IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2409.00658.html
   My bibliography  Save this paper

Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market

Author

Listed:
  • Seyed Mohammad Ali Jafari
  • Ehsan Chitsaz

Abstract

The emergence of new and disruptive technologies makes the economy and labor market more unstable. To overcome this kind of uncertainty and to make the labor market more comprehensible, we must employ labor market intelligence techniques, which are predominantly based on data analysis. Companies use job posting sites to advertise their job vacancies, known as online job vacancies (OJVs). LinkedIn is one of the most utilized websites for matching the supply and demand sides of the labor market; companies post their job vacancies on their job pages, and LinkedIn recommends these jobs to job seekers who are likely to be interested. However, with the vast number of online job vacancies, it becomes challenging to discern overarching trends in the labor market. In this paper, we propose a data mining-based approach for job classification in the modern online labor market. We employed structural topic modeling as our methodology and used the NASDAQ-100 indexed companies' online job vacancies on LinkedIn as the input data. We discover that among all 13 job categories, Marketing, Branding, and Sales; Software Engineering; Hardware Engineering; Industrial Engineering; and Project Management are the most frequently posted job classifications. This study aims to provide a clearer understanding of job market trends, enabling stakeholders to make informed decisions in a rapidly evolving employment landscape.

Suggested Citation

  • Seyed Mohammad Ali Jafari & Ehsan Chitsaz, 2024. "Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market," Papers 2409.00658, arXiv.org.
    • Handle: RePEc:arx:papers:2409.00658
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2409.00658
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    2. Hugo Hopenhayn & Julian Neira & Rish Singhania, 2022. "From Population Growth to Firm Demographics: Implications for Concentration, Entrepreneurship and the Labor Share," Econometrica, Econometric Society, vol. 90(4), pages 1879-1914, July.
    3. Pejic-Bach, Mirjana & Bertoncel, Tine & Meško, Maja & Krstić, Živko, 2020. "Text mining of industry 4.0 job advertisements," International Journal of Information Management, Elsevier, vol. 50(C), pages 416-431.
    4. Papoutsoglou, Maria & Rigas, Emmanouil S. & Kapitsaki, Georgia M. & Angelis, Lefteris & Wachs, Johannes, 2022. "Online labour market analytics for the green economy: The case of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    5. Mekhail Mustak & Joni Salminen & Loïc Plé & Jochen Wirtz, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Post-Print hal-03269994, HAL.
    6. Ali Reza Yusefi & Mehrdad Sharifi & Narjes sadat Nasabi & Esmat Rezabeigi Davarani & Peivand Bastani, 2022. "Health human resources challenges during COVID-19 pandemic; evidence of a qualitative study in a developing country," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-20, January.
    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. Erik Karger & Marvin Jagals & Frederik Ahlemann, 2021. "Blockchain for Smart Mobility—Literature Review and Future Research Agenda," Sustainability, MDPI, vol. 13(23), pages 1-32, November.
    2. Reyes-Menendez, Ana & Clemente-Mediavilla, Jorge & Villagra, Nuria, 2023. "Understanding STI and SDG with artificial intelligence: A review and research agenda for entrepreneurial action," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    3. Raniah Alsahafi & Ahmed Alzahrani & Rashid Mehmood, 2023. "Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations," Sustainability, MDPI, vol. 15(5), pages 1-64, February.
    4. Mariani, Marcello M. & Hashemi, Novin & Wirtz, Jochen, 2023. "Artificial intelligence empowered conversational agents: A systematic literature review and research agenda," Journal of Business Research, Elsevier, vol. 161(C).
    5. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    6. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    7. Soheila Khajoui & Saeid Dehyadegari & Sayyed Abdolmajid Jalaee, 2023. "Forecasting exports in selected OECD countries and Iran using MLP Artificial Neural Network," Papers 2312.15535, arXiv.org.
    8. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    9. wael AL-khatib, Ayman, 2023. "Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework," Technology in Society, Elsevier, vol. 75(C).
    10. Paulin Gohoungodji, 2024. "Innovation in creative industries: Bibliometrix analysis and research agenda," Journal of Economic Analysis, Anser Press, vol. 0(1), pages 1-1, March.
    11. Soheila Khajoui & Saeid Dehyadegari & Sayyed Abdolmajid Jalaee, 2024. "Forecasting Imports in OECD Member Countries and Iran by Using Neural Network Algorithms of LSTM," Papers 2402.01648, arXiv.org.
    12. Debesh Mishra & Biswajit Mohapatra & Abhaya Sanatan Satpathy & Kamalakanta Muduli & Binayak Mishra & Swagatika Mishra & Upma Paliwal, 2024. "The pandemic COVID-19 and associated challenges with implementation of artificial intelligence (AI) in Indian agriculture," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(6), pages 2715-2729, June.
    13. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    14. Shivam Gupta & Jakob Rhyner, 2022. "Mindful Application of Digitalization for Sustainable Development: The Digitainability Assessment Framework," Sustainability, MDPI, vol. 14(5), pages 1-23, March.
    15. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris & Jabeen, Fauzia, 2022. "Digital transformation of organization using AI-CRM: From microfoundational perspective with leadership support," Journal of Business Research, Elsevier, vol. 153(C), pages 46-58.
    16. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    17. Salminen, Joni & Kandpal, Chandrashekhar & Kamel, Ahmed Mohamed & Jung, Soon-gyo & Jansen, Bernard J., 2022. "Creating and detecting fake reviews of online products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    18. Krey, Nina & Picot-Coupey, Karine & Cliquet, Gérard, 2022. "Shopping mall retailing: A bibliometric analysis and systematic assessment of Chebat's contributions," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    19. Potrawa, Tomasz & Tetereva, Anastasija, 2022. "How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market," Journal of Business Research, Elsevier, vol. 144(C), pages 50-65.
    20. Aleem, Majid & Sufyan, Muhammad & Ameer, Irfan & Mustak, Mekhail, 2023. "Remote work and the COVID-19 pandemic: An artificial intelligence-based topic modeling and a future agenda," Journal of Business Research, Elsevier, vol. 154(C).

    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:arx:papers:2409.00658. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.