JOURNAL ARTICLE

Volunteers and resource management competencies of nonprofit organizations during challenging times.

  • Published In: Nonprofit Management & Leadership, 2023, v. 34, n. 1. P. 107 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Lee, Jeongyoon; Park, Young Joo; Kim, Sung‐eun 3 of 3

Abstract

Nonprofit organizations increasingly compete for limited resources and recognize volunteers as vital assets. That said, which nonprofits are more successful in harnessing volunteers than others and enlisting more volunteers while responding to environmental changes? Guided by the resource‐based theory, this study theorizes that a nonprofit's human, social, and financial resource management competencies relate to its volunteer use. Also, applying contingent resource‐based theory extends our theoretical arguments as to why a nonprofit needs to develop its resource management competencies for the use of volunteers facing a community's financial hardship. Our zero‐inflated negative binomial analysis using the IRS 990 and U.S. Census data (2010–2012) finds that (1) a nonprofit's higher human, social, and financial resource management competencies have an important influence on using volunteers, and (2) a nonprofit having higher financial resource management competencies can use more volunteers when its community experiences economic hardship. These findings pinpoint that volunteer use is not separable from organizational holistic resource management competencies and suggest that turbulent environments can cause a significant shift in the management competencies required for volunteer use. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Nonprofit Management & Leadership. 2023/09, Vol. 34, Issue 1, p107
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:1048-6682
  • DOI:10.1002/nml.21562
  • Accession Number:171348337
  • Copyright Statement:Copyright of Nonprofit Management & Leadership 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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