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Lookup NU author(s): Dr Yin LiangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This paper advances Human Resource Management (HRM) scholarship by introducing an accessible method to analyse of both visual and textual social media content in combination. Although HRM studies increasingly mobilise social media data, most approaches remain text-centric, overlooking the HR-relevant cues, embedded in images, that can inform micro, meso and macro level interpretations. We propose a method that classifies latent features from images and texts by leveraging the potential of a Large Language Model, namely GPT-4o-mini. We illustrate the method with an example that reports a promising performance of the GPT-4o-mini model. We highlight the conceptual potential of our method for theory development through multimodal data, enabling multi-level analysis of HRM phenomena, and we discuss practical applications for HR practitioners in recruitment and selection, gauging employee engagement, and assessing organisational image, alongside limitations and considerations for responsible use.
Author(s): Liang Y, Aroles J, Li Y
Publication type: Article
Publication status: Published
Journal: Human Resource Management Journal
Year: 2026
Issue: ePub ahead of Print
Online publication date: 30/03/2026
Acceptance date: 11/02/2026
Date deposited: 11/02/2026
ISSN (print): 0954-5395
ISSN (electronic): 1748-8583
Publisher: Wiley-Blackwell Publishing Ltd.
URL: https://doi.org/10.1111/1748-8583.70035
DOI: 10.1111/1748-8583.70035
Data Access Statement: The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions
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