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

Do Finns accept nudging?

2022· other· en· W6925307347 sur OpenAlexaboutno aff

Notice bibliographique

RevueOpen Science Framework · 2022
Typeother
Langueen
DomaineMathematics
ThématiqueHistory and Theory of Mathematics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNudge theoryPsychological interventionIntrusivenessWork (physics)Choice architectureSurvey data collection

Résumé

récupéré en direct d'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).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Science ouverte, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,284
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0080,003
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,2850,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,065
Tête enseignante GPT0,383
Écart entre enseignants0,318 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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