Human resources ranking in a cloud-based knowledge sharing framework using the quality control criteria

Abstract

Purpose

This paper aims to propose a new method for evaluating the quality and prioritizing of the human resources (HRs) based on trust, reputation, agility, expertise and cost criteria in the expert cloud. To evaluate some quality control (QC) factors, a model based on the SERVQUAL is used.

Design/methodology/approach

The aim of this paper is to offer a fast and simple method for selecting the HRs by the customers. To achieve this goal, the ranking diagram of different HRs based on the different criteria of QC is provided. By means of this method, the customer can rapidly decide on the selection of the required HRs. By using the proposed method, the scores for various criteria are evaluated. These criteria are used in the ranking of each HR which is obtained based on the evaluation conducted by previous customers and their colleagues. First, customers were asked to select their needed criteria and then by constructing a hierarchical structure, the ranking diagram of different HRs is achieved. Using a ranking system based on evaluating the quality of the model, satisfy the customer needs to be based on the properties of HRs. Also, an analytical hierarchical process-based ranking mechanism is proposed to solve the problem of assigning weights to features for considering the interdependence between them to rank the HRs in the expert cloud.

Findings

The obtained results showed the applicability of the radar graph using a case study and also numerically obtained results showed that a hierarchical structure increases the quality and speed rating of HR ranking than the previous works.

Originality/value

The suggested ranking method in this paper allows the optimal selection due to the special needs of any given customer in the expert cloud.

Citation

Fouladi, P. and Jafari Navimipour, N. (2017), "Human resources ranking in a cloud-based knowledge sharing framework using the quality control criteria", Kybernetes, Vol. 46 No. 5, pp. 876-892. https://doi.org/10.1108/K-01-2017-0007