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Implicit Knowledge-Oriented New Product Development Based on Online Review

  • Huiliang ZhaoEmail author
  • Zhenghong Liu
  • Jian Lyu
Conference paper
  • 52 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

As one of the most important stages of product design, the early stage of new product development is knowledge-intensive creative work, whose essence is the evolution of knowledge. There is a lot of complex tacit knowledge in the early stage of new product development. Therefore, organizing and applying this knowledge is the key to the success of product conceptual design and even the whole product design. It is also the embodiment of the user-centered design concept. This paper presents a method of implicit knowledge-oriented new product development based on online review. The method of user requirement acquisition and product feature characterization based on online review is studied. This method can provide accurate user requirement analysis for the early stage of new product development, providing reference and support for product positioning.

Keywords

Implicit knowledge New product development Online review 

Notes

Acknowledgements

Project supported by the Natural Science Foundation of the Guizhou Higher Education Institutions of China (Grant No. [2018]152, No. [2017]239). Project supported by the Humanity and Social Science Foundation of the Guizhou Higher Education Institutions of China (Grant No. 2018qn46). Technology projects of Guizhou province (LH [2016]7467, [2017]1046, [2017]2016, [2018]1049, [2016]12, YJSCXJH (2018) 088).

References

  1. 1.
    Guo Q, Xue C, Yu M, Shen Z (2019) A new user implicit requirements process method oriented to product design. J Comput Inf Sci Eng 19:011010CrossRefGoogle Scholar
  2. 2.
    Münte TF, Brack M, Grootheer O, Wieringa BM, Matzke M, Johannes S (1997) Event-related brain potentials to unfamiliar faces in explicit and implicit memory tasks. Neurosci Res 28:223–233CrossRefGoogle Scholar
  3. 3.
    Yongtai L, Zhengying L (2006) Analysis on demand and definition of implicit demand. Nankai Bus Rev 3:22–27Google Scholar
  4. 4.
    Polanyi M (2012) Personal knowledge. Routledge, AbingdonCrossRefGoogle Scholar
  5. 5.
    Borgianni Y, Rotini F (2015) Towards the fine-tuning of a predictive Kano model for supporting product and service design. Total Qual Manage Bus Excellence 26:263–283CrossRefGoogle Scholar
  6. 6.
    Osgood CE (2010) Semantic differential technique in the comparative study of cultures. Am Anthropol 66:171–200CrossRefGoogle Scholar
  7. 7.
    Jian J, Ping J, Rui G (2016) Identifying comparative customer requirements from product online reviews for competitor analysis. Eng Appl Artif Intell 49:61–73CrossRefGoogle Scholar
  8. 8.
    Mauri AG, Minazzi R (2013) Web reviews influence on expectations and purchasing intentions of hotel potential customers. Int J Hospitality Manage 34:99–107CrossRefGoogle Scholar
  9. 9.
    Wang H, Yue L, Zhai C Latent aspect rating analysis on review text data: a rating regression approach. ACM SIGKDD Int Conf Knowl Disc Data MinGoogle Scholar
  10. 10.
    Decker R, Trusov M (2010) Estimating aggregate consumer preferences from online product reviews. Int J Res Mark 27:0–307CrossRefGoogle Scholar
  11. 11.
    Zhang H, Rao H, Feng J (2018) Product innovation based on online review data mining: a case study of Huawei phones. Electron Commer Res 18:3–22CrossRefGoogle Scholar
  12. 12.
    Hsiao YH, Chen MC, Liao WC (2017) Logistics service design for cross-border e-commerce using Kansei engineering with text-mining-based online content analysis. Telematics Inform 34:S0736585316303136Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Fine ArtsGuizhou Minzu UniversityGuiyangChina
  2. 2.School of Mechanical EngineeringGuiyang UniversityGuiyangChina
  3. 3.Key Laboratory of Advanced Manufacturing Technology, Ministry of EducationGuizhou UniversityGuiyangChina

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