IEEE Technology and Society Magazine - March 2018 - 87

Table 4. Results of hierarchical regression analyses predicting support
with experimentally varied and measured variables.
Step 1

Step 2
Indep.
Variance

(Constant)

5.26***

0.165

Security use (vs. envir.)

-1.36***

0.133

10.52%

Economic use (vs. envir.)

-0.58***

0.129

Business (vs. Government)

-0.26*

0.106

Autonomous (vs. partially)

-0.04

Manual (vs. partially)

-0.22+

UAS term (vs. drone)
UAV term (vs. drone)

Indep.
Variance

5.15***

0.138

-1.15***

0.112

7.46%

2.08%

-0.58***

0.108

2.05%

0.59%

-0.30**

0.088

0.80%

0.129

0.01%

0.02

0.108

0.00%

0.129

0.29%

-0.08

0.108

0.04%

-0.18

0.149

0.15%

-0.09

0.124

0.04%

0.04

0.149

0.01%

-0.04

0.124

0.01%

Aerial robot (vs. drone)

0.09

0.147

0.04%

0.12

0.123

0.06%

Promotion (vs. prevention)

-0.23*

0.106

0.48%

-0.24**

0.088

0.52%

Trustworthiness

0.52***

0.063

4.69%

Distrustworthiness

-0.16*

0.064

0.45%

Competence

0.27***

0.059

1.53%

Issue attitude

0.12*

0.046

0.46%

Ideology

-0.01

0.044

0.00%

Step 1

Step 2 a

(9 867) = 13.79, p < 0.001

(14 862) = 40.12, p < 0.001

0.13

0.40

+p<0.10, *p<0.05, **p<0.01, ***p<0.001. B indicates the expected change in support relating to a 1-unit change in the predictor variable. Indep. Variance
indicates the non-overlapping variance accounted for by the predictor above and beyond the variance accounted for by the other variables.
a
Step 2 variables were transformed to z-scores such that 0 equals the mean response and the B value refers to the change in support for a 1 standard
deviation increase in the predictor.

increase in support on the 7-point scale) compared to
promotion framing. However, the overall variance
accounted for by prevention or promotion framing
(beyond that accounted for by other variables) was
small (independently only accounting for less than onehalf-percent of the total variance in support for UAVs).
Note that, although terminology did not impact support for the technology, it did impact familiarity. A total
of 92% of respondents across both years indicated
"yes" they had heard of drones. Only 59% indicated they
had heard of UAVs, 37% had heard of UASs, and 33%
had heard of aerial robots. These results were similar
across both years of the survey.

Response to RQ2: UAV Autonomy did not
Impact UAV Support, Regardless of Purpose
Table 4 results indicate that, as a main effect, autonomy
of the UAVs does not appear to affect public support,
although there was slightly less support for fully manual
march 2018

∕

UAVs than partially autonomous UAVs in Step 1 of the
model. To examine whether the effect of autonomy
depends on the purpose of the UAVs, we conducted
another regression analysis (not shown in Table 4) that
tested for the interaction between the purpose and autonomy variables. The interaction was not statistically significant, which indicates that our hypothesis (H2) that
autonomy will have different effects on support depending on purpose, was not supported. Autonomy did not
affect our respondents' reported levels of support for
UAVs, regardless of the purpose of the UAVs.

Response to RQ3: Purpose and End-User Trustworthiness are the Most Important Predictors of Support
The results presented in Table 4 further confirm the
importance of UAV purpose for impacting support, as purpose accounts for about 13% (11+2%) of the independent
variance in the Step 1 model, while end user and framing
each independently account for vastly less - only about

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