Emotional Preferences in Metaverse Library Interface: A Kansei Analysis
While metaverse technology continues to advance, catching user attention ultimately relies on the appealing qualities of its user interface (UI), making it essential to include emotional perception. A well-designed UI aligned with user preferences fosters user engagement and contributes to product s...
Published in: | Communications in Computer and Information Science |
---|---|
Main Author: | |
Format: | Conference paper |
Language: | English |
Published: |
Springer Science and Business Media Deutschland GmbH
2024
|
Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210258733&doi=10.1007%2f978-981-97-9890-2_1&partnerID=40&md5=b7b24b600863bfd02da92268b5f12db4 |
id |
2-s2.0-85210258733 |
---|---|
spelling |
2-s2.0-85210258733 Ahmad N.A.N.; Lokman A.M.; Suhaimi A.I.H.; Abdullah M. Emotional Preferences in Metaverse Library Interface: A Kansei Analysis 2024 Communications in Computer and Information Science 2313 CCIS 10.1007/978-981-97-9890-2_1 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210258733&doi=10.1007%2f978-981-97-9890-2_1&partnerID=40&md5=b7b24b600863bfd02da92268b5f12db4 While metaverse technology continues to advance, catching user attention ultimately relies on the appealing qualities of its user interface (UI), making it essential to include emotional perception. A well-designed UI aligned with user preferences fosters user engagement and contributes to product success. However, creating a UI that prioritizes emotions remains an ongoing challenge. For that reason, this study attempts to explore the relationship between UI design factors and the user perception of the demand experience. This research utilizes Kansei Engineering (KE) method to investigate user preferences on the metaverse library UI and examine the correlation between emotional responses. A three-step methodology consisting of instrument, evaluation, and analysis was applied in this study. The evaluation utilized a total of 28 specimens and 60 Kansei Words (KWs). The data were analyzed using multivariate statistical analysis; Cronbach's Alpha, Principal Component Analysis (PCA) and Factor Analysis (FA) to identify the significant UI design for the metaverse library. The results of this research take the form of recommendations for UI design concepts based on the most dominant emotional factors. The data analysis results revealed that “radiance”, “orderliness”, and “conciseness” were the most significant emotional factors for the metaverse library application. Through the application of KE analysis, the emotionally demanding UI concepts within the metaverse library can be determined. This analysis provides valuable guidance for designing or redesigning the library interface, which in turn, contributes to an enhanced metaverse experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Springer Science and Business Media Deutschland GmbH 18650929 English Conference paper |
author |
Ahmad N.A.N.; Lokman A.M.; Suhaimi A.I.H.; Abdullah M. |
spellingShingle |
Ahmad N.A.N.; Lokman A.M.; Suhaimi A.I.H.; Abdullah M. Emotional Preferences in Metaverse Library Interface: A Kansei Analysis |
author_facet |
Ahmad N.A.N.; Lokman A.M.; Suhaimi A.I.H.; Abdullah M. |
author_sort |
Ahmad N.A.N.; Lokman A.M.; Suhaimi A.I.H.; Abdullah M. |
title |
Emotional Preferences in Metaverse Library Interface: A Kansei Analysis |
title_short |
Emotional Preferences in Metaverse Library Interface: A Kansei Analysis |
title_full |
Emotional Preferences in Metaverse Library Interface: A Kansei Analysis |
title_fullStr |
Emotional Preferences in Metaverse Library Interface: A Kansei Analysis |
title_full_unstemmed |
Emotional Preferences in Metaverse Library Interface: A Kansei Analysis |
title_sort |
Emotional Preferences in Metaverse Library Interface: A Kansei Analysis |
publishDate |
2024 |
container_title |
Communications in Computer and Information Science |
container_volume |
2313 CCIS |
container_issue |
|
doi_str_mv |
10.1007/978-981-97-9890-2_1 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210258733&doi=10.1007%2f978-981-97-9890-2_1&partnerID=40&md5=b7b24b600863bfd02da92268b5f12db4 |
description |
While metaverse technology continues to advance, catching user attention ultimately relies on the appealing qualities of its user interface (UI), making it essential to include emotional perception. A well-designed UI aligned with user preferences fosters user engagement and contributes to product success. However, creating a UI that prioritizes emotions remains an ongoing challenge. For that reason, this study attempts to explore the relationship between UI design factors and the user perception of the demand experience. This research utilizes Kansei Engineering (KE) method to investigate user preferences on the metaverse library UI and examine the correlation between emotional responses. A three-step methodology consisting of instrument, evaluation, and analysis was applied in this study. The evaluation utilized a total of 28 specimens and 60 Kansei Words (KWs). The data were analyzed using multivariate statistical analysis; Cronbach's Alpha, Principal Component Analysis (PCA) and Factor Analysis (FA) to identify the significant UI design for the metaverse library. The results of this research take the form of recommendations for UI design concepts based on the most dominant emotional factors. The data analysis results revealed that “radiance”, “orderliness”, and “conciseness” were the most significant emotional factors for the metaverse library application. Through the application of KE analysis, the emotionally demanding UI concepts within the metaverse library can be determined. This analysis provides valuable guidance for designing or redesigning the library interface, which in turn, contributes to an enhanced metaverse experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. |
publisher |
Springer Science and Business Media Deutschland GmbH |
issn |
18650929 |
language |
English |
format |
Conference paper |
accesstype |
|
record_format |
scopus |
collection |
Scopus |
_version_ |
1820775438255915008 |