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Career adaptability is emerging as an important research area in today's uncertain, volatile world of work created by the COVID-19 pandemic. The present study focuses on career adaptability research post-COVID-19 by scientifically capturing the literature evolution, hotspots and future trends using bibliometric analysis.

The Scopus database, due to its vast and quality literature, was used to search the papers from the period 2020 to 2023. Bibliometric data were extracted and analyzed from the relevant literature. For further scientific mapping, VOSviewer and Biblioshiny software tools were used.

Findings of the analysis suggest a positive research trend related to career adaptability research post-Covid. Keyword analysis revealed noteworthy clusters and important themes. Bibliometric visual networks regarding authors, sources, citations, future themes, etc. are also presented from the 441 analyzed publications with comprehensive interpretation.

The literature for carrying out the bibliometric analysis was confined to the Scopus database. Other databases in combination with different software can be used for future niche research. From the analysis, future research avenues and practical interventions are presented which have significant implications for future researchers, career counselors and managers.

The study summarizes the recent literature on career adaptability in the aftermath of the pandemic and makes a novel contribution to the existing literature. A reliable study has been provided by the authors using the scientific bibliometric technique. The study highlights emerging research trends post the pandemic. The results are concluded with further suggestions which can guide future research related to the topic.

Entrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial competencies (ECs) can predict entrepreneurial action (EA) for distinct profiles, such as male versus female, start-up versus established and for entrepreneurs within different age groups and educational levels.

The research was conducted using a survey method on a large sample of 1,150 South African entrepreneurs. Chi-squared automatic interaction detection (CHAID) algorithms were used to build decision trees to illustrate distinct entrepreneurial profiles.

Each profile has a different set of ECs that predict EA, with a growth mindset being the most significant predictor of action. Therefore, this study confirms that a “one-size-fits-all” approach cannot be applied when profiling entrepreneurs.

From a pedagogical standpoint, different combinations of these ECs for each profile provide priority information for identification of appropriate candidates (e.g. the highest potential for success) and training initiatives, effective pedagogies and programme design (e.g. which individual ECs should be trained and how should they be trained).

Previous work has mostly focused on demographic variables and included a single sample to profile entrepreneurs. This study maintains much wider applicability in terms of examining profiles in a systematic way. The large sample size supports quantitative analysis of the comparisons between different entrepreneurial profiles using unconventional analyses. Furthermore, as far as can be determined, this represents the first CHAID conducted in a developing country context, especially South Africa, focusing on individual ECs predicting EA.