TripAware: Emotional and Informational Approaches to Encourage Sustainable Transportation via Mobile Applications

To combat climate change, we need to change user transportation behavior to be less carbon intensive. Prior work on motivating this behavior change has been predominantly qualitative and lacks comparison. This makes it challenging to determine which interventions should be deployed at scale. The behavior change community needs a process to compare interventions against each other in pilot studies before committing deployment resources. We perform the first quantitative comparison, to our knowledge, of behavior change strategies in the transportation behavior domain. Since this is a pilot with a limited recruitment budget, we design a Randomized Controlled Trial (RCT) using an open source platform. We assign 41 users to three mobile applications: Emotion, Information, Control. The RCT allows us to draw statistically valid inferences that can suggest future avenues for larger-scale studies. We found that Emotion resulted in greater engagement with the application (p=0.006, 0.035, 0.031, 0.040) while Information improved the sustainability of travel behavior (p = 0.043). These exploratory statistical results can motivate the design of future studies to further explore combinations of these approaches for sustainable transportation behavior.

[1]  Randy H. Katz,et al.  e-mission: An Open-Source, Smartphone Platform for Collecting Human Travel Data , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[2]  Randy H. Katz,et al.  TripAware: Separate Related Works Document , 2019 .

[3]  Tatsuo Nakajima,et al.  ECOISLAND: A System for Persuading Users to Reduce CO2 Emissions , 2009, 2009 Software Technologies for Future Dependable Distributed Systems.

[4]  Paul Holleis,et al.  TRIPZOOM: a System to Motivate Sustainable Urban Mobility , 2012 .

[5]  Antti Jylhä,et al.  MatkaHupi: a persuasive mobile application for sustainable mobility , 2013, UbiComp.

[6]  R. Sokal,et al.  Introduction to biostatistics , 1974 .

[7]  M. Harjumaa,et al.  A Systematic Framework for Designing and Evaluating Persuasive Systems , 2008, PERSUASIVE.

[8]  Phoebe Sengers,et al.  Mapping the landscape of sustainable HCI , 2010, CHI.

[9]  Faruk Gul,et al.  A Theory of Addiction , 2001 .

[10]  Haiying Liu,et al.  Mobile technologies and personalized environmental information for supporting sustainable mobility in Oslo: The Citi-Sense-MOB approach , 2014, EnviroInfo.

[11]  G. Carrus,et al.  Emotions, habits and rational choices in ecological behaviours: The case of recycling and use of public transportation , 2008 .

[12]  Noah J. Goldstein,et al.  The Constructive, Destructive, and Reconstructive Power of Social Norms , 2007, Psychological science.

[13]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[14]  James A. Landay,et al.  UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits , 2009, CHI.

[15]  Martin Raubal,et al.  Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App , 2016 .

[16]  S. Siegel,et al.  Nonparametric Statistics for the Behavioral Sciences , 2022, The SAGE Encyclopedia of Research Design.

[17]  David Keatley,et al.  CALO-RE taxonomy of behavior change techniques , 2014 .

[18]  S. Michie,et al.  The behaviour change wheel: A new method for characterising and designing behaviour change interventions , 2011, Implementation science : IS.

[19]  Marko Turpeinen,et al.  Climate persuasive services: changing behavior towards low-carbon lifestyles , 2009, Persuasive '09.