Araştırma Makalesi
BibTex RIS Kaynak Göster

NETWORK MAPPING OF AI-DRIVEN INNOVATION STUDIES: A CONCEPTUAL BIBLIOMETRIC COUPLING REVIEW

Yıl 2025, Cilt: 14 Sayı: 2, 201 - 214, 22.12.2025

Öz

Purpose: The aim of this study is investigate the subareas, the research trends and the conceptual structure artificial intelligence studies in innovation researches. The paper will first introduce a coherent framework based on the prevalent state of knowledge in the area and will then help in a stepwise development of future research.
Methodology: This study has citation and bibliometric coupling analysis. The dataset was collected by scanning papers containing the phrases "innovation" and "artificial intelligence" in their titles, abstracts or keywords together with their accompanying ratings of 3, 4, 4* in the Association of Business Schools Journal List (ABS Journal List) in the Web of Science database. The research has 908 papers were published until September 2025.
Findings: The study shows the field is divided into four clusters. These clusters include dynamic capabilities theory and digital transformation, value creation processes, product-service transformation and business model development and the dynamics of AI adoption and organizational transformation.
Practical Implications: total number of articles in this article research area increased dramatically after 2020. They were divided into more subtopics. In order to populate the interdisciplinary knowledge stream involved, it is a must to collect data in different ways from the databases by the use of variety of key phrases. Also applying varied analytical methods is going to assist in determining the current status of the field from diverse insights.
Originality: This work represents a pioneering effort among the few bibliometric studies that particularly focus on the AI innovation literature in the realm of high impact business journals. The resulting cluster structure is indeed a valuable tool for researchers as it gives quantitative indicators to gaps and emerging themes in the field.

Kaynakça

  • Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
  • Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60(2), 323–351. https://doi.org/10.2307/2951599
  • Amabile, T. M., & Pratt, M. G. (2016). The dynamic componential model of creativity and innovation in organizations: Making progress, making meaning. Research in Organizational Behavior, 36, 157–183. https://doi.org/10.1016/j.riob.2016.10.001
  • Anderson, N., Potočnik, K., & Zhou, J. (2014). Innovation and creativity in organizations: A state-of-the-science review. Journal of Management, 40(5), 1297–1333. https://doi. org/10.1177/0149206314527128
  • Artz, K. W., Norman, P. M., Hatfield, D. E., & Cardinal, L. B. (2010). A longitudinal study of the impact of R&D, patents, and product innovation on firm performance. Journal of Product Innovation Management, 27(5), 725–740. https://doi.org/10.1111/j.1540-5885.2010.00747.x
  • Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776. https://doi. org/10.1016/j.ijpe.2020.107776
  • Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
  • Bocken, N. M. P., Rana, P., & Short, S. W. (2019). Sustainable business model design: Five steps to develop sustainable business models. Journal of Cleaner Production, 235, 1368–1382. https://doi. org/10.1016/j.jclepro.2019.06.267
  • Boons, F., & Lüdeke-Freund, F. (2013). Business models for sustainable innovation: State-of-theart and steps towards a research agenda. Journal of Cleaner Production, 45, 9–19. https://doi.org/ 10.1016/j.jclepro.2012.07.007
  • Boschert, S., & Rosen, R. (2016). Digital twin—The simulation aspect. In P. Hehenberger & D. Bradley (Eds.), Mechatronic futures (pp. 59–74). Springer. https://doi.org/10.1007/978-3-319-32156-1_5
  • Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(4), 3–11.
  • Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530–1534. https://doi.org/10.1126/science.aap8062
  • Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business School Press.
  • Chesbrough, H. W. (2010). Business model innovation: Opportunities and barriers. Long Range Planning, 43(2–3), 354–363. https://doi.org/10.1016/j.lrp.2009.07.010
  • Chesbrough, H., & Bogers, M. (2014). Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. In H. Chesbrough, W. Vanhaverbeke, & J. West (Eds.), New frontiers in open innovation (pp. 3–28). Oxford University Press.
  • Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 115–146). University of Chicago Press. https://doi.org/10.7208/chicago/9780226613338.003.0010
  • Coad, A., & Rao, R. (2008). Innovation and firm growth in high-tech sectors: A quantile regression approach. Research Policy, 37(4), 633–648. https://doi.org/10.1016/j.respol.2008.01.003
  • Colombo, G., Dell’Era, C., & Frattini, F. (2021). External search through solution crowdsourcing, talent crowdsourcing, and open innovation contests: A literature review. Technovation, 102, 102223. https://doi.org/10.1016/j.technovation.2020.102223

YAPAY ZEKÂ ODAKLI İNOVASYON ÇALIŞMALARININ AĞ HARİTALANDIRMASI: KAVRAMSAL BİBLİYOMETRİK EŞLEME İNCELEMESİ

Yıl 2025, Cilt: 14 Sayı: 2, 201 - 214, 22.12.2025

Öz

Amaç: Bu çalışmanın temel amacı, inovasyon literatüründe yapay zekâ (YZ) temasını ele alan akademik çalışmaların kavramsal yapısını, araştırma eğilimlerini ve entelektüel iş birliklerini ortaya koymaktır. Böylece söz konusu alandaki bilgi birikiminin mevcut durumuna ilişkin bütüncül bir çerçeve sunularak gelecekteki araştırmalara yön gösterilmesi hedeflenmektedir.
Yöntem: Çalışmada Bibliyometrik analiz yöntemlerinden atıf analizi ve bibliyometrik eşleme yöntemleri beraber kullanılmıştır. Veri seti, Web of Science veritabanında İşletme Okulları Derneği (ABS) dergi listesinde 4*, 4 ve 3 puanına sahip dergilerde yayımlanmış başlık, özet veya anahtar kelimelerinde hem “innovation” hem de “artificial intelligence” kavramlarını içeren makalelerin taranmasıyla elde edilmiştir. Araştırma 2025 Kasım ayına kadar olan tüm dönemi kapsamış ve toplam 908 özgün makale analiz edilmiştir.
Bulgular: Yapılan analizde alanın dört ana kümede toplandığını göstermektedir. Bu kümeler dinamik yetkinlikler ve dijital dönüşüm, değer yaratma süreçleri, ürün-hizmet dönüşümü ve iş modeli gelişimi ve son olarak yapay zekâ benimseme dinamikleri ve kurumsal dönüşümdür.
Sonuç ve Öneriler: Alandaki çalışmalar 2020 yılı sonrasında hızla artarak ve farklı alt başlıklara ayrılmıştır. Gün geçtikçe disiplinler arası önem kazanan bu alandaki oluşan akademik bilgi dağarcığını arttırmak için farklı veri tabanlarında, farklı anahtar cümleler veri toplanması gerekmektedir. Ayrıca farklı analiz yöntemleri alandaki bilgi birikimini farklı açılardan değerlendirmeye katkı sağlayacaktır.
Özgünlük/Değer: Bu çalışma, inovasyon alanındaki YZ odaklı literatürü yüksek etki faktörlü işletme dergileri çerçevesinde sistematik biçimde haritalayan ilk bibliyometrik incelemelerden biridir. Elde edilen kümelenme yapısı, araştırmacılara alandaki teorik boşlukları ve yükselen temaları nicel göstergelerle sunarak özgün katkı sağlamaktadır.

Kaynakça

  • Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
  • Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60(2), 323–351. https://doi.org/10.2307/2951599
  • Amabile, T. M., & Pratt, M. G. (2016). The dynamic componential model of creativity and innovation in organizations: Making progress, making meaning. Research in Organizational Behavior, 36, 157–183. https://doi.org/10.1016/j.riob.2016.10.001
  • Anderson, N., Potočnik, K., & Zhou, J. (2014). Innovation and creativity in organizations: A state-of-the-science review. Journal of Management, 40(5), 1297–1333. https://doi. org/10.1177/0149206314527128
  • Artz, K. W., Norman, P. M., Hatfield, D. E., & Cardinal, L. B. (2010). A longitudinal study of the impact of R&D, patents, and product innovation on firm performance. Journal of Product Innovation Management, 27(5), 725–740. https://doi.org/10.1111/j.1540-5885.2010.00747.x
  • Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776. https://doi. org/10.1016/j.ijpe.2020.107776
  • Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
  • Bocken, N. M. P., Rana, P., & Short, S. W. (2019). Sustainable business model design: Five steps to develop sustainable business models. Journal of Cleaner Production, 235, 1368–1382. https://doi. org/10.1016/j.jclepro.2019.06.267
  • Boons, F., & Lüdeke-Freund, F. (2013). Business models for sustainable innovation: State-of-theart and steps towards a research agenda. Journal of Cleaner Production, 45, 9–19. https://doi.org/ 10.1016/j.jclepro.2012.07.007
  • Boschert, S., & Rosen, R. (2016). Digital twin—The simulation aspect. In P. Hehenberger & D. Bradley (Eds.), Mechatronic futures (pp. 59–74). Springer. https://doi.org/10.1007/978-3-319-32156-1_5
  • Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(4), 3–11.
  • Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530–1534. https://doi.org/10.1126/science.aap8062
  • Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business School Press.
  • Chesbrough, H. W. (2010). Business model innovation: Opportunities and barriers. Long Range Planning, 43(2–3), 354–363. https://doi.org/10.1016/j.lrp.2009.07.010
  • Chesbrough, H., & Bogers, M. (2014). Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. In H. Chesbrough, W. Vanhaverbeke, & J. West (Eds.), New frontiers in open innovation (pp. 3–28). Oxford University Press.
  • Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 115–146). University of Chicago Press. https://doi.org/10.7208/chicago/9780226613338.003.0010
  • Coad, A., & Rao, R. (2008). Innovation and firm growth in high-tech sectors: A quantile regression approach. Research Policy, 37(4), 633–648. https://doi.org/10.1016/j.respol.2008.01.003
  • Colombo, G., Dell’Era, C., & Frattini, F. (2021). External search through solution crowdsourcing, talent crowdsourcing, and open innovation contests: A literature review. Technovation, 102, 102223. https://doi.org/10.1016/j.technovation.2020.102223
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

İsmail Çağrı Doğan 0000-0001-7875-0897

Gönderilme Tarihi 25 Kasım 2025
Kabul Tarihi 18 Aralık 2025
Yayımlanma Tarihi 22 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 14 Sayı: 2

Kaynak Göster

APA Doğan, İ. Ç. (2025). NETWORK MAPPING OF AI-DRIVEN INNOVATION STUDIES: A CONCEPTUAL BIBLIOMETRIC COUPLING REVIEW. Journal of Entrepreneurship and Innovation Management, 14(2), 201-214.