IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v12y2025i1d10.1057_s41599-025-05607-z.html
   My bibliography  Save this article

Why might AI-enabled interviews reduce candidates’ job application intention? The role of procedural justice and organizational attractiveness

Author

Listed:
  • Wenhao Luo

    (North China University of Technology)

  • Yuelin Zhang

    (North China University of Technology)

  • Maona Mu

    (North China University of Technology)

Abstract

Despite substantial scientific developments, companies and professionals are unaware of or do not appreciate the negative repercussions linked to AI-enabled interviews. From the standpoint of job seekers, this raises questions regarding the reasons why certain candidates decline job chances that incorporate AI-enabled interviews. By integrating the capacity-personality framework, fairness heuristic theory, and signaling theory, we investigate the interactive effect between interview format (traditional video interviews vs. AI-enabled interviews) and industry type (high-tech industries vs. low-tech industries) on candidates’ intention to apply for a job. The results from an online scenario-based experiment suggested that interview format and industry type interactively influence candidates’ job application intention, with candidates being more inclined to attend AI-enabled interviews in high-tech industries. We also found that both perceived procedural justice and organizational attractiveness mediate the relationship between interview format and candidates’ intention to apply for jobs, but they do not mediate the interactive effects between interview format and industry type on candidates’ job application intention. Finally, we discuss the theoretical and practical implications of our findings, which contribute to the sustainable use of AI-enabled tools in the job application process.

Suggested Citation

  • Wenhao Luo & Yuelin Zhang & Maona Mu, 2025. "Why might AI-enabled interviews reduce candidates’ job application intention? The role of procedural justice and organizational attractiveness," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05607-z
    DOI: 10.1057/s41599-025-05607-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-025-05607-z
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-025-05607-z?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    2. Suen, Hung-Yue & Hung, Kuo-En, 2024. "Revealing the influence of AI and its interfaces on job candidates' honest and deceptive impression management in asynchronous video interviews," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    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. Suen, Hung-Yue & Hung, Kuo-En, 2024. "Revealing the influence of AI and its interfaces on job candidates' honest and deceptive impression management in asynchronous video interviews," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    2. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
    3. Colak Murat & Saridogan Berkay C., 2023. "Exploring the Remote Work Revolution: A Managerial View of the Tech Sector’s Response to the New Normal," International Journal of Contemporary Management, Sciendo, vol. 59(4), pages 18-33, December.
    4. Mahmoud Abdulhadi Alabdali & Sami A. Khan & Muhammad Zafar Yaqub & Mohammed Awad Alshahrani, 2024. "Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis," Sustainability, MDPI, vol. 16(11), pages 1-30, June.
    5. Zhang, Lixuan & Yencha, Christopher, 2022. "Examining perceptions towards hiring algorithms," Technology in Society, Elsevier, vol. 68(C).
    6. Trautwein, Yannik & Zechiel, Felix & Coussement, Kristof & Meire, Matthijs & Büttgen, Marion, 2025. "Opening the ‘black box’ of HRM algorithmic biases – How hiring practices induce discrimination on freelancing platforms," Journal of Business Research, Elsevier, vol. 192(C).
    7. Mallory Avery & Andreas Leibbrandt & Joseph Vecci, 2023. "Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech," Monash Economics Working Papers 2023-09, Monash University, Department of Economics.
    8. Mahmut Ozer & Matjaz Perc & Hayri Eren Suna, 2024. "Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market," Journal of Economy Culture and Society, Istanbul University, Faculty of Economics, vol. 69(69), pages 159-168, June.
    9. Leyer, Michael & Schneider, Sabrina, 2021. "Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?," Business Horizons, Elsevier, vol. 64(5), pages 711-724.
    10. Hayley Clatterbuck & Clinton Castro & Arvo Mu~noz Mor'an, 2024. "Risk Alignment in Agentic AI Systems," Papers 2410.01927, arXiv.org.
    11. Yuan Xiang & Jing Du & Xue Ni Zheng & Li Rong Long & Huan Yan Xie, 2025. "Judging in the Dark: How Delivery Riders Form Fairness Perceptions Under Algorithmic Management," Journal of Business Ethics, Springer, vol. 199(3), pages 653-670, July.
    12. Roanne Voorst & Tanja Ahlin, 2024. "Key points for an ethnography of AI: an approach towards crucial data," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-5, December.
    13. Allal-Chérif, Oihab & Puertas, Rosa & Carracedo, Patricia, 2024. "Intelligent influencer marketing: how AI-powered virtual influencers outperform human influencers," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    14. Arab ul Mateen & Qasim Ali Nisar & Samia Jamshed & Sumaira Rehman & Muhammad Ali, 2025. "HRM Effectiveness as an Outcome of Big Data: The Role of Big Data–Driven HR Practices and Electronic HRM," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(3), pages 11339-11373, September.
    15. Dirk Lindebaum & Christine Moser & Gazi Islam, 2024. "Big Data, Proxies, Algorithmic Decision‐Making and the Future of Management Theory," Journal of Management Studies, Wiley Blackwell, vol. 61(6), pages 2724-2747, September.
    16. Matthew O. Jackson & Qiaozhu Me & Stephanie W. Wang & Yutong Xie & Walter Yuan & Seth Benzell & Erik Brynjolfsson & Colin F. Camerer & James Evans & Brian Jabarian & Jon Kleinberg & Juanjuan Meng & Se, 2025. "AI Behavioral Science," Papers 2509.13323, arXiv.org.
    17. Jing Wang & Zeyu Xing & Rui Zhang, 2023. "AI technology application and employee responsibility," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    18. Carsten D. Schultz & Christian Koch & Rainer Olbrich, 2024. "Dark sides of artificial intelligence: The dangers of automated decision‐making in search engine advertising," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(5), pages 550-566, May.
    19. Gabriel Koman & Patrik Boršoš & Milan Kubina, 2024. "Sustainable Human Resource Management with a Focus on Corporate Employee Recruitment," Sustainability, MDPI, vol. 16(14), pages 1-21, July.
    20. Weisman, Hannah & Wu, Chia-Huei & Yoshikawa, Katsuhiko & Lee, Hyun-Jung, 2022. "Antecedents of organizational identification: a review and agenda for future research," LSE Research Online Documents on Economics 117626, London School of Economics and Political Science, LSE Library.

    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:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05607-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/palcomms/ .

    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.