IDEAS home Printed from https://ideas.repec.org/r/taf/marpmg/v47y2020i5p577-597.html
   My bibliography  Save this item

Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Sel, Burakhan & Minner, Stefan, 2022. "A hedging policy for seaborne forward freight markets based on probabilistic forecasts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
  2. Yili Chen & Congdong Li & Han Wang, 2022. "Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)," Forecasting, MDPI, vol. 4(4), pages 1-20, September.
  3. Rachid Oucheikh & Tuwe Löfström & Ernst Ahlberg & Lars Carlsson, 2021. "Rolling Cargo Management Using a Deep Reinforcement Learning Approach," Logistics, MDPI, vol. 5(1), pages 1-18, February.
  4. Büşra Ayan & Elif Güner & Semen Son-Turan, 2022. "Blockchain Technology and Sustainability in Supply Chains and a Closer Look at Different Industries: A Mixed Method Approach," Logistics, MDPI, vol. 6(4), pages 1-39, December.
  5. Ricardo Arencibia-Jorge & Rosa Lidia Vega-Almeida & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet, 2022. "Evolutionary stages and multidisciplinary nature of artificial intelligence research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5139-5158, September.
  6. Zhang, Jinfen & Liu, Jiongjiong & Hirdaris, Spyros & Zhang, Mingyang & Tian, Wuliu, 2023. "An interpretable knowledge-based decision support method for ship collision avoidance using AIS data," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  7. Zhao, Guoqing & Xie, Xiaotian & Wang, Yi & Liu, Shaofeng & Jones, Paul & Lopez, Carmen, 2024. "Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
  8. Nayak, Bishwajit & Bhattacharyya, Som Sekhar & Krishnamoorthy, Bala, 2022. "Exploring the black box of competitive advantage – An integrated bibliometric and chronological literature review approach," Journal of Business Research, Elsevier, vol. 139(C), pages 964-982.
  9. Olushina Olawale Awe & Dennis Makafui Dogbey & Ronel Sewpaul & Derrick Sekgala & Natisha Dukhi, 2021. "Anaemia in Children and Adolescents: A Bibliometric Analysis of BRICS Countries (1990–2020)," IJERPH, MDPI, vol. 18(11), pages 1-13, May.
  10. Marino-Romero, Jorge Alberto & Palos-Sánchez, Pedro R. & Velicia-Martín, Félix, 2024. "Evolution of digital transformation in SMEs management through a bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
  11. Suneet Singh & Ashish Dwivedi & Saurabh Pratap, 2023. "Sustainable Maritime Freight Transportation: Current Status and Future Directions," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
  12. Carine Dominguez-Péry & Lakshmi Narasimha Raju Vuddaraju & Isabelle Corbett-Etchevers & Rana Tassabehji, 2021. "Reducing maritime accidents in ships by tackling human error: a bibliometric review and research agenda," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-32, December.
  13. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
  14. Merlyn Tjimuku & Sulaiman Olusegun Atiku, 2024. "Mapping Emotional Intelligence and Psychological Capital Research: A Bibliometric Analysis and Future Research Agenda," Businesses, MDPI, vol. 4(2), pages 1-24, April.
  15. Tymoteusz Miller & Irmina Durlik & Ewelina Kostecka & Polina Kozlovska & Andrzej Jakubowski & Adrianna Łobodzińska, 2024. "Waste Heat Utilization in Marine Energy Systems for Enhanced Efficiency," Energies, MDPI, vol. 17(22), pages 1-29, November.
  16. Theodore Styliadis & Jason Angelopoulos & Panagiota Leonardou & Petros Pallis, 2022. "Promoting Sustainability through Assessment and Measurement of Port Externalities: A Systematic Literature Review and Future Research Paths," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
  17. Bogdan Florian Socoliuc & Florin Nicolae & Doru Alexandru Pleșea & Andrei Alexandru Suciu, 2024. "EU Maritime Industry Blue-Collar Recruitment: Sustainable Digitalization," Sustainability, MDPI, vol. 16(20), pages 1-25, October.
  18. Lingjie Tang & Chang’an Zhang, 2023. "Global Research on International Students’ Intercultural Adaptation in a Foreign Context: A Visualized Bibliometric Analysis of the Scientific Landscape," SAGE Open, , vol. 13(4), pages 21582440231, December.
  19. Mohamad Issa & Adrian Ilinca & Fahed Martini, 2022. "Ship Energy Efficiency and Maritime Sector Initiatives to Reduce Carbon Emissions," Energies, MDPI, vol. 15(21), pages 1-37, October.
  20. Feliciano-Cestero, María M. & Ameen, Nisreen & Kotabe, Masaaki & Paul, Justin & Signoret, Mario, 2023. "Is digital transformation threatened? A systematic literature review of the factors influencing firms’ digital transformation and internationalization," Journal of Business Research, Elsevier, vol. 157(C).
  21. Tijan, Edvard & Jović, Marija & Aksentijević, Saša & Pucihar, Andreja, 2021. "Digital transformation in the maritime transport sector," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
  22. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.
  23. Yang, Ying & Liu, Yang & Li, Guorong & Zhang, Zekun & Liu, Yanbin, 2024. "Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
  24. Dharm Dev Bhatta & Muddassar Sarfraz & Larisa Ivascu & Marius Pislaru, 2023. "The Nexus of Corporate Affinity for Technology and Firm Sustainable Performance in the Era of Digitalization: A Mediated Model," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
  25. Marija Jović & Edvard Tijan & Doroteja Vidmar & Andreja Pucihar, 2022. "Factors of Digital Transformation in the Maritime Transport Sector," Sustainability, MDPI, vol. 14(15), pages 1-18, August.
  26. Changhee Lee & Yulseong Kim & Youngran Shin, 2021. "Data Usage and the Legal Stability of Transactions for the Commercial Operation of Autonomous Vessels Based on Digital Ownership in Korean Civil Law," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
  27. Wohlleber, Annika & Münch, Christopher & Hartmann, Evi, 2022. "Prevailing technologies and adoption obstacles in maritime logistics," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 559-588, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
  28. Benjamin Mosses Sakita & Berit Irene Helgheim & Svein Bråthen, 2024. "The Principal-Agent Theoretical Ramifications on Digital Transformation of Ports in Emerging Economies," Logistics, MDPI, vol. 8(2), pages 1-39, May.
  29. Concepta McManus & Abilio Afonso Baeta Neves & Alvaro Toubes Prata, 2021. "Scientific publications from non-academic sectors and their impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8887-8911, November.
  30. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.