Added: Lelia Dendy - Date: 15.03.2022 21:23 - Views: 39502 - Clicks: 4932
Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. With the increasing of overseas talent tasks in China, overseas talent and job fit are ificant issues that aim to improve the utilization of this key human resource.
Many studies based on fuzzy sets have been conducted on this topic. Among the many fuzzy set methods, intuitionistic fuzzy sets are usually utilized to express and handle the evaluation information. In recent years, various intuitionistic fuzzy decision-making methods have been rapidly developed and used to solve evaluation problems, but none of them can be used to solve the person-job fit problem with intuitionistic best-worst method BWM and TOPSIS methods considering large-scale group decision making LSGDM and evaluator social network relations SNRs.
Finally, we build a model of high-level overseas talent and job fit and establish a mutual criteria system that is applied to a case study to illustrate the efficiency and reasonableness of the model. High-level overseas talent, which can be considered a part of natural globalization, has both advanced knowledge and international vision 1. Such talent is an important force in Chinese economics, society, and development. To achieve the Chinese Dream, the Chinese government has paid close attention to the introduction and management of overseas talent, proposing that proper high-level overseas talent should be treated as an important force.
There is a problem, which should not be ignored. The introduction of high-level overseas talent in China is largely driven by government policies, but the original initiative of both the supply and demand of human resources was limited.
Although many overseas workers have great enthusiasm for coming to China, their knowledge and ability have not been fully utilized. Person-job fit is the key point to effectively achieve human resource allocation and management. Therefore, we need to underline the need for person-job fit in the process of overseas talent introduction and utilization.
The two sides of person-job fit should take preferences and benefits into consideration.
To achieve this goal, we propose an optimization algorithm whose goal is to obtain the best matching degree of the person and job. The two-sided matching method is simply an appropriate method that considers the preferences of the two sides at the same time.
InGale and Shapley 2 first proposed two-sided theory to solve the marriage problem. Since then, many researchers have shown great interest in this topic, extending this theory with different fuzzy language to different problems 345. Some papers have used two-sided matching methodologies to solve human resource management problems 6789. Yu and Xu 10 proposed a novel intuitionistic fuzzy two-sided matching model for the person-job problem.
Compared with the mentioned papers, this paper discusses the Person-job fit problem for High-level overseas talent, which needs a systematic evaluation standard system determined by a large of decision makers. With the development of management practice, the classical two-sided matching method is not sufficient. Additionally, an increasing of academic studies have generally accepted that person-job fit should be characterized as the adaptability of characteristics and the compatibility between personnel and organizations.
However, the interpretation of adaptation is not the same. There are three views explaining the connotation of person-job fit from different angles. The first is the requirements and capabilities view 1112which considers that adaptation refers to the correspondence between job requirements and personal capability. The second is the similar or consistent view 13which believes that the person-job fit is due to certain similarities and mutual attractions, such as highly consistent values. Many scholars tend to agree with the third view of demand and supply 14under which people will choose organizations that have similar goals or can help them achieve their goals.
Overall, regardless of the kind of view, it is essentially stressed that person-job fit is a mutual evaluation based on a specific criterion set. Therefore, a scientific criteria system is necessary. Due to the complexity of the evaluation standard system itself, this paper introduces a large-scale decision group to discuss the evaluation criteria and criteria weights. The traditional group evaluation problem will be difficult to utilize.
Because most decision makers come from the same human resource management field, their relationship has a considerable impact on the weight of the evaluation criteria. In recent years, a large group decision-making method considering social networks SN-LSGDM has taken the social relationships of decision makers intobringing decisions closer to reality.
For example, some decision makers may occupy important positions in the network structure. Their decision may have a greater influence on the implementation of the decision. Chu et al. In particular, some papers 1617 have proposed the concept of leadership in social network analysis.
Specifically, we let nodes represent the decision makers. If there is a relationship between two nodes, then we connect them. Some scholars have studied simple graphs, whose edges do not have directions and weights. Wu utilized the Louvain method to detect communities and calculated node weights by their degree centrality and eigenvector centrality Wu et al. In reference 18Furthermore, this paper considers the importance of nodes and the influence of modularity on the overall structure of a network. The BWM is a different idea for ranking alternatives than the pairwise comparison methods proposed by Rezaei 20 inapplying to a phone choosing problem.
The BWM has attracted much attention from scholars 22 in different fields. Liang 23 used linguistic variables and fuzzy s from a qualitative and quantitative perspective to not only enrich the expression of decision-makers but also make their opinions very direct and effective. Guo and Zhao 24 extended the BWM to fuzzy environments.
Yang et al. Ahmadi et al. Kheybari et al. As the idea of the BWM decreases the of comparisons, the BWM will not be suitable when the of decision makers is large and their relationships are complex. In this paper, the BWM will be discussed under the environment of preference relations. In general, the BWM has 3 main steps: Step 1: Determine the best and worst criteria from among n alternatives. Considering the non-negativity of weights, we obtain:.
As noted for the BWM method, it is not difficult for people to choose the best and the worst among the alternatives under a certain criterion. Determining by how much the best alternative is superior to the others and by how much the others are superior to the worst are the difficult steps. The BWM is an effective method for dealing with comparison times. In this paper, the decision body expresses its preference attitude with the I-BWM.
We introduce a transformation formula to obtain the corresponding intuitionistic fuzzy s, aggregating the initial evaluation more effectively. To handle the satisfactory weight calculation problem, we introduce the technique for order preference by similarity to ideal solution TOPSIS method to calculate satisfactory weights. TOPSIS is a well-known MCDM method, proposed by Huang and Yang 28 in and studied by many researchers, policymakers and stakeholders, who have mostly aimed to promote and improve the core functions of this method 2930 The core idea of the TOPSIS method is that the closer to the positive ideal solution and the farther from the negative ideal solution an alternative is, the better it is, as shown in Fig.
Ye 33 used it to solve the partner choosing problem, which depends on interval-valued intuitionistic fuzzy sets. Wang et al. Joshi et al. Paritosh et al. Opricovic et al. Then, Kuo 38 constructed a ranking method for both of the positive and negative ideal distances. Some authors have studied this method in different situations, such as using type-2 fuzzy s 18 and intuitionistic fuzzy s The main idea of TOPSIS is to find the best alternative that has the greatest distance from the negative ideal solution and the least distance from the positive solution.
Measuring distance is a complex problem that has been researched by many scholars. Thus far, the mentioned papers have not been suitable for large-scale group decision-making problems. Works in this field are still related to traditional decision-making methods. In our study, the extended TOPSIS method is improved on the basis of multiobjective optimization and is then used to identify the optimal de scheme.
We propose a novel distance measure to calculate the distance between different intuitionistic fuzzy s. When the of decision makers is more than 20, the traditional multicriteria decision-making methods are invalid. There is a trend in which a larger of experts are becoming involved in the decision-making process. Therefore, the large-scale group decision-making problem has become a much-discussed topic Tang et al. It is necessary to consider the decision with their connections. Ding et al. In this paper, we also weight the criteria and decision makers by their importance degree.
On the other hand, our distance measures based on the BWM are more suitable for evaluating problems. The motivations of this paper are summarized in three parts, as shown in Fig. China regards talent as the primary driving force of innovation, and talent evaluation covers a wide range. Additionally, the of decision bodies for the person-job matching problem is larger than that for a traditional evaluation problem. With the increase in the of decision-makers, the impact of their social network relationship on the determination of evaluation is becoming increasingly prominent.
The proposed clustering algorithm based on social network analysis is helpful in enriching and improving the LSGDM problem system. For the person-job fit problem, it is of great ificance to evaluate the efficient matching of both decision bodies.
Therefore, the two-sided matching model proposed in this paper will enrich the research method system of this problem. To carry out the evaluation process more smoothly, this paper systematically constructs an evaluation standard system covering both sides and applies it to solve practical problems. In the process of constructing the methodology, some innovative definitions and theorems are given to support the methodology. Pang et al. To address large-scale s, large-scale group decision makers will be ased to subgroups by applying Algorithm 1 below.
LTS can be also named linguistic evaluation scale, which consists of an odd of ordered linguistic terms It determines the range of linguistic terms, which are available for the linguistic computational models. An important definition in this section is modularity, which is used to address aggregation problems and is defined by Eq.
A principle of network analysis in the LSGDM problem is to let each node be a separate community initially. To maximize the modularity of the whole community, we calculate the largest local contribution of each node community. The basic algorithm process contains four steps. Algorithm 1 Step 1: Let each node to be an original community. Each community is regarded as a new node.Btm in need of top
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