A Weighted Statistical Network Modeling Approach to Product Competition Analysis
Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary (i.e., whether a relationship exists or not...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/9417869 |
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| _version_ | 1832559131711504384 |
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| author | Yaxin Cui Faez Ahmed Zhenghui Sha Lijun Wang Yan Fu Noshir Contractor Wei Chen |
| author_facet | Yaxin Cui Faez Ahmed Zhenghui Sha Lijun Wang Yan Fu Noshir Contractor Wei Chen |
| author_sort | Yaxin Cui |
| collection | DOAJ |
| description | Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary (i.e., whether a relationship exists or not), while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are ultimately purchased by customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences (i.e., aggregated choice decisions) and car competition (i.e., customers’ co-consideration decisions) together using weighted directed networks. |
| format | Article |
| id | doaj-art-3d09018529b24899a45bd89c89edff40 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3d09018529b24899a45bd89c89edff402025-02-03T01:30:39ZengWileyComplexity1099-05262022-01-01202210.1155/2022/9417869A Weighted Statistical Network Modeling Approach to Product Competition AnalysisYaxin Cui0Faez Ahmed1Zhenghui Sha2Lijun Wang3Yan Fu4Noshir Contractor5Wei Chen6Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringInsight and AnalyticsInsight and AnalyticsDepartment of Industrial Engineering & Management SciencesDepartment of Mechanical EngineeringStatistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary (i.e., whether a relationship exists or not), while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are ultimately purchased by customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences (i.e., aggregated choice decisions) and car competition (i.e., customers’ co-consideration decisions) together using weighted directed networks.http://dx.doi.org/10.1155/2022/9417869 |
| spellingShingle | Yaxin Cui Faez Ahmed Zhenghui Sha Lijun Wang Yan Fu Noshir Contractor Wei Chen A Weighted Statistical Network Modeling Approach to Product Competition Analysis Complexity |
| title | A Weighted Statistical Network Modeling Approach to Product Competition Analysis |
| title_full | A Weighted Statistical Network Modeling Approach to Product Competition Analysis |
| title_fullStr | A Weighted Statistical Network Modeling Approach to Product Competition Analysis |
| title_full_unstemmed | A Weighted Statistical Network Modeling Approach to Product Competition Analysis |
| title_short | A Weighted Statistical Network Modeling Approach to Product Competition Analysis |
| title_sort | weighted statistical network modeling approach to product competition analysis |
| url | http://dx.doi.org/10.1155/2022/9417869 |
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