IDEAS home Printed from https://ideas.repec.org/a/vra/journl/v10y2021i3p21-32.html
   My bibliography  Save this article

Digital Advantages for the Construction Industry

Author

Listed:
  • Plamen Yankov

    (University of Economics - Varna, Varna, Bulgaria)

  • Stefka Petrova

    (University of Economics - Varna, Varna, Bulgaria)

  • Svetlana Todorova

    (University of Economics - Varna, Varna, Bulgaria)

Abstract

Currently, the digital transformation is recognized as a source of many potential effects for the business organization from all sectors of the economy. The purpose of this study is to identify and highlight the possible advantages of the application of digital technologies in the construction sector. A scientometric analysis of the existing literature is performed. The scope of the study is given from three perspectives through three search criteria - digitalization, big data, and forecasting. A total of 2371 articles are abstracted. Then, the extracted data is visualized through Vosviewer software tool. The growth of publications increases significantly over the last decade. The results illustrate that the digitalization in the construction sector affect all aspects of construction projects, with the strongest impact on the architecture design and building information modelling. Big data in construction is associated with the data storage, data analytics and information management, during the whole life of the buildings. The third search criterion shows that construction companies most often forecast the total costs using regression analysis, machine learning algorithms, artificial neural networks, etc The research findings could support decision makers and practitioners with-depth understanding for the possible advantages of digital technologies in the construction industry. The current study is part of a larger project called "Digitalization of Economy in a Big Data Environment" BG05M2OP001-1.002-0002-C02.

Suggested Citation

  • Plamen Yankov & Stefka Petrova & Svetlana Todorova, 2021. "Digital Advantages for the Construction Industry," Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, Union of Scientists - Varna, Economic Sciences Section, vol. 10(3), pages 21-32, December.
    • Handle: RePEc:vra:journl:v:10:y:2021:i:3:p:21-32
    as

    Download full text from publisher

    File URL: http://su-varna.org/journal/IJUSV-ESS/2021.10.3/21-32.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ritu Agarwal & Vasant Dhar, 2014. "Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research," Information Systems Research, INFORMS, vol. 25(3), pages 443-448, September.
    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. Bram Klievink & Bart-Jan Romijn & Scott Cunningham & Hans Bruijn, 2017. "Big data in the public sector: Uncertainties and readiness," Information Systems Frontiers, Springer, vol. 19(2), pages 267-283, April.
    2. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    3. Anke Joubert & Matthias Murawski & Markus Bick, 2023. "Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa," Information Systems Frontiers, Springer, vol. 25(1), pages 327-350, February.
    4. Aleš Popovič & Ray Hackney & Rana Tassabehji & Mauro Castelli, 2018. "The impact of big data analytics on firms’ high value business performance," Information Systems Frontiers, Springer, vol. 20(2), pages 209-222, April.
    5. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    6. Mazanec, Josef A., 2020. "Hidden theorizing in big data analytics: With a reference to tourism design research," Annals of Tourism Research, Elsevier, vol. 83(C).
    7. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    8. Yuan Li & William J. Kettinger, 2022. "Testing the Relationship Between Information and Knowledge in Computer-Aided Decision-Making," Information Systems Frontiers, Springer, vol. 24(6), pages 1827-1843, December.
    9. Jinyang Zheng & Zhengling Qi & Yifan Dou & Yong Tan, 2019. "How Mega Is the Mega? Exploring the Spillover Effects of WeChat Using Graphical Model," Information Systems Research, INFORMS, vol. 30(4), pages 1343-1362, December.
    10. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    11. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    12. Giorgi Shuradze & Yevgen Bogodistov & Heinz-Theo Wagner, 2018. "The Role Of Marketing-Enabled Data Analytics Capability And Organisational Agility For Innovation: Empirical Evidence From German Firms," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-32, May.
    13. Pratyush Bharati & Abhijit Chaudhury, 2019. "Assimilation of Big Data Innovation: Investigating the Roles of IT, Social Media, and Relational Capital," Information Systems Frontiers, Springer, vol. 21(6), pages 1357-1368, December.
    14. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Roubaud, David & Fosso Wamba, Samuel & Giannakis, Mihalis & Foropon, Cyril, 2019. "Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain," International Journal of Production Economics, Elsevier, vol. 210(C), pages 120-136.
    15. Shahriar Akter & Samuel Fosso Wamba, 2019. "Big data and disaster management: a systematic review and agenda for future research," Annals of Operations Research, Springer, vol. 283(1), pages 939-959, December.
    16. Issam Laguir & Sachin Modgil & Indranil Bose & Shivam Gupta & Rebecca Stekelorum, 2023. "Performance effects of analytics capability, disruption orientation, and resilience in the supply chain under environmental uncertainty," Annals of Operations Research, Springer, vol. 324(1), pages 1269-1293, May.
    17. Hajer Kefi & Sitesh Indra & Talel Abdessalem, 2016. "Social media marketing analytics : a multicultural approach applied to the beauty & cosmetic sector," Post-Print hal-01456580, HAL.
    18. Ahmed Abbasi & Jingjing Li & Donald Adjeroh & Marie Abate & Wanhong Zheng, 2019. "Don’t Mention It? Analyzing User-Generated Content Signals for Early Adverse Event Warnings," Information Systems Research, INFORMS, vol. 30(3), pages 1007-1028, September.
    19. M. M. Malik & S. Abdallah & M. Ala’raj, 2018. "Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review," Annals of Operations Research, Springer, vol. 270(1), pages 287-312, November.
    20. Soroosh Nalchigar & Eric Yu, 2020. "Designing Business Analytics Solutions," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(1), pages 61-75, February.

    More about this item

    Keywords

    construction industry; big data; digitalization; forecast; scientometric analysis; Vosviewer;
    All these keywords.

    JEL classification:

    • E02 - Macroeconomics and Monetary Economics - - General - - - Institutions and the Macroeconomy

    Statistics

    Access and download statistics

    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:vra:journl:v:10:y:2021:i:3:p:21-32. 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: Pavel Petrov (email available below). General contact details of provider: https://edirc.repec.org/data/uevecea.html .

    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.