JOURNAL ARTICLE
Who Gives Back? Evidence from India on Successful Entrepreneurial Exit and Involvement in Philanthropy.
Published In: Organization Science (INFORMS), 2023, v. 34, n. 1. P. 329 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Hans, Leena Kinger; Vissa, Balagopal 3 of 3
Abstract
This article examines how the personal social backgrounds of successful commercial entrepreneurs in India influence their involvement in philanthropy after achieving financial exit from their ventures. Using the status characteristics framework, it finds that entrepreneurs from disadvantaged ascribed-status groups (notably lower caste) and those with privileged achieved-status markers (specifically foreign tertiary education) are more likely to engage in systematic philanthropic activities aimed at social change. Quantitative analysis of 673 Indian entrepreneurs who exited between 2003 and 2013, supplemented by qualitative interviews, supports the view that these social structural positions shape entrepreneurs' perceptions of societal problems and motivate their philanthropic engagement. The study highlights founder transitions from entrepreneurship to philanthropy as an understudied mechanism linking commercial success to positive social welfare outcomes, particularly in emerging economies. It also suggests that experiences of social disadvantage, even among traditionally privileged groups (e.g., high-caste entrepreneurs educated abroad), can increase philanthropic propensity, offering nuanced insights into elite philanthropy beyond notions of noblesse oblige or self-dealing.
Additional Information
- Source:Organization Science (INFORMS). 2023/01, Vol. 34, Issue 1, p329
- Document Type:Article
- Subject Area:Economics
- Publication Date:2023
- ISSN:1047-7039
- DOI:10.1287/orsc.2022.1572
- Accession Number:161794184
- Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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