首页> 外文期刊>Computers in Human Behavior >Self-selection and attrition biases in app-based persuasive technologies for mobility behavior change: Evidence from a Swiss case study
【24h】

Self-selection and attrition biases in app-based persuasive technologies for mobility behavior change: Evidence from a Swiss case study

机译:基于应用的有说服力技术的自我选择和磨损偏见,用于移动行为的改变:来自瑞士案例研究的证据

获取原文
获取原文并翻译 | 示例
           

摘要

App-based persuasive technologies emerged as promising tools to promote sustainable travel behavior. However, the opt-in, self-selection framework characterizing their use in real-life conditions might actually lead to wrongly estimate their potential and actual impact in analyses that do not rely on strict randomized controlled trials (RCTs). To investigate evidence of such biases, we analyze mobility data gathered from users of a persuasive app promoting public transport and active mobility launched in 2018 in Bellinzona (Switzerland). We consider the users' baseline mobility data: km per day (total and by car) traveled during the app validation period, when behavior change motivational features were not enabled. To estimate the possible self-selection bias, we compare these data with the reference population, using data from the Swiss Mobility and Transport Census; to study the possible attrition bias, we look at the relations between baseline mobility and the number of weeks of app's active use. We find evidence of neither self-selection nor critical attrition biases. This strengthens findings by earlier non RCT-based analyses and confirms the relevance of app-based persuasive technologies for mobility behavior change.
机译:基于应用的有说服力技术作为推动可持续旅行行为的有希望的工具。然而,选择他们在现实生活条件下使用的选择选择,实际上可能导致错误地估计它们在不依赖严格随机对照试验(RCT)的分析中的潜在和实际影响。为了调查此类偏见的证据,我们分析了从有说服力应用程序的用户收集的移动数据,促进2018年在Bellinzona(瑞士)推出的公共交通和积极流动。我们考虑在App验证期间在App Validation期间旅行的用户的基准移动数据:KM每天(总和乘CAR),当没有启用行为改变动机功能时。要估计可能的自选偏差,我们将这些数据与参考群体进行比较,使用来自瑞士流动性和运输人口普查的数据;为了研究可能的磨损偏见,我们看看基线移动性与应用程序主动使用数周数之间的关系。我们发现证据既不是自我选择也没有关键的消磨偏见。这种基于非RCT的分析得到了加强了结果,并确认了基于应用的说服技术对移动性行为变化的相关性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号