A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations
Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes) are deciding factors in customers’ consideration decisions, it is not clear how t...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/2753638 |
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| _version_ | 1832551376543023104 |
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| author | Zhenghui Sha Yun Huang Jiawei Sophia Fu Mingxian Wang Yan Fu Noshir Contractor Wei Chen |
| author_facet | Zhenghui Sha Yun Huang Jiawei Sophia Fu Mingxian Wang Yan Fu Noshir Contractor Wei Chen |
| author_sort | Zhenghui Sha |
| collection | DOAJ |
| description | Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes) are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products) would influence such decisions. This paper presents a network-based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles) besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision-making. |
| format | Article |
| id | doaj-art-178c924dcd734c41ba2a15729d66b832 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-178c924dcd734c41ba2a15729d66b8322025-02-03T06:01:33ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/27536382753638A Network-Based Approach to Modeling and Predicting Product Coconsideration RelationsZhenghui Sha0Yun Huang1Jiawei Sophia Fu2Mingxian Wang3Yan Fu4Noshir Contractor5Wei Chen6Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR, USADepartment of Industrial Engineering & Management Sciences and Department of Management & Organizations and Department of Communication Studies, Northwestern University, Evanston, IL, USAMedia, Technology, and Society, Northwestern University, Evanston, IL, USAGlobal Data Insight & Analytics, Ford Motor Company, Dearborn, MI, USAGlobal Data Insight & Analytics, Ford Motor Company, Dearborn, MI, USADepartment of Industrial Engineering & Management Sciences and Department of Management & Organizations and Department of Communication Studies, Northwestern University, Evanston, IL, USADepartment of Mechanical Engineering, Northwestern University, Evanston, IL, USAUnderstanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes) are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products) would influence such decisions. This paper presents a network-based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles) besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision-making.http://dx.doi.org/10.1155/2018/2753638 |
| spellingShingle | Zhenghui Sha Yun Huang Jiawei Sophia Fu Mingxian Wang Yan Fu Noshir Contractor Wei Chen A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations Complexity |
| title | A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations |
| title_full | A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations |
| title_fullStr | A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations |
| title_full_unstemmed | A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations |
| title_short | A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations |
| title_sort | network based approach to modeling and predicting product coconsideration relations |
| url | http://dx.doi.org/10.1155/2018/2753638 |
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