Can Social Comparison Feedback Affect Indicators of Eco-Friendly Travel Choices? Insights from Two Online Experiments
Rouven Doran; Daniel Hanss; Torvald Øgaard · 2017 · Sustainability
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Two online experiments explored the effects of social comparison feedback on indicators of eco-friendly travel choices. It was tested whether the chosen indicators are sensitive to the information conveyed, and if this varies as a function of in-group identification. Study 1 (N = 134) focused on unfavourable feedback (i.e., being told that one has a larger ecological footprint than the average member of a reference group). People who received unfavourable feedback reported stronger intentions to choose eco-friendly travel options than those who received nondiscrepant feedback, when in-group id
Abstract by Rouven Doran; Daniel Hanss; Torvald Øgaard, Sustainability (2017) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Is CO 2 an Indoor Pollutant? Direct Effects of Low-to-Moderate CO 2 Concentrations on Human Decision-Making Performance
Negative / Null Result ReportIngestion of Nanoplastics and Microplastics by Pacific Oyster Larvae
Negative / Null Result ReportHealth Effects of Chronic Arsenic Exposure
Negative / Null Result ReportSevere Air Pollution and Labor Productivity: Evidence from Industrial Towns in China
Negative / Null Result ReportLand-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers
Negative / Null Result ReportTraffic-Related Air Pollution and Cognitive Function in a Cohort of Older Men
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.3390/su9020196
