Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2018 (2018), Article ID 2753638, 14 pages
https://doi.org/10.1155/2018/2753638
Research Article

A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations

1Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR, USA
2Department of Industrial Engineering & Management Sciences and Department of Management & Organizations and Department of Communication Studies, Northwestern University, Evanston, IL, USA
3Media, Technology, and Society, Northwestern University, Evanston, IL, USA
4Global Data Insight & Analytics, Ford Motor Company, Dearborn, MI, USA
5Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA

Correspondence should be addressed to Wei Chen; ude.nretsewhtron@nehciew

Received 23 September 2017; Accepted 18 December 2017; Published 28 January 2018

Academic Editor: Jesús Gómez-Gardeñes

Copyright © 2018 Zhenghui Sha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.