Megatrends affecting the world of work: Implications for human resource management
Bibliographic record
Abstract
Purpose The purpose of the anthology is to explore how major societal shifts or “megatrends” are impacting the world of work and to provide guidance for human resource management (HRM) professionals. Design/methodology/approach The anthology adopts a varied approach encompassing literature reviews, empirical research and conceptual frameworks to offer informed perspectives on identifying and interpreting megatrends' impact on HRM. Findings The synthesis highlights several key impacts on the future of work: the transformative power of technological advancements, particularly AI and other new technologies; the challenges posed by globalization and shifting demographics; the lasting effects of the COVID-19 pandemic on work practices; the significant risks of climate change; the negative influence of populism and political polarization on diversity, equity and inclusion (DEI) initiatives; and the need for nuanced HRM approaches to address generational differences. Research limitations/implications There is inherent subjectivity in identifying and interpreting megatrends. Individual authors’ perspectives and biases might influence their analyses of megatrends and their recommendations for HRM. The analyses predominantly focus on Western contexts, limiting the generalizability of findings to other geographical regions and cultures. Practical implications The anthology encourages a more proactive, adaptable and inclusive approach to HRM, emphasizing the need for strategic foresight, investment in employee development and a focus on building organizational resilience in the face of significant societal changes. Social implications The anthology underscores the social responsibility of organizations and policymakers to mitigate negative social consequences arising from megatrends, promoting social justice, equity and the well-being of all members of society, particularly those most vulnerable to disruption. The findings highlight a need for societal adaptation and proactive measures to address potential inequities. Originality/value The anthology offers a comprehensive and insightful exploration of the significant transformations in the world of work, offering actionable guidance and laying the groundwork for future research into how HRM can successfully adapt to the evolving landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".