TY - GEN
T1 - Forecasting technological impacts on customers' co-consideration behaviors
T2 - ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2016
AU - Wang, Mingxian
AU - Sha, Zhenghui
AU - Huang, Yun
AU - Contractor, Noshir
AU - Fu, Yan
AU - Chen, Wei
N1 - Funding Information:
The authors gratefully acknowledge financial support from National Science Foundation (CMMI-1436658) and Ford- Northwestern Alliance Project.
Publisher Copyright:
© Copyright 2016 by ASME.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85008196667&partnerID=8YFLogxK
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U2 - 10.1115/DETC2016-60015
DO - 10.1115/DETC2016-60015
M3 - Conference contribution
AN - SCOPUS:85008196667
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 42nd Design Automation Conference
PB - American Society of Mechanical Engineers (ASME)
Y2 - 21 August 2016 through 24 August 2016
ER -