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The effect of paternalistic alternatives on attitudes toward default nudges

Published online by Cambridge University Press:  30 September 2019

WILLIAM HAGMAN
Affiliation:
Department of Behavioral Science and Learning, Linköping University, Linköping, Sweden
ARVID ERLANDSSON
Affiliation:
Department of Behavioral Science and Learning, Linköping University, Linköping, Sweden
STEPHAN DICKERT
Affiliation:
School of Business and Management, Queen Mary University of London, London, UK and Klagenfurt University, Klagenfurt, Austria
GUSTAV TINGHÖG
Affiliation:
Department of Management and Engineering, Division of Economics, Linköping University, Linköping, Sweden
DANIEL VÄSTFJÄLL*
Affiliation:
Department of Behavioral Science and Learning, Linköping University, Linköping, Sweden and Decision Research, Eugene, OR, USA
*
*Correspondence to: Daniel Västfjäll, Department of Behavioral Science and Learning, Linköping University, Linköping, Sweden. Email: daniel.vastfjall@liu.se
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Abstract

Nudges are increasingly being proposed and used as a policy tool around the world. The success of nudges depends on public acceptance. However, several questions about what makes a nudge acceptable remain unanswered. In this paper, we examine whether policy alternatives to nudges influence the public's acceptance of these nudges: Do attitudes change when the nudge is presented alongside either a more paternalistic policy alternative (legislation) or a less paternalistic alternative (no behavioral intervention)? In two separate samples drawn from the Swedish general public, we find a very small effect of alternatives on the acceptability of various default nudges overall. Surprisingly, we find that when the alternative to the nudge is legislation, acceptance decreases and perceived intrusiveness increases (relative to conditions where the alternative is no regulation). An implication of this finding is that acceptance of nudges may not always automatically increase when nudges are explicitly compared to more paternalistic alternatives.

Information

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use.
Copyright
Copyright © The Author(s) 2019
Figure 0

Table 1. Descriptive statistics of participants for each condition.

Figure 1

Figure 1. Acceptance rates of nudges divided by condition. Error bars are 95% confidence intervals.

Figure 2

Figure 2. Intrusiveness rates of nudges divided by condition. Error bars are 95% confidence intervals.

Figure 3

Figure 3. Acceptance rates of pro-social and pro-self nudges divided by condition. Error bars are 95% confidence intervals.

Figure 4

Figure 4. Intrusiveness rates of pro-social and pro-self nudges divided by condition. Error bars are 95% confidence intervals.

Figure 5

Table 2. Linear regression analyses exploring the influences of individual differences on acceptance rates.

Figure 6

Table 3. Linear regression analyses exploring the influences of individual differences on intrusiveness rates.

Figure 7

Table 4. Descriptive statistics of participants for each condition.

Figure 8

Figure 5. Acceptance rates for each condition and manipulation check. Error bars are 95% confidence intervals.

Figure 9

Figure 6. Intrusiveness rates for each condition and manipulation check. Error bars are 95% confidence intervals.

Figure 10

Figure 7. Acceptance rates of pro-social and pro-self nudges divided by condition. Error bars are 95% confidence intervals.

Figure 11

Figure 8. Intrusiveness rates of pro-social and pro-self nudges divided by condition. Error bars are 95% confidence intervals.

Figure 12

Table 5. Linear regression analyses exploring the influences of individual differences on acceptance rate.

Figure 13

Table 6. Linear regression analyses exploring the influences of individual differences on intrusiveness rate.