The Impact of Abstract vs. Concrete Feedback Design on Behavior Insights from a Large Eco-Driving Field Experiment

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems Pub Date : 2018-04-21 DOI:10.1145/3173574.3173953

André Dahlinger, Felix Wortmann, Benjamin Ryder, Bernhard Gahr

{"title":"The Impact of Abstract vs. Concrete Feedback Design on Behavior Insights from a Large Eco-Driving Field Experiment","authors":"André Dahlinger, Felix Wortmann, Benjamin Ryder, Bernhard Gahr","doi":"10.1145/3173574.3173953","DOIUrl":null,"url":null,"abstract":"About 17% of the worldwide CO2-emissions can be ascribed to road transportation. Using information systems (IS)-enabled feedback has shown to be very efficient in promoting a less fuel-consuming driving style. Today, in-car IS that provide feedback on driving behavior are in the midst of a fundamental change. Increasing digitalization of in-car IS enables virtually any kind of feedback. Still, we see a gap in the empirical evidence on how to leverage this potential, raising questions on future HCI-based feedback design. To address this knowledge gap, we designed an eco-driving feedback IS and, building upon construal level theory, hypothesize that abstract feedback is more effective in reducing fuel consumption than concrete feedback. Deployed in a large field experiment with 56 participants covering over 297,000km, we provide first empirical evidence that supports this hypothesis. Despite its limitations, this research may have general implications for the design of real-time feedback.","PeriodicalId":20512,"journal":{"name":"Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3173574.3173953","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 14

Abstract

About 17% of the worldwide CO2-emissions can be ascribed to road transportation. Using information systems (IS)-enabled feedback has shown to be very efficient in promoting a less fuel-consuming driving style. Today, in-car IS that provide feedback on driving behavior are in the midst of a fundamental change. Increasing digitalization of in-car IS enables virtually any kind of feedback. Still, we see a gap in the empirical evidence on how to leverage this potential, raising questions on future HCI-based feedback design. To address this knowledge gap, we designed an eco-driving feedback IS and, building upon construal level theory, hypothesize that abstract feedback is more effective in reducing fuel consumption than concrete feedback. Deployed in a large field experiment with 56 participants covering over 297,000km, we provide first empirical evidence that supports this hypothesis. Despite its limitations, this research may have general implications for the design of real-time feedback.

查看原文本刊更多论文

抽象与具体反馈设计对大型生态驾驶现场实验行为洞察的影响

全球约17%的二氧化碳排放量可归因于道路运输。使用信息系统(IS)支持的反馈已被证明是非常有效的推广一种更少燃料消耗的驾驶方式。如今，为驾驶行为提供反馈的车载信息系统正处于一场根本性的变革之中。车载信息系统日益数字化，几乎可以提供任何形式的反馈。尽管如此，我们在如何利用这一潜力的经验证据中看到了差距，这为未来基于hci的反馈设计提出了问题。为了解决这一知识差距，我们设计了一个生态驾驶反馈系统，并基于解释水平理论，假设抽象反馈比具体反馈在降低油耗方面更有效。在一项涉及56名参与者、覆盖29.7万公里的大型野外实验中，我们首次提供了支持这一假设的经验证据。尽管有其局限性，但该研究可能对实时反馈的设计具有普遍意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

自引率

0.00%

发文量