Investigating the impact of cognitive assistive technologies on human performance and well-being: an experimental study in assembly and picking tasks
Andrea Lucchese,
Sotirios Panagou and
Fabio Sgarbossa
International Journal of Production Research, 2025, vol. 63, issue 6, 2038-2057
Abstract:
Current industrial scenarios are characterised by increasingly demanding activities, especially order picking and assembly tasks. These activities require high levels of adaptability and manual dexterity, requirements that workers can fulfil, thus underscoring their paramount role. However, these tasks are becoming more complex and subjecting workers to greater cognitive strain. In this context, Industry 4.0 (I4.0) technologies that provide cognitive support (cognitive assistive technologies) are essential for reducing cognitive load, facilitating decision-making, improving performance and safeguarding workers’ well-being. This study investigates the effectiveness of cognitive assistive technologies through laboratory experiments with 37 participants performing assembly and order picking tasks. Performance and well-being outcomes are evaluated based on task completion time and perceived workload. Results suggest that among the technologies investigated, pick-by-light is the most effective in assisting users, easing decision-making, and ensuring performance and well-being. This study contributes to explorative works that focus on the human-centric outcomes of assistive technologies, examining their effectiveness in providing cognitive support. Practical and managerial insights are derived to help engineers and managers choose cognitive assistive technologies that effectively support workers and enhance their performance and well-being.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2024.2394090 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:63:y:2025:i:6:p:2038-2057
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20
DOI: 10.1080/00207543.2024.2394090
Access Statistics for this article
International Journal of Production Research is currently edited by Professor A. Dolgui
More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().