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Forecasting Technological Impacts on Customers’ Co-Consideration Behaviors: A Data-Driven Network Analysis Approach

[+] Author Affiliations
Mingxian Wang, Zhenghui Sha, Yun Huang, Noshir Contractor, Wei Chen

Northwestern University, Evanston, IL

Yan Fu

Ford Motor Company, Dearborn, MI

Paper No. DETC2016-60015, pp. V02AT03A040; 13 pages
doi:10.1115/DETC2016-60015
From:
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 42nd Design Automation Conference
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5010-7
  • Copyright © 2016 by ASME

abstract

Forecasting customers’ responses and market competitions is essential before launching major technological changes in product design. In this research, we present a data-driven network analysis approach to understand the interactions among technologies, products, and customers. Such an approach provides a quantitative assessment of the impact of technological changes on customers’ co-consideration behaviors. The multiple regression quadratic assignment procedure (MRQAP) is employed to quantitatively predict product co-consideration relations as a function of various effect networks created by associations of product attributes and customer demographics. The uniqueness of the proposed approach is its capability of predicting complex relationships of product co-consideration as a network. Using vehicles as a case study, we forecast the impacts of two technological changes — adopting the fuel economy-boosting technology and the turbo engine technology by individual auto companies. The case study provides vehicle designers with insights into the change of market competitions brought by new technological developments. Our proposed approach links the market complexity to technology features and subsequently product design attributes to guide engineering design decisions in the complex customer-product systems.

Copyright © 2016 by ASME

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