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 accessAbstract
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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“AI is intensively used for learning about human demand patterns to serve as a technology that enhances the efficiency of marketing (Kwong 2016) and retailing (Weber and Schütte 2019).”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Kwong 2016
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Cites
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Cited by
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
Artificial Intelligence in Supply Chain Management: A Systematic Literature Review
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.009 · Cited by 1,020
“Kwong et al. (2016) propose a methodology of integrating affective design, engineering and marketing to define the design specifications of new products using a GA and fuzzy models.”
Ren, S., Zhang, Y., Liu, Y., Sakao, T., Huisingh, D., & Almeida, C. M. V. B. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.11.025 · Cited by 563
Tian, G., Zhang, H., Feng, Y., Wang, D., Peng, Y. L., & Jia, H. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.050 · Cited by 267
AI based decision making: combining strategies to improve operational performance
Al-Surmi, A., Bashiri, M., & Koliousis, I. G. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1966540 · Cited by 321
Metadata
- Authors
- Kwong, C. K., Jiang, H., & Luo, X. G.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Kwong, C.K.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 47
- Pages
- 49-60
- ISSN
- 0952-1976 (ISSN-L)
- 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
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- 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.
