对共享数字足迹数据的态度:一个离散选择实验

Rebecca McDonald, Anya Skatova, Carsten Maple
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 Objectives & ApproachWe used responses from a nationally representative sample of 2,087 UK residents to estimate public preferences towards sharing different types of digital footprint data in scenarios with different features. The main part of our experiment consisted of a Discrete Choice Experiment which allows the relative importance of the different features of data sharing scenarios to be established, revealing the tradeoffs participants make between them. Participants made a series of choices between two hypothetical data sharing scenario options or could “opt out” by choosing neither specified option. For example, we examined the differences in responses when data are shared for different purposes (e.g., for research vs private benefit), as well as when data are shared with more or less granular details about identity or location. The data were analysed using a logistic regression with an alternative-specific constant.
 Relevance to Digital FootprintsWe focused on understanding whether varied features of six different types of digital footprints data - namely banking transactions, electricity use at home, retail loyalty cards use, browsing history, social media, and physical activity data - affect people’s decision whether to share these data.
 ResultsParticipants were more likely to share their data with a university for academic research than with a private company or government. Participants were also most reluctant to share data alongside their personal identity. Participants were concerned with the recipient of the data and their purpose in requesting it; whether the data would be shared along with their location and if so, to what specificity; and with the level of aggregation of the data (i.e. whether it would be shared in fine detail or as a monthly summary). In addition, we demonstrated the importance of the type of data to be shared, with people most reluctant to share bank transactions data, but relatively unconcerned about sharing their physical activity, electricity use and loyalty cards data.
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引用次数: 0

摘要

介绍,数字足迹数据是经济的关键,支撑着商业模式和服务提供。这些信息也可以为公共利益带来好处,然而数字足迹数据的共享是基于个人态度的,这取决于这些数据对消费者的价值。在这项研究中,我们调查了个人如何做出共享其数字足迹数据的决定,以及数据共享场景的哪些特征影响了他们共享数据的决定。 目标,我们使用了来自2087名英国居民的全国代表性样本的回复,以估计公众在不同特征的场景中对共享不同类型的数字足迹数据的偏好。我们实验的主要部分包括一个离散选择实验,该实验允许建立数据共享场景的不同特征的相对重要性,揭示参与者在它们之间做出的权衡。参与者在两个假设的数据共享场景选项之间做出一系列选择,或者可以不选择任何指定选项而“选择退出”。例如,我们检查了出于不同目的共享数据时(例如,用于研究与私人利益)的反应差异,以及当数据与更多或更少的关于身份或位置的粒度细节共享时。使用具有替代特定常数的逻辑回归对数据进行分析。 与数字足迹的相关性我们专注于了解六种不同类型的数字足迹数据(即银行交易、家庭用电、零售会员卡使用、浏览历史、社交媒体和体育活动数据)的不同特征是否会影响人们是否分享这些数据的决定。 结果:与私人公司或政府相比,参与者更有可能与大学分享他们的数据进行学术研究。参与者也最不愿意在分享个人身份的同时分享数据。参加者关心资料的接收人及其索取资料的目的;这些数据是否会与他们的位置一起共享,如果是,具体到什么程度;以及数据的聚合程度(即,是详细共享还是按月汇总)。此外,我们证明了共享数据类型的重要性,人们最不愿意共享银行交易数据,但相对不关心共享他们的身体活动,用电量和会员卡数据。 结论,我们通过强调个人愿意在数据共享情况的不同元素之间做出的权衡,以及这些不同方面的相对重要性来贡献。我们还表明,个人对分享数字足迹数据以进行有利于公共利益的研究持积极态度。通过将这些偏好整合到道德和负责任的研究模型中,我们可以创建更公平、更平衡的数据共享框架,最终帮助人们对个人数字足迹数据做出更好的选择。
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Attitudes towards Sharing Digital Footprint Data: a Discrete Choice Experiment
Introduction & BackgroundDigital footprints data are key for the economy, underpinning business models and service provision. This information can also bring benefit to public good, yet sharing of digital footprints data are predicated on individual attitudes which in term depend on the value these data have to consumers. In this study, we investigated how individuals make decisions about sharing their digital footprints data, as well as which features of the data sharing scenario affect their decision to share the data. Objectives & ApproachWe used responses from a nationally representative sample of 2,087 UK residents to estimate public preferences towards sharing different types of digital footprint data in scenarios with different features. The main part of our experiment consisted of a Discrete Choice Experiment which allows the relative importance of the different features of data sharing scenarios to be established, revealing the tradeoffs participants make between them. Participants made a series of choices between two hypothetical data sharing scenario options or could “opt out” by choosing neither specified option. For example, we examined the differences in responses when data are shared for different purposes (e.g., for research vs private benefit), as well as when data are shared with more or less granular details about identity or location. The data were analysed using a logistic regression with an alternative-specific constant. Relevance to Digital FootprintsWe focused on understanding whether varied features of six different types of digital footprints data - namely banking transactions, electricity use at home, retail loyalty cards use, browsing history, social media, and physical activity data - affect people’s decision whether to share these data. ResultsParticipants were more likely to share their data with a university for academic research than with a private company or government. Participants were also most reluctant to share data alongside their personal identity. Participants were concerned with the recipient of the data and their purpose in requesting it; whether the data would be shared along with their location and if so, to what specificity; and with the level of aggregation of the data (i.e. whether it would be shared in fine detail or as a monthly summary). In addition, we demonstrated the importance of the type of data to be shared, with people most reluctant to share bank transactions data, but relatively unconcerned about sharing their physical activity, electricity use and loyalty cards data. Conclusions & ImplicationsWe contribute by highlighting the trade-offs individuals are willing to make between different elements of a data sharing situation, and the relative importance of these different aspects. We also demonstrate that individuals’ have positive attitudes to share digital footprints data for research benefiting public good. By integrating these preferences into ethical and responsible research models, we can create fairer and more balanced data sharing frameworks, which can ultimately help people to make better choices about their personal digital footprints data.
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