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The future is yesterday: Use of AI-driven facial recognition to enhance value in the travel and tourism industry.

作者信息

Gupta Shivam, Modgil Sachin, Lee Choong-Ki, Sivarajah Uthayasankar

机构信息

Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, 59 Rue Pierre Taittinger, 51100 Reims, France.

Department of Operations Management, International Management Institute (IMI) Kolkata, 2/4 C, Judges Ct Rd, Alipore, Kolkata, West Bengal 700027 India.

出版信息

Inf Syst Front. 2023;25(3):1179-1195. doi: 10.1007/s10796-022-10271-8. Epub 2022 Apr 30.

DOI:10.1007/s10796-022-10271-8
PMID:35529102
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9059456/
Abstract

This study aims to investigate the role of artificial intelligence (AI) driven facial recognition to enhance a value proposition by influencing different areas of services in the travel and tourism industry. We adopted semi-structured interviews to derive insights from 26 respondents. Thematic analysis reveals the development of four main themes (personalization, data-driven service offering, security and safety, and seamless payments). Further, we mapped the impact of AI- driven facial recognition to enhance value and experience for corporate guests. Findings indicate that AI-based facial recognition can facilitate the travel and tourism industry in understanding travelers' needs, optimization of service offers, and value-based services, whereas data-driven services can be realized in the form of customized trip planning, email, and calendar integration, and quick bill summarization. This contributes to strengthening the tourism literature through the lens of organizational information processing theory.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da04/9059456/6e58c7e6be14/10796_2022_10271_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da04/9059456/c52fe3bf0f2f/10796_2022_10271_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da04/9059456/19cb5e58b7be/10796_2022_10271_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da04/9059456/d73344bfe315/10796_2022_10271_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da04/9059456/6e58c7e6be14/10796_2022_10271_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da04/9059456/c52fe3bf0f2f/10796_2022_10271_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da04/9059456/19cb5e58b7be/10796_2022_10271_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da04/9059456/d73344bfe315/10796_2022_10271_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da04/9059456/6e58c7e6be14/10796_2022_10271_Fig4_HTML.jpg

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本文引用的文献

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AI Ethics. 2021;1(2):159-172. doi: 10.1007/s43681-020-00014-3. Epub 2020 Oct 10.
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The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence: a comparative analysis of US, EU, and UK regulatory frameworks.人工智能时代面部识别技术、监控与问责的伦理：美国、欧盟和英国监管框架的比较分析
AI Ethics. 2022;2(3):377-387. doi: 10.1007/s43681-021-00077-w. Epub 2021 Jul 29.
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A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts.
美国公众对人脸识别技术以及在健康和研究领域使用面部成像数据的看法调查。
PLoS One. 2021 Oct 14;16(10):e0257923. doi: 10.1371/journal.pone.0257923. eCollection 2021.
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Facial expression recognition in image sequences using geometric deformation features and Support Vector Machines.使用几何变形特征和支持向量机的图像序列中的面部表情识别。
IEEE Trans Image Process. 2007 Jan;16(1):172-87. doi: 10.1109/tip.2006.884954.