Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach

Hüseyin Parmaksız; Osman Akarsu

doi:10.52642/susbed.1617370

EN TR

Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach

Öz

The field of digital transformation continues to develop and expand rapidly due to technological advances. This study uses text mining techniques to analyze 5280 articles published between 2014 and 2024 using the LDA model and the Gibbs sampling method, the study identifies the most prominent topics on digital transformation research. Traditional methods, which rely on predefined categories and subjective judgment, are inadequate for identifying underlying themes in large datasets. The study identifies the most prominent topics in digital transformation research and tracks trends by tracking changes in topic rankings across different periods. It also explores sub- specialization areas across 1065 digital transformation journals and assesses how shifts in these areas impact the broader topic landscape. The findings provide valuable insights for practitioners, researchers, journal editors, and policymakers involved in digital transformation.

Anahtar Kelimeler

Digital Transformation Research, Text Mining, Topic Model Approach

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

İnovasyon Yönetimi

Bölüm

Araştırma Makalesi

Yazarlar

Hüseyin Parmaksız
0000-0001-8455-5625
Türkiye

Osman Akarsu ^*
0000-0002-0595-6795
Türkiye

Yayımlanma Tarihi

30 Ağustos 2025

Gönderilme Tarihi

10 Ocak 2025

Kabul Tarihi

28 Temmuz 2025

Yayımlandığı Sayı

Yıl 2025 Sayı: 57

DOI

https://doi.org/10.52642/susbed.1617370

IZ

https://izlik.org/JA77EY97BE

APA

Parmaksız, H., & Akarsu, O. (2025). Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 57, 32-54. https://doi.org/10.52642/susbed.1617370

AMA

1.Parmaksız H, Akarsu O. Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach. SUSBED. 2025;(57):32-54. doi:10.52642/susbed.1617370

Chicago

Parmaksız, Hüseyin, ve Osman Akarsu. 2025. “Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach”. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy 57: 32-54. https://doi.org/10.52642/susbed.1617370.

EndNote

Parmaksız H, Akarsu O (01 Ağustos 2025) Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 57 32–54.

IEEE

[1]H. Parmaksız ve O. Akarsu, “Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach”, SUSBED, sy 57, ss. 32–54, Ağu. 2025, doi: 10.52642/susbed.1617370.

ISNAD

Parmaksız, Hüseyin - Akarsu, Osman. “Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach”. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 57 (01 Ağustos 2025): 32-54. https://doi.org/10.52642/susbed.1617370.

JAMA

1.Parmaksız H, Akarsu O. Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach. SUSBED. 2025;:32–54.

MLA

Parmaksız, Hüseyin, ve Osman Akarsu. “Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach”. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy 57, Ağustos 2025, ss. 32-54, doi:10.52642/susbed.1617370.

Vancouver

1.Hüseyin Parmaksız, Osman Akarsu. Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach. SUSBED. 01 Ağustos 2025;(57):32-54. doi:10.52642/susbed.1617370

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TRT Akademi

https://doi.org/10.37679/trta.1832541

24108 28027

Bu eser Creative Commons Attribution-NonCommercial 4.0 International Lisansı ile lisanslanmıştır.

Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach

Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach

Öz

Anahtar Kelimeler

Dijital Dönüşüm Çalışmalarındaki Temel Konuların Topik Model Yaklaşımı ile Belirlenmesi

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Diplomasi Odaklı Soyutlama mı, İnsanî Kriz Odaklı Yoğunlaşma mı? Gazze Krizi Bağlamında Reuters ve Anadolu Ajansında Yapay Zeka Destekli Tema Analizi