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Linking Creativity Measurements to Product Market Favorability: A Data-Mining Approach

[+] Author Affiliations
Christian E. Lopez B., Xuan Zheng, Scarlett R. Miller

Pennsylvania State University, State College, PA

Paper No. DETC2017-67622, pp. V02AT03A013; 13 pages
doi:10.1115/DETC2017-67622
From:
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 43rd Design Automation Conference
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5812-7
  • Copyright © 2017 by ASME

abstract

While creative ideas can lead to market success and payoff, they are also associated with high risks and uncertainties. One way to reduce these uncertainties is to provide decision makers with valuable information about the innovative potential and future success of an idea. Even though several metrics have been proposed in the literature to evaluate the creativity of early design-stage ideas, these metrics do not provide information about the future product success or market favorability of new product ideas. Hence, existing metrics fail to link the creativity of early-stage ideas to their future market favorability. In order to bridge this gap, the current work proposes a new metric to estimate early design-stage ideas’ favorability and analyzes its relationship with current creativity metrics. A data-mining driven method to assess the future favorability of new product ideas using customers’ reviews of current market products that shared similar features with the new ideas of interest is presented. The results suggest that the new product idea favorability is positively correlated with relative creativity metrics and existing product market favorability ratings. This method can be used to help designers gain a better insight into the creativity and market favorability potential of new product ideas in early design-stages via a systematic approach; hence, helping reduce the risks and uncertainties associated with early-phase ideas during the screening and selecting process.

Copyright © 2017 by ASME

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