Financial threat and individuals’ willingness to change financial behavior
Notice bibliographique
Résumé
Purpose Financial threat is defined as fearful-anxious uncertainty regarding one’s current and future financial situation. The purpose of this paper is to examine predictors and outcomes of financial threat in two samples of students who completed an online questionnaire for course credit. The theoretical model the authors proposed tested the association between personal debt, anxiety, and economic hardship with financial threat, and in turn, financial threat’s relationship with willingness to change financial behavior (e.g. increase income, cut expenses, and reduce debt), job search activity, and psychological distress. Consistent across samples, structural equation modeling (SEM) revealed that the data fit the model and supported all four hypotheses. Debt, economic hardship, and anxiety were all related positively to financial threat, which itself related positively to willingness to change, job search, and psychological distress. Importantly, financial threat mediated the relationship between these economic-situational predictors and affective-behavioral outcomes of financial stain. Theoretical and practical implications of the findings are discussed. Design/methodology/approach Using an online questionnaire, participants completed measures of economic hardship, intolerance of uncertainty, job search behavior, financial threat, life satisfaction, general health, perceived stress, and willingness to change to financial behavior. The authors developed and tested a model that explores emotional and cognitive reactions to financial stressors following the recession. Findings Results of SEM revealed that the data fit the model and no modification indices were suggested. Examination of parameter estimates indicated that total debt, economic hardship, and anxiety were positively related to financial threat. Financial threat, in turn, positively related to willingness to change one’s financial behaviors, job search, and psychological distress. In addition, economic hardship and anxiety were positively related to psychological distress. That is, individuals who were feeling more threatened by their financial situation were more willing to change their financial situation and were more likely to engage in job search behavior. They were also more likely to report more psychological distress than individuals reporting lower levels of financial threat. Research limitations/implications This study was cross-sectional and therefore precludes causal interpretations of the findings. Longitudinal data with repeated assessments of all measures would help determine the direction of causation. Also, the study relied on self-report data, which is prone to bias. For example, it is possible that some participants did not know their exact debt levels, which may have resulted in an under- or overestimation of debt levels. Future research should extend this line of research using objective measures. While the model tested in this study examined the impact of economic factors on perceived threat, behavior, and psychological distress, it did not include social and psychological resources. For example, the authors did not include measures of social support, coping, or personality, which may moderate the impact of economic variables and stress on psychological distress. Although financial knowledge/literacy was not studied here, future research could include it since it has been associated with a variety of financial behaviors such as cash-flow management, credit management, saving, and investing. There is some evidence that financial literacy can decrease emotional stress and anxiety (Vitt et al. , 2000). Practical implications The current study can help researchers and practitioners understand the concept of financial threat among university students. For example, if students have incurred student loans and debt and begin displaying symptoms of distress, like anxiousness, worry, and irritability, they could be referred to a professional experienced in working with emotional and behavioral disorders related to financial issues. It can also help practitioners gain an understanding and insight into clients’ poor financial decision making. Government could initiate programs that help individuals cope with the negative effects of unemployment. Given that young people are experiencing disproportionately high unemployment that can have a lasting adverse effect on employment prospects and future earnings, the current post-secondary curriculum needs to prepare young people for the world of work, and gain a footing in the labor market. One way to achieve this is through high-quality work experiences (e.g. internships/apprenticeships). Identifying ways to mitigate the effects of debt and economic hardship is also imperative. For example, money and debt advice may improve one’s financial circumstances, which, in turn, may improve their physical and psychological well-being. Social implications Future studies could focus on developing models predicting to financial stress using personality, psychological resources, and an objective measure of financial knowledge. Despite these limitations, this research demonstrates how emotional factors need to be included in economic models that also include debt and economic hardship. The study contributes to the economic and psychological literature by documenting how economic hardship and debt influence perceptions of threat, planned behavior, and psychological distress. The authors take a unique approach to describing economic hardship and financial threat as antecedents of distress, job search, and willingness to change. Future research could be directed toward employing the model for predicting behavior that would lessen economic stress and thereby leading to increased psychological well-being. Originality/value The study develops and tests an original theoretical model linking financial, emotional, and psychological variable in a comprehensive framework that is then tested empirically. This model is original with this paper.
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».