JOURNAL ARTICLE
Personality and Retirement Planning Behavior: Future Orientation and Risk Tolerance as Mediators.
Published In: Journal of Financial Counseling & Planning, 2026, v. 37, n. 1. P. 98 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Choudhary, Diksha; Khanna, Ashu 3 of 3
Abstract
Recent demographic shifts, an aging population, and longer life expectancies have raised concerns about retirement financial stability. The privatization of pension systems has shifted the responsibility of wealth accumulation to individuals. Yet many workers approaching retirement remain inadequately prepared. In light of these challenges, the present study examines how the Big Five personality traits, future orientation, and financial risk tolerance are associated with retirement financial planning behavior. Data were collected via a structured questionnaire survey from a sample of 443 working individuals in India. Analyses were then conducted using the partial least squares structural equation modeling technique. The results reveal that among the Big Five personality traits, conscientiousness and agreeableness are positively associated with retirement planning behavior. The results also support the mediating effect of future orientation on the relationship between personality traits and retirement planning behavior. Neuroticism is positively associated with risk tolerance. The findings of this study offer valuable insights for policymakers, financial advisors, and counselors. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Financial Counseling & Planning. 2026/01, Vol. 37, Issue 1, p98
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2026
- ISSN:1052-3073
- DOI:10.1891/JFCP-2023-0082
- Accession Number:193016645
- Copyright Statement:Copyright of Journal of Financial Counseling & Planning is the property of Springer Publishing Company, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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