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Trends And Gaps In Ai Research Related To Labor Market And Productivity: A Bibliometric Perspective

Author

Listed:
  • BATUSARU Cristina-Maria

    (Nicolae Bălcescu Land Forces Academy)

  • OLTEAN Delia

    (Transilvania University of Brasov)

  • SBARCEA Ioana-Raluca

    (Lucian Blaga University of Sibiu)

Abstract

This study investigates the evolving structure of scientific research at the intersection of artificial intelligence (AI), labor markets and productivity. As economies continue to change and new technologies influence how people work and how efficiently tasks are completed, it is important to understand how these developments are reflected in academic studies. The main goal of this research is to identify major trends, key contributions, and areas that are still underexplored.The study is based on a selection of academic articles retrieved from the Web of Science database. A bibliometric analysis was carried out using the Bibliometrix package in R, through the Biblioshiny interface. The analysis focused on the evolution of publications, collaboration among author, and the most common research topics in the field. The results show a growing interest in how technology affects jobs and productivity, especially in recent years. However, the research in this area is still fragmented, with limited connections between different fields. This study offers a useful overview for researchers and decision-makers who want to better understand the relationship between technology, employment and economic performance. The findings suggest the need for more interdisciplinary work and highlight research directions that deserve more attention.

Suggested Citation

  • BATUSARU Cristina-Maria & OLTEAN Delia & SBARCEA Ioana-Raluca, 2025. "Trends And Gaps In Ai Research Related To Labor Market And Productivity: A Bibliometric Perspective," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 77(1), pages 36-51, May.
    • Handle: RePEc:blg:reveco:v:77:y:2025:i:1:p:36-51
    DOI: 10.56043/reveco-2025-0004
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    References listed on IDEAS

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    1. Nick Drydakis, 2022. "Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 24(4), pages 1223-1247, August.
    2. Maria Denisa Vasilescu & Andreea Claudia Serban & Gina Cristina Dimian & Mirela Ionela Aceleanu & Xose Picatoste, 2020. "Digital divide, skills and perceptions on digitalisation in the European Union—Towards a smart labour market," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-39, April.
    3. Amankwah-Amoah, Joseph & Khan, Zaheer & Wood, Geoffrey & Knight, Gary, 2021. "COVID-19 and digitalization: The great acceleration," Journal of Business Research, Elsevier, vol. 136(C), pages 602-611.
    4. Baha M. Mohsen, 2024. "AI-Driven Optimization of Urban Logistics in Smart Cities: Integrating Autonomous Vehicles and IoT for Efficient Delivery Systems," Sustainability, MDPI, vol. 16(24), pages 1-24, December.
    5. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

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    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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