A network-based discrete choice model for decision-based design
Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based ap...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2023-01-01
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| Series: | Design Science |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2053470123000045/type/journal_article |
| _version_ | 1797860943687843840 |
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| author | Zhenghui Sha Yaxin Cui Yinshuang Xiao Amanda Stathopoulos Noshir Contractor Yan Fu Wei Chen |
| author_facet | Zhenghui Sha Yaxin Cui Yinshuang Xiao Amanda Stathopoulos Noshir Contractor Yan Fu Wei Chen |
| author_sort | Zhenghui Sha |
| collection | DOAJ |
| description | Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations. |
| first_indexed | 2024-04-09T21:54:01Z |
| format | Article |
| id | doaj.art-4500b58d11424b1bb7d6941d65c5c6da |
| institution | Directory Open Access Journal |
| issn | 2053-4701 |
| language | English |
| last_indexed | 2024-04-09T21:54:01Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Design Science |
| spelling | doaj.art-4500b58d11424b1bb7d6941d65c5c6da2023-03-24T08:39:10ZengCambridge University PressDesign Science2053-47012023-01-01910.1017/dsj.2023.4A network-based discrete choice model for decision-based designZhenghui Sha0https://orcid.org/0000-0003-3267-0941Yaxin Cui1Yinshuang Xiao2Amanda Stathopoulos3Noshir Contractor4Yan Fu5Wei Chen6Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USADepartment of Mechanical Engineering, Northwestern University, Evanston, IL, USAWalker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USADepartment of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USADepartment of Industrial Engineering & Management Sciences, Northwestern University, Evanston, IL, USAGlobal Data Insight and Analytics, Ford Motor Company, Dearborn, MI, USADepartment of Mechanical Engineering, Northwestern University, Evanston, IL, USACustomer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.https://www.cambridge.org/core/product/identifier/S2053470123000045/type/journal_articlecustomer preference modellingexponential random graph modelmultinomial logit modeldecision-based design |
| spellingShingle | Zhenghui Sha Yaxin Cui Yinshuang Xiao Amanda Stathopoulos Noshir Contractor Yan Fu Wei Chen A network-based discrete choice model for decision-based design Design Science customer preference modelling exponential random graph model multinomial logit model decision-based design |
| title | A network-based discrete choice model for decision-based design |
| title_full | A network-based discrete choice model for decision-based design |
| title_fullStr | A network-based discrete choice model for decision-based design |
| title_full_unstemmed | A network-based discrete choice model for decision-based design |
| title_short | A network-based discrete choice model for decision-based design |
| title_sort | network based discrete choice model for decision based design |
| topic | customer preference modelling exponential random graph model multinomial logit model decision-based design |
| url | https://www.cambridge.org/core/product/identifier/S2053470123000045/type/journal_article |
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