Muhasebe Dijital Dönüşüm: AKEA ve LDA ile Yapısal Bir Analiz

Eda Çevik

doi:10.69599/izd.3027

Yazarlar

Eda Çevik Dokuz Eylül Üniversitesi / Türkiye 0000-0001-9878-8622

10.69599/izd.3027

Özet

Bu çalışma, muhasebe ve dijital dönüşüm alanındaki bilimsel yapıyı incelemek amacıyla 2020-2024 yılları arasında Web of Science veri tabanından elde edilen 2232 makalelik veri setine dayanan kapsamlı bir analiz sunmaktadır. Çalışma, bibliyometrik haritalamayı (AKEA), Latent Dirichlet Allocation (LDA) algoritması ile çapraz doğrulamaya tabi tutarak literatüre metodolojik özgünlük kazandırmaktadır. Python ile JEL kod dönüşümü ve sinonim standardizasyonu uygulanan analizlerde; AKEA araştırma gündemini nicel metotlar, piyasalar, yönetişim ve performans olarak dört odakta toplarken, LDA optimum konu sayısını üç olarak belirlemiştir. Karşılaştırmalı analiz çarpıcı bir bulgu ortaya koymuştur: LDA, finansal ve yönetim kümelerini doğrulamış; ancak ağ analizindeki ‘yeşil küme’ (sürdürülebilirlik/inovasyon), makine öğrenmesi tabanında bağımsız bir konu olarak ayrışamamıştır. Bu durum, literatürün finansal sonuçlara odaklandığını, ancak etik ve sürdürülebilirlik gibi beşerî boyutların henüz sistematik

bir araştırma omurgasına oturmadığını kanıtlamaktadır. Sonuç olarak çalışma, şeffaf bir gelecek için veri bilimi ve

beşerî boyutların entegrasyonunu vurgulamaktadır.

Anahtar Kelimeler:

Muhasebede Dijital Dönüşüm Eş- Oluşum Ağı Analizi LDA

İndirmeler

İndirme verileri henüz mevcut değil.

Referanslar

Barde, B. V., & Bainwad, A. M. (2017). An overview of topic modeling methods and tools. 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 745-750. https://doi.org/10.1109/ICCONS.2017.8250563

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.

Cheng, Z., Xie, Y., & Wen, H. (2022). Visualization analysis of research on climate innovation on CiteSpace. Frontiers in Environmental Science, 10, 1025128. https://doi.org/10.3389/fenvs.2022.1025128

Das, A., Upadhya, P., & Sanyal, S. (2019). Optimality of Feature Selection After Dimensionality Reduction (Latent Semantic Analysis). İçinde H. S. Saini, R. Sayal, A. Govardhan, & R. Buyya (Ed.), Innovations in Computer Science and Engineering (C. 32, ss. 271-279). Springer Singapore. https://doi.org/10.1007/978-981-10-8201-6_31

Dragomirescu, O.-A., Crăciun, P.-C., & Bologa, A. R. (2025). Enhancing Invoice Processing Automation Through the Integration of DevOps Methodologies and Machine Learning. Systems, 13(2), 87. https://doi.org/10.3390/systems13020087

Ebert, C., & Duarte, C. H. C. (2018). Digital transformation. IEEE Softw., 35(4), 16-21.

Gradillas, M., & Thomas, L. D. W. (2025). Distinguishing digitization and digitalization: A systematic review and conceptual framework. Journal of Product Innovation Management, 42(1), 112-143. https://doi.org/10.1111/jpim.12690

Islam, N., Rashid, M. M., Pasandideh, F., Ray, B., Moore, S., & Kadel, R. (2021). A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming. Sustainability, 13(4), 1821. https://doi.org/10.3390/su13041821

Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: Models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169-15211. https://doi.org/10.1007/s11042-018-6894-4

Jesus, J. B., Kilag, O. K. T., Gamboa, J. C. M., Solatorio, R. F., & Matis, P. J. A. (2024). Exploring the Role of Digital Transformation in Modern Accounting and Business Practices: A Systematic Review. https://doi.org/10.5281/ZENODO.11669590

Kherwa, P., & Bansal, P. (2018). Topic Modeling: A Comprehensive Review. ICST Transactions on Scalable Information Systems, 0(0), 159623. https://doi.org/10.4108/eai.13-7-2018.159623

Lang, V. (2021). Digitalization and Digital Transformation. Içinde V. Lang, Digital Fluency (ss. 1-50). Apress. https://doi.org/10.1007/978-1-4842-6774-5_1

Lozano, S., Calzada-Infante, L., Adenso-Díaz, B., & García, S. (2019). Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature. Scientometrics, 120(2), 609-629. https://doi.org/10.1007/s11192-019-03132-w

Mayegun, K. L., & Nwenavu, C. (2025). Harnessing Big Data and AI to Revolutionize Sustainability Accounting and Integrated Corporate Financial Reporting. International Journal of Computer Applications Technology and Research. https://doi.org/10.7753/IJCATR1406.1008

Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 262-272.

Nguyen, M.-H., Nguyen, H. T. T., Le, T.-T., Luong, A.-P., & Vuong, Q.-H. (2022). Gender issues in family business research: A bibliometric scoping review. Journal of Asian Business and Economic Studies, 29(3), 166-188. https://doi.org/10.1108/JABES-01-2021-0014

O’Callaghan, D., Greene, D., Carthy, J., & Cunningham, P. (2015). An analysis of the coherence of descriptors in topic modeling. Expert Systems with Applications, 42(13), 5645-5657. https://doi.org/10.1016/j.eswa.2015.02.055

Ostrowski, D. A. (2015). Using latent dirichlet allocation for topic modelling in twitter. Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), 493-497. https://doi.org/10.1109/ICOSC.2015.7050858

Owens, B. (2024). China’s research clout leads to growth in homegrown science publishing. Nature, 630(8015), S2-S4. https://doi.org/10.1038/d41586-024-01596-2

Ozek, B., Lu, Z., Pouromran, F., Radhakrishnan, S., & Kamarthi, S. (2023). Analysis of pain research literature through keyword Co-occurrence networks. PLOS Digital Health, 2(9), e0000331. https://doi.org/10.1371/journal.pdig.0000331

Özgür, A., Cetin, B., & Bingol, H. (2008). CO-OCCURRENCE NETWORK OF REUTERS NEWS. International Journal of Modern Physics C, 19(05), 689-702. https://doi.org/10.1142/S0129183108012431

Power, M. (2022). Theorizing the Economy of Traces: From Audit Society to Surveillance Capitalism. Organization Theory, 3(3). https://doi.org/10.1177/26317877211052296

Radhakrishnan, S., Erbis, S., Isaacs, J. A., & Kamarthi, S. (2017). Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLOS ONE, 12(3), e0172778. https://doi.org/10.1371/journal.pone.0172778

Raj, P., Pandey, M., & Khatoon, A. (2023). Breaking the Mold-Analyzing Gender Stereotyping in the Workplace Through Bibliometric and Content Analysis. Sage Open, 13(4), 21582440231215154. https://doi.org/10.1177/21582440231215154

Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the Space of Topic Coherence Measures. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 399-408. https://doi.org/10.1145/2684822.2685324

Saad, R. (2022). The role of artificial intelligence techniques in achieving audit quality. Acad. Account. Financ. Stud. J, 26(5), 1-18.

Schumacher, A., Sihn, W., & Erol, S. (2016). Automation, digitization and digitalization and their implications for manufacturing processes. Innovation and Sustainability Conference Bukarest, 1-5.

Syed, S., & Spruit, M. (2017). Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation. 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 165-174. https://doi.org/10.1109/DSAA.2017.61

Tiwari, K., & Khan, M. S. (2020). Sustainability accounting and reporting in the industry 4.0. Journal of Cleaner Production, 258, 120783. https://doi.org/10.1016/j.jclepro.2020.120783

Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94, 101582. https://doi.org/10.1016/j.is.2020.101582

Vrana, J., & Singh, R. R. (2025). Digitization, digitalization, digital transformation, and beyond. İçinde Handbook of nondestructive evaluation 4.0 (ss. 145-170). Springer.

Yuan, C., Li, G., Kamarthi, S., Jin, X., & Moghaddam, M. (2022). Trends in intelligent manufacturing research: A keyword co-occurrence network based review. Journal of Intelligent Manufacturing, 33(2), 425-439. https://doi.org/10.1007/s10845-021-01885-x

Muhasebe Dijital Dönüşüm: AKEA ve LDA ile Yapısal Bir Analiz

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