Purpose

During the challenging time of lockdown and isolation due to the coronavirus disease (COVID-19), contact-tracing apps have played a critical role in health communication and preventive healthcare. This study proposed and tested an extended technology acceptance model (TAM) with key health factors (i.e. health risk perception from COVID-19, health information orientation to COVID-19 and health consciousness) to understand individuals' adoption of COVID-19 contact-tracing apps.

Design/methodology/approach

A two-stage online survey was conducted to collect data on US individuals’ intention and actual use of COVID-19 contact-tracing apps. The sample comprises 288 valid responses. Partial least squares structural equation modeling (PLS-SEM) and fuzzy set/qualitative comparative analysis (fsQCA) were employed as the complementary approaches.

Findings

The findings from PLS-SEM revealed that health risk perception, health information orientation and perceived usefulness have positive net effects on behavioral intention, which, in turn, affects actual use. The results from fsQCA highlighted the explanatory power of the extended TAM to COVID-19 contact-tracing app adoption.

Originality/value

Although TAM is considerably effective in measuring technology acceptance, the phenomenon is highly context-driven. How technological and health factors simultaneously motivate the use of contact-tracing apps has not been well documented. The present study offers some implications for practitioners concerned about fostering the adoption of mobile health services in the time of COVID-19. Methodologically, this study is among the first to blend PLS-SEM and fsQCA to measure the explanatory power of a structural model.

中文翻译：

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探索个人对 COVID-19 接触者追踪应用程序的采用：一种混合方法

目的

在冠状病毒病 (COVID-19) 导致的封锁和隔离的艰难时期，接触者追踪应用程序在健康沟通和预防保健方面发挥了关键作用。本研究提出并测试了一个扩展技术接受模型 (TAM)，其中包含关键健康因素（即对 COVID-19 的健康风险感知、对 COVID-19 的健康信息导向和健康意识），以了解个人对 COVID-19 接触者追踪的采用情况应用。

设计/方法/方法

进行了一项两阶段的在线调查，以收集有关美国个人使用 COVID-19 接触者追踪应用程序的意图和实际使用情况的数据。该样本包含 288 个有效响应。偏最小二乘结构方程模型（PLS-SEM）和模糊集/定性比较分析（fsQCA）被用作补充方法。

发现

PLS-SEM 的研究结果表明，健康风险感知、健康信息导向和感知有用性对行为意图有积极的净影响，进而影响实际使用。fsQCA 的结果强调了扩展 TAM 对 COVID-19 接触者追踪应用程序采用的解释力。

原创性/价值

尽管 TAM 在衡量技术接受度方面相当有效，但这种现象是高度受环境驱动的。技术和健康因素如何同时激发接触者追踪应用程序的使用尚未得到很好的记录。本研究为关注在 COVID-19 时期促进采用移动医疗服务的从业人员提供了一些启示。在方法论上，这项研究是第一个将 PLS-SEM 和 fsQCA 混合来测量结构模型的解释能力的研究之一。