Technology, Megatrends and Work: Thoughts on the Future of Business Ethics
Bibliographic record
Abstract
. Of all the profound changes in business, technology is perhaps the most ubiquitous. There is not a facet of our lives unaffected by internet technologies and artificial intelligence. The Journal of Business Ethics established a dedicated section that focuses on Technology and Business Ethics, yet issues related to this phenomenon run right through all the sections. Kirsten Martin, editor of the Technology and Business Ethics section, joins our interim social media editor, Hannah Trittin-UIbrich, to advance a human-centric approach to the development and application of digital technologies that places Business Ethics at centre of the analysis. For Shuili Du, technology is the defining condition for a new era of Corporate Social Responsibility-CSR 3.0-which she defines as "a company's socially responsible strategies and practices that deal with key ethical and socio-technical issues associated with AI and related technologies on the one hand and leverage the power of AI and related technologies to tackle social and environmental problems on the other hand." It is not just technologies that are a determining feature of our lives but technology companies, an argument made by Glen Whelan as he examines Big Business and the need for a Big Business Ethics as we try to understand the impact of Big Tech on our post-work world. Indeed, as noted by Ernesto Noronha and Premilla D'Cruz, megatrends in addition to advancement in technologies, namely globalization, the greening of economies, and changes in demographics and migration, are shaping the future for workers in ways previously unimaginable. Contributing to this important debate, Praveen Parboteeah considers the influence of another longstanding but oft overlooked megatrend, the role of religion in the workplace. Given the enormity of the influence of technology and other megatrends in our world, it is not surprising that this essay introduces ground-breaking ideas that speak to the future of business ethics research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".