IDEAS home Printed from https://ideas.repec.org/a/vrs/otamic/v13y2021i2p2472-2483n4.html
   My bibliography  Save this article

Construction health and safety: A topic landscape study

Author

Listed:
  • Cao Xiaorui

    (Data Science institute, Shandong University, China)

  • Lu Ruodan

    (Darwin College, University of Cambridge, UK)

  • Guo Liang

    (Data Science institute, Shandong University, China)

  • Liu Jianya

    (Data Science institute, Shandong University, China)

Abstract

We aim to draw in-depth insights into the current literature in construction health and safety and provide perspectives for future research efforts. The existing literature on construction health and safety is not only diverse and rich in sight, but also complex and fragmented in structure. It is essential for the construction industry and research community to understand the overall development and existing challenges of construction health and safety to adapt to future new code of practice and challenges in this field. We mapped the topic landscape followed by identifying the salient development trajectories of this research area over time. We used the topic modeling algorithm to extract 10 distinct topics from 662 abstracts (filtered from a total of 895) of articles published between 1991 and 2020. In addition, we provided the most cited references and the most popular journal per topic as well. The results from a time series analysis suggested that the construction health and safety would maintain its popularity in the next 5 years. Research efforts would be devoted to the topics including “Physical health and disease”, “Migrant and race”, “Vocational ability and training”, and “Smart devices.” Among these topics, “Smart devices” would be the most promising one.

Suggested Citation

  • Cao Xiaorui & Lu Ruodan & Guo Liang & Liu Jianya, 2021. "Construction health and safety: A topic landscape study," Organization, Technology and Management in Construction, Sciendo, vol. 13(2), pages 2472-2483, July.
    • Handle: RePEc:vrs:otamic:v:13:y:2021:i:2:p:2472-2483:n:4
    DOI: 10.2478/otmcj-2021-0027
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/otmcj-2021-0027
    Download Restriction: no
    File URL: https://libkey.io/10.2478/otmcj-2021-0027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Aoife M. Finneran & Alistair Gibb, 2013. "Construction Safety and Health," Construction Management and Economics, Taylor & Francis Journals, vol. 31(5), pages 501-502, May.
    2. Arcury, T.A. & Grzywacz, J.G. & Chen, H. & Mora, D.C. & Quandt, S.A., 2014. "Work organization and health among immigrant women: Latina manual workers in North Carolina," American Journal of Public Health, American Public Health Association, vol. 104(12), pages 2445-2452.
    3. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    2. Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    3. Daoud, Adel & Kohl, Sebastian, 2016. "How much do sociologists write about economic topics? Using big data to test some conventional views in economic sociology, 1890 to 2014," MPIfG Discussion Paper 16/7, Max Planck Institute for the Study of Societies.
    4. Cardinale, Roberto & Cardinale, Ivano & Zupic, Ivan, 2024. "The EU's vulnerability to gas price and supply shocks: The role of mismatches between policy beliefs and changing international gas markets," Energy Economics, Elsevier, vol. 131(C).
    5. Shr-Wei Kao & Pin Luarn, 2020. "Topic Modeling Analysis of Social Enterprises: Twitter Evidence," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    6. Hsia-Ching Chang, 2016. "The Synergy of Scientometric Analysis and Knowledge Mapping with Topic Models: Modelling the Development Trajectories of Information Security and Cyber-Security Research," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-33, December.
    7. Holand, Øystein & Contiero, Barbara & Næss, Marius W. & Cozzi, Giulio, 2024. "“The Times They Are A-Changin' “ – research trends and perspectives of reindeer pastoralism – A review using text mining and topic modelling," Land Use Policy, Elsevier, vol. 136(C).
    8. Ozgun, Burcu & Broekel, Tom, 2021. "The geography of innovation and technology news - An empirical study of the German news media," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    9. Anita Mendiratta & Shveta Singh & Surendra Singh Yadav & Arvind Mahajan, 2023. "Bibliometric and Topic Modeling Analysis of Corporate Social Irresponsibility," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(3), pages 319-339, September.
    10. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    11. Lüdering Jochen & Winker Peter, 2016. "Forward or Backward Looking? The Economic Discourse and the Observed Reality," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(4), pages 483-515, August.
    12. Sabrina L. Woltmann & Lars Alkærsig, 2018. "Tracing university–industry knowledge transfer through a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 449-472, October.
    13. Alexander Brem & Petra A. Nylund & Saeed Roshani, 2024. "Unpacking the complexities of crisis innovation: a comprehensive review of ecosystem-level responses to exogenous shocks," Review of Managerial Science, Springer, vol. 18(8), pages 2441-2464, August.
    14. Motta Queiroz, Mariza & Roque, Carlos & Moura, Filipe & Marôco, João, 2024. "Understanding the expectations of parents regarding their children's school commuting by public transport using latent Dirichlet Allocation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    15. Prithwiraj Choudhury & Dan Wang & Natalie A. Carlson & Tarun Khanna, 2019. "Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles," Strategic Management Journal, Wiley Blackwell, vol. 40(11), pages 1705-1732, November.
    16. Ulrich Fritsche & Johannes Puckelwald, 2018. "Deciphering Professional Forecasters’ Stories - Analyzing a Corpus of Textual Predictions for the German Economy," Macroeconomics and Finance Series 201804, University of Hamburg, Department of Socioeconomics.
    17. Lino Wehrheim, 2019. "Economic history goes digital: topic modeling the Journal of Economic History," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 13(1), pages 83-125, January.
    18. Luo, Shuli & He, Sylvia Y., 2021. "Understanding gender difference in perceptions toward transit services across space and time: A social media mining approach," Transport Policy, Elsevier, vol. 111(C), pages 63-73.
    19. Seongyoun Hong & Taejung Park & Jaewon Choi, 2020. "Analyzing Research Trends in University Student Experience Based on Topic Modeling," Sustainability, MDPI, vol. 12(9), pages 1-11, April.
    20. Müller, Henrik & Rieger, Jonas & Hornig, Nico, 2021. ""We're rolling". Our Uncertainty Perception Indicator (UPI) in Q4 2020: introducing RollingLDA, a new method for the measurement of evolving economic narratives," DoCMA Working Papers 6, TU Dortmund University, Dortmund Center for Data-based Media Analysis (DoCMA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:otamic:v:13:y:2021:i:2:p:2472-2483:n:4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.