Competing Motivations (the Need to Help and Need to Conserve) Bias Attitudes and Helping Behavior Toward Natural Disaster Victims
Notice bibliographique
Résumé
In this investigation, Weiner’s (1995) attribution theory is integrated with Hobfoll’s conservation of resources theory to build upon previous research explaining donor support variations in natural disasters (Marjanovic et al., 2008). Utilizing three experiments conducted within three months of real disasters (Hurricane Dorian, Australian Bushfires, Typhoon Rai) and diverse Canadian samples from Amazon’s Mechanical Turk, the study manipulated victim preparedness and plea timing to examine their biasing effects on attribution processes and helping behavior. Hypothesis 1 asserts that victims who are portrayed as having done everything they could to minimize or avoid a foreseeable disaster before it occurred will evoke participants’ need to help. As reflected through the stages of Weiner’s model, these victims will be judged not responsible for their predicament, be afforded sympathy and a willingness to help, and elicit helping behavior. In contrast, victims portrayed as unprepared for a foreseeable disaster will be judged responsible and blamed for the event. They will elicit anger, little willingness to help, and generate low levels of helping behavior. Hypothesis 2 asserts that Low-NCR (i.e., Late Plea Timing) participants will judge victims less harshly than High-NCR (i.e., Ealy Plea Timing). To a lesser extent but still significant, Low-NCR participant attitudes will be less angry, more sympathetic, and more willing to help victims. Lastly, Low-NCR participants engage in greater Helping Behavior than High-NCR participants. Hypothesis 3 posits that in the Early Plea condition, participants will be inclined to conserve resources and motivated not to allocate their participation payment generously. Consequently, they may denigrate victims in the Responsible condition and offer minimal assistance. In contrast, as they find no faults with the Not Responsible victims, they are likely to sympathize with them, express willingness to help, and donate a larger portion of their participation payments to aid in their recovery. Thus, in the Early Plea condition, distinctions in attitudes and behavior toward victims between the Responsible and Not Responsible groups are expected to be perceptible and pronounced. Conversely, in the Late Plea condition, differences between Responsible and Not Responsible outcomes are predicted to be minimal. Participants are expected to have formed attitudes before scrutinizing accountability, resulting in less sensible attitudes and prosocial behavior towards victims, with a more equitable distribution of help to both Responsible and Not Responsible victims, and less pronounced distinctions between the two.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».