Multi-attribute dynamic two-sided matching method of talent sharing market in incomplete preference ordinal environment

Liang, Decui; He, Xin; Zhang, Xu

doi:10.1016/j.asoc.2020.106427

Cited by 43 publications

(10 citation statements)

References 25 publications

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“…Also associated with the concept of human resource virtualization, platforms have been designed to share talent in activities related to the collaborative economy, i.e. cloud platforms specialized in crowdsourcing (Xu et al , 2015; Liang et al , 2020; Lin et al , 2020).…”

Section: Resultsmentioning

confidence: 99%

Cloud computing and human resource management: systematic literature review and future research agenda

Marín

Dias

Navimipour

et al. 2021

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PurposeThis study aims to provide an overview of what characterizes the current state of research in the field of cloud computing use in human resource management (HRM) with the identification, analysis and classification of the existing literature and lines of research addressed and to provide guidance for future research.Design/methodology/approachThe systematic literature review (SLR) technique has been used to identify, select, analyze and evaluate the existing publications on cloud computing and HRM. A total of 35 papers published up to December 2020 have been obtained from the Web of Science (WoS) scientific database. The research design has allowed us to determine what characterizes the current state of research on the use of cloud computing in HRM and obtain a novel classification of the literature that identifies four lines of research and the contributions in each line and has allowed us to define the future research agenda.FindingsThe four groups into which the papers on the cloud computing-HRM relationship have been classified are: (1) studies focused on the development of cloud platforms for HRM that highlight technical aspects, (2) papers that focus on the concept of human resource elasticity, (3) papers on the adoption and/or implantation of cloud platforms for HRM and (4) studies that highlight the effects or implications of cloud platforms for HRM. This paper proposes some new opportunities for future research and presents some helpful implications from the theoretical and management perspectives.Research limitations/implicationsThis study uses only scientific articles in the WoS database with a Journal Citation Report (JCR) or SCImago Journal Rank (SJR) impact.Originality/valueThis paper provides an overview of the knowledge on cloud computing and HRM research and offers recommendations for future research.

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Section: Resultsmentioning

confidence: 99%

Cloud computing and human resource management: systematic literature review and future research agenda

Marín

Dias

Navimipour

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Zhao et al 14 proposed a dynamic bilateral matching decision method for the preference information given by the subject. Liang et al 15 proposed a multi-attribute dynamic bilateral matching method for talent sharing market under incomplete preference order environment. Li et al 16 discussed the problem of dynamic resource allocation on multi-category bilateral platforms.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Person-position Matching Decision Method Based on Hesitant Fuzzy Number Information

Yue,

Liu,

Tao

2023

Preprint

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In view of the fact that people pay more and more attention to the principle of "getting the position according to the person" and "adapting the person to the position" in person-position matching, a dynamic person-position matching decision method based on hesitant fuzzy numbers is proposed. First, the dynamic person-position matching problem with the hesitant fuzzy numbers is described. Then, according to the hesitant fuzzy evaluation matrices of the positions and the candidates, the expected score matrices of bilateral subjects are calculated. Furthermore, based on the idea of the generalized optimal order method and the dominant correlation and the missing correlation coefficient, the satisfaction mean of both people and positions is calculated. According to the satisfaction mean, the growth satisfaction of each period is obtained, and then the exponential decay formula is used to determine the weights of the growth satisfaction. The dynamic satisfaction of bilateral subjects is calculated by aggregating the initial satisfaction mean and the growth satisfaction. On this basis, a stable person-position matching model considering dynamic satisfaction is established and then is solved to obtain the optimal stable person-position matching scheme. Finally, the feasibility and effectiveness of the proposed method are verified by the example analysis of person-position matching. The main contributions of this article are as follows: an effective calculation method for the missing correlation coefficient is presented; a novel effective calculation method for the dynamic satisfaction is proposed by introducing the correlation parameter to combine the missing correlation with the dominant correlation coefficient; an effective stable person-position matching model considering the dynamic satisfaction is established.

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“…Yang et al [8] focused on the MATSM problem of 2-tuple preference and proposed a multi-stage matching framework composed of the matching process and the feedback process. Liang et al [9] proposed a new MATSM decision method from the perspective of prospect theory and constructed a multi-stage dynamic matching model for talent sharing based on the attribute priorities. Liang et al [10] discussed a strict two-sided matching problem based on multi-attribute interval-valued preference ordinal information.…”

Section: Introductionmentioning

confidence: 99%

A Multi-attribute Two-Sided Matching Decision Method Based on Multi-granularity Probabilistic Linguistic MARCOS

Jing

2022

Mathematical Problems in Engineering

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With the complexity of the matching environment, individual differences in matching objects and the uncertainty of evaluation information should be considered. The probabilistic linguistic term set (PLTS) is a useful tool to describe the uncertainty and limited cognition of matching objects. Thus, this paper proposes a multi-attribute two-sided matching method based on multi-granularity probabilistic linguistic MARCOS. First, we use a probabilistic linguistic term set with different granularities to express the evaluation information of different matching objects. Then, a conversion function is used to unify different granularity probabilistic linguistic terms. Second, a linguistic scale function is introduced to improve the expectation function, deviation degree, and distance of PLTS. The processed probabilistic linguistic evaluation information is transformed into accurate utility values through the transformation function. The evaluation attribute weights are determined by PLTS distance entropy. Based on this, this paper proposes a multi-granularity probabilistic linguistic MARCOS method to obtain the two-sided satisfaction degree. Finally, an optimization model which aims to maximize the overall satisfaction degree of matching objects by considering the stable matching condition is then established and solved to determine the matching between matching objects. The multi-objective two-sided matching model is constructed with the objective of maximizing the two-sided satisfaction degree. A case study of the service outsourcing matching is presented to validate the proposed method. The comparative analyses and discussions are also provided to demonstrate its effectiveness and scientific character.

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Multi-attribute dynamic two-sided matching method of talent sharing market in incomplete preference ordinal environment

Cited by 43 publications

References 25 publications

Cloud computing and human resource management: systematic literature review and future research agenda

Cloud computing and human resource management: systematic literature review and future research agenda

Dynamic Person-position Matching Decision Method Based on Hesitant Fuzzy Number Information

A Multi-attribute Two-Sided Matching Decision Method Based on Multi-granularity Probabilistic Linguistic MARCOS

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