COMPARATIVE ASSESSMENT OF FINANCIAL PERFORMANCE AMONG AIR NAVIGATION SERVICE PROVIDERS.
Published In: Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 2025, n. 66. P. 35 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: TUNCAL, Arif 3 of 3
Abstract
The significant expansion of the aviation industry highlights the crucial need for financial resilience and strategic governance among stakeholders, particularly emphasizing the essential role of air navigation service providers (ANSPs). The aim of the study was to present a model for the assessment and comparison of the financial performance of seventeen ANSPs. The financial performance of the seventeen ANSPs was evaluated using nine financial ratios, with the combined scores subsequently analyzed using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method over a five-year period (2018-2022). The study revealed that DSNA demonstrated resilience in the face of the crisis, whereas ENAIRE was unable to sustain its previous financial performance among the seventeen ANSPs over the past years. It is recommended that further analyses be conducted using a range of criteria, that financial strategies for crisis resilience be investigated, and that global aviation trends across regions be explored. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 2025/01, Issue 66, p35
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:1308-2922
- DOI:10.30794/pausbed.1508218
- Accession Number:182888267
- Copyright Statement:Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute 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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