沉浸式欺骗:对扩展现实中欺骗性设计的系统分析

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-04-17 DOI:10.1145/3659945
Hilda Hadan, Lydia Choong, Leah Zhang-Kennedy, Lennart E. Nacke
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引用次数: 0

摘要

成熟的欺骗性设计文献主要集中在传统的用户界面上。随着扩展现实(XR)的兴起,了解欺骗性设计在这一沉浸式领域的独特表现至关重要。然而,现有的研究缺乏全面、跨学科的分析,无法分析 XR 技术是如何实现新形式的欺骗性设计的。我们的研究回顾了有关 XR 环境中欺骗性设计的文献。我们采用主题综合法来确定关键主题。我们发现,XR 的沉浸式功能和广泛的数据收集使得微妙而强大的操纵策略成为可能。我们确定了概述这些策略的八个主题,并讨论了现有的应对措施。我们的研究结果表明了 XR 中欺骗性设计的独特风险,强调了对研究人员、设计人员和政策制定者的影响。我们提出了未来的研究方向,以探索无意的欺骗性设计、数据驱动的操纵解决方案、用户教育以及道德设计与政策法规之间的联系。
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Deceived by Immersion: A Systematic Analysis of Deceptive Design in Extended Reality

The well-established deceptive design literature has focused on conventional user interfaces. With the rise of extended reality (XR), understanding deceptive design’s unique manifestations in this immersive domain is crucial. However, existing research lacks a full, cross-disciplinary analysis that analyzes how XR technologies enable new forms of deceptive design. Our study reviews the literature on deceptive design in XR environments. We use thematic synthesis to identify key themes. We found that XR’s immersive capabilities and extensive data collection enable subtle and powerful manipulation strategies. We identified eight themes outlining these strategies and discussed existing countermeasures. Our findings show the unique risks of deceptive design in XR, highlighting implications for researchers, designers, and policymakers. We propose future research directions that explore unintentional deceptive design, data-driven manipulation solutions, user education, and the link between ethical design and policy regulations.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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