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Sustainable development as well as sustainability is desired globally. However, the knowledge and consciousness of people on issues around sustainability remains questionable. Hence, the purpose of this study is to explore the sustainability consciousness (SC) of university students in South Africa.

Quantitative research methodology was adopted for this study. The sample comprised 1,591 randomly selected students from four South African universities. The data was collected through an online survey. Statistical Package for the Social Sciences was used for analysis.

The findings of this study showed that there is need to consider country-specific contextual issues when considering factors capable of promoting the SC of students. Also, the incorporation of modules on sustainability has the potency of increasing the SC of students.

This study recommends, amongst others, the need for the revision of the curricula of universities to accommodate topical issues on sustainability that are capable of increasing the consciousness of students on the subject. In addition, pedagogical approaches that enhance sustainability knowledge and action (like green pedagogies) are required.

The purpose of this study is to provide an overview of artificial intelligence (AI) in the talent management sphere. The study seeks to contribute to the body of knowledge with respect to human resource management and AI by conducting a literature review on the integration of AI in talent management, synthesising existing approaches and frameworks, as well as emphasising potential benefits.

The study adopts desk research, computational literature review (CLR) and uses topic modelling [with bidirectional encoder representations from transformers (BERTopic)] to throw light on the diffusion of AI in talent management.

The study’s main finding is that the area of AI in talent management is on the verge of gradual development and is in tandem with the growth of AI. We deduced that there is a link between talent management practices (planning, recruitment, compensation and rewards, performance management, employee empowerment, employee engagement and organisational culture) and AI. Though there are some known fears with regards to using the innovation, the benefits outweigh the demerits.

The current study has some limitations. The scope and size of the sample are the primary limitations of this study. No form of qualitative analytics was used in this study; as a result, the information obtained was limited. The study provides a snapshot of AI in talent management and contributes to the lack of literature in the joint fields. Also, the study provides practitioners and experts an overview of where to target investments and resources if need be.

The originality of this study comes from the combination of CLR methods and the use topic modelling with BERTopic which has not been used by previous reviews. In addition, the salient machine learning algorithms are identified in the study, which other studies have not identified.