DOI: 10.25338/b81s5m
title: Employee airline travel preferences survey data, UC Davis GreenFLY project

description: This survey data is from a study exploring the potential to promote
 lower-emissions air travel by providing consumers with information about
 the carbon emissions of possible flight choices in the context of online
 flight search and booking. We surveyed over 450 faculty, researchers, and
 staff at the University of California, Davis, and asked them to choose
 among hypothetical flight options for a domestic and an international
 university-related business trip. Emissions estimates for different flight
 alternatives were displayed as prominently as price; this simple
 intervention has been promoted in several demonstration projects,
 including GreenFLY, a demo we created at UC Davis.
description: The flight choice experiment involved an online survey in which
 UC Davis employees were asked to make a series of binary discrete choices
 between roundtrip flight alternatives, that varied in terms of cost,
 carbon emissions, layovers (0 or 2: one layover each way), and airport
 (SMF or SFO), for two hypothetical UC Davis-related business trips, one to
 Washington, DC and the other to London. We based these hypothetical
 scenarios (trip destinations and attribute levels of flight alternatives)
 on data about actual UC Davis employee air travel. For
 the layover flight alternatives, we created eight possible cost-carbon
 combinations, using each cost level and each carbon level twice, and not
 repeating any pairing. There are many ways to do this, and we chose one
 which tended to pair high cost with low carbon, to create trade-offs. Our
 eight layover flights to DC appear in Table 2. The same cost-carbon
 pairings were used for layover flight alternatives from SFO.
 We organized the flight alternatives into sets of two for the
 choice experiment questions. Criteria for pairing flight alternatives were
 as follows:<br> 1. Every flight alternative should appear roughly
 the same number of times in the survey,<br> 2. The distribution of
 kinds of flights in the questions (eg. layover out of SFO) should match
 the distribution in the entire set,<br> 3. Avoid questions in which
 the two flights have the same cost, or the same carbon, and<br> 4.
 Focus on pairs that might have competitive utility (e.g. an alternative
 that is lower cost, lower carbon, nonstop and out of SMF is likely to be
 selected in most cases, so it is not useful for understanding potential
 trade-offs). From this, we created seven
 "buckets" of questions for Washington, and seven for London, and
 asked each participant a randomly-chosen question from each bucket.  We
 made an error in the online questionnaire-design software, which caused a
 random bucket to be skipped for London.  Nonetheless, each flight option
 appears freqently (between 40 and 120 times) in the questions we
 asked.  The original data output from Qualtrics was
 processed into a format suitable for processing with the mlogit package in
 R. 
description: The R file we used for analysis is included.  It is called
 <em>ModelsFromSurveyData.R</em> The data
 file is called Organized_Survey_Data.csv There are two
 rows in the file for each question presented to a user, one for each of
 the two alternative flights the question asked the user to
 compare. The columns in the file are:
 rowIndex - The user's ID from the Qualtrics data.  In the
 original data each user's entire questionaire was one long
 row.<br> columnIndex - The question column from the Qualtrics data. 
 Each question was a column in the original data.<br> QID - The
 question ID in the Qualtrics file for each question. These should be in
 1:1 correspondence with the columnIndex values.<br> Code - The
 descriptive name of the specific flight corresponding to this
 row.<br> L.N - Layover or non-stop<br> Carbon - CO2 emissions
 for the flight, in pounds<br> Cost - cost in dollars<br>
 Airport - SMF or SFO<br> Choice - the flight the user chose has
 Choice=1, the other has Choice=0<br> perferred_airport - SFO or
 SMF.  About 10% of participants live in the Bay Area and would prefer to
 fly out of SFO. Other columns included other
 demographic data, which hopefully are self-explanatory.