JOURNAL ARTICLE
A New MCDM Model for the Optimal Selection of Sales Representatives in Cable Television Service System Providers.
Published In: Journal of Multiple-Valued Logic & Soft Computing, 2024, v. 42, n. 5/6. P. 439 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Shu-Che Chi; Yu-Ching Lin, Michael; Kacprzyk, Janusz; Kuei-Lun Chang; Tung-Lin Chuang 3 of 3
Abstract
A new approach to the solution of an important problem of a better use of personnel, sales representatives, for cable television service system providers in Taiwan is discussed. A new multi-criteria decision-making (MCDM) model is proposed for personnel selection meant as a process of selecting candidates who are qualified to perform specific jobs in the best possible way. The new approach involves first the selection of criteria for the sales representatives. Fuzzy importanceperformance analysis (FIPA) is used to design a questionnaire, and interviews with executives are performed. A new method for the determination of importance and degree of satisfaction of the criteria proposes is developed. Decision making trial and evaluation laboratory (DEMATEL) method is used to establish interactions between the dimensions and criteria. Analytic network process (ANP) is then used to find the weights of each dimension and selection criterion based on their interaction via pairwise comparison. The integrated weights obtained for each selection criterion are then employed in technique for order preference by similarity to ideal solution (TOPSIS) to select the best sales representative. A realistic example is provided. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Multiple-Valued Logic & Soft Computing. 2024/05, Vol. 42, Issue 5/6, p439
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2024
- ISSN:15423980
- Accession Number:177268121
- Copyright Statement:Copyright of Journal of Multiple-Valued Logic & Soft Computing is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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