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Record W6925307347 · doi:10.17605/osf.io/q4xv2

Do Finns accept nudging?

2022· other· en· W6925307347 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2022
Typeother
Languageen
FieldMathematics
TopicHistory and Theory of Mathematics
Canadian institutionsnot available
Fundersnot available
KeywordsNudge theoryPsychological interventionIntrusivenessWork (physics)Choice architectureSurvey data collection

Abstract

fetched live from OpenAlex

Nudges are choice-preserving interventions that steer people's behavior in specific directions without imposing significant costs (Thaler & Sunstein, 2008). Policymakers have recently adopted nudging interventions to increase society's wellbeing and help citizens make better choices. Hence, it has become essential to have a psychological understanding of who opposes nudges, how they are perceived, and when alternative methods could work better. Recent studies from Sweden, Denmark, Australia, Italy, UK, Hungary, France, Germany, South Korea, Brazil, South Africa, Russia, China, Japan, Canada, and the USA, indicate that citizens generally accept state nudging (Almqvist & Andersson, 2021; Reisch and Sunstein, 2016; Sunstein, Reisch, & Rauber, 2018). People show moderate to high levels of approval with nudging across all countries, and the level of intrusiveness and nudging approval have been negatively associated. Nudges implemented by experts and industry, as opposed to policymakers, were more approved of. This study analyzes the relationship between the support for nudging and several psychological variables, a research gap recently identified. The analyses will be based on a representative population-based survey in Finland. Data will be collected in May 2022 through a TNS Kantar Web panel. Reisch and Sunstein's (2016) questionnaire will be replicated (study I), and the questionnaire will be complemented with a survey experiment (study II). Finally, we investigate several factors that influence Finnish citizens' attitudes toward nudges – types of nudges, individual dispositions, nudge frames, and nudge perceptions. In study I, the purpose is to analyze what type of nudges are supported and what individual dispositions explain the support. In study II, we extend the focus and investigate how framing and perceptions of the nudge affect acceptance. To this end, we conduct a survey experiment. In vignettes, the nudge objective (pro-social or pro-self) and proposer (Government or researcher) will be varied. Nudges can be classified as targeting either System 1 or System 2 thinking, referring to decision-making according to dual-process theory. The theory proposes that individuals make decisions with either a quick and heuristic intuition or slower, analytical thinking (Kahneman, 2011). In previous studies, "System 1" nudges (e.g., defaults) have been perceived as less acceptable than "System 2" nudges (e.g., educational messages or reminders). System 1 nudges were also considered more autonomy threatening, whereas System 2 nudges were viewed as more effective and even necessary to change behavior (Jung & Mellers, 2016). In addition to system 1 ja system 2 classification, nudges may also be classified according to their objective: an individual's benefit (pro-self) or the more significant benefit to society (pro-social) (Hagman, 2015). In previous studies, people preferred pro-self nudges over pro-social ones (Hagman, 2015). Also, studies show that the acceptance may vary depending on who does the nudging (Tannenbaum et al., 2014). For example, nudges implemented by experts have received more approval than those by policymakers. In general, acceptance increased with the trustworthiness of the source. (Evers et al., 2018; Junghans et al., 2015). People seem to consider the choice architect's (person or institute implementing the nudge) intentions when implementing the nudges. It is expected that the trustworthiness of the nudge source will explain the considerable variation of the nudge approval. Although Finland is one of the highest institutional trust countries globally (Newton, 2007), there is variation among the population in the Finnish Government and science trust. The people with the highest education trust more than those with lower education (Tiedebarometri, 2019). Also, rural residents, lower-income households (OECD, 2021), and supporters of the populist party (Jallinoja & Väliverronen, 2021; Saarinen, Koivula, and Keipi, 2020) show consistently lower trust in Government and universities. Specifically, the inhabitants in the most sparsely populated area of Eastern and Northern Finland experience lower economic and wellbeing outcomes than Finland on average (OECD, 2021). Further, several other individual dispositions are expected to explain the nudge approval or disapproval, respectively, although not previously studied. Despite the robust scientific evidence of global warming, many people continue to deny the severity and the efforts to tackle the problem. Extensive findings suggest that general ideological attitudes influence environmental attitudes to maintain the societal status quo (e.g., Jost, Glaser, Kruglanski, & Sulloway, 2003). One of these ideologies is right-wing authoritarianism, which has been shown to impact pro-environmentalism (Sabbagh, 2005) negatively. It is expected to affect lower approval of "green" nudges. Furthermore, people who hold culturally and economically conservative attitudes and generally support social dominance are less likely than others to support pro-environmental actions (Dunlap & Van Liere, 1984; Kilbourne, Beckmann, & Thelen, 2002). Moreover, so-called system justification is a mindset predisposing to opposition to such scientific knowledge that would threaten the status quo. Furthermore, the research findings show that system justification tendencies are associated with more significant denial of environmental realities and less commitment to pro-environmental action (Feygina et al., 2009).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0080.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.2850.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.383
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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