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Modeling Multi-Year Customers’ Considerations and Choices in China’s Auto Market Using Two-Stage Bipartite Network Analysis

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Abstract

Choice modeling is important in transportation planning, marketing and engineering design, as it can quantify the influence of product attributes and customer demographics on customers’ choice behaviors. Consumer studies suggest that customers’ choice-making process often consists of two different stages: customers first consider subsets of available products on the market, and then make the final choice from the subsets. As existing preference modeling is mostly focused on the choice stage, there is a need to develop methods for understanding customer preferences at both stages, and investigate how customer preferences change from “consideration” to “choice”, and whether such changes will be consistent over time. In this paper, we study customers’ consideration and purchase behaviors in China’s auto market using multi-year survey datasets. We demonstrate how descriptive network analysis and analytic network models (bipartite Exponential Random Graph Model (ERGM)) capture the change of customers’ preferences from the consideration stage to the choice stage in multiple consecutive years. Our results show that factors such as fuel consumption per unit power, car make origin, and place of production influence customers’ considerations and final purchase decisions in different ways, and this difference between consideration and purchase is consistent over time. The main contribution of this study is that we validate the two-stage network-based modeling approach and its utility in preference elicitation using multiple-year dataset, which sheds lights on understanding the trend of customers’ consideration and choice behaviors across years. Our study also contributes to a refined interpretation of the ERGM results with categorization of continuous variables into ranges, which shows that customer choice decisions may be more qualitatively influenced by product attributes rather than quantitatively. Our approach is generic and thus can be applied to solving broader choice modeling problems, such as the transportation mode selection and the adoption of clean technology (e.g., electric vehicles).

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Notes

  1. “Edges” are defined as the number of links in the network, and its coefficient can be considered as the intercept of a regression. However, here the intercept by itself does not necessarily provide meaningful interpretation. To explain the intercept, one needs to set all input categorical variables as baselines and all continuous variables as zeros, which is not physically meaningful in our case. Network researchers commonly interpret the meaning of “Edges” only when it is the single input variable in an ERGM.

  2. We studied an additional dataset with 5000 non-overlapped random samples, and found the new results are quite similar to what we reported here, and the same conclusions hold.

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Acknowledgments

The authors gratefully acknowledge the financial support from NSF-CMMI-1436658 and the Ford-Northwestern Alliance Project.

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Authors and Affiliations

  1. University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, 200240, China

    Youyi Bi

  2. Department of Aerospace & Mechanical Engineering, University of Southern California, 934 Downey Way, Los Angeles, CA, 90089, USA

    Yunjian Qiu

  3. Department of Mechanical Engineering, University of Arkansas, 863 W. Dickson St, Fayetteville, AR, 72701, USA

    Zhenghui Sha

  4. Global Data Insight & Analytics, Ford Motor Company, Dearborn, MI, 48124, USA

    Mingxian Wang & Yan Fu

  5. Department of Industrial Engineering & Management Sciences, Northwestern University, 2145 Sheridan Road, Tech C210, Evanston, IL, 60208, USA

    Noshir Contractor

  6. Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Road, Tech A216, Evanston, IL, 60208, USA

    Wei Chen

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  1. Youyi Bi
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Correspondence to Wei Chen.

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Bi, Y., Qiu, Y., Sha, Z. et al. Modeling Multi-Year Customers’ Considerations and Choices in China’s Auto Market Using Two-Stage Bipartite Network Analysis. Netw Spat Econ 21, 365–385 (2021). https://doi.org/10.1007/s11067-021-09526-9

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