Designing meaningful industrial workplaces: a framework for collaborative research projects

Abstract The transition towards Industry 5.0 emphasises human-centricity, resilience, and sustainability in industrial workplaces. Despite well-meaning efforts, implementing the Industry 5.0 agenda has proven to be challenging. This paper describes the complexity of future industrial work environments and underlines important human-technology interaction issues to consider. Complexity may arise, for instance, from the division of tasks between humans and autonomous intelligent systems, as well as digital systems, within hybrid work environments. Usually, the human-centred design (HCD) approach is used to solve human-technology interaction design problems in workplaces as it encompasses themes related to safety, ergonomics, usability and user experience. However, these alone are no longer sufficient in today’s context; it is equally essential to consider the meaningfulness of work to ensure an adequate workforce for industrial work environments. Thus, this paper provides steps for enlarging HCD to encompass meaningfulness of work and addresses the challenges of implementing HCD practices in collaborative research projects, resulting in an HCD framework that emphasises human-centricity and collaborative knowledge-sharing actions. It aims to support the renewal of manufacturing in alignment with Industry 5.0 values and the creation of meaningful, enjoyable and productive hybrid industrial workplaces for the future.

Publication Details

Journal
Quality and User Experience
Published
2026-05-19
DOI
https://doi.org/10.1007/s41233-026-00077-w
Primary Topic
Ergonomics and Human Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Designing meaningful industrial workplaces: a framework for collaborative research projects

Quality and User Experience
Ergonomics and Human Factors
article

Designing meaningful industrial workplaces: a framework for collaborative research projects

article en

Abstract

Abstract The transition towards Industry 5.0 emphasises human-centricity, resilience, and sustainability in industrial workplaces. Despite well-meaning efforts, implementing the Industry 5.0 agenda has proven to be challenging. This paper describes the complexity of future industrial work environments and underlines important human-technology interaction issues to consider. Complexity may arise, for instance, from the division of tasks between humans and autonomous intelligent systems, as well as digital systems, within hybrid work environments. Usually, the human-centred design (HCD) approach is used to solve human-technology interaction design problems in workplaces as it encompasses themes related to safety, ergonomics, usability and user experience. However, these alone are no longer sufficient in today’s context; it is equally essential to consider the meaningfulness of work to ensure an adequate workforce for industrial work environments. Thus, this paper provides steps for enlarging HCD to encompass meaningfulness of work and addresses the challenges of implementing HCD practices in collaborative research projects, resulting in an HCD framework that emphasises human-centricity and collaborative knowledge-sharing actions. It aims to support the renewal of manufacturing in alignment with Industry 5.0 values and the creation of meaningful, enjoyable and productive hybrid industrial workplaces for the future.

Quality and User ExperienceVol. 11(1)
Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Ergonomics and Human Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.