Artikel

Corporate governance, shariah governance, and credit rating: A cross-country analysis from Asian Islamic banks

This study aimed to investigate the association between corporate governance characteristics, shariah governance characteristics, and the credit rating of Asian Islamic banks. To do so, we collected data from 22 banks during the 2006-2018 period. In total, we observed 286 data points. Credit rating was measured through an adaption of the credit rating scale that measured the long term credit of Islamic banks on an ordinal scale. From these data, 19 scores (Aaa) were considered high credit ratings and 1 score (C) was considered a low credit rating. Descriptive statistics, correlations, and the ordered logit regression model were applied in a panel setting. We found that the board interlock, board independence, CEO duality, and board foreign directorship negatively affected credit ratings. We also found that the board size, board accounting, finance knowledge, presence of women on the board, shariah board size, presence of supervisory shariah board, the shariah board interlock, and presence of female shariah scholars all were positively associated with credit ratings. This study suggests that Islamic banks can access more funds with higher shariah compliance. As such, we concluded that evaluating organizations' credit ratings must consider shariah governance attributes as determinants of the credit rating of Islamic banks.

Language
Englisch

Bibliographic citation
Journal: Journal of Open Innovation: Technology, Market, and Complexity ; ISSN: 2199-8531 ; Volume: 6 ; Year: 2020 ; Issue: 4 ; Pages: 1-15 ; Basel: MDPI

Classification
Management
Subject
credit rating institutions
order logit model
shariah governance

Event
Geistige Schöpfung
(who)
Baig, Muhammad Mansoor
Ellahi, Nazima
Hassan, Arshad
Malik, Qaisar Ali
Waheed, Abdul
Ullah, Naeem
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2020

DOI
doi:10.3390/joitmc6040170
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Artikel

Associated

  • Baig, Muhammad Mansoor
  • Ellahi, Nazima
  • Hassan, Arshad
  • Malik, Qaisar Ali
  • Waheed, Abdul
  • Ullah, Naeem
  • MDPI

Time of origin

  • 2020

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