team formation problem using sociometry.” Comput. Oper. Res. 75 (Nov): 150–162. https://doi.org/10.1016/j.cor.2016.05.012.
Hendriks, M. H. A., B. Voeten, and L. Kroep. 1999. “Human resource allocation in a multi-project R&D environment: Resource capacity allocation and project portfolio planning in practice.” Int. J. Project Manage. 17 (3): 181–188. https://doi.org/10.1016/S0263-7863(98)00026-X.
Lappas, T., K. Liu, and E. Terzi. 2009. “Finding a team of experts in social networks.” In Proc., 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining-KDD ‘09, 467. New York: ACM.
Lau, D. C., and J. K. Murnighan. 2005. “Interactions within groups and subgroups: The effects of demographic faultlines.” Acad. Manage. J. 48 (4): 645–659. https://doi.org/10.5465/amj.2005.17843943.
Li, C.-T., and M.-K. Shan. 2010. “Team formation for generalized tasks in expertise social networks.” In Proc., 2010 IEEE 2nd Int. Conf. on Social Computing, 9–16. Piscataway, NJ: IEEE.
Lott, A. J., and B. E. Lott. 1965. “Group cohesiveness as interpersonal attraction: A review of relationships with antecedent and consequent variables.” Psychol. Bull. 64 (4): 259–309. https://doi.org/10.1037/h0022386.
Mathieu, J. E., M. R. Kukenberger, L. D’Innocenzo, and G. Reilly. 2015. “Modeling reciprocal team cohesion—Performance relationships, as impacted by shared leadership and members’ competence.” J. Appl. Psychol. 100 (3): 713–734. https://doi.org/10.1037/a0038898.
Meyer, B., and A. Glenz. 2013. “Team faultline measures: A computational comparison and a new approach to multiple subgroups.” Organ. Res. Methods 16 (3): 393–424. https://doi.org/10.1177/1094428113484970.
Moreno, J. L. 1941. “Foundations of sociometry: An introduction.” Sociometry 4 (1): 15–35. https://doi.org/10.2307/2785363.
Phua, F. T. T. 2004. “The antecedents of co-operative behaviour among project team members: An alternative perspective on an old issue.” Constr. Manage. Econ. 22 (10): 1033–1045. https://doi.org/10.1080/01446190310001649092.
Phua, F. T. T., and S. Rowlinson. 2004. “How important is cooperation to construction project success? A grounded empirical quantification.” Eng., Constr. Archit. Manage. 11 (1): 45–54. https://doi.org/10.1108/09699980410512656.
Salo, M. 2006. The relation between sociometric choices and group cohesion. Washington, DC: US Government.
Shi, Z., and F. Hao. 2013. “A strategy of multi-criteria decision-making task ranking in social-networks.” J. Supercomputing 66 (1): 556–571. https://doi.org/10.1007/s11227-013-0934-7.
Sorkhi, M., H. Alvari, S. Hashemi, and A. Hamzeh. 2012. “A game-theoretic framework to identify top-k teams in social networks.” In Proc., Int. Conf. on Knowledge Discovery and Information Retrieval KDIR 2012, 252–257. Barcelona, Spain: INSTICC.
Sturgis, P., I. Brunton-Smith, J. Kuha, and J. Jackson. 2014. “Ethnic diversity, segregation and the social cohesion of neighbourhoods in London.” Ethnic Racial Stud. 37 (8): 1286–1309. https://doi.org/10.1080/01419870.2013.831932.
Teixeira, L. O., and E. H. M. Huzita. 2014. “DiSEN-AlocaHR: A multi-agent mechanism for human resources allocation in a distributed software development environment.” In Proc., Int. Conf. Advances in Intelligent Systems and Computing, edited by S. Omatu, H. Bersini, J. M. Corchado, S. Rodríguez, P. Pawlewski, and E. Bucciarelli. Cham, Switzerland: Springer International.
Tseng, T.-L. B., C.-C. Huang, H.-W. Chu, and R. R. Gung. 2004. “Novel approach to multi-functional project team formation.” Int. J. Project Manage. 22 (2): 147–159. https://doi.org/10.1016/S0263-7863(03)00058-9.
Xia, N., R. Zhong, C. Wu, X. Wang, and S. Wang. 2017. “Assessment of stakeholder-related risks in construction projects: Integrated analyses of risk attributes and stakeholder influences.” J. Constr. Eng. Manage. 143 (8): 4017030. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001322.
Yin, H., B. Cui, and Y. Huang. 2011. “Advanced data mining and applications. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics).” In Lecture notes in computer science, edited by J. Tang, I. King, L. Chen, and J. Wang. Berlin: Springer.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 145 • Issue 2 • February 2019
Copyright
©2018 American Society of Civil Engineers.
History
Received: Feb 28, 2018
Accepted: Aug 20, 2018
Published online: Dec 4, 2018
Published in print: Feb 1, 2019
Discussion open until: May 4, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.