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Cited by 98Color perception and designProduct Development and CustomizationMulti-Criteria Decision Making

The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.

AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products

Open access
Kwong, C.K., Jiang, Huimin, Luo, X.G.·2016·Engineering Applications of Artificial Intelligence
DOI 10.1016/j.engappai.2015.04.001
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Abstract

In the early stage of product design, particularly for consumer products, affective design, engineering, and marketing issues must be taken into considerationand they are commonly performed respectively by product designers, engineers, and marketing personnel. However, they have different concerns and focuses with regard to the new product design. Thus, these three processes are commonly conducted separately, leading to a sub-optimal and even sub-standard design. Such scenario indicates the need to incorporate the concerns of the three processes in the early stage of product design. However, no study has explored the incorporation of the concerns of the three processes into the product design. In this paper, an artificial intelligence (AI)-based methodology for integrating affective design, engineering, and marketing for defining design specifications of new products is proposed by which the concerns of the three processes can be considered simultaneously in the early design stage. The proposed methodology mainly involves development of customer satisfaction and cost models using fuzzy regression, generation of product utility functions using chaos-based fuzzy regression, formulation of a multi-objective optimization model and its solving using a non-dominated sorting genetic algorithm-II (NSGA-II). A case study was conducted for electric iron design to evaluate the effectiveness of the proposed methodology.

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Crossref · 10.1016/j.engappai.2015.04.001

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Metadata

Authors
Kwong, C. K., Jiang, H., & Luo, X. G.
Affiliations
Hong Kong Polytechnic University (HK)
Corresponding author
Kwong, C.K.
ORCID
Venue
Engineering Applications of Artificial Intelligence
Publisher
Elsevier BV
Published
2016-01
Volume
47
Pages
49-60
ISSN
0952-1976 (ISSN-L)
DOI
10.1016/j.engappai.2015.04.001
OpenAlex
W569792995
Cited by
98
References
62
Citations by year
2026: 6 · 2025: 11 · 2024: 6 · 2023: 8 · 2022: 9 · 2021: 15 · 2020: 10 · 2019: 8 · 2018: 15 · 2017: 7 · 2016: 3
FWCI
13.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Engineering Applications of Artificial Intelligence (publishedVersion)
  • PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by-nc-nd)
Funders
  • Research Grant Council
  • Hong Kong Special Administrative Region, China (PolyU 517113)
Topics
  • Social Sciences › Psychology › Social Psychology › Color perception and design
  • Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Product Development and Customization
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
Keywords
affective design, new product design, design specifications, customer satisfaction, cost model, fuzzy regression, multi-objective optimization, NSGA-II, early-stage product design, design integration

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.