Bibliometric Analysis of Studies on Digital Transformation in Auditing

Şerife Kılıçarslan

Makale detayları

Bibliometric Analysis of Studies on Digital Transformation in Auditing

Yazarlar:

Şerife Kılıçarslan

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

Denetim ve Mali Sorumluluk | Finansal Muhasebe | İç Denetim | İç Kontrol | Muhasebe | Denetim ve Mali Sorumluluk (Diğer)

Dergi:

Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi Vol.29 Issue 3, pp.211-224

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001338135

pISSN:

1301-0603

Anahtar kelimeler:

Özet:

 In recent years, technological advancements have accelerated digital transformation, making it inevitable in auditing activities. While the literature on digital transformation is extensive, studies addressing its structure and development dynamics in auditing at the national level remain limited. This study examines the current state of academic research on digital transformation in auditing within Turkey. Using a descriptive survey method, the research employed criterion sampling, with the inclusion criteria being accessibility via Google Scholar, publication between 2015-2024, full-text availability, and appearance in peer-reviewed journals. Document analysis served as the data collection tool, and bibliometric methods were applied for analysis. The findings show that most studies were published in 2022, while the least appeared in 2024. Artificial intelligence was the most frequently used keyword, and the primary JEL code was M42. Many studies utilized qualitative methodologies, with the journal Denetişim standing out as a prominent publication outlet. Qualitative methods dominated the studies, and the journal Denetişim emerged as a key publication outlet. Journals often emphasized JEL codes, with contributions from twenty-nine institutions, seven of which produced multiple studies. The title lecturer was the most common among authors, with most studies co-authored by two individuals. The average article length was twenty pages.

 In recent years, technological advancements have accelerated digital transformation, making it inevitable in auditing activities. While the literature on digital transformation is extensive, studies addressing its structure and development dynamics in auditing at the national level remain limited. This study examines the current state of academic research on digital transformation in auditing within Turkey. Using a descriptive survey method, the research employed criterion sampling, with the inclusion criteria being accessibility via Google Scholar, publication between 2015-2024, full-text availability, and appearance in peer-reviewed journals. Document analysis served as the data collection tool, and bibliometric methods were applied for analysis. The findings show that most studies were published in 2022, while the least appeared in 2024. Artificial intelligence was the most frequently used keyword, and the primary JEL code was M42. Many studies utilized qualitative methodologies, with the journal Denetişim standing out as a prominent publication outlet. Qualitative methods dominated the studies, and the journal Denetişim emerged as a key publication outlet. Journals often emphasized JEL codes, with contributions from twenty-nine institutions, seven of which produced multiple studies. The title lecturer was the most common among authors, with most studies co-authored by two individuals. The average article length was twenty pages.

 In recent years, technological advancements have accelerated digital transformation, making it inevitable in auditing activities. While the literature on digital transformation is extensive, studies addressing its structure and development dynamics in auditing at the national level remain limited. This study examines the current state of academic research on digital transformation in auditing within Turkey. Using a descriptive survey method, the research employed criterion sampling, with the inclusion criteria being accessibility via Google Scholar, publication between 2015-2024, full-text availability, and appearance in peer-reviewed journals. Document analysis served as the data collection tool, and bibliometric methods were applied for analysis. The findings show that most studies were published in 2022, while the least appeared in 2024. Artificial intelligence was the most frequently used keyword, and the primary JEL code was M42. Many studies utilized qualitative methodologies, with the journal Denetişim standing out as a prominent publication outlet. Qualitative methods dominated the studies, and the journal Denetişim emerged as a key publication outlet. Journals often emphasized JEL codes, with contributions from twenty-nine institutions, seven of which produced multiple studies. The title lecturer was the most common among authors, with most studies co-authored by two individuals. The average article length was twenty pages.

 Son yıllarda, teknolojik gelişmelerin hız kazanmasıyla birlikte denetim faaliyetlerinde dijital dönüşüm kaçınılmaz hale gelmiştir. Her ne kadar dijital dönüşüm üzerine kapsamlı bir literatür bulunsa da, ulusal düzeyde bu dönüşümün denetim alanındaki yapısını ve gelişim dinamiklerini ele alan çalışmaların sınırlı olduğu gözlemlenmiştir. Bu kapsamda çalışmanın amacı, denetimde dijital dönüşüm üzerine yapılan akademik çalışmaların Türkiyedeki durumunu ortaya koymaktır. Araştırma, nicel araştırma desenlerinden betimsel tarama yöntemiyle yürütülmüştür. Araştırmada uygun örnekleme ulaşmak amacıyla, amaçlı örneklem yöntemlerinden birisi olan ölçüt örneklem yöntemi kullanılmıştır. Bu kapsamda, çalışmaların örnekleme dâhil edilme ölçütleri; Google Akademik veri tabanı, 2015-2024 yılları, tam metin olarak erişim hakemli dergilerde yayımlanmış olma olarak belirlenmiştir. Araştırmada veri toplama aracı olarak belge incelemesi; elde edilen verilerin analizinde, bibliyometrik analiz kullanılmıştır. Araştırma sonucunda, en çok çalışmanın 2022de, en az çalışmanın ise 2024te yayımlandığı; en sık kullanılan anahtar kelimenin yapay zekâ, JEL kodun ise M42 olduğu; çalışmaların çoğunlukla nitel yöntemlerle gerçekleştirildiği; yayımlanan çalışmalarda Denetişim dergisinin ön plana çıktığı; dergilerin genel olarak JEL kod kullanımına önem verdiği; yirmi dokuz farklı kurumun alana katkı sağladığı ve bu kurumlardan 7 kurumun birden fazla çalışmasının olduğu; öğretim görevlisi unvanın ön plana çıktığı; çoğunlukla 2 yazarlı çalışmaların olduğu; ve son olarak ortalama sayfa sayısının yirmi olduğu tespit edilmiştir.

Makale detayları

Bibliometric Analysis of Studies on Digital Transformation in Auditing

Yazarlar:

Şerife Kılıçarslan

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

Denetim ve Mali Sorumluluk | Finansal Muhasebe | İç Denetim | İç Kontrol | Muhasebe | Denetim ve Mali Sorumluluk (Diğer)

Dergi:

Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi Vol.29 Issue 3, pp.211-224

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001338135

pISSN:

1301-0603

Audit

Digital transformation

Artificial intelligence (AI)

Automation

BlockchainDenetim

Dijital dönüşüm

Yapay zekâ

Otomasyon

Blokchange

Sayı

Makale detayları

Yazarlar: Şerife Kılıçarslan

Yayıncı: Süleyman Demirel University

Ana Başlıklar: Denetim ve Mali Sorumluluk | Finansal Muhasebe | İç Denetim | İç Kontrol | Muhasebe | Denetim ve Mali Sorumluluk (Diğer)

Dergi: Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi Vol.29 Issue 3, pp.211-224

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001338135

pISSN: 1301-0603

Sayı

Yazarlar:

Şerife Kılıçarslan

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

Denetim ve Mali Sorumluluk | Finansal Muhasebe | İç Denetim | İç Kontrol | Muhasebe | Denetim ve Mali Sorumluluk (Diğer)

Dergi:

Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi Vol.29 Issue 3, pp.211-224

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001338135

pISSN:

1301-0603