Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings
With the remarkable promotion of e-commerce platforms, consumers increasingly prefer to purchase products online. Online ratings facilitate consumers to choose among products. Thus, to help consumers effectively select products, it is necessary to provide decision support methods for consumers to tr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-05-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-8994/10/5/178 |
| _version_ | 1798005601491484672 |
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| author | Xia Liang Peide Liu Zhengmin Liu |
| author_facet | Xia Liang Peide Liu Zhengmin Liu |
| author_sort | Xia Liang |
| collection | DOAJ |
| description | With the remarkable promotion of e-commerce platforms, consumers increasingly prefer to purchase products online. Online ratings facilitate consumers to choose among products. Thus, to help consumers effectively select products, it is necessary to provide decision support methods for consumers to trade online. Considering the decision makers are bounded rational, this paper proposes a novel decision support model for product selection based on online ratings, in which the regret aversion behavior of consumers is formulated. Massive online ratings provided by experienced consumers for alternative products associated with several evaluation attributes are obtained by software finders. Then, the evaluations of alternative products in format of stochastic variables are conducted. To select a desirable alternative product, a novel method is introduced to calculate gain and loss degrees of each alternative over others. Considering the regret behavior of consumers in the product selection process, the regret and rejoice values of alternative products for consumer are computed to obtain the perceived utility values of alternative products. According to the prior order of the evaluation attributes provided by the consumer, the prior weights of attributes are determined based on the perceived utility values of alternative products. Furthermore, the overall perceived utility values of alternative products are obtained to generate a ranking result. Finally, a practical example from Zol.com.cn for tablet computer selection is used to demonstrate the feasibility and practically of the proposed model. |
| first_indexed | 2024-04-11T12:41:49Z |
| format | Article |
| id | doaj.art-44fe4f3a930f495aa52ac53d316fd8af |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T12:41:49Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-44fe4f3a930f495aa52ac53d316fd8af2022-12-22T04:23:27ZengMDPI AGSymmetry2073-89942018-05-0110517810.3390/sym10050178sym10050178Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online RatingsXia Liang0Peide Liu1Zhengmin Liu2School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, ChinaWith the remarkable promotion of e-commerce platforms, consumers increasingly prefer to purchase products online. Online ratings facilitate consumers to choose among products. Thus, to help consumers effectively select products, it is necessary to provide decision support methods for consumers to trade online. Considering the decision makers are bounded rational, this paper proposes a novel decision support model for product selection based on online ratings, in which the regret aversion behavior of consumers is formulated. Massive online ratings provided by experienced consumers for alternative products associated with several evaluation attributes are obtained by software finders. Then, the evaluations of alternative products in format of stochastic variables are conducted. To select a desirable alternative product, a novel method is introduced to calculate gain and loss degrees of each alternative over others. Considering the regret behavior of consumers in the product selection process, the regret and rejoice values of alternative products for consumer are computed to obtain the perceived utility values of alternative products. According to the prior order of the evaluation attributes provided by the consumer, the prior weights of attributes are determined based on the perceived utility values of alternative products. Furthermore, the overall perceived utility values of alternative products are obtained to generate a ranking result. Finally, a practical example from Zol.com.cn for tablet computer selection is used to demonstrate the feasibility and practically of the proposed model.http://www.mdpi.com/2073-8994/10/5/178decision support modelproduct selectiononline ratingsregret theorystochastic variables |
| spellingShingle | Xia Liang Peide Liu Zhengmin Liu Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings Symmetry decision support model product selection online ratings regret theory stochastic variables |
| title | Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings |
| title_full | Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings |
| title_fullStr | Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings |
| title_full_unstemmed | Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings |
| title_short | Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings |
| title_sort | selecting products considering the regret behavior of consumer a decision support model based on online ratings |
| topic | decision support model product selection online ratings regret theory stochastic variables |
| url | http://www.mdpi.com/2073-8994/10/5/178 |
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