Evolution and Emerging Trends of Kansei Engineering: A Visual Analysis Based on CiteSpace

Lin, Shibin; Shen, Tao; Guo, Wenping

doi:10.1109/access.2021.3102606

Cited by 28 publications

(17 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional approaches for Kansei engineering work on questionnaires to measure a user's feelings towards a customer need where groups of words called ''Kansei attributes'' are used to measure their emotions. Recent computational approaches try to implement Kansei engineering on UGC so that time-consuming questionnaires don't need to be carried out [13], [14], [44], [60], [61]. [44] extracts customer needs from Amazon reviews using the linguistic information of words and then expands on the list of Kansei attributes (first identified by numerous research works in the product development literature) using WordNet.…”

Section: B Application Scenarios and Methods Performedmentioning

confidence: 99%

Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

2022

View full text Add to dashboard Cite

Section: B Application Scenarios and Methods Performedmentioning

confidence: 99%

Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

2022

View full text Add to dashboard Cite

“…Terefore, in a market tailored to emotional and personalized needs, the emotional imagery delivered by the design has become a key factor for improving the competitiveness of the products in the market. As an important method to study the emotional needs of products, Kansei engineering adopts the semantic diferential (SD) method to qualify the users' Kansei imagery and translates it into design indicators for innovative design [2]. Te previous researches dedicated to the modeling of Kansei engineering mainly used Quantifcation Teory Type I (QTTI) to establish the prediction model.…”

Section: Introductionmentioning

confidence: 99%

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

Lin

Woo

Luo

et al. 2023

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The market scale of electric shavers in China has reached ¥ 26.3 billion in 2021. Consumers currently place an increasing emphasis on the Kansei image conveyed by products rather than just concerning with functional satisfaction. To meet consumers’ expectations, the emotional message conveyed by product design is essential under multisensory channels. This research first collected 230 electric shavers samples and 135 pairs of consumers’ Kansei words, then reduced them into 34 representative samples using multidimensional scale and clustering analysis, with 4 groups of representative Kansei words selected via the expert group. Moreover, consumers’ Kansei images were evaluated via questionnaire using the semantic differential scales, with 416 valid samples acquired in total. Meanwhile, design elements of the samples (including item and category) were classified by ways of morphological analysis and audio software. At last, the prediction models of the electric shavers were established between the overall design elements and user’s Kansei evaluation under the multisensory channel of visual model and auditory audio taking advantage of Quantification Theory Type I , back propagation neural network, and genetic algorithm-based BPNN. The proposed models can provide defined design indexes and references in multisensory design, facilitating designers to design in a logical and scientific manner rather than designing as per experience.

show abstract

“…Regarding knowledge clustering, scholars mainly rely on data analysis and visualization software to analyze the research status and hotspots of user experience. For example, Lin et al (2021) used keyword co-occurrence and multivariate statistical analysis methods based on the visual analysis of knowledge map to output the frontier co-word knowledge map and mainstream fields of domestic perceptual engineering research. Li et al (2022) used bibliometrics and knowledge map analysis to analyze literature on user experience design from 1999 to 2019 and systematically reviewed the development process of user experience design from different perspectives, such as keywords, references, and author institutions.…”

Section: Introductionmentioning

confidence: 99%

Research hotspots and trends analysis of user experience: Knowledge maps visualization and theoretical framework construction

Song

Yin

2022

Front. Psychol.

View full text Add to dashboard Cite

This study focuses on user experience from the perspective of big data to complete the aggregation, clustering, and visual presentation of knowledge. Using a combination of sample literature review, visualization technologies, knowledge map analysis, Carrot2 clustering, and other methodologies, this study intends to examine user experience from three perspectives: research state, hotspots, and trends. First, based on the double-map overlay, core institutions, core countries, core authors, core journals, and core references distribution research, the knowledge flow, research power, and research subjects of user experience are analyzed. Secondly, through keyword clustering analysis, this research intuitively presents the research topics of user experience and reveals the research hotspots and the evolution path of research methods. Finally, with the help of the subject clustering algorithm, the emerging trends of user experience research are predicted: the immersive experience upgrade of multi-scenario integration, the innovative design of multi-role collaboration, and the cross-disciplinary interactive exploration of multi-discipline. Following this, the user experience knowledge map is constructed, providing a global view and macro-cognition for subsequent research.

show abstract

Evolution and Emerging Trends of Kansei Engineering: A Visual Analysis Based on CiteSpace

Cited by 28 publications

References 83 publications

Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

Research hotspots and trends analysis of user experience: Knowledge maps visualization and theoretical framework construction

Contact Info

Product

Resources

About