İlkokul Düzeyinde Yapay Zekâ Alanındaki Araştırmaların R Programı ile Bibliyometrik Analizi

Year 2025, Volume: 7 Issue: Özel Sayı, 87 - 116, 29.11.2025

Yasin Uzun

Abstract

Bu araştırmanın amacı, ilkokul düzeyinde yapay zekâ alanında gerçekleştirilen çalışmaları bibliyometrik yöntemlerle incelemektir. Araştırmanın veri seti, Web of Science veri tabanında, belirlenen anahtar kelimeler kullanılarak yapılan tarama sonucunda oluşturulmuştur. Sorgu sonucunda toplam 614 kayıt elde edilmiş, bu kayıtların tamamı uygunluk kriterleri açısından değerlendirilmiştir. Belirlenen ölçütleri karşılayan 190 kayıt analiz kapsamına alınmıştır. Veri setinin analiz süreci, bibliyometrik analizler için geliştirilen “Biblioshiny” paketi kullanılarak, R programlama dili ortamında gerçekleştirilmiştir. Elde edilen bulgular, ilkokul düzeyinde yapay zekâ araştırmalarının son yıllarda belirgin bir artış eğilimi sergilediğini ortaya koymuştur. Üniversite ve yazar analizleri, çalışmaların çok merkezli bir yapıya sahip olduğunu; farklı kıtalardan kurum ve araştırmacıların katkılarıyla güçlü bir kültürler arası bilgi paylaşımının gerçekleştiğini göstermiştir. Ülke bazlı incelemeler, Çin’in yüksek ulusal üretim kapasitesine karşın sınırlı uluslararası iş birliği yürüttüğünü, buna karşılık Birleşik Krallık, Japonya, Finlandiya ve Malezya gibi ülkelerin çok uluslu yayınlarda öne çıktığını ortaya çıkarmıştır. Dergi dağılımı, alanın eğitim bilimleri, mühendislik, bilişsel bilimler ve halk sağlığı gibi disiplinleri bir araya getiren yapısını yansıtmış; anahtar kelime analizleri ise teknik ve pedagojik boyutların bütünleştiğini ortaya sermiştir. Ortak yazar, kurum ve ülke ağ haritaları, alanın hem bölgesel hem de küresel ölçekte çok merkezli bir iş birliği ağına sahip olduğunu kanıtlamıştır. Ortak atıf analizi ise alanın hem pedagojik temele hem de algoritmik altyapıya dayandığını, güncel çalışmaların ise dil modelleri ve etik boyutları gündeme taşıdığını göstermiştir.

Keywords

Yapay zekâ , İlkokul , Bibliyometrik analiz , Eğitim , R-Studio

Bibliometric Analysis of Research on Artificial Intelligence at the Primary School Level Using R

Abstract

The aim of this study is to conduct a bibliometric analysis of research on artificial intelligence at the primary school level. The dataset was compiled from the Web of Science database through a search performed using predetermined keywords. The search yielded a total of 614 records, all of which were examined in terms of eligibility criteria. A total of 190 records that met the specified criteria were included in the analysis. The dataset was analyzed in the R programming environment using the “Biblioshiny” package, developed for bibliometric analysis. The findings revealed a marked upward trend in artificial intelligence research at the primary school level in recent years. University and author analyses indicated that the studies possess a highly multi-centered structure, with contributions from institutions and researchers across different continents, facilitating robust intercultural knowledge exchange. Country-level examinations showed that, despite China’s high national production capacity, its level of international collaboration remains limited, whereas countries such as the United Kingdom, Japan, Finland, and Malaysia stand out in multinational publications. The distribution of journals reflected the field’s interdisciplinary nature, encompassing disciplines such as educational sciences, engineering, cognitive sciences, and public health. Keyword analyses demonstrated the integration of technical and pedagogical dimensions. Co-author, institutional, and country network maps confirmed the existence of a multi-centered collaboration network at both regional and global scales. Co-citation analysis showed that the field is grounded in both pedagogical foundations and computational infrastructure, while recent studies have brought issues such as language models and ethical dimensions to the forefront.

Keywords

Artificial intelligence , Primary school , Bibliometric analysis , Education , R-Studio

References

• Abdelghani, R., Wang, Y. H., Yuan, X., Wang, T., Lucas, P., Sauzéon, H. ve Oudeyer, P. Y. (2024). GPT-3-driven pedagogical agents to train children’s curious question-asking skills. International Journal of Artificial Intelligence in Education, 34(2), 483-518. https://doi.org/10.1007/s40593-023-00340-7
• Akhtar, N. (2024). Breaking silos: Interdisciplinary education in the era of artificial intelligence. https://doi.org/10.13140/RG.2.2.19570.68802
• Akkol, S. ve Balkan, Z. E. (2024). Yapay zekânın ilkokul öğretmenleri tarafından kullanımı: 50 öğretmen üzerinde uygulama. Social Sciences Studies Journal (Sssjournal), 10(10), 1754-1770.
• Altunışık, M. (2023). Eğitim ve Bilim Dergisi’nin bibliyometrik ve içerik analizi [Yayımlanmamış Yüksek Lisans Tezi] Necmettin Erbakan Üniversitesi.
• Angeli, C. ve Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 106185. https://doi.org/10.1016/j.chb.2019.106185
• Aria, M. ve Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
• Ateş, S. K., Kaleci, F. ve Erdoğan, A. (2025). Artificial intelligence in education: A bibliometric analysis. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, 7(1), 14-36. https://doi.org/10.38151/akef.2025.147
• Atmatzidou, S. ve Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661-670. https://doi.org/10.1016/j.robot.2015.10.008
• Azevedo, R., Moos, D. C., Greene, J. A., Winters, F. I., ve Cromley, J. G. (2008). Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia?. Educational Technology Research and Development, 56(1), 45-72. https://doi.org/10.1007/s11423-007-9067-0
• Baek, C. ve Doleck, T. (2020). A bibliometric analysis of the papers published in the Journal of Artificial Intelligence in Education from 2015–2019. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), 2(1), 67–84. https://doi.org/10.3991/ijai.v2i1.14481
• Beghetto, R. A. (2009). Correlates of intellectual risk taking in elementary school science. Journal of Research in Science Teaching, 46(2), 210-223. https://doi.org/10.1002/tea.20270
• Bers, M. U., González-González, C. ve Armas–Torres, M. B. (2019). Coding as a playground: Promoting positive learning experiences in childhood classrooms. Computers & Education, 138, 130-145. https://doi.org/10.1016/j.compedu.2019.04.013
• Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
• Broadus, R. N. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12(5-6), 373-379. https://doi.org/10.1007/BF02016680
• Cai, C., Zhu, G. ve Ma, M. (2024). A systemic review of AI for interdisciplinary learning: Application contexts, roles, and influences. Education and Information Technologies, 30, 1-47. https://doi.org/10.1007/s10639-024-13193-x
• Ceylan, A. (2025, Mayıs 15–17). T.C. Millî Eğitim Bakanlığı’nın yapay zekâ üzerine yapmış olduğu çalışmalar ve ilkokul kademesinin yeri. İçinde E. Ö. Özçatal ve N. Abdullazade (Ed.), Proceedings of the 15th International “Başkemt” Conference on Social, Human, Administrative and Educational Sciences. BZT Turan Academy Publishing House. Ankara, Türkiye.
• Chang, C. C. ve Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions On Intelligent Systems And Technology (TIST), 2(3), 1-27. https://doi.org/10.1145/1961189.1961199
• Chassignol, M., Khoroshavin, A., Klimova, A. ve Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24. https://doi.org/10.1016/j.procs.2018.08.233
• Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X. ve Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162-175. https://doi.org/10.1016/j.compedu.2017.03.001
• Corbett, A. T. ve Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling And User-Adapted Interaction, 4(4), 253-278. https://doi.org/10.1007/BF01099821
• Cortes, C. ve Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1023/A:1022627411411
• Çalışkan, G. ve Çınaroğlu, S. (2023). Yapay zekânın maliyet etkililiğini inceleyen yayınların bibliyometrik, kelime bulutu ve duygu analizi. Bilişim Teknolojileri Dergisi, 16(2), 151-165. https://doi.org/10.17671/gazibtd.1197021
• Dağlı, G., Muhtaroğlu, M. B., Baştaş, M., Altınay, F. ve Altınay, Z. (2023). Evaluation of primary school managers' duties in digital transformation. Revista de Gestão e Secretariado, 14(9), 15227-15249. https://doi.org/10.7769/gesec.v14i9.2524
• Dai, Y., Lin, Z., Liu, A. ve Wang, W. (2024). An embodied, analogical and disruptive approach of AI pedagogy in upper elementary education: An experimental study. British Journal of Educational Technology, 55(1), 417-434. https://doi.org/10.1111/bjet.13371
• Dai, Y., Liu, A., Qin, J., Guo, Y., Jong, M. S. Y., Chai, C. S. ve Lin, Z. (2023). Collaborative construction of artificial intelligence curriculum in primary schools. Journal of Engineering Education, 112(1), 23-42. https://doi.org/10.1002/jee.20503
• Dikmen, S. ve Bahçeci, F. (2024). Eğitim bilimleri alanında yapay zekâ konulu bilimsel yayınların eğilimleri: Bibliyometrik bir inceleme. Socrates Journal of Interdisciplinary Social Researches, 10(46), 98–112. https://doi.org/10.58830/ozgur.pub383.c1716
• Durak, G., Çankaya, S., Özdemir, D. ve Can, S. (2024). Artificial intelligence in education: A bibliometric study on its role in transforming teaching and learning. International Review of Research in Open and Distributed Learning, 25(3), 219-244. https://doi.org/10.19173/irrodl.v25i3.7757
• Duran, V. ve Aydın, E. (2024). Eğitimde yapay zekanın kapsamlı incelenmesi: Web of science veri tabanı üzerinden bir AI destekli bibliyometrik analiz. International Journal of Social and Humanities Sciences Research (JSHSR), 11(104), 468-484. https://doi.org/10.5281/zenodo.10737254
• Ellegaard, O. ve Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact?. Scientometrics, 105(3), 1809-1831. https://doi.org/10.1007/s11192-015-1645-z
• Galaz, Z., Mucha, J., Zvoncak, V., Mekyska, J., Smekal, Z., Safarova, K., Ondrackova, A., Urbanek, T., Havigerova, J. M., Bednarova, J. ve Faundez-Zanuy, M. (2020). Advanced parametrization of graphomotor difficulties in school-aged children. IEEE Access, 8, 112883-112897. https://doi.org/10.1109/ACCESS.2020.3003214
• Gavenciak, M., Mucha, J., Mekyska, J., Galaz, Z., Zvoncakova, K. ve Faundez-Zanuy, M. (2025). Computer-aided diagnosis of graphomotor difficulties utilizing direction-based fractional order derivatives. Cognitive Computation, 17(1), 13. https://doi.org/10.1007/s12559-024-10360-7
• Gomede, E., Gaffo, F. H., Briganó, G. U., De Barros, R. M. ve Mendes, L. D. S. (2018). Application of computational intelligence to improve education in smart cities. Sensors, 18(1), 267. https://doi.org/10.3390/s18010267
• Hallinger, P. ve Kovačević, J. (2019). A bibliometric review of research on educational administration: Science mapping the literature, 1960 to 2018. Review of Educational Research, 89(3), 335-369. https://doi.org/10.3102/0034654319830380
• Horvers, A., Kooi, R., Knoop-van Campen, C. A., Dijkstra, R., Baars, M. ve Molenaar, I. (2024, December). How does co-regulation with Adaptive Learning Technologies affect primary school students' goal-setting, regulation of practice behavior and learning outcomes?. Frontiers in Education, Vol. 9, 1435483. https://doi.org/10.3389/feduc.2024.1435483
• Hsu, T. C., Chang, S. C. ve Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296-310. https://doi.org/10.1016/j.compedu.2018.07.004
• Jauhiainen, J. S. ve Garagorry Guerra, A. (2024). Generative AI and education: Dynamic personalization of pupils’ school learning material with ChatGPT. Frontiers in Education 9, 1288723. https://doi.org/10.3389/feduc.2024.1288723
• Jauhiainen, J. S. ve Guerra, A. G. (2023). Generative AI and ChatGPT in school children’s education: Evidence from a school lesson. Sustainability, 15(18) 14025. https://doi.org/10.3390/su151814025
• Kamalov, F., Santandreu Calonge, D. ve Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451
• Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J. ve Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
• Knoop‐van Campen, C. A., van Der Graaf, J., Horvers, A., Kooi, R., Dijkstra, R. ve Molenaar, I. (2024). Enacting control with student dashboards: The role of motivation. Journal of computer assisted learning, 40(3), 1137-1153. https://doi.org/10.1111/jcal.12936
• Köse, B., Radıf, H., Uyar, B., Baysal, İ. ve Demirci, N. (2023). Öğretmen Görüşlerine GöreEğitimde Yapay Zekanın Önemi, Journal of Social, Humanities and Administrative Sciences, 9(71), 4203-4209. https://doi.org/10.29228/JOSHAS.74125
• Kuprenko, V. (2020). Artificial ıntelligence in education: Benefits, challenges, and use cases. Medium. https://pub.towardsai.net/artificial-intelligence-in-education-benefits-challenges-and-use-cases-db52d8921f7a
• Lala, B., Biju, A., Rastogi, A., Dahiya, K., Kala, S. M. ve Hagishima, A. (2023). The Challenge of Multiple Thermal Comfort Prediction Models: Is TSV Enough?. Buildings, 13(4), 890. https://doi.org/10.3390/buildings13040890
• Lala, B., Rizk, H., Kala, S. M. ve Hagishima, A. (2022). Multi-task learning for concurrent prediction of thermal comfort, sensation and preference in winters. Buildings, 12(6), 750. https://doi.org/10.3390/buildings12060750
• Lété, B., Sprenger-Charolles, L. ve Colé, P. (2004). MANULEX: A grade-level lexical database from French elementary school readers. Behavior Research Methods, Instruments, & Computers, 36(1), 156-166. https://doi.org/10.3758/BF03195560
• Li, W., Tu, Y., Zhou, L., Ma, R., Li, Y., Hu, D., Zhang, C. ve Lu, Y. (2024). Study of myopia progression and risk factors in Hubei children aged 7–10 years using machine learning: a longitudinal cohort. BMC Ophthalmology, 24(1), 93. https://doi.org/10.1186/s12886-024-03331-x
• Lin, Y. ve Yu, Z. (2024). A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interactive Technology and Smart Education, 21(2), 189-213. https://doi.org/10.1108/ITSE-12-2022-0165
• Lis, A. ve Tomanek, M. (2021). Mapping the intellectual and conceptual structure of physical education research: Direct citation analysis. Physical Education of Students, 25(2), 67-84. https://doi.org/10.15561/20755279.2021.0201
• Metli, A. (2023). Articles on education and artificial intelligence: A bibliometric analysis. Journal of Social Sciences and Education, 6(Education Special Issue), 279-312. https://doi.org/10.53047/josse.1352197
• Mhlanga, D. (2023). Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning. In FinTech and artificial intelligence for sustainable development: The role of smart technologies in achieving development goals (pp. 387-409). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-37776-1_17
• Millî Eğitim Bakanlığı. (2025a). Eğitimde yapay zekâ politika belgesi ve eylem planı (2025–2029). Millî Eğitim Bakanlığı.
• Millî Eğitim Bakanlığı. (2025b). Yapay zekâ ve eğitim: Öğretmenler için uygulamalı prompt mühendisliği ve üretken araçlarla yenilikçi öğrenme stratejileri. Millî Eğitim Bakanlığı.
• Molenaar, I. ve Knoop-van Campen, C. A. (2018). How teachers make dashboard information actionable. IEEE Transactions on Learning Technologies, 12(3), 347-355. https://doi.org/10.1109/TLT.2018.2851585
• Nabiyev, V. V. (2012). Yapay zekâ: İnsan-bilgisayar etkileşimi. Seçkin Yayıncılık.
• Negussie Tolossa, D., Sahibzada, A., Abdulhussein, H. F. ve Pandya, D. H. B. (2023). The Impact of Social Media on Tourism: Bibliometric Analysis using Scopus Database. International Journal of Advanced Research in Science, Communication and Technology, 557-563. https://doi.org/10.48175/IJARSCT-11488
• Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422
• Prahani, B., Rizki, I., Jatmiko, B., Suprapto, N. ve Tan, A. (2022). Artificial intelligence in education research during the last ten years: A review and bibliometric study. International Journal of Emerging Technologies in Learning (iJET), 17(8), 169-188. https://doi.org/10.3991/ijet.v17i08.29833
• Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348-349. https://doi.org/10.1108/eb026482
• Pritchard, A. ve Wittig, G. (1981). Bibliometrics: A bibliography and index. ALLM Books.
• Quiles-Rodriguez, J. ve Palau, R. (2024). New evidence on the influence of coloured lighting on students’ cognitive processes. Electronics, 13(15), 3005. https://doi.org/10.3390/electronics13153005
• Quiles-Rodriguez, J., Palau, R. ve Mateo-Sanz, J. M. (2025). How artificial intelligence-assisted colour lighting can improve learning: Evidence from recent classrooms studies. Applied Sciences-Basel, 15(7), 1-33. https://doi.org/10.3390/app15073657
• Randall, N. (2019). A survey of robot-assisted language learning (RALL). ACM Transactions on Human-Robot Interaction (THRI), 9(1), 1-36. https://doi.org/10.1145/3345506
• Saçan, S., Tozduman Yaralı, K. ve Kavruk, S. Z. (2022). Çocukların “yapay zeka” kavramına ilişkin metaforik algılarının incelenmesi. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, (64), 274-296. https://doi.org/10.21764/maeuefd.1074024
• Sarıdağ, U. ve Doğan, O. (2022). Dünyada ve Türkiye’de davranışsal finans kavramının bibliyometrik analiz teknikleri kullanılarak incelenmesi. İşletme ve İktisat Çalışmaları Dergisi, 10(2), 70-94.
• Sayed, W. S., Noeman, A. M., Abdellatif, A., Abdelrazek, M., Badawy, M. G., Hamed, A. ve El-Tantawy, S. (2023). AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform. Multimedia Tools and Applications, 82(3), 3303-3333. https://doi.org/10.1007/s11042-022-13076-8
• Schiff, D. (2021). Out of the laboratory and into the classroom: The future of artificial intelligence in education. AI & society, 36(1), 331-348. https://doi.org/10.1007/s00146-020-01033-8
• Song, P. ve Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473-486. https://doi.org/10.1007/s12564-020-09640-2
• Talan, T. (2021). Artificial intelligence in education: A bibliometric study. International Journal of Research in Education and Science, 7(3), 822-837. https://doi.org/10.46328/ijres.2409
• Tozduman Yaralı, K. (2021). Dikkat, bellek ve öğrenmede medyanın etkisi. İçinde Şükran K. (Ed.) Çocuk ve Medya. Pegem Akademi.
• Tu, Y., Hu, X., Zeng, C., Ye, M., Zhang, P., Jin, X., Zhang, J. ve Zhou, L. (2022). A machine-learning approach to discerning prevalence and causes of myopia among elementary students in Hubei. International Ophthalmology, 42(9), 2889-2902. https://doi.org/10.1007/s10792-022-02279-5
• UNESCO. (2019). Beijing consensus on artificial intelligence and education. UNESCO. http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/s5987/201908/W020190828311234688933.pdf
• Vahabzadeh, A., Keshav, N. U., Abdus-Sabur, R., Huey, K., Liu, R. ve Sahin, N. T. (2018). Improved socio-emotional and behavioral functioning in students with autism following school-based smartglasses intervention: Multi-stage feasibility and controlled efficacy study. Behavioral Sciences, 8(10), 85. https://doi.org/10.3390/bs8100085
• Van Eck, N. ve Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
• Wing, J. M. (2006). Computational Thinking, Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
• Wu, C. H., Liu, C. H. ve Huang, Y. M. (2022). The exploration of continuous learning intention in STEAM education through attitude, motivation, and cognitive load. International Journal of STEM Education, 9(1), 35. https://doi.org/10.1186/s40594-022-00346-y
• Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y. ve Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370
• Yeter, I. H., Yang, W. ve Sturgess, J. B. (2024). Global initiatives and challenges in integrating artificial intelligence literacy in elementary education: Mapping policies and empirical literature. Future in Educational Research, 2(4), 382-402. https://doi.org/10.1002/fer3.59
• Yılmaz, K. (2021). Sosyal bilimlerde ve eğitim bilimlerinde sistematik derleme, meta değerlendirme ve bibliyometrik analizler. Manas Sosyal Araştırmalar Dergisi, 10(2), 1457-1490. https://doi.org/10.33206/mjss.791537
• Zhang, LC. ve Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, 103607. https://doi.org/10.1016/j.compedu.2019.103607
• Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. İçinde Monique B., Paul, R. P. ve Moshe, Z. (Ed.) Handbook of self-regulation. Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7
• Zupic, I. ve Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629