İşletme Yönetiminde Yapay Zeka: Bibliyometrik Analiz

Yıl 2024, Cilt: 8 Sayı: Eğitim Bilimleri Özel Sayısı, 504 - 531, 31.12.2024

Murat Sağbaş , Sebahattin Kılınç

https://doi.org/10.30561/sinopusd.1561011

Öz

Yapay zekâ günümüzde her türlü sektör ve alanında kullanılan yenilikçi bir teknolojidir. Bu teknolojinin işletme yönetimine katkıları çok yönlüdür. Nitekim literatür, bu teknolojinin dünyaya etkili bir biçimde yayılmasından dolayı ivmeli bir şekilde genişlemiştir. Bu alan-da çok fazla çalışma bulunması sebebiyle araştırmacılara rehberlik edecek çalışmalara ihtiyaç duyulmuştur. Bu makalenin amacı, işletme yönetiminde yapay zekâya ilişkin alt araştırma alanları tespit etmek, konuya ilişkin en önemli makale, dergi ve yazarları belirlemektir. Böylelikle gelecekteki araştırmacılara işletme yönetiminde yapay zekâya yönelik çalışma-larını geliştirmelerinde karar vermelerine yardımcı olmayı hedeflemektedir. Bu makalede, işletme yönetiminde yapay zekâya yönelik çalışmalara bibliyometrik ve görselleştirme analizleri uygulanmıştır. Bu analizleri gerçekleştirmek için VOSviewer adlı uygulama kullanılmıştır. Yapılan analizler sonucunda, önde gelen dergilerin Sustainability, Cogent Business & Management, Information Systems And E-Business Management, International Journal Of Information Management dergileri olduğu, en popüler anahtar kelimelerin yapay zekâ, makine öğrenme, büyük veri, derin öğrenme, sürdürülebilirlik olduğu tespit edilmiştir. Alt araştırma konuları olarak, iş modellerinde inovasyon, yapay zekanın sektörde benimsenmesi, yapay zekâ ile işletme yönetimi arasındaki ilişkiyi ölçmeye yara-yan analiz teknikleri, yapay zekanın iş dünyasına dair geleceği, büyük veri analitiği ve yapay zekâ ilişkisi tespit edilmiştir.

Anahtar Kelimeler

İşletme, Yapay Zekâ, Bibliyometrik Analiz

Kaynakça

• Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in human behavior, 114, 106548.
• Basri, W. (2020). Examining the impact of artificial intelligence (AI)-assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the Saudi Arabian context. International Journal of Computational Intelligence Systems, 13(1), 142-152.
• Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880.
• Chen, H., Li, L., & Chen, Y. (2021). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36-68.
• Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257.
• Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). Technology acceptance model. J Manag Sci, 35(8), 982-1003.
• Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal, 32(4), 869-896.
• Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., ... & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International journal of production economics, 226, 107599.
• Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., ... & Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International journal of information management, 55, 102211.
• Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.
• Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International journal of information management, 59, 102168.
• Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., ... & Wamba, S. F. (2022). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 66, 102542.
• Dwivedi, Y. K., Hughes, L., Kar, A. K., Baabdullah, A. M., Grover, P., Abbas, R., ... & Wade, M. (2022). Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action. International Journal of Information Management, 63, 102456.
• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). Opinion Paper:“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
• Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169.
• Haenlein, M., Kaplan, A., Tan, C. W., & Zhang, P. (2019). Artificial intelligence (AI) and management analytics. Journal of Management Analytics, 6(4), 341-343.
• Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
• Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172.
• Iaia, L., Nespoli, C., Vicentini, F., Pironti, M., & Genovino, C. (2024). Supporting the implementation of AI in business communication: the role of knowledge management. Journal of Knowledge Management, 28(1), 85-95.
• Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International journal of production research, 58(10), 2904-2915.
• Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695.
• Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Suman, R. (2021). Blockchain technology applications for Industry 4.0: A literature-based review. Blockchain: Research and Applications, 2(4), 100027.
• Kaggwa, S., Eleogu, T. F., Okonkwo, F., Farayola, O. A., Uwaoma, P. U., & Akinoso, A. (2024). AI in decision making: transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), 423-444.
• Kassarjian, H.H. (1977) Content analysis in consumer research. Journal of Consumer Research 4(1): 8–18.
• Kumar, S. (2015). Co-authorship networks: a review of the literature. Aslib Journal of Information Management, 67(1), 55-73.
• Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: the case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44.
• Lin, H., Chi, O. H., & Gursoy, D. (2020). Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services. Journal of Hospitality Marketing & Management, 29(5), 530-549.
• Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36-51.
• Lyu, P., Liu, X., & Yao, T. (2023). A bibliometric analysis of literature on bibliometrics in recent half-century. Journal of Information Science, 01655515231191233.
• Ma, C., Xu, Q., & Li, B. (2022). Comparative study on intelligent education research among countries based on bibliographic coupling analysis. Library hi tech, 40(3), 786-804.
• Mantri, A., & Mishra, R. (2023). Empowering small businesses with the force of big data analytics and AI: A technological integration for enhanced business management. The Journal of High Technology Management Research, 34(2), 100476.
• Maseda, A., Iturralde, T., Cooper, S., & Aparicio, G. (2022). Mapping women's involvement in family firms: A review based on bibliographic coupling analysis. International Journal of Management Reviews, 24(2), 279-305.
• Mas-Tur, A., Roig-Tierno, N., Sarin, S., Haon, C., Sego, T., Belkhouja, M., ... & Merigó, J. M. (2021). Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of Technological Forecasting and Social Change. Technological Forecasting and Social Change, 165, 120487.
• Mishra, S., & Tripathi, A. R. (2021). AI business model: an integrative business approach. Journal of Innovation and Entrepreneurship, 10(1), 18.
• Oh, C., Denton, G., & Gursoy, D. (2020). Artificially intelligent device use in service delivery: A systematic review, synthesis, and research agenda. Journal of Hospitality Marketing & Management, 29(7), 757-786.
• Ong, K. L., Stafford, L. K., McLaughlin, S. A., Boyko, E. J., Vollset, S. E., Smith, A. E., ... & Brauer, M. (2023). Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet, 402(10397), 203-234.
• Papaioannou, G., & Wilson, J. M. (2010). The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research. European journal of operational research, 206(3), 509-521.
• Pallathadka, H., Ramirez-Asis, E. H., Loli-Poma, T. P., Kaliyaperumal, K., Ventayen, R. J. M., & Naved, M. (2023). Applications of artificial intelligence in business management, e-commerce and finance. Materials Today: Proceedings, 80, 2610-2613. Phan Tan, L. (2022). Bibliometrics of social entrepreneurship research: Cocitation and bibliographic coupling analyses. Cogent Business & Management, 9(1), 2124594.
• Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199-3226.
• Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57, 102207.
• Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
• Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., & De Pablo, J. (2021). Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98-117.
• Sestino, A., & De Mauro, A. (2022). Leveraging artificial intelligence in business: Implications, applications and methods. Technology Analysis & Strategic Management, 34(1), 16-29.
• Shneiderman, B. (2020). Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), 1-31.
• Song, Y., Lei, L., Wu, L., & Chen, S. (2023). Studying domain structure: a comparative analysis of bibliographic coupling analysis and co-citation analysis considering all authors. Online Information Review, 47(1), 123-137.
• Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: the case of bank failure predictions. Management science, 38(7), 926-947.
• Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic management journal, 18(7), 509-533.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
• Walter, C., & Ribière, V. (2013). A citation and co-citation analysis of 10 years of KM theory and practices. Knowledge Management Research & Practice, 11(3), 221-229.
• Xu, D.L. (2011). Enterprise systems: state-of-the-art and future trends. IEEE transactions on industrial informatics, 7(4), 630-640.
• Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational research methods, 18(3), 429-472.