Research on the Game Relationship and Behavior Optimization between Banks and Customers in the Omni-Channel Environment
In an omni-channel environment of banks, the information transmission between banks and customers is the basis for decision-making on both sides. This dissertation analyzes the characteristics of signals sent between banks and customers at different stages and proposes a game model of bank–customer...
| Hlavní autoři: | , |
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| Médium: | Článek |
| Jazyk: | English |
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MDPI AG
2023-03-01
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| Edice: | Systems |
| Témata: | |
| On-line přístup: | https://www.mdpi.com/2079-8954/11/4/171 |
| _version_ | 1827743618665807872 |
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| author | Jianzhong Xu Xiaolei Cui |
| author_facet | Jianzhong Xu Xiaolei Cui |
| author_sort | Jianzhong Xu |
| collection | DOAJ |
| description | In an omni-channel environment of banks, the information transmission between banks and customers is the basis for decision-making on both sides. This dissertation analyzes the characteristics of signals sent between banks and customers at different stages and proposes a game model of bank–customer signals in an omni-channel environment. The model explores the types of banks and customers and the influence of six signals on both parties’ action decisions. Building on this model, a genetic algorithm of the signaling game between banks and customers is developed. This algorithm improves the adaptability of customers to the bank’s omni-channel environment through the “selection–crossover–mutation” process. The algorithm determines the signal that brings the greatest utility among multiple bank–customer combinations. This is carried out by calculating the choices made, resulting in the greatest total utility. Finally, a case study is carried out on the omni-channel transformation of Agricultural Bank of China, illustrating the validity of the research results of the game relationship and action optimization. Overall, this study provides a quantitative tool for the action decision-making of banks and customers and the optimization of the relationship between the two. It also provides a reference for how banks should manage customer relationships in an omni-channel environment. |
| first_indexed | 2024-03-11T04:29:08Z |
| format | Article |
| id | doaj.art-7ace9ac0c6d44d05a3b78f9717e12bb5 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-11T04:29:08Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-7ace9ac0c6d44d05a3b78f9717e12bb52023-11-17T21:35:14ZengMDPI AGSystems2079-89542023-03-0111417110.3390/systems11040171Research on the Game Relationship and Behavior Optimization between Banks and Customers in the Omni-Channel EnvironmentJianzhong Xu0Xiaolei Cui1School of Economics and Management, Harbin Engineering University (HEU), Harbin 150009, ChinaSchool of Economics and Management, Harbin Engineering University (HEU), Harbin 150009, ChinaIn an omni-channel environment of banks, the information transmission between banks and customers is the basis for decision-making on both sides. This dissertation analyzes the characteristics of signals sent between banks and customers at different stages and proposes a game model of bank–customer signals in an omni-channel environment. The model explores the types of banks and customers and the influence of six signals on both parties’ action decisions. Building on this model, a genetic algorithm of the signaling game between banks and customers is developed. This algorithm improves the adaptability of customers to the bank’s omni-channel environment through the “selection–crossover–mutation” process. The algorithm determines the signal that brings the greatest utility among multiple bank–customer combinations. This is carried out by calculating the choices made, resulting in the greatest total utility. Finally, a case study is carried out on the omni-channel transformation of Agricultural Bank of China, illustrating the validity of the research results of the game relationship and action optimization. Overall, this study provides a quantitative tool for the action decision-making of banks and customers and the optimization of the relationship between the two. It also provides a reference for how banks should manage customer relationships in an omni-channel environment.https://www.mdpi.com/2079-8954/11/4/171bank omni-channelsignaling gamegenetic algorithmcustomer relationship |
| spellingShingle | Jianzhong Xu Xiaolei Cui Research on the Game Relationship and Behavior Optimization between Banks and Customers in the Omni-Channel Environment Systems bank omni-channel signaling game genetic algorithm customer relationship |
| title | Research on the Game Relationship and Behavior Optimization between Banks and Customers in the Omni-Channel Environment |
| title_full | Research on the Game Relationship and Behavior Optimization between Banks and Customers in the Omni-Channel Environment |
| title_fullStr | Research on the Game Relationship and Behavior Optimization between Banks and Customers in the Omni-Channel Environment |
| title_full_unstemmed | Research on the Game Relationship and Behavior Optimization between Banks and Customers in the Omni-Channel Environment |
| title_short | Research on the Game Relationship and Behavior Optimization between Banks and Customers in the Omni-Channel Environment |
| title_sort | research on the game relationship and behavior optimization between banks and customers in the omni channel environment |
| topic | bank omni-channel signaling game genetic algorithm customer relationship |
| url | https://www.mdpi.com/2079-8954/11/4/171 |
| work_keys_str_mv | AT jianzhongxu researchonthegamerelationshipandbehavioroptimizationbetweenbanksandcustomersintheomnichannelenvironment AT xiaoleicui researchonthegamerelationshipandbehavioroptimizationbetweenbanksandcustomersintheomnichannelenvironment |