Human-Centered Future: The Rise of Industry 5.0 in Corporate Sustainability Literature
Bibliographic record
Abstract
In the second quarter of the 21st century, Industry 5.0 is considered as the most advanced stage of the industrial revolution, where the interaction between people and smart technologies has reached a new level. This assessment is not limited to the digital transformation of production processes, but also shaped by multidimensional sustainability elements such as environmental sensitivity, social benefit and ethical responsibility. The concept of Industry 5.0 seeks to build a manufacturing ecosystem grounded in sustainability and human-centric principles. The aim of this study is to reveal the current research trends in the international academic literature within the framework of the keywords “Industry 5.0” and “Sustainability” and to analyze the areas in which this conceptual framework is concentrated through bibliometric data. Building on this context, a total of 664 English-language scientific publications published between 2020 and 2025 in the Web of Science (WoS) Core Collection database were included in the scope of the analysis. The retrieved data were processed using RStudio Biblioshiny and VOSviewer software, and the publications were systematically evaluated according to their distribution across research fields, open access status, funding institutions, ongoing projects, influential conferences, leading journals, and prolific authors. The findings reveal that engineering, computer-related fields, and environmental sciences stand out as the disciplines contributing most to this area, while the fact that 71% of the publications are open access has gained particular attention. Furthermore, it was identified that funding bodies originating from the European Union and China provide the majority of financial support for this research domain. International conferences and publishers such as IFIP, IEEE, and ACM are among the most prominent platforms where the research is disseminated. As a result of the research, it is seen that the themes of Industry 5.0 and sustainability are met with increasing interest in the scientific literature; this interest is supported by multidisciplinary collaborations, global research funds and open access policies. This study contributes to identifying gaps in the literature and possible future research directions by revealing how Industry 5.0 is positioned in the context of sustainability.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".