A quality function deployment model by social network and group decision making: Application to product design of e-commerce platforms
Engineering Applications of Artificial Intelligence, ISSN: 0952-1976, Vol: 133, Page: 108509
2024
- 16Citations
- 34Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Quality function deployment (QFD) is an effective method to convert customer requirements (CRs) into design requirements (DRs) by constructing house of quality (HOQ). With the rapid growth of the e-commerce market, it is a new challenge to utilize the available online reviews to facilitate the implementation of QFD. Therefore, this paper proposes a novel QFD model from the perspective of group decision making (GDM) and social network analysis (SNA), then applies the proposed model to product design under Chinese e-commerce scene. Firstly, this paper extracts CRs from online reviews on e-commerce platforms, and the initial HOQs can be constructed. Then a bilateral negotiation GDM method based on SNA is carried out to generate a consensus-based HOQ, and therefore the final priorities of DRs can be obtained. Finally, a case study is provided to illustrate the applicability, and some discussions and comparative analysis are also conducted. The result indicates that the proposed method can generate effective and stable results for QFD implementation in real-world e-commerce scenario.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0952197624006675; http://dx.doi.org/10.1016/j.engappai.2024.108509; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85191347894&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0952197624006675; https://dx.doi.org/10.1016/j.engappai.2024.108509
Elsevier BV
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