JOURNAL ARTICLE

Longitudinal Study on the Relationships between Organizational Factors and Autonomous and Controlled Motivation among Older Japanese Bridge Employees1.

  • Published In: Japanese Psychological Research, 2024, v. 66, n. 1. P. 28 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Horiguchi, Kouta 3 of 3

Abstract

Longitudinal relationships between organizational factors and autonomous and controlled motivation among older Japanese bridge employees were investigated. The study sample consisted of 331 older people working in bridge employment at the same organizations where they worked before retirement (Mage = 63.45 years, SD = 3.28 years). Data were taken at two time points 10 months apart. Multiple regression analysis indicated that organizational justice predicted autonomous motivation at Time 2, after controlling for demographic variables and autonomous motivation at Time 1, whereas organizational justice did not predict controlled motivation. Moreover, support from coworkers positively predicted autonomous motivation and negatively predicted controlled motivation at Time 2. These results indicate that optimal organizational environments might promote autonomous motivation and reduce controlled motivation among older workers. It is suggested that organizations develop environments where older workers can receive fair evaluations from their superiors and close relationships with their coworkers. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Japanese Psychological Research. 2024/01, Vol. 66, Issue 1, p28
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:0021-5368
  • DOI:10.1111/jpr.12395
  • Accession Number:174546916
  • Copyright Statement:Copyright of Japanese Psychological Research is the property of Wiley-Blackwell 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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