A Bibliometric Analysis on Potential Trends in Artificial Intelligence and Organization Studies

Year 2024, , 669 - 698, 31.10.2024

Deniz Dirik , Tuğba Erhan , İnan Eryılmaz

https://doi.org/10.25229/beta.1487924

Abstract

The impact of artificial intelligence (AI) technologies on organizations and organized life has become a central issue in both the business world and academic studies. AI is increasingly being integrated into organizational studies and has shown potential to enhance organizational performance, particularly in areas such as data analysis, decision-making processes, and human resources management. In this context, the study aims to identify the current and potential trends in AI and organization-based research, explore the intersections between AI and organizational studies, and uncover areas that require further investigation. A bibliometric analysis was conducted on studies focusing on AI and organizational research, examining publication and citation trends, key concepts, and interactions. This analysis was performed using the Biblioshiny program based on RStudio, and it utilized data from 1,085 articles sourced from the Web of Science database. The findings indicate that publications and citations related to the use of AI in organizational settings are on the rise, highlighting that the integration of these technologies into business processes can support strategic innovations. Additionally, the necessity for managing AI applications in an ethical and transparent manner is emphasized. The study provides a systematic overview of the existing literature at the intersection of AI and organizational studies, offering modest contributions to academic discussions in this field. It also presents recommendations that can serve as a reference for future research.

Keywords

Artificial Intelligence, RStudio, Biblioshiny, Organization Studies, Human Resource Management, Machine Learning

Yapay Zekâ ve Örgüt Temelli Araştırmaların Potansiyel Eğilimleri Üzerine Bibliyometrik Bir Analiz

Abstract

Yapay zekâ teknolojilerinin örgütler ve örgütlü yaşam üzerindeki etkisi, iş dünyasının ve akademik çalışmaların ana meselelerinden biri haline gelmiştir. Yapay zekânın örgüt çalışmalarında giderek daha fazla yer bulduğu ve özellikle veri analizi, karar verme süreçleri ve insan kaynakları yönetimi alanlarında örgütlerin performansını artırma potansiyeline sahip olduğu görülmektedir. Bu bağlamda, çalışmada, yapay zekâ ve örgüt temelli araştırmaların mevcut ve potansiyel eğilimlerini tespit etmek, yapay zekâ ve örgüt çalışmaları arasındaki kesişim noktalarını ve araştırılmayı bekleyen alanları keşfetmek hedeflenmiştir. Yapay zekâ ve örgüt araştırmalarını konu eden çalışmaların yayın ve atıf trendleri, anahtar kavramlar ve etkileşimler üzerinden bibliyometrik bir analiz yapılmıştır. RStudio tabanlı Biblioshiny programı kullanılarak yapılan bibliyometrik analiz, Web of Science veri tabanından alınan 1085 makale üzerinden gerçekleştirilmiştir. Yapay zekâ teknolojilerinin örgütsel alanda kullanımıyla ilgili yayınların ve atıfların arttığı, bu teknolojilerin iş süreçlerine entegrasyonunun stratejik yenilikleri destekleyebileceği görüşü yayınlarda öne çıkmaktadır. Ayrıca, yapay zekâ uygulamalarının etik ve şeffaf bir şekilde yönetilmesi gerekliliği vurgulanmaktadır. Çalışma, yapay zekâ ve örgüt çalışmalarının kesişimindeki mevcut literatüre sistematik bir bakış sunarak, bu alandaki akademik tartışmalara mütevazi bir katkı sağlamakta ve gelecekteki araştırmalar için bir referans kaynağı olarak kullanılabilecek öneriler sunmaktadır.

Keywords

Yapay Zeka, Rstudio, Biblioshiny, Örgüt Araştırmaları, İnsan Kaynakları Yönetimi, Makine Öğrenmesi

References

• Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation, and work. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 197-236). University of Chicago Press.
• Al Mansoori, S., Salloum, S. A., & Shaalan, K. (2020). The impact of artificial intelligence and information technologies on the efficiency of knowledge management at modern organizations: A systematic review. In M. Al-Emran, K. Shaalan, & A. E. Hassanien (Eds.), Recent advances in intelligent systems and smart applications (pp. 163-182). Springer. https://doi.org/10.1007/978-3-030-47411-9
• American Psychological Association. (2022). Industrial and organizational psychology. https://www.apa.org/ed/graduate/specialize/industrial
• André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., & Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and big data. Customer Needs and Solutions, 5(1), 28-37.
• Argote, L., & Fahrenkopf, E. (2016). Knowledge transfer in organizations: The roles of members, tasks, tools, and networks. Organizational Behavior and Human Decision Processes, 136, 146-159. https://doi.org/10.1016/j.obhdp.2016.08.003
• Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive, 19(4), 108-116. https://doi.org/10.2139/ssrn.3741983
• Bray, D. A. (2018). The future of artificial intelligence. In M. A. Abramson, D. J. Chenok, & J. M. Kamensky (Eds.), Government for the future: Reflection and vision for tomorrow’s leaders (pp. 221-230). Rowman & Littlefield.
• Brock, J. K. U., & von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. California Management Review, 61(4), 110-134. https://doi.org/10.1177/1536504219865226
• Brynjolfsson, E., & McAfee, A. (2012). Thriving in the automated economy. The Futurist, 46(2), 27-31.
• Brynjolfsson, E., Rock, D., & Syverson, C. (2019). Artificial intelligence and the modern productivity paradox. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 23-57). University of Chicago Press. https://doi.org/10.7208/9780226613475-003
• Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute. https://scholar.google.com/scholar_lookup?title=Notes%20from%20the%20AI%20frontier%3A%20Modeling%20the%20Impact%20of%20AI%20on%20the%20World%20Economy&author=J.%20Bughin&publication_year=2018
• Chatterjee, S., Ghosh, S. K., & Chaudhuri, R. (2020). Knowledge management in improving business process: An interpretative framework for successful implementation of AI–CRM–KM system in organizations. Business Process Management Journal, 26(6), 1261-1281.
• Chatterjee, S., Nguyen, B., Ghosh, S. K., Bhattacharjee, K. K., & Chaudhuri, S. (2020). Adoption of artificial intelligence integrated CRM system: An empirical study of Indian organizations. The Bottom Line, 33(4), 359-375.
• 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.
• Coombs, C., Hislop, D., Taneva, S. K., & Barnard, S. (2020). The strategic impacts of intelligent automation for knowledge and service work: An interdisciplinary review. The Journal of Strategic Information Systems, 29(4), 101600.
• Daugherty, P. R., & Wilson, H. J. (2018). Human+ machine: Reimagining work in the age of AI. Harvard Business Press.
• Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., & Rahwan, I. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences, 116(14), 6531-6539.
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
• Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392.
• IBM. (2018). Unplug from the past: 19th global C-Suite study. IBM Institute for Business Value. https://www.ibm.com/downloads/cas/D2KEJQRO
• Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586.
• Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99. https://doi.org/10.1016/J.BUSHOR.2022.03.002
• Jeste, D. V., Graham, S. A., Nguyen, T. T., Depp, C. A., Lee, E. E., & Kim, H. (2020). Beyond artificial intelligence: Exploring artificial wisdom. International Psychogeriatrics, 32(8), 993-1001.
• Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172.
• Kaul, V., Enslin, S., & Gross, S. A. (2020). History of artificial intelligence in medicine. Gastrointestinal Endoscopy, 92(4), 807-812.
• Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., & Ziemba, E. (2023). Generative artificial intelligence as a new context for management theories: Analysis of ChatGPT. Central European Management Journal, 31(1), 3-13.
• Larivière, B., Bowen, D., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., & De Keyser, A. (2017). “Service Encounter 2.0”: An investigation into the roles of technology, employees, and customers. Journal of Business Research, 79, 238-246.
• McKinsey & Co. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-AI-frontier-modeling-the-impact-of-ai-on-the-world-economy
• Messeri, L., & Crockett, M. J. (2024). Artificial intelligence and illusions of understanding in scientific research. Nature, 627, 49-58.
• Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434.
• Mirbabaie, M., Brünker, F., Möllmann, N. R., & Stieglitz, S. (2022). The rise of artificial intelligence–understanding the AI identity threat at the workplace. Electronic Markets, 32(1), 1-27
• Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102-104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
• Njuguna, C., & McSharry, P. (2017). Constructing spatiotemporal poverty indices from big data. Journal of Business Research, 70, 318–327. https://doi.org/10.1016/j.jbusres.2016.08.005
• Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., ... Staab, S. (2020). Bias in data-driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356. https://doi.org/10.1002/widm.1356
• Østerlund, C., Jarrahi, M. H., Willis, M., Boyd, K., & Wolf, C. T. (2021). Artificial intelligence and the world of work, a co-constitutive relationship. Journal of the Association for Information Science and Technology, 72(1), 128-135. https://doi.org/10.1002/asi.24388
• Peifer, Y., Jeske, T., & Hille, S. (2022). Artificial intelligence and its impact on leaders and leadership. Procedia Computer Science, 200, 1024-1030. https://doi.org/10.1016/j.procs.2022.01.373
• Raj, M., & Seamans, R. (2018). Artificial intelligence, labor, productivity, and the need for firm-level data. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 553-565). University of Chicago Press.
• Rowell-Jones, A., & Howard, C. (2019). CIO survey: CIOs have awoken to the importance of AI. Gartner. Retrieved April 12, 2024, from https://www.gartner.com/en/documents/3897266/2019-cio-survey-cios-have-awoken-to-the-importance-of-ai
• Stank, T., Esper, T., Goldsby, T. J., Zinn, W., & Autry, C. (2019). Toward a digitally dominant paradigm for twenty-first century supply chain scholarship. International Journal of Physical Distribution & Logistics Management, 49(10), 956-971. https://doi.org/10.1108/IJPDLM-08-2019-0230
• Stone, D. L., Lukaszewski, K. M., & Johnson, R. D. (2024). Will artificial intelligence radically change human resource management processes? Organizational Dynamics, 53(1), 101034. https://doi.org/10.1016/j.orgdyn.2023.101034
• 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
• Toshav-Eichner, N., & Bareket-Bojmel, L. (2022). Yesterday's workers in tomorrow's world. Personnel Review, 51(5), 1553-1569. https://doi.org/10.1108/PR-02-2021-0073
• Tredinnick, L. (2017). Artificial intelligence and professional roles. Business Information Review, 34(1), 37-41. https://doi.org/10.1177/0266382116685720
• Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460. https://doi.org/10.1093/mind/LIX.236.433
• Van Esch, P., & Stewart Black, J. (2021). Artificial intelligence (AI): Revolutionizing digital marketing. Australasian Marketing Journal, 29(3), 199-203. https://doi.org/10.1016/j.ausmj.2021.06.001
• 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
• Yorks, L., & Jester, M. Y. (2024). Applying generative AI ethically in HRD practice. Human Resource Development International, 1-18. https://doi.org/10.1080/13678868.2024.1012347