Determinants for university students’ location data sharing with public institutions during COVID-19: The Italian case

IF 1.8 Q3 PUBLIC ADMINISTRATION Data & policy Pub Date : 2024-01-11 DOI:10.1017/dap.2023.42
V. M. Urbano, Federico Bartolomucci, Giovanni Azzone
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Abstract

Abstract Data on real-time individuals’ location may provide significant opportunities for managing emergency situations. For example, in the case of outbreaks, besides informing on the proximity of people, hence supporting contact tracing activities, location data can be used to understand spatial heterogeneity in virus transmission. However, individuals’ low consent to share their data, proved by the low penetration rate of contact tracing apps in several countries during the coronavirus disease-2019 (COVID-19) pandemic, re-opened the scientific and practitioners’ discussion on factors and conditions triggering citizens to share their positioning data. Following the Antecedents → Privacy Concerns → Outcomes (APCO) model, and based on Privacy Calculus and Reasoned Action Theories, the study investigates factors that cause university students to share their location data with public institutions during outbreaks. To this end, an explanatory survey was conducted in Italy during the second wave of COVID-19, collecting 245 questionnaire responses. Structural equations modeling was used to contemporary investigate the role of trust, perceived benefit, and perceived risk as determinants of the intention to share location data during outbreaks. Results show that respondents’ trust in public institutions, the perceived benefits, and the perceived risk are significant predictor of the intention to disclose personal tracking data with public institutions. Results indicate that the latter two factors impact university students’ willingness to share data more than trust, prompting public institutions to rethink how they launch and manage the adoption process for these technological applications.
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COVID-19 期间大学生与公共机构共享位置数据的决定因素:意大利案例
摘要 个人实时位置数据可为管理紧急情况提供重要机会。例如,在疫情爆发的情况下,位置数据除了可以告知人们距离的远近,从而支持联系人追踪活动外,还可用于了解病毒传播的空间异质性。然而,在冠状病毒病-2019(COVID-19)大流行期间,一些国家的接触追踪应用程序普及率很低,这证明了个人对分享其数据的同意程度很低,这重新引发了科学界和从业人员对引发公民分享其定位数据的因素和条件的讨论。本研究遵循 "前因→隐私关注→结果(APCO)"模型,以 "隐私计算 "和 "合理行动 "理论为基础,探讨了导致大学生在疫情爆发时与公共机构共享定位数据的因素。为此,在 COVID-19 第二波期间,在意大利进行了一项解释性调查,收集了 245 份问卷。调查采用结构方程模型对信任、感知利益和感知风险作为疫情爆发期间共享位置数据意愿的决定因素所起的作用进行了当代研究。结果表明,受访者对公共机构的信任、感知到的好处和感知到的风险是向公共机构披露个人追踪数据意愿的重要预测因素。结果表明,与信任相比,后两个因素对大学生分享数据意愿的影响更大,这促使公共机构重新思考如何启动和管理这些技术应用的采用过程。
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CiteScore
3.10
自引率
0.00%
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0
审稿时长
12 weeks
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