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The authors aim to contribute to the understanding of the enduring underrepresentation of women in the IT industry by analysing media discourse triggered by a campaign intended to encourage women to join the IT industry.

Internet media coverage of the Little Miss Geek campaign in the UK was analysed as qualitative data to reveal systematic and coherent patterns contributing to the social construction of the role of women with respect to the IT industry and IT employment.

While ostensibly supporting women's empowerment, the discourse framed women's participation in the IT industry as difficult to achieve, focused on women's presumed “feminine” essential features (thus, effectively implying that they are less suitable for IT employment than men), and tasked women with overcoming the barrier via individual efforts (thus, implicitly blaming them for the imbalance). In these ways, the discourse worked against the broader aims of the campaign.

Campaigns and organisations that promote women's participation should work to establish new frames, rather than allowing the discourse to be shaped by the established frames.

The authors interpret the framing in the discourse using Bourdieu's perspective on symbolic power: the symbolic power behind the existing patriarchal order expressed itself via framing, thus contributing to the maintenance of that order. By demonstrating the relevance of Bourdieu's symbolic power, the authors offer a novel understanding of how underrepresentation of women in the IT sector is produced and maintained.

The purpose of this study is to investigate the usage of digital technologies (DTs) in improving the mental health of workers on construction sites.

A mixed research approach was used in the study, which comprised a questionnaire survey and two phases of semi-structured interviews. Purposive sampling was used to determine the interviewees and respondents of the questionnaire survey. Weighted mean rating (WMR) and manual content analysis were used to rank and evaluate the collected data.

The findings of this study revealed bipolar disorder, anxiety disorders, attention-deficit/hyperactivity disorder, obsessive-compulsive disorder, work-related stress and depression as the six most significant mental disorders (MDs) among the construction workforce and 30 causes for them. Moreover, 27 symptoms were related to the six most significant MDs, and sweating was the most significant symptom among them. Despite that, 16 DTs were found to be suitable in mitigating the causes for the most significant MDs.

There are numerous studies conducted on the application of DTs to construction operations. However, insufficient studies have been conducted focusing on the application of DTs in improving the mental health of workers at construction sites. This study can thus influence the use of DTs for tackling the common causes for MDs by bringing a new paradigm to the construction industry.

Artificial intelligence (AI) is a significant game changer in human resource development (HRD). The launch of ChatGPT has accelerated its progress and amplified its impact on organizations and employees. This study aims to review and examine literature on AI in HRD, using a bibliometric approach.

This study is a bibliometric review. Scopus was used to identify studies in the field. In total, 236 papers published in the past 10 years were examined using the VOSviewer program.

The obtained results showed that most cited documents and authors are mainly from computer sciences, emphasizing machine learning over human learning. While it was expected that HRD authors and studies would have a more substantial presence, the lesser prominence suggests several interesting avenues for explorations.

This study provides insights and recommendations for researchers, managers, HRD practitioners and policymakers. Prioritizing the development of both humans and machines becomes crucial, as an exclusive focus on machines may pose a risk to the sustainability of employees' skills and long-term career prospects.

There is a dearth of bibliometric studies examining AI in HRD. Hence, this study proposes a relatively unexplored approach to examine this topic. It provides a visual and structured overview of this topic. Also, it highlights areas of research concentration and areas that are overlooked. Shedding light on the presence of more research originating from computer sciences and focusing on machine learning over human learning represent an important contribution of this study, which may foster interdisciplinary collaboration with experts from diverse fields, broadening the scope of research on technologies and learning in workplaces.