A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction
Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can b...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9506941 |
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| author | Amir Ahmad Omar Barukab |
| author_facet | Amir Ahmad Omar Barukab |
| author_sort | Amir Ahmad |
| collection | DOAJ |
| description | Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can be prioritized by a manager. The Kano model is a well-known method to perform this task for an individual customer. However, it requires filling in a form, which is a difficult and time-consuming exercise. Many existing methods require less effort from the customer side to perform data collection and can be used for a group of customers; however, they are not applicable to individuals. In the present study, we develop a data-analytic method that also uses the dataset; however, it can identify the attribute category for an individual customer. The proposed method is based on the probabilistic approach to analyze changes in the customer satisfaction corresponding to variations in attribute values. We employ this information to reveal the relationship between an attribute and the level of customer satisfaction, which, in turn, allows identifying the attribute category. We considered the synthetic and real housing datasets to test the efficiency of the proposed approach. The method correctly categorizes the attributes for both datasets. We also compare the result with the existing method to show the superiority of the proposed method. The results also suggest that the proposed method can accurately capture the behavior of individual customers. |
| format | Article |
| id | doaj-art-a1bed8cff02e4c518d555fc1e757600e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a1bed8cff02e4c518d555fc1e757600e2025-02-03T06:46:21ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/95069419506941A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer SatisfactionAmir Ahmad0Omar Barukab1College of Information Technology, United Arab Emirates University, Al Ain, UAEFaculty of Computing and Information Technology, P.O. Box 411, King Abdulaziz University, Rabigh 21911, Jeddah, Saudi ArabiaEvaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can be prioritized by a manager. The Kano model is a well-known method to perform this task for an individual customer. However, it requires filling in a form, which is a difficult and time-consuming exercise. Many existing methods require less effort from the customer side to perform data collection and can be used for a group of customers; however, they are not applicable to individuals. In the present study, we develop a data-analytic method that also uses the dataset; however, it can identify the attribute category for an individual customer. The proposed method is based on the probabilistic approach to analyze changes in the customer satisfaction corresponding to variations in attribute values. We employ this information to reveal the relationship between an attribute and the level of customer satisfaction, which, in turn, allows identifying the attribute category. We considered the synthetic and real housing datasets to test the efficiency of the proposed approach. The method correctly categorizes the attributes for both datasets. We also compare the result with the existing method to show the superiority of the proposed method. The results also suggest that the proposed method can accurately capture the behavior of individual customers.http://dx.doi.org/10.1155/2020/9506941 |
| spellingShingle | Amir Ahmad Omar Barukab A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction Complexity |
| title | A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction |
| title_full | A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction |
| title_fullStr | A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction |
| title_full_unstemmed | A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction |
| title_short | A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction |
| title_sort | novel approach to identify the categories of attributes for the three factor structure in customer satisfaction |
| url | http://dx.doi.org/10.1155/2020/9506941 |
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