ABSTRACT

In this study, we extended and tested the privacy calculus framework in the context of a hypothetical AI-based contact-tracing technology for application during the COVID-19 pandemic that is based on the communication privacy management and contextual integrity theories. Specifically, we investigated how the perceived privacy risks and benefits of information disclosure affect the public’s willingness to opt in and adopt contact-tracing technologies and how social and contextual factors influence their decision-making process. Four hundred eighteen adults in the United States participated in the study via Amazon Mechanical Turk in August 2020. A percentile bootstrap method with 5,000 resamples and bias-corrected 95% confidence intervals in structural equation modeling was used for data analysis. The participants’ privacy concerns and perceived benefits significantly influenced their opt-in and adoption intentions, which suggests that the privacy calculus framework applies to the context of COVID-19 contact-tracing technologies. Perceived social, personal, and reciprocal benefits were identified as crucial mediators that link contextual variables to both opt-in and adoption intentions. Although this study was based on a hypothetical AI-based contact-tracing app, our findings provide meaningful theoretical and practical implications for future research investigating the public’s technology adoption in contexts where tradeoffs between privacy risks and public health coexist.

摘要

在这项研究中，我们在基于通信隐私管理和上下文完整性理论的 COVID-19 大流行期间应用的假设的基于 AI 的接触者追踪技术的背景下扩展和测试了隐私演算框架。具体来说，我们调查了感知到的隐私风险和信息披露的好处如何影响公众选择和采用接触者追踪技术的意愿，以及社会和背景因素如何影响他们的决策过程。2020 年 8 月，美国有 418 名成年人通过 Amazon Mechanical Turk 参与了这项研究。使用结构方程建模中具有 5,000 次重采样和偏差校正 95% 置信区间的百分位引导法进行数据分析。参与者的隐私担忧和感知利益显着影响了他们的选择加入和采用意图，这表明隐私计算框架适用于 COVID-19 接触者追踪技术的背景。感知到的社会、个人和互惠利益被确定为将上下文变量与选择加入和采用意图联系起来的关键中介。尽管这项研究是基于一个假设的基于 AI 的接触者追踪应用程序，但我们的研究结果为未来调查公众在隐私风险和公共卫生之间权衡的情况下采用技术的研究提供了有意义的理论和实践意义。这表明隐私计算框架适用于 COVID-19 接触者追踪技术的背景。感知到的社会、个人和互惠利益被确定为将上下文变量与选择加入和采用意图联系起来的关键中介。尽管这项研究是基于一个假设的基于 AI 的接触者追踪应用程序，但我们的研究结果为未来调查公众在隐私风险和公共卫生之间权衡的情况下采用技术的研究提供了有意义的理论和实践意义。这表明隐私计算框架适用于 COVID-19 接触者追踪技术的背景。感知到的社会、个人和互惠利益被确定为将上下文变量与选择加入和采用意图联系起来的关键中介。尽管这项研究是基于一个假设的基于 AI 的接触者追踪应用程序，但我们的研究结果为未来调查公众在隐私风险和公共卫生之间权衡的情况下采用技术的研究提供了有意义的理论和实践意义。