IDEAS home Printed from https://ideas.repec.org/a/taf/tjmaxx/v7y2020i2p139-172.html
   My bibliography  Save this article

Natural language processing (NLP) in management research: A literature review

Author

Listed:
  • Yue Kang
  • Zhao Cai
  • Chee-Wee Tan
  • Qian Huang
  • Hefu Liu

Abstract

Natural language processing (NLP) is gaining momentum in management research for its ability to automatically analyze and comprehend human language. Yet, despite its extensive application in management research, there is neither a comprehensive review of extant literature on such applications, nor is there a detailed walkthrough on how it can be employed as an analytical technique. To this end, we review articles in the UT Dallas List of 24 Leading Business Journals that employ NLP as their focal analytical technique to elucidate how textual data can be harnessed for advancing management theories across multiple disciplines. We describe the available toolkits and procedural steps for employing NLP as an analytical technique as well as its advantages and disadvantages. In so doing, we highlight the managerial and technological challenges associated with the application of NLP in management research in order to guide future inquires.

Suggested Citation

  • Yue Kang & Zhao Cai & Chee-Wee Tan & Qian Huang & Hefu Liu, 2020. "Natural language processing (NLP) in management research: A literature review," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 139-172, April.
    • Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:2:p:139-172
    DOI: 10.1080/23270012.2020.1756939
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23270012.2020.1756939
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23270012.2020.1756939?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Xueling Li & Yujie Long & Meixi Fan & Yong Chen, 2022. "Drilling down artificial intelligence in entrepreneurial management: A bibliometric perspective," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 379-396, May.
    2. Jing Ge & Feng Wang & Hongxia Sun & Liuliu Fu & Mingwei Sun, 2020. "Research on the maturity of big data management capability of intelligent manufacturing enterprise," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 646-662, July.
    3. Segun Akinola & Arnesh Telukdarie, 2023. "Sustainable Digital Transformation in Healthcare: Advancing a Digital Vascular Health Innovation Solution," Sustainability, MDPI, vol. 15(13), pages 1-23, July.
    4. Zimei Liu & Kefan Xie & Ling Li & Yong Chen, 2020. "A paradigm of safety management in Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 632-645, July.
    5. Tao Shu & Zhiyi Wang & Huading Jia & Wenjin Zhao & Jixian Zhou & Tao Peng, 2022. "Consumers’ Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China," IJERPH, MDPI, vol. 19(19), pages 1-19, October.
    6. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    7. Mohammad Alamgir Hossain & Md. Maruf Hossan Chowdhury & Ilias O. Pappas & Bhimaraya Metri & Laurie Hughes & Yogesh K. Dwivedi, 2023. "Fake news on Facebook and their impact on supply chain disruption during COVID-19," Annals of Operations Research, Springer, vol. 327(2), pages 683-711, August.
    8. Chen, Shiuann-Shuoh & Choubey, Bhaskar & Singh, Vinay, 2021. "A neural network based price sensitive recommender model to predict customer choices based on price effect," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    9. Weifeng Jia & Shuo Wang & Yongping Xie & Zifeng Chen & Kaixin Gong, 2022. "Disruptive technology identification of intelligent logistics robots in AIoT industry: Based on attributes and functions analysis," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 557-568, May.
    10. Indu Khurana & Daniel J. Lee, 2023. "Gender bias in high stakes pitching: an NLP approach," Small Business Economics, Springer, vol. 60(2), pages 485-502, February.
    11. Alin-Gabriel Vaduva & Simona-Vasilica Oprea & Dragos-Catalin Barbu, 2023. "Understanding Customers' Opinion using Web Scraping and Natural Language Processing," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 537-544, August.
    12. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
    13. Lu, Qinli & Chesbrough, Henry, 2022. "Measuring open innovation practices through topic modelling: Revisiting their impact on firm financial performance," Technovation, Elsevier, vol. 114(C).
    14. Adrian LUPASC, 2023. "The Potential of Natural Language Technology in Transforming Educational Processes," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 142-147.
    15. Aavash Raj Pandey & Mahdi Seify & Udoka Okonta & Amin Hosseinian-Far, 2023. "Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire," IJERPH, MDPI, vol. 20(12), pages 1-11, June.
    16. Wei Zhang & Linhui Sun & Xinping Wang & Anbo Wu, 2022. "The influence of AI word‐of‐mouth system on consumers' purchase behaviour: The mediating effect of risk perception," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 516-530, May.

    More about this item

    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:taf:tjmaxx:v:7:y:2020:i:2:p:139-172. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjma .

    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.