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논문 기본 정보

자료유형
학술저널
저자정보
Alvarez, Gabriela (한양대학교) Min, Aram (한맥기술)
저널정보
한국실내디자인학회 한국실내디자인학회 논문집 한국실내디자인학회 논문집 제31권 제6호(통권 제155호)
발행연도
2022.12
수록면
13 - 22 (10page)
DOI
10.14774/JKIID.2022.31.6.013

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In the last couple of decades, the use of social media has consistently increased. Different social media platforms allow users to interact and create diverse types of content, making them able to share their opinions. With brands getting involved in these platforms, it is important to know how their image is portrayed by their customers on social media. This includes the café industry. The purpose of our study is twofold. One is to understand which design attributes make a café appealing to social media users, and another is to examine whether the Google Cloud Vision API is able to differentiate the key design attributes using user-generated content, which are Instagram posts in this research. In order to achieve these aims, we conducted a comparative case study using two cafés of unique interior designs located in Yeonnam-dong in Seoul, South Korea. Specifically, Greem Cafe and Perception Coffee were used. Using these cases as hashtags, we scraped the posts using a Python web scraper and screened out the posts irrelevant to the cafés. After, we ran them through the Google Cloud Vision API to obtain the labels and screened out the labels irrelevant to the interior designs, such as people and amenities. Finally, we were able to categorize the label to different design attributes and compare and contrast the labels from two cafés. The main differences shown in the results from the labels are that Greem Café had Color and Cartoon attribute labels like “Black-and-White” and “Drawing” that clearly represent the cartoonish interior design style. On the other hand, Perception Coffee’s most frequent label, “Wood” along with other labels like “Aeolian Landform” identify their wooden ceiling design. With these results, it is shown that the Google Cloud Vision API is able to distinguish the main design elements from both cafés. This research utilizes a new tool that can be useful for future researchers and designers that deal with big data, also this research brings insight for designers at the time of creating new places by analyzing data made by consumers and promotes the usage of social media data as a tool for design thinking.
#Social Media #User-Generated Contents #Design Attribute #Instagram #Café

목차

Abstract
1. Introduction
2. Literature Review
3. Materials and Methods
4. Data Analysis
5. Analysis Results and Discussions
6. Conclusions
References

참고문헌 (23)

참고문헌 신청
Abbott, W., Donaghey, J., Hare, J., & Hopkins, P. (2013). An Instagram is worth at housand words: an industry panel and audience Q&A. Library Hi Tech News, 30(7), 1-6. https://doi.org/http://dx.doi.org/10.1108/LHTN-08-2013-0047 Crossref Apaolaza, V., Paredes, M. R., Hartmann, P., & D’Souza, C. (2021). How does restaurant’s symbolic design affect photo-posting on instagram? The moderating role of community commitment and coolness. Journal of Hospitality Marketing and Management, 30(1), 21-37. https://doi.org/10.1080/19368623.2020.1768195 Crossref Chen, P. T. & Hu, H. H. (2010). How determinant attributes of service quality influence customer-perceived value: An empirical investigation of the Australian coffee outlet industry. International Journal of Contemporary Hospitality Management, 22(4), 535- 551. https://doi.org/10.1108/09596111011042730 Crossref Chen, S. H. & Chen, Y. H. (2017). A content-based image retrieval method based on the google cloud vision API and wordnet. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10191 LNAI(840), 651-662. https://doi.org/10.1007/978-3-319-54472-4_61 Crossref Lee, M., Rajaratnam, S. D., & Konar, R. (2018). Key Attributes of Malaysian Specialty Coffee Shops: An Exploratory Investigation. Asia-Pacific Journal of Innovation in Hospitality and Tourism, 7(2), 69-83. https://doi.org/10.7765/9781784992118.00022 Crossref

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