Some q ‐rung orthopair fuzzy Hamy mean operators in multiple attribute decision‐making and their application to enterprise resource planning systems selection

2019 ◽  
Vol 34 (10) ◽  
pp. 2429-2458 ◽  
Author(s):  
Jie Wang ◽  
Guiwu Wei ◽  
Jianping Lu ◽  
Fuad E. Alsaadi ◽  
Tasawar Hayat ◽  
...  
Keyword(s):  
Decision Making ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Multiple Attribute Decision Making ◽  
Planning Systems ◽  
Multiple Attribute ◽  
Enterprise Resource Planning Systems

Model for Multiple Attribute Decision Making Based on Picture 2-Tuple Linguistic Power Aggregation Operators

2019 ◽  
Vol 11 (1) ◽  
pp. 35-65 ◽  
Author(s):  
Guiwu Wei
Keyword(s):  
Decision Making ◽  
Enterprise Resource Planning ◽  
Weighted Average ◽  
Resource Planning ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Erp System ◽  
Multiple Attribute ◽  
Linguistic Power ◽  
System Selection

In this article, the authors investigate the multiple attribute decision making problems with picture 2-tuple linguistic information. The utilized power average and power geometric operations used to develop some picture 2-tuple linguistic power aggregation operators: picture 2-tuple linguistic power weighted average (P2TLPWA) operator, picture 2-tuple linguistic power weighted geometric (P2TLPWG) operator, picture 2-tuple linguistic power ordered weighted average (P2TLPOWA) operator, picture 2-tuple linguistic power ordered weighted geometric (P2TLPOWG) operator, picture 2-tuple linguistic power hybrid average (P2TLPHA) operator and picture 2-tuple linguistic power hybrid geometric (P2TLPHG) operator. The prominent characteristic of these proposed operators is studied. This article has utilized these operators to develop some approaches to solve the picture 2-tuple linguistic multiple attribute decision making problems. Finally, a practical example for enterprise resource planning (ERP) system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.

scholarly journals Approaches to multiple attribute decision making based on picture 2-tuple linguistic power Hamy mean aggregation operators

2019 ◽  
Author(s):  
Guiwu Wei ◽  
Jie Wang ◽  
Hui Gao ◽  
Cun Wei
Keyword(s):  
Decision Making ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Erp System ◽  
Multiple Attribute ◽  
Linguistic Power ◽  
System Selection ◽  
Selection Of

In this paper, the multiple attribute decision making (MADM) problems are investigated with picture 2-tuple linguistic information. Then, based on Hamy mean (HM) operator and dual Hamy mean (DHM) operator, the power average and power geometric operations are utilized to develop some picture 2-tuple linguistic power Hamy mean aggregation operators: picture 2-tuple linguistic power weighted Hamy mean (P2TLPWHM) operator, picture 2-tuple linguistic power weighted dual Hamy mean (P2TLPWDHM) operator, picture 2-tuple linguistic power ordered weighted Hamy mean (P2TLPOWHM) operator, picture 2-tuple linguistic power ordered weighted dual Hamy mean (P2TLPOWDHM) operator, picture 2-tuple linguistic power hybrid Hamy mean (P2TLPHHM) operator and picture 2-tuple linguistic power hybrid dual Hamy mean (P2TLPHDHM) operator. The prominent characteristic of these proposed operators are studied. Then, these operators are utilized to develop some approaches to solve the picture 2-tuple linguistic multiple attribute decision making problems. Finally, the proposed method is demonstrated through a practical example for enterprise resource planning (ERP) system selection of how the proposed methods help us and is effective in MADM problems.

Do cloud-based enterprise resource planning systems affect the productivity of human resources in the COVID-19 era?

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaojun Yao ◽  
Masoumeh Azma
Keyword(s):  
Human Resources ◽  
Human Resource ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Equation Modeling ◽  
Planning Systems ◽  
Privacy And Security ◽  
Content Type ◽  
Enterprise Resource Planning Systems ◽  
The Impact

PurposeThis study aims to investigate the impact of skills and knowledge of employees, economic situations of the company, current IT infrastructure, payment fashion, cloud availability, and cloud privacy and security on the productivity of the human resources in the COVID-19 era.Design/methodology/approachOver the past few years, the advent of cloud-assisted technologies has dramatically advanced the Information Technology (IT)-based industries by providing everything as a service. Cloud computing is recognized as a growing technology among companies around the world. One of the most critical cloud applications is deploying systems and organizational resources, especially systems whose deployment costs are high. Manpower is one of the basic and vital resources of the organization, and organizations need an efficient workforce to achieve their goals. But, in the COVID-19 era, human resources' productivity can be reduced due to stress, high labor force, reduced organizational performance and profits, unfavorable organizational conditions, inability to manage and lack of training. Therefore, this study tries to investigate the productivity of human resources in the COVID-19 era. Data were collected from the medium-sized companies through a questionnaire. Distributed questionnaires were conducted on the Likert scale. The model is assessed using the structural equation modeling technique to examine its reliability and validity. The study is a library method and literature review. A case study was conducted through a questionnaire and statistical analysis by SPSS 25 and SMART-PLS.FindingsBased on the findings, the skills and knowledge of employees, the economic situations of the company, payment fashion, cloud availability and the current IT infrastructures of the company have a positive impact on human resource efficiency in the COVID-19 era. But cloud privacy and security have a negative effect on the productivity of human resources. The findings can be the basis for companies and organizations in the COVID-19 era.Research limitations/implicationsThis study has some restrictions that need to be considered in evaluating the obtained results. First, due to the prevalence of Coronavirus, access to information from the companies under study was limited. Second, this research may have overlooked other variables that affect human resource productivity in the COVID-19 era. Prospective researchers can examine the impact of Customer Relationship Management (CRM) and Supply Chain Management (SCM) on the human resource's productivity in the COVID-19 era.Practical implicationsThe results of this research are applicable for all companies, their departments and human resources in the COVID-19 era.Originality/valueIn this paper, human resources' productivity in the COVID-19 era is pointed out. The presented new model provides a complete framework for investigating cloud-based enterprise resource planning systems affect the productivity of human resources in the COVID-19 era.

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