IEEE Technology and Society Magazine - March 2018 - 85

Table 3. Pearson correlations between UaV support and each of our manipulated and measured variables.
2014

2015

Both Years

%Var

Manipulated Variables
Purposes
Security use

−0.30***

−0.27***

−0.29***

8.41%

Environmental use

0.24***

0.31***

0.27***

7.29%

Economic use

0.06

−0.04

0.03

0.09%

−0.11**

−0.03

−0.08*

0.64%

End-User
Business (vs. Government)
Autonomy
Autonomous

−0.03

0.09

0.01

0.01%

Manual

−0.04

−0.07

−0.05

0.25%

Partially Autonomous

0.07

−0.02

0.04

0.16%

−0.03

−0.09

−0.05

0.25%

Terminology
UAS term
UAV term

0.00

0.02

0.01

0.01%

Aerial robot term

0.05

0.01

0.03

0.09%

Drone term

−0.02

0.06

0.01

0.01%

−0.12**

−0.03

−0.09*

0.81%

Framing
Promotion (vs. prevention)
Measured Variables
Female

−0.03

−0.02

−0.02

0.04%

Age

−0.01

−0.08

−0.03

0.09%

Ideology (Conservativism)

−0.05

0.07

−0.01

0.01%

Issue attitude

0.17***

0.24***

0.20***

4.00%

0.50***

0.55***

0.52***

27.04%

Perceptions of end-users
Trustworthiness
Dis-trustworthiness

−0.42***

−0.48***

−0.44***

19.36%

Competence

0.39***

0.47***

0.42***

17.64%

Notes: 2014 N = 576, 2015 N=301. +p<0.10, *p<0.05, **p<0.01, ***p<0.001. %Var is the square of the Pearson correlation across both years and estimates
the variance shared by the predictor and UAV support.

uses, indicates support was lowest for security purposes
and highest for environmental purposes.
Figure 3 illustrates the distribution of responses to
questions assessing public support or resistance by UAV
purpose and year. There was a relatively bi-modal distribution of support ratings for security purposes in both
2014 and 2015, indicating public polarization. Ratings
of support for economic purposes were negatively
skewed, resulting in more support on average than for
security purposes. However, there appeared to be
increasing polarization of responses in 2015 relative to
2014. That is, the percentage of those strongly resisting
use of UAVs for economic purposes was greater than
those expressing more moderate resistance in 2015.
Finally, support ratings for use of UAVs for environmental purposes were negatively skewed in 2014, and even
march 2018

∕

more negatively skewed in 2015, resulting in the highest
average levels of support for environmental purposes.
Among the other variables listed in Table 3, there
were weaker but significant relationships with UAV support favoring prevention-focused framing and end use
by the government over private business. Among the
non-experimentally varied variables, as expected, there
were relatively strong effects of end-user trustworthiness and of issue attitudes relevant to the scenario
assigned to the participant.
Multiple regression procedures provide a different
measure of the importance of variables for predicting
public support by identifying variables that account for
independent variance above and beyond other variables. We first tested whether the effects of each of our
variables depended on time (this is done by testing for

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