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Management science in the age of the internet and digital revolution

Author

Listed:
  • Rameshwar Dubey

    (MBS School of Business)

  • Pierre-Luc Fournier

    (Université de Sherbrooke)

  • Daniel Jugend

    (São Paulo State University (UNESP))

  • David J. Bryde

    (Liverpool John Moore’s University)

  • Gary Graham

    (Liverpool John Moore’s University)

  • Cyril Foropon

    (MBS School of Business)

Abstract

This special issue was organized to explore the impact of the internet and digital revolution on the field of management science (MS). Our goal was to highlight the scientific developments that have reshaped management practices and methodologies, enabling organizations to effectively navigate the increasingly complex challenges they encounter in today’s fast-paced environment. We received high-quality submissions from diverse contributors, which illustrate the broad interest and relevance of this topic. After a rigorous and thorough peer review, we successfully published 31 insightful articles. Each article provides a valuable perspective, contributing not only to theoretical frameworks but also to practical applications across various industries. This comprehensive collection serves as an essential resource, enriching the knowledge base for both scholars and practitioners in MS.

Suggested Citation

  • Rameshwar Dubey & Pierre-Luc Fournier & Daniel Jugend & David J. Bryde & Gary Graham & Cyril Foropon, 2025. "Management science in the age of the internet and digital revolution," Annals of Operations Research, Springer, vol. 348(3), pages 1109-1125, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:3:d:10.1007_s10479-025-06608-6
    DOI: 10.1007/s10479-025-06608-6
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    References listed on IDEAS

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    1. Rameshwar Dubey & David J. Bryde & Cyril Foropon, 2024. "Design and management of humanitarian supply chains for pandemics: lessons from COVID-19," Annals of Operations Research, Springer, vol. 335(3), pages 885-898, April.
    2. Marshall Fisher & Ananth Raman, 2018. "Using Data and Big Data in Retailing," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1665-1669, September.
    3. Alexandre Dolgui & Dmitry Ivanov, 2025. "Internet of behaviors: conceptual model, practical and theoretical implications for supply chain and operations management," International Journal of Production Research, Taylor & Francis Journals, vol. 63(1), pages 1-8, January.
    4. Tsan‐Ming Choi & James H. Lambert, 2017. "Advances in Risk Analysis with Big Data," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1435-1442, August.
    5. Stephen C. Graves, 2021. "Reflections on the Evolution of Operations Management," Management Science, INFORMS, vol. 67(9), pages 5379-5388, September.
    6. Rameshwar Dubey, 2022. "Design and management of humanitarian supply chains: challenges, solutions, and frameworks," Annals of Operations Research, Springer, vol. 319(1), pages 1-14, December.
    7. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    8. Weiwei Chen & Tsan-Ming Choi & Alexandre Dolgui & Dmitry Ivanov & Erwin Pesch, 2025. "Digital manufacturing and supply chain: creating benefits through operations research and artificial intelligence," Annals of Operations Research, Springer, vol. 344(2), pages 569-574, January.
    9. Ilya Jackson & Dmitry Ivanov & Alexandre Dolgui & Jafar Namdar, 2024. "Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation," International Journal of Production Research, Taylor & Francis Journals, vol. 62(17), pages 6120-6145, September.
    10. Samuel Fosso Wamba & Maciel M. Queiroz & Eric W. T. Ngai & Fred Riggins & Ygal Bendavid, 2024. "The interplay between artificial intelligence, production systems, and operations management resilience," International Journal of Production Research, Taylor & Francis Journals, vol. 62(15), pages 5361-5366, August.
    11. Mehrbakhsh Nilashi & Abdullah Baabdullah & Rabab Ali Abumalloh & Keng-Boon Ooi & Garry Wei-Han Tan & Mihalis Giannakis & Yogesh Dwivedi, 2023. "How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry?," Post-Print hal-05081422, HAL.
    12. Hakold F. Smiddy & Lionel Naum, 1954. "Evolution of a "Science of Managing" in America," Management Science, INFORMS, vol. 1(1), pages 1-31, October.
    13. Dmitry Ivanov & Christopher S. Tang & Alexandre Dolgui & Daria Battini & Ajay Das, 2021. "Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 2055-2078, April.
    14. Miles Lubin & Iain Dunning, 2015. "Computing in Operations Research Using Julia," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 238-248, May.
    15. Benjamin Rolf & Ilya Jackson & Marcel Müller & Sebastian Lang & Tobias Reggelin & Dmitry Ivanov, 2023. "A review on reinforcement learning algorithms and applications in supply chain management," International Journal of Production Research, Taylor & Francis Journals, vol. 61(20), pages 7151-7179, October.
    16. Merigó, José M. & Yang, Jian-Bo, 2017. "A bibliometric analysis of operations research and management science," Omega, Elsevier, vol. 73(C), pages 37-48.
    17. Marshall Fisher & Marcelo Olivares & Bradley R. Staats, 2020. "Why Empirical Research Is Good for Operations Management, and What Is Good Empirical Operations Management?," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 170-178, January.
    18. Ivanov, Dmitry & Dolgui, Alexandre, 2021. "OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications," International Journal of Production Economics, Elsevier, vol. 232(C).
    19. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
    20. Shrey Jain & Sunil Kumar Jauhar & Piyush, 2024. "A machine-learning-based framework for contractor selection and order allocation in public construction projects considering sustainability, risk, and safety," Annals of Operations Research, Springer, vol. 338(1), pages 225-267, July.
    21. Mingers, John & White, Leroy, 2010. "A review of the recent contribution of systems thinking to operational research and management science," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1147-1161, December.
    22. S. Kumari & V.G. Venkatesh & F.T.C. Tan & S.V. Bharathi & M. Ramasubramanian & Y. Shi, 2023. "Application of Machine Learning and Artificial Intelligence on Agriculture Supply Chain: A Comprehensive Review and Future Research Directions," Post-Print hal-04433057, HAL.
    23. Pervaiz Akhtar & Arsalan Mujahid Ghouri & Haseeb Ur Rehman Khan & Mirza Amin ul Haq & Usama Awan & Nadia Zahoor & Zaheer Khan & Aniqa Ashraf, 2023. "Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions," Annals of Operations Research, Springer, vol. 327(2), pages 633-657, August.
    24. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    25. Maciel M. Queiroz & Samuel Fosso Wamba & Charbel Jose Chiappetta Jabbour & Ana Beatriz Lopes De Sousa Jabbour & Marcio Cardoso Machado, 2022. "Adoption of Industry 4.0 technologies by organizations: a maturity levels perspective," Post-Print hal-04275087, HAL.
    26. Samuel Fosso Wamba & Angappa Gunasekaran & Rameshwar Dubey & Eric W. T. Ngai, 2018. "Big data analytics in operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 1-4, November.
    27. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    28. Spanaki, Konstantina & Dennehy, Denis & Papadopoulos, Thanos & Dubey, Rameshwar, 2025. "Data-driven digital transformation in operations and supply chain management," International Journal of Production Economics, Elsevier, vol. 284(C).
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