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Record W4409271644 · doi:10.1080/19397038.2025.2486343

Outlook on human-centred design in industry 5.0: towards mass customisation, personalisation, co-creation, and co-production

2025· article· en· W4409271644 on OpenAlex
Nastaran Hasani, Seyed Abbas Hosseini, Yasaman Ashjazadeh, Victoria Diederichs, Salar Ghotb, Mariapaola Riggio, Eric Hansen, Vahid Nasir

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Sustainable Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMass customizationProduction (economics)BusinessIndustry 4.0Co-creationPersonalizationManufacturing engineeringEngineeringProcess managementMarketingEconomics

Abstract

fetched live from OpenAlex

This study reviews and expands upon the traditional human-centred design within the emerging paradigm of Industry 5.0. It differentiates human-centred design from user-centred approaches by advocating beyond usability encompassing social, environmental, and ethical considerations. The framework considers humans at multiple levels and explores how designers can benefit from Industry 4.0 technologies to interact with non-designer humans. It examines human-centred design approach in co-creation and co-production practices. Furthermore, it investigates the role of technological enablers (artificial intelligence, digital twins, computational design, digital fabrication, robotics, augmented/virtual reality), in facilitating communication and collaboration throughout the design process from observation and ideation to prototyping and testing. The framework leverages a smart data-centre to ensure interconnectivity and integration across all stages. By incorporating technological enablers of Industry 4.0 and addressing the concerns and values of Industry 5.0, we explore human-centricity at both the consumption and production levels, ultimately fostering sustainable, ethical, and socially responsible design practices. Successful implementation of human-centred design requires addressing several challenges related to data security, transparency, and explainability focusing on technological scalability, and stakeholder engagement. Investing in developing industry-wide standards, open-source solutions, and digital literacy initiatives is crucial to keep human-centric design at the core of Industry 5.0 advancements.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.288
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it