Abstract
As part of human resource management policies and practices, construction firms need to define competency requirements for project staff and recruit the necessary team for completion of project assignments. Traditionally, potential candidates are interviewed and the most qualified are selected. Applicable methodologies that could take various candidate competencies and inherent uncertainties of human evaluation into consideration and then pinpoint the most qualified person with a high degree of reliability would be beneficial. In the last decade, computing with words (CWW) has been the center of attention of many researchers for its intrinsic capability of dealing with linguistic, vague, interdependent, and imprecise information under uncertain environments. This paper presents a CWW approach, based on the specific architecture of Perceptual Computer (Per-C) and the Linguistic Weighted Average (LWA), for competency-based selection of human resources in construction firms. First, human resources are classified into two types of main personnel: project manager and engineer. Then, a hierarchical criteria structure for competency-based evaluation of each main personnel category is established upon the available literature and survey. Finally, the perceptual computer approach is utilized to develop a practical model for the competency-based selection of personnel in construction companies. We believe that the proposed approach provides a useful tool to handle personnel selection problem in a more reliable and intelligent manner.
Keywords:
Computing with words (CWW), Perceptual computing, Linguistic Weighted Average (LWA), Fuzzy multiple criteria decision making (FMCDM), Interval type-2 fuzzy set (IT2 FS), Competency, Construction personnel
Introduction
Human resource management(HRM)is defined as the processes that organize, manage, and lead a project team [1]. It contributes to the success of the project [2,3] and creates a competitive advantage for the organization [4,5]. The HRM policies, processes, and practices in the construction company are in some way supportive of project-oriented working and are different from more traditional HRM processes and practices [6], which are designed for the classically managed organization. While in classically managed organizations, the emphasis is not on projects but instead on routine products and services where the job requirements are well defined and stable [7], in competency-based HRM, practices, and policies are designed, developed and implemented based on personnel competencies in order to support the integration of human resource management. The latter forms a solid basis, which unifies the different steps of human resource management.
The project team is comprised of appropriate people with assigned roles and responsibilities for completing the project [1]. Project team members may also be referred to as the project staff or personnel [1,7]. Developing the project team improves the people skills, technical competencies, and overall team environment and project performance [8], which is a critical factor for project success [9,10]. Effective team development strategies and activities are expected to increase the team’s performance, which increases the likelihood of meeting project objectives [11]. The degree or extent of this impact may vary, depends on certain factors such as project type, characteristic, and organizational context. If project team members do not possess the required competencies, performance can be jeopardized [1]. Competency is the knowledge, skills, and behaviors a person needs to fulfill his or her role [12]. Assessing competencies, skills, and abilities, and knowing personality traits and key behaviors of individuals increase the chances of choosing a team that has the potential to succeed [8].
Traditionally, expert interviews the candidates for job positions, and the best person is selected based on the capability analysis. The statistical techniques support the engaging decision through the arrangement of test scores and the measure of accomplishment for the candidate [12]. However, the process is often ambiguous, biased, and lacking in accuracy [13]. In contrast, in the competency-based selection procedures, the overall competencies of personnel are evaluated by the competency framework established for each specific job class. Therefore, this procedure is carried out for each class of jobs and careers rather than tasks and duties, which definitely leads to the fair evaluation of personnel.
As part of project HRM policies and practices, construction firms need to define competencies required for all project personnel and obtaining the team necessary to complete project assignments. Therefore, the purpose of this research is to construct and practice a competency-based model for selecting personnel in construction companies, which are classified into two types of main personnel: project manager and engineer. A survey is undertaken to develop the competency criteria model of each main personnel. With the consideration of the main personnel competencies, we develop a CWW model based on the specific architecture of Perceptual Computer and the Linguistic Weighted Average for competency-based selection of personnel in construction companies, where all linguistic terms are characterized by the interval type-2 fuzzy sets (IT2 FSs), directly exploited from the perception of a group of experts. The rest of this paper is organized as follows: in Section 2, some recent literature on personnel selection problems is reviewed. In Section 3, some basic concepts, definitions, and methods for IT2 FSs are briefly reviewed. In Section 4, some background and technical materials about CWW in decision-making are reviewed. We also go into more detail about the Perceptual Computer concept and the Linguistic Weighted Average method. In Section 5, the complete procedure for handling the human resource selection problem is considered. In Section 6, we validate the proposed methodology for personnel selection based on an empirical example. In Section 7, we discuss the conclusion.
Conclusions
The aim of this paper was to develop the Perceptual Computer model, as a specific architecture and as an instantiation of Zadeh’s CWW paradigm, for competency-based selection of personnel in construction companies. Competency-based evaluation and selection is performed for each class of jobs and careers and serves as a link between different steps of human resource management. Classical job classification and evaluation processes take position roles and responsibilities into account for the selection problem. By contrast, in the competency-based selection procedures, the overall competencies of personnel are evaluated by the competency framework established for each specific job class. The latter, indeed, leads to the fair evaluation of personnel.
In the proposed methodology, all the inputs to the personnel selection model are words, represented by IT2 FSs, to make up for some shortages of previous models and to ease the decision-making process when the experts can only provide qualitative information based on their subjective perceptions. The specificity of this problem stemmed from its vague and imprecise information and judgments, i.e., candidate evaluations are under uncertainty, their competencies cannot be evaluated correctly without considering the related uncertainties, and linguistic descriptors are usually used by experts in this area. To tackle these issues, we exploited the remarkable vagueness modeling capability of interval type-2 fuzzy sets in order to address the uncertainties associated with words used normally by experts. It should be noted that interval type-2 fuzzy representation of a word is the least realistic descriptor concerning the word nature since itis an obvious contradiction to mold an uncertain concept with a certain one. Other reduced representations, such as type-1 fuzzy sets, are just rough approximations. By modeling the words with IT2 FSs, no pre-processing and reduction approaches are utilized, and as a result, all accompanying uncertainties flow all through the calculation processes.
The distinguishing feature of using Per-C architecture and the LWA method in this problem is that all the uncertainties about the words, sub-criteria scores and criteria weights can flow and be aggregated all through the calculations and be reflected in the overall results, and consequently, no information is lost. Besides, all word’s FOUs are established directly, without any prior assumptions, upon the interval endpoint data collected from a group of experts and as a result, are realistic representations of the linguistic terms. In addition, the IT2 FS result of performance evaluation contains much more information than just a single ranking number, since the UMF and LMF of each result represent the upper bound and lower bound of possibility associated with each grade. In other words, each individual is actually evaluated by the FOU, which contain uncertainty information in addition to ranking and similarity information. This advantage can be effortlessly seen in Figs. 7–10. The complying of the empirical experiment results with the existing performance records and the intuitive preferences of the experts also showed that the proposed approach has practical implementations for real-world problems. This approach provides the experts with a useful tool to handle the problem in a more flexible and intelligent manner.
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The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
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FULL Paper PDF file:
Computing_with_words_for_hierarchical_co (1)
Bibliography
author
Year
2012
Title
Computing with words for hierarchical competency-based selection of personnel in construction companies
Publish in
Elsevier, Applied Soft Computing
Doi
https://doi.org/10.1016/j.asoc.2011.10.004
PDF reference and original file: Click here
Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
