JOURNAL ARTICLE
A Buddhist Mindfulness View of Paradox: Silence and Skepticism of Language to Dismantle Paradoxes.
Published In: Organization Science (INFORMS), 2025, v. 36, n. 1. P. 361 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Song, Hee-Chan 3 of 3
Abstract
This study investigates how Buddhist mindfulness, particularly from the Yogācāra tradition, enables Korean Buddhist managing monks to navigate and ultimately dismantle the paradox between their spiritual values and the financial demands of temple management. Through nine months of ethnographic fieldwork in three Korean Buddhist temples, the research identifies a temporal cognitive process where monks initially use differentiation tactics (building role boundaries and switching roles) and later integration tactics (removing linguistic boundaries and searching for mutual benefits) to cope with this paradox. Central to this process is "mindful awareness," manifested as silence and skepticism of language, which acts as a mental buffer and cognitive trigger that leads monks to question and dissolve the linguistic categories underpinning the paradox, resulting in the paradox no longer being experienced. The study challenges prevailing paradox theory by highlighting language's paradoxical role in creating tensions and proposes that deep silence and emptying the mind—core aspects of Buddhist mindfulness—offer an alternative pathway to transcending paradoxical conflicts rather than merely managing them.
Additional Information
- Source:Organization Science (INFORMS). 2025/01, Vol. 36, Issue 1, p361
- Document Type:Article
- Subject Area:History
- Publication Date:2025
- ISSN:1047-7039
- DOI:10.1287/orsc.2023.17606
- Accession Number:182452594
- 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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