Use of Artificial Intelligence in Operational Efficiency and Business Management Strategic
DOI:
https://doi.org/10.58812/wsist.v2i03.1533Keywords:
Artificial Intelligence, Operational Efficiency, Strategic Business Management, Bibliometric Analysis, VOSviewerAbstract
Artificial Intelligence (AI) has emerged as a transformative technology, reshaping operational efficiencies and strategic business management across industries. This study employs a bibliometric analysis using VOSviewer to explore the intellectual structure, global collaboration, and thematic trends in AI research from 2000 to 2024. The findings reveal AI’s pivotal role in enhancing operational processes, particularly in cost reduction, efficiency improvement, and data-driven decision-making. Furthermore, AI’s integration into diverse fields such as healthcare, energy management, and cybersecurity underscores its multidisciplinary impact. The visualizations highlight the strong global collaboration among nations, with China, India, and the United States as major contributors to AI research. Despite these advancements, challenges such as ethical concerns, data privacy, and workforce displacement persist. This study emphasizes the need for ethical frameworks, workforce reskilling, and robust international cooperation to maximize AI's benefits while mitigating its challenges. By mapping current trends and identifying future directions, this research contributes to a deeper understanding of AI’s transformative potential in operational and strategic domains.
References
A. Kaplan and M. Haenlein, “Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence,” Bus. Horiz., vol. 62, no. 1, pp. 15–25, 2019.
K. Siau and W. Wang, “Building trust in artificial intelligence, machine learning, and robotics,” Cut. Bus. Technol. J., vol. 31, no. 2, pp. 47–53, 2018.
D. Ivanov, “Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case,” Transp. Res. Part E Logist. Transp. Rev., vol. 136, p. 101922, 2020.
N. Kaloudi and J. Li, “The ai-based cyber threat landscape: A survey,” ACM Comput. Surv., vol. 53, no. 1, pp. 1–34, 2020.
S. Makridakis, “The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms,” Futures, vol. 90, pp. 46–60, 2017.
S. J. Russell and P. Norvig, Artificial intelligence: a modern approach. Pearson, 2016.
T. H. Davenport, “From analytics to artificial intelligence,” J. Bus. Anal., vol. 1, no. 2, pp. 73–80, 2018.
M. Aria and C. Cuccurullo, “A brief introduction to bibliometrix,” J. Informetr., vol. 11, no. 4, pp. 959–975, 2017.
M. Haenlein, A. Kaplan, C.-W. Tan, and P. Zhang, “Artificial intelligence (AI) and management analytics,” J. Manag. Anal., vol. 6, no. 4, pp. 341–343, 2019.
E. Brynjolfsson and A. McAfee, The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & company, 2014.
J. Bughin, E. Hazan, P. Sree Ramaswamy, W. DC, and M. Chu, “Artificial intelligence the next digital frontier,” 2017.
Y. K. Dwivedi et al., “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” Int. J. Inf. Manage., vol. 57, p. 101994, 2021.
S. Raisch and S. Krakowski, “Artificial intelligence and management: The automation–augmentation paradox,” Acad. Manag. Rev., vol. 46, no. 1, pp. 192–210, 2021.
X. Luo, S. Tong, Z. Fang, and Z. Qu, “Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases,” Mark. Sci., vol. 38, no. 6, pp. 937–947, 2019.
T. J. M. Bench-Capon and P. E. Dunne, “Argumentation in artificial intelligence,” Artif. Intell., vol. 171, no. 10–15, pp. 619–641, 2007.
A. Di Vaio, R. Palladino, R. Hassan, and O. Escobar, “Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review,” J. Bus. Res., vol. 121, pp. 283–314, 2020.
P. Mikalef and M. Gupta, “Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance,” Inf. Manag., vol. 58, no. 3, p. 103434, 2021.
T. Ahmad et al., “Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities,” J. Clean. Prod., vol. 289, p. 125834, 2021.
R. Nishant, M. Kennedy, and J. Corbett, “Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda,” Int. J. Inf. Manage., vol. 53, p. 102104, 2020.
D. Vrontis, M. Christofi, V. Pereira, S. Tarba, A. Makrides, and E. Trichina, “Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review,” Artif. Intell. Int. HRM, pp. 172–201, 2023.
J. H. Thrall et al., “Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success,” J. Am. Coll. Radiol., vol. 15, no. 3, pp. 504–508, 2018.
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Copyright (c) 2024 Loso Judijanto, Ahmad Zaelani Adnan, Gilang Pranajasakti, Arnes Yuli Vandika, Wahyuni Sri Astutik

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