JOURNAL ARTICLE
Exploring Krishna-Arjuna Sambada in the Bhagavad Gita through a Non-Western Communication Model.
Published In: International Journal of Communication & Linguistic Studies, 2024, v. 22, n. 2. P. 175 1 of 3
Database: Communication Source 2 of 3
Authored By: Baral, Raj K. 3 of 3
Abstract
This study applies the Sadharanikaran Model of Communication (SMC) to examine Krishna-Arjuna sambada (dialogue) in the Bhagavad Gita and reveals the model's efficacy to elucidate the dynamics inherent in their conversation. It explores how communication progresses between divine and mortal beings, focusing on how Krishna tailors his guidance to align with Arjuna's emotions and intellect. The SMC framework is utilized to explore how Krishna's teachings navigate Arjuna's moral and existential crises, emphasizing the adaptability and effectiveness of communication even in a hierarchical Hindu society. This analysis delves into the complexities of emotional expression and reception, revealing how Krishna's dialogue involves not only words but also emotions, intentions, and actions. As sahridayas (people with similar heart), their dialogues are not only guided by the intent of persuasion, but they enjoy every moment of their exchanges. This examination highlights the depth of communication within the text, revealing Arjuna's enlightenment in the form of ananda (bliss) and his willingness to act in accordance with Krishna's teachings. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Communication & Linguistic Studies. 2024/12, Vol. 22, Issue 2, p175
- Document Type:Article
- Subject Area:Religion and Philosophy
- Publication Date:2024
- ISSN:2327-7882
- DOI:10.18848/2327-7882/CGP/v22i02/175-189
- Accession Number:181719133
- Copyright Statement:Copyright of International Journal of Communication & Linguistic Studies is the property of Common Ground Research Networks 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.