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Abbasian-Hosseini, S A, Hsiang, S M, Leming, M L and Liu, M (2014) From Social Network to Data Envelopment Analysis: Identifying Benchmarks at the Site Management Level. Journal of Construction Engineering and Management, 140(08).

  • Type: Journal Article
  • Keywords: Benchmark; Social factors; Data analysis; Internet; Construction industry; Contract management; Benchmarking; Social network; Data envelopment analysis; Social learning; Construction trades; Labor and personnel issues;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000875
  • Abstract:
    It is widely accepted in the construction industry that contract documents, specifying the responsibility and risk of each participant, are the basis for project managers’ and superintendents’ decision making (DM). However, in practice strict adherence to the formal procedures and chains of command would not always be possible without an unacceptable expenditure of time and money. Although much attention is given to the decisions at the project manager and superintendent level, the underlying rules and mechanisms for the moment-to-moment DM at the site management level has not been documented. In this paper, a social network (SN)–based data envelopment analysis (DEA) benchmarking procedure (SDBP), which combines DEA (assessing the relative efficiency of DM units) and SN (concentrating on the relationships amongst DM units) to identify the benchmarks for the inefficient specialty trades (STs). This paper also uses a case study to illustrate how to implement the SDBP. This research contributes to the body of knowledge because it combines the DEA and SN analysis for the first time to propose a new method of identifying the improvement direction (benchmarks) for STs of a project. This technique is beneficial to project managers because it (1) untangles the role and effectiveness of the existing interactions and interdependencies among the STs, (2) evaluates the STs’ potential for being benchmarks, and (3) outlines how an inefficient ST can improve its performance through learning from the practices of other good performers (social learning).