The Impact of Emotional Intelligence on the Relationship between Financial Literacy and Financial Behavior: The Case of the Lebanese Banking Consumers
Notice bibliographique
Résumé
The global financial crisis of 2007 led researchers to study the underlying reasons behind people’s loan/credit disrespecting behaviors which caused tremendous setbacks to major economies. After a handful amount of academic research, banks and other financial institutions admitted their unethical behaviors towards their clients. Being knowledgeable about people’s lack of knowledge in financial principles, they had provided them with novel and tricky financial services which an ordinary client couldn’t comprehend. Since then, many governments such as USA, Canada, Australia and Switzerland started educating their citizens the financial principles which might be of their need to avoid the reoccurrence of a similar global financial crises. Nonetheless, throughout the several years of research in this field, scholars didn’t find a holistic model in which all the variables influencing people’s financial behavior are present. That is why this topic remains worth studying about further. Recently, calls for the need of behavioral finance approach rose in the academic world. Therefore, this study checks the impact of emotional intelligence on the relationship between financial literacy and financial behavior focused on the Lebanese banking consumers. By the literature collected about the three mentioned variables – emotional intelligence, financial literacy, financial behavior – five hypotheses are proposed. Based on these hypotheses, the research model is drawn following Hayes (2013)’s Process Models in which emotional intelligence mediates the relation of financial literacy and financial behavior. Nonetheless, as per the research’s literature, one type of a financial behavior, savings & investment behavior, is treated as a dependent variable. In order to collect the necessary data to test the posed hypotheses, a questionnaire is designed and conducted following the convenient sampling method and using SurveyMonkey online survey platform in December 2018 on Lebanese banking consumers owning either a debit or a credit card. To analyze a total of 187 valid responses, quantitative techniques are used through IBM Spss and its macro, Process Model 1 and Model 74. The results failed to show a significant relation between the dependent variable, financial behavior, and the two independent variables, financial literacy and emotional intelligence. However, significant results exist in the further exploration of the data by moderated mediation in which emotional intelligence moderates the mediation between financial literacy and a type of financial behavior: savings & investments behavior. Some interesting significant results are also attained using the respondents socio-demographics information such as the lack of any significant relation of people’s years of professional experience with the research’s three variables. This research enriches the literature and gives practical insights to policymakers. Being the first of its kind in terms of relating emotional intelligence to financial literacy and financial behavior, it gives itself a unique sense and importance in the field of financial behavioral studies. It also recommends further research on financial behaviors to be focused on savings and investments behaviors. A practical value-added of this research is the recommendation to the concerned policymakers in enhancing both the emotional intelligence and the financial literacy in order to have citizens making good financial decisions.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 source (Gemma direct ou Codex distillé), 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 ».