Computational Social Science-Research Center for Educational and Network Studies (CSS-RECENS), Centre for Social Sciences, Tóth Kálmán Utca 4, 1097 Budapest, Hungary.
Institute of Communication and Sociology, Corvinus University, Fővám tér 8, 1093 Budapest, Hungary.
Sensors (Basel). 2021 Sep 10;21(18):6085. doi: 10.3390/s21186085.
In this paper, we present the results of an exploratory study conducted in Hungary using a factorial design-based online survey to explore the willingness to participate in a future research project based on active and passive data collection via smartphones. Recently, the improvement of smart devices has enabled the collection of behavioural data on a previously unimaginable scale. However, the willingness to share this data is a key issue for the social sciences and often proves to be the biggest obstacle to conducting research. In this paper we use vignettes to test different (hypothetical) study settings that involve sensor data collection but differ in the organizer of the research, the purpose of the study and the type of collected data, the duration of data sharing, the number of incentives and the ability to suspend and review the collection of data. Besides the demographic profile of respondents, we also include behavioural and attitudinal variables to the models. Our results show that the content and context of the data collection significantly changes people's willingness to participate, however their basic demographic characteristics (apart from age) and general level of trust seem to have no significant effect. This study is a first step in a larger project that involves the development of a complex smartphone-based research tool for hybrid (active and passive) data collection. The results presented in this paper help improve our experimental design to encourage participation by minimizing data sharing concerns and maximizing user participation and motivation.
在本文中，我们展示了在匈牙利进行的一项探索性研究的结果，该研究采用基于因子设计的在线调查，以探索基于智能手机的主动和被动数据收集参与未来研究项目的意愿。最近，智能设备的改进使得以前难以想象的规模的行为数据收集成为可能。然而，分享这些数据的意愿是社会科学的一个关键问题，并且往往被证明是进行研究的最大障碍。在本文中，我们使用情景描述来测试不同的（假设的）研究设置，这些设置涉及传感器数据收集，但在研究的组织者、研究的目的和收集的数据类型、数据共享的持续时间、激励措施的数量以及暂停和审查数据收集的能力方面有所不同。除了受访者的人口统计特征外，我们还将行为和态度变量纳入模型。我们的研究结果表明，数据收集的内容和背景显著改变了人们参与的意愿，但他们的基本人口统计特征（除了年龄）和一般信任水平似乎没有显著影响。这项研究是一个更大项目的第一步，该项目涉及开发一种用于混合（主动和被动）数据收集的复杂基于智能手机的研究工具。本文介绍的研究结果有助于改进我们的实验设计，通过最小化数据共享问题并最大化用户参与度和积极性来鼓励参与。
