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A systematic approach to prioritizing R&D projects based on customer-perceived value using opinion mining

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  • Yoon, Byungun
  • Jeong, Yujin
  • Lee, Keeeun
  • Lee, Sungjoo

Abstract

As product development has recently emphasized user innovation, it should necessarily reflect customer-perceived value, as well as technological value itself. However, while previous studies for technology planning have focused on analyzing the potential of technology, they have not considered the customer-perceived value that technology can create in a new product. Therefore, this research suggests a new approach to assessing the level of technology and evaluating R&D projects, by investigating customer-perceived value on technology through the use of the structural equation model and opinion mining. For this, the assessment framework is developed in terms of technology, product quality, and customer satisfaction, respectively, by investigating a variety of databases on each factor. The relationship between technology level and customer satisfaction is analyzed, using structural equation model and opinion mining. Based on the results, a strategy for technology development is established through gap analysis and simulation, after selecting and evaluating technologies that need to be developed. The proposed approach is applied to the real case of a moving system, in particular an automobile door, and we obtained that an R&D project for hinge-related technology would be promising, enhancing the customer satisfaction. It can suggest a future direction for new technology development. This paper contributes to enhancing the efficiency of technology planning based on the customer-perceived value, enabling the launch of new R&D projects.

Suggested Citation

  • Yoon, Byungun & Jeong, Yujin & Lee, Keeeun & Lee, Sungjoo, 2020. "A systematic approach to prioritizing R&D projects based on customer-perceived value using opinion mining," Technovation, Elsevier, vol. 98(C).
  • Handle: RePEc:eee:techno:v:98:y:2020:i:c:s0166497218306874
    DOI: 10.1016/j.technovation.2020.102164
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    3. Nataliya Chukhray & Nataliya Shakhovska & Oleksandra Mrykhina & Lidiya Lisovska & Ivan Izonin, 2022. "Stacking Machine Learning Model for the Assessment of R&D Product’s Readiness and Method for Its Cost Estimation," Mathematics, MDPI, vol. 10(9), pages 1-28, April.
    4. Lee, Keeeun & Kim, Sunhye & Yoon, Byungun, 2022. "A systematic idea generation approach for developing a new technology: Application of a socio-technical transition system," Technological Forecasting and Social Change, Elsevier, vol. 176(C).

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