Abstract：
BACKGROUND: In pandemic situations, digital contact tracing (DCT) can be an effective way to assess one\'s risk of infection and inform others in case of infection. DCT apps can support the information gathering and analysis processes of users aiming to trace contacts. However, users\' use intention and use of DCT information may depend on the perceived benefits of contact tracing. While existing research has examined acceptance in DCT, automation-related user experience factors have been overlooked.
OBJECTIVE: We pursued three goals: (1) to analyze how automation-related user experience (ie, perceived trustworthiness, traceability, and usefulness) relates to user behavior toward a DCT app, (2) to contextualize these effects with health behavior factors (ie, threat appraisal and moral obligation), and (3) to collect qualitative data on user demands for improved DCT communication.
METHODS: Survey data were collected from 317 users of a nationwide-distributed DCT app during the COVID-19 pandemic after it had been in app stores for >1 year using a web-based convenience sample. We assessed automation-related user experience. In addition, we assessed threat appraisal and moral obligation regarding DCT use to estimate a partial least squares structural equation model predicting use intention. To provide practical steps to improve the user experience, we surveyed users\' needs for improved communication of information via the app and analyzed their responses using thematic analysis.
RESULTS: Data validity and perceived usefulness showed a significant correlation of r=0.38 (P<.001), goal congruity and perceived usefulness correlated at r=0.47 (P<.001), and result diagnosticity and perceived usefulness had a strong correlation of r=0.56 (P<.001). In addition, a correlation of r=0.35 (P<.001) was observed between Subjective Information Processing Awareness and perceived usefulness, suggesting that automation-related changes might influence the perceived utility of DCT. Finally, a moderate positive correlation of r=0.47 (P<.001) was found between perceived usefulness and use intention, highlighting the connection between user experience variables and use intention. Partial least squares structural equation modeling explained 55.6% of the variance in use intention, with the strongest direct predictor being perceived trustworthiness (β=.54; P<.001) followed by moral obligation (β=.22; P<.001). Based on the qualitative data, users mainly demanded more detailed information about contacts (eg, place and time of contact). They also wanted to share information (eg, whether they wore a mask) to improve the accuracy and diagnosticity of risk calculation.
CONCLUSIONS: The perceived result diagnosticity of DCT apps is crucial for perceived trustworthiness and use intention. By designing for high diagnosticity for the user, DCT apps could improve their support in the action regulation of users, resulting in higher perceived trustworthiness and use in pandemic situations. In general, automation-related user experience has greater importance for use intention than general health behavior or experience.
OBJECTIVE: We pursued three goals: (1) to analyze how automation-related user experience (ie, perceived trustworthiness, traceability, and usefulness) relates to user behavior toward a DCT app, (2) to contextualize these effects with health behavior factors (ie, threat appraisal and moral obligation), and (3) to collect qualitative data on user demands for improved DCT communication.
METHODS: Survey data were collected from 317 users of a nationwide-distributed DCT app during the COVID-19 pandemic after it had been in app stores for >1 year using a web-based convenience sample. We assessed automation-related user experience. In addition, we assessed threat appraisal and moral obligation regarding DCT use to estimate a partial least squares structural equation model predicting use intention. To provide practical steps to improve the user experience, we surveyed users\' needs for improved communication of information via the app and analyzed their responses using thematic analysis.
RESULTS: Data validity and perceived usefulness showed a significant correlation of r=0.38 (P<.001), goal congruity and perceived usefulness correlated at r=0.47 (P<.001), and result diagnosticity and perceived usefulness had a strong correlation of r=0.56 (P<.001). In addition, a correlation of r=0.35 (P<.001) was observed between Subjective Information Processing Awareness and perceived usefulness, suggesting that automation-related changes might influence the perceived utility of DCT. Finally, a moderate positive correlation of r=0.47 (P<.001) was found between perceived usefulness and use intention, highlighting the connection between user experience variables and use intention. Partial least squares structural equation modeling explained 55.6% of the variance in use intention, with the strongest direct predictor being perceived trustworthiness (β=.54; P<.001) followed by moral obligation (β=.22; P<.001). Based on the qualitative data, users mainly demanded more detailed information about contacts (eg, place and time of contact). They also wanted to share information (eg, whether they wore a mask) to improve the accuracy and diagnosticity of risk calculation.
CONCLUSIONS: The perceived result diagnosticity of DCT apps is crucial for perceived trustworthiness and use intention. By designing for high diagnosticity for the user, DCT apps could improve their support in the action regulation of users, resulting in higher perceived trustworthiness and use in pandemic situations. In general, automation-related user experience has greater importance for use intention than general health behavior or experience.
摘要：
背景：在大流行情况下，数字接触者追踪（DCT）是评估感染风险并在感染时告知他人的有效方法。DCT应用程序可以支持旨在跟踪联系人的用户的信息收集和分析过程。然而,用户对DCT信息的使用意图和使用可能取决于联系人追踪的感知好处。虽然现有的研究已经检查了DCT的接受度，自动化相关的用户体验因素被忽视。
目标：我们追求三个目标：（1）分析与自动化相关的用户体验如何（即，感知的可信度，可追溯性,和有用性)与用户对DCT应用程序的行为有关，(2)将这些影响与健康行为因素联系起来(即，威胁评估和道德义务)，（3）收集有关用户需求的定性数据，以改善DCT通信。
方法：在COVID-19大流行期间，使用基于网络的便利样本，从全国范围内分布的DCT应用程序的317名用户那里收集了数据。我们评估了与自动化相关的用户体验。此外,我们评估了DCT使用的威胁评估和道德义务，以估计预测使用意图的偏最小二乘结构方程模型。为了提供实际步骤来改善用户体验，我们调查了用户对通过应用程序改善信息交流的需求，并使用主题分析分析了他们的回答。
结果：数据有效性和感知有用性显示出r=0.38（P<.001）的显着相关性，目标一致性和感知有用性相关，r=0.47(P<.001)，结果诊断性和感知有用性有很强的相关性，r=0.56(P<.001)。此外,在主观信息处理意识和感知有用性之间观察到r=0.35(P<.001)的相关性，这表明与自动化相关的变化可能会影响DCT的感知效用。最后,感知有用性和使用意向之间存在r=0.47(P<.001)的中度正相关，突出用户体验变量与使用意图之间的联系。偏最小二乘结构方程模型解释了55.6%的使用意图方差，最强的直接预测指标是感知可信度(β=.54；P<.001)，其次是道德义务(β=.22；P<.001)。根据定性数据，用户主要要求更详细的联系人信息(例如，联系的地点和时间)。他们还想分享信息(例如，他们是否戴着口罩），以提高风险计算的准确性和诊断性。
结论：DCT应用程序的感知结果诊断对感知可信度和使用意图至关重要。通过为用户设计高诊断性，DCT应用程序可以提高他们对用户行为监管的支持，导致更高的可信性和在大流行情况下的使用。总的来说,与自动化相关的用户体验对使用意图的重要性高于一般的健康行为或体验。
目标：我们追求三个目标：（1）分析与自动化相关的用户体验如何（即，感知的可信度，可追溯性,和有用性)与用户对DCT应用程序的行为有关，(2)将这些影响与健康行为因素联系起来(即，威胁评估和道德义务)，（3）收集有关用户需求的定性数据，以改善DCT通信。
方法：在COVID-19大流行期间，使用基于网络的便利样本，从全国范围内分布的DCT应用程序的317名用户那里收集了数据。我们评估了与自动化相关的用户体验。此外,我们评估了DCT使用的威胁评估和道德义务，以估计预测使用意图的偏最小二乘结构方程模型。为了提供实际步骤来改善用户体验，我们调查了用户对通过应用程序改善信息交流的需求，并使用主题分析分析了他们的回答。
结果：数据有效性和感知有用性显示出r=0.38（P<.001）的显着相关性，目标一致性和感知有用性相关，r=0.47(P<.001)，结果诊断性和感知有用性有很强的相关性，r=0.56(P<.001)。此外,在主观信息处理意识和感知有用性之间观察到r=0.35(P<.001)的相关性，这表明与自动化相关的变化可能会影响DCT的感知效用。最后,感知有用性和使用意向之间存在r=0.47(P<.001)的中度正相关，突出用户体验变量与使用意图之间的联系。偏最小二乘结构方程模型解释了55.6%的使用意图方差，最强的直接预测指标是感知可信度(β=.54；P<.001)，其次是道德义务(β=.22；P<.001)。根据定性数据，用户主要要求更详细的联系人信息(例如，联系的地点和时间)。他们还想分享信息(例如，他们是否戴着口罩），以提高风险计算的准确性和诊断性。
结论：DCT应用程序的感知结果诊断对感知可信度和使用意图至关重要。通过为用户设计高诊断性，DCT应用程序可以提高他们对用户行为监管的支持，导致更高的可信性和在大流行情况下的使用。总的来说,与自动化相关的用户体验对使用意图的重要性高于一般的健康行为或体验。
