ORGANIZATIONAL AGILITY IN THE AGE OF ARTIFICIAL INTELLIGENCE: BIBLIOMETRIC ANALYSIS AND EMERGING TRENDS

Öz

Artificial intelligence (AI) and organizational agility have become crucial concepts for organizations in today’s business world. AI refers to technological systems that enable computer programs to analyze data, automate processes, and solve problems in a way that mimics human decision-making. Organizational agility, on the other hand, is the ability of an organization to quickly adapt to changing conditions, embrace innovation, exhibit flexibility, and use these capabilities as a competitive advantage. By integrating AI with organizational agility, businesses can operate more efficiently, flexibly, and at a higher speed, allowing them to respond to environmental changes more rapidly and gain a competitive edge. This study aims to identify trends in AI and organizational agility, provide a comprehensive research framework, and outline future research directions. A bibliometric analysis was conducted to achieve these objectives. While the Web of Science database facilitated data collection, the RStudio-based Biblioshiny interface played a crucial role in mapping the scientific landscape. According to the research data, the first study in this field was conducted in 2008, with the most recent one published in 2024. A total of 38 studies were identified. The most frequently used keywords were "information technology," "organizational agility," and "artificial intelligence." The analysis also revealed that while publications in this field were relatively scarce before 2019, there was a significant surge in research, particularly in 2023, highlighting the increasing relevance and growth potential of the topic. AI and organizational agility are complementary concepts, as confirmed by keyword analysis, which demonstrated a strong connection between the two. As one of the pioneering bibliometric studies on this topic, this research represents a meaningful contribution to literature. Furthermore, by identifying current trends and defining new research areas, it contributes to the theoretical foundations of future scientific activities and the development of the literature.

Anahtar Kelimeler

• Artificial Intelligence
• Organizational Agility
• Bibliometric Analysis

YAPAY ZEKÂ ÇAĞINDA ÖRGÜTSEL ÇEVİKLİK: BİBLİYOMETRİK ANALİZ VE ÖNE ÇIKAN EĞİLİMLER

Öz

Yapay zekâ ve örgütsel çeviklik, günümüz iş dünyasında örgütler açısından önemli birer kavram haline gelmiştir. Yapay zekâ; veri analizi, otomasyon, problem çözme gibi alanlarda güçlü bir kaynak olarak karşımıza çıkan bilgisayar sistemlerinin insana benzer şekilde karar almasını sağlayan teknolojik sistemlerdir. Örgütsel çeviklik ise örgütün değişen şartlara hızlı şekilde uyum sağlayabilmesi, yeniliklere açık olması, esnek davranabilmesi ve bunu rekabet avantajı olarak kullanabilmesidir. Yapay zekânın örgütsel çeviklik ile bütünleştirilmesi ile örgütte faaliyetler daha hızlı, verimli ve esnek olarak gerçekleştirilebilecektir. Örgütler değişen çevreye daha hızlı cevap vererek rekabet avantajı sağlayabilecektir. Bu çalışma yapay zekâ ve örgütsel çeviklik alanındaki eğilimlerin ortaya konulması, araştırma çerçevesine ilişkin kapsamlı bir bakış sunulması ve araştırma için ileriye dönük yolların belirlenmesini amaçlamaktadır. Çalışma kapsamında bu amaca ulaşmak için bibliyometrik analiz yöntemi kullanılmıştır. Web of Science veri tabanı veri sağlamayı kolaylaştırırken, RStudio temelli Biblioshiny arayüzü bilimsel üretim haritasının oluşturulmasında etkili olmuştur. Araştırma verilerine göre bu konudaki çalışmaların ilki 2008 yılında, en sonuncusu ise 2024 yılında yapılmıştır. Bu konuda yapılmış çalışma sayısı 38’dir. En fazla kullanılan sözcükler information-technology (bilgi teknolojisi), organizational agility (örgütsel çeviklik), artificial-intelligence (yapay zekâ) olarak bulunmuştur. Analiz verileri, 2019'a kadar seyrek şekilde ilerleyen yayınların özellikle 2023'te belirgin şekilde artması, konunun güncelliğini ve büyüme potansiyelini doğrulamaktadır. Yapay zekâ ve örgütsel çeviklik birbirini tamamlayan kavramlardır. Yapılan kelime analizlerinde de yapay zekâ ve örgütsel çeviklik kavramlarının güçlü ilişkisi ortaya konulmuştur. Bu çalışma, bu konuda yapılan öncü bibliyometrik çalışmalardan biri olduğu için literatürde anlamlı bir çabayı temsil etmektedir. Ayrıca, mevcut eğilimleri belirleyip yeni araştırma sahalarını tanımlayarak, gelecekteki bilimsel faaliyetlerin teorik temellerine ve literatürün gelişimine katkı sunmaktadır.

Anahtar Kelimeler

• Yapay Zeka
• Örgütsel Çeviklik
• Bibliyometrik Analiz

Kaynakça

1. Arias-Pérez, J., Chacón-Henao, J. & López-Zapata, E. (2023). Unlocking Agility: Trapped in the Antagonism Between Co-İnnovation in Digital Platforms, Business Analytics Capability and External Pressure for AI Adoption? Business Process Management Journal, 29(6), 1791-1809.
2. Atienza-Barba, M., del Río, M. D. L. C., Meseguer-Martínez, Á. & Barba-Sánchez, V. (2024). Artificial Intelligence and Organizational Agility: an Analysis of Scientific Production and Future Trends. European Research on Management and Business Economics, 30(2), 100253.
3. Baabdullah, A. M., Alalwan, A. A., Slade, E. L., Raman, R. & Khatatneh, K. F. (2021). SMEs and Artificial Intelligence (AI): Antecedents and Consequences of AI-based B2B Practices. Industrial Marketing Management, 98, 255-270.
4. Barba-S´anchez, V., Orozco-Barbosa, L. & Arias-Antúnez, E. (2021). On the Impact of Information Technologies Secondary-School Capacity in Business Development: Evidence from Smart Cities Around the World. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.731443
5. Blancia, G. V. V., Fetalvero, E. G., Baldera, P. R. & Mani, M. C. (2024). The Mediating Effects of Artificial Intelligence Literacy on the Association between Computational Thinking Skills and Organizational Agility among Secondary School Teachers. Problems of Education in the 21st Century, 82(5), 616-629.
6. Bouwman, H., Heikkilä, J., Heikkilä, M., Leopold, C. & Haaker, T. (2018). Achieving Agility Using Business Model Stress Testing. Electron Markets, 28(2), 149–162. https://doi.org/10.1007/s12525-016-0243-0
7. Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A. & Truong, L. (2023). Unlocking the Value of Artificial Intelligence in Human Resource Management Through AI Capability Framework. Human resource management review, 33(1), 100899.
8. Creswell J.W. & Plano V.L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.), Sage, California: Thousand Oaks.

9. Dahmardeh, N. & Banihashemi, S. A. (2010). Organizational Agility and Agile Manufacturing. European Journal of Economics, Finance and Administrative Sciences, 27, 178-184.
10. Felipe, C. M., Leidner, D. E., Roldán, J. L. & Leal‐Rodríguez, A. L. (2020). Impact of IS Capabilities on Firm Performance: The Roles of Organizational Agility and Industry Technology Intensity. Decision sciences, 51(3), 575-619.
11. Fosso Wamba, S., Queiroz, M. M. & Braganza, A. (2022). Preface: Artificial Intelligence in Operations Management. Annals of Operations Research, 1-6.
12. Giacosa, E., Culasso, F., & Crocco, E. (2022). Customer agility in the modern automotive sector: how lead management shapes agile digital companies. Technological Forecasting and Social Change, 175, 121362.
13. Greco, D., Tursunbayeva, A., Capurro, R. & Staffa, M. (2024). Digital Transformation of Teaching and Learning in Higher Education Institutions: A Case Study of the University of Naples “Parthenope”. In Workshop on Artificial Intelligence with and for Learning Sciences: Past, Present, and Future Horizons (pp. 99-111). Cham: Springer Nature Switzerland.
14. Groher, W. & Riss, U. V. (2023). Digital Twin of The Organization for Support of Customer Journeys and Business Processes. In International Conference on Business Process Management (pp. 341-352). Cham: Springer Nature Switzerland.
15. Haenlein, M. & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, And Future Of Artificial İntelligence. Calif Manage Rev, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
16. Kaplan, A. & Haenlein, M. (2019). Siri, Siri, in My Hand: Who’s the fairest in the land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence. Business horizons, 62(1), 15-25.
17. Kiani, A. (2024). Artificial Intelligence in Entrepreneurial Project Management: A Review, Framework And Research Agenda. International Journal of Managing Projects in Business.
18. Lu, Y. & Ramamurthy, K. (2011). Understanding the Link Between Information Technology Capability and Organizational Agility: An Empirical Examination. MIS Quarterly: Management Information Systems, 35(4), 931–954. https://doi.org/10.2307/41409967
19. Passas, I. (2024). Bibliometric Analysis: The Main Steps. Encyclopedia, 4(2), 1014-1025.
20. Project Management Institute (PMI) (2021). A guide to the project management body of knowledge (PMBOK® Guide) (7th ed). Newtown Square, PA: Project Management Institute.
21. Ridwandono, D. & Subriadi, A. P. (2019). IT and Organizational Agility: A Critical Literature Review. Procedia Computer Science, 161, 151–159. https://doi.org/10.1016/j.procs.2019.11.110
22. Shafiabady, N., Hadjinicolaou, N., Din, F. U., Bhandari, B., Wu, R. M., & Vakilian, J. (2023). Using Artificial Intelligence (AI) to Predict Organizational Agility. Plos one, 18(5), e0283066.
23. Ştefan, S. C., Olariu, A. A. & Popa, S. C. (2024). Implications of Artificial Intelligence on Organizational Agility: A PLS-SEM and PLS-POS Approach. Amfiteatru Economic, 26(66), 403-420.
24. Sullivan, Y. & Wamba, S. F. (2024). Artificial Intelligence and Adaptive Response to Market Changes: A Strategy to Enhance Firm Performance and Innovation. Journal of Business Research, 174, 114500.
25. Tambe, P., Cappelli, P. & Yakubovich, V. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. California Management Review, 61(4), 15-42. https://doi.org/10.1177/0008125619867910
26. Vesterinen, M., Mero, J. & Skippari, M. (2024). Big Data Analytics Capability, Marketing Agility, and Firm Performance: A Conceptual Framework. Journal of Marketing Theory and Practice, 1-21.
27. Wahab, M. D. A. & Radmehr, M. (2024). The Impact of AI Assimilation on Firm Performance in Small and Medium-Sized Enterprises: A Moderated Multi-Mediation Model. Heliyon, 10(8).
28. Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R. & Tchatchouang Wanko, C. E. (2020). Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-Based Transformation Projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411
29. Wamba, S. F. (2022). Impact of Artificial Intelligence Assimilation on Firm Performance: The Mediating Effects of Organizational Agility and Customer Agility. International Journal of Information Management, 67. https://doi.org/10.1016/j.ijinfomgt.2022.102544
30. Yang, Y. (2022). Artificial Intelligence-Based Organizational Human Resource Management and Operation System. Frontiers in psychology, 13, 962291.
31. Zhang, D., Pee, L. G. & Cui, L. (2021). Artificial intelligence in E-commerce Fulfillment: A Case Study of Resource Orchestration at Alibaba’s Smart Warehouse. International Journal of Information Management, 57, 102304.
32. Zhang, B. Z., Ashta, A. & Barton, M. E. (2021). Do FinTech and Financial Incumbents Have Different Experiences And Perspectives On The Adoption Of Artificial Intelligence? Strategic Change, 30(3), 223-234.
33. Zhang, S. & Suntrayuth, S. (2024). The Synergy of Ambidextrous Leadership, Agility, and Entrepreneurial Orientation to Achieve Sustainable AI Product Innovation. Sustainability, 16(10), 4248.