Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2024
License: CC BY
Data sources: ZENODO
https://doi.org/10.54941/ahfe1...
Article . 2023 . Peer-reviewed
Data sources: Crossref
http://dx.doi.org/10.54941/ahf...
Conference object
Data sources: Sygma
versions View all 4 versions
addClaim

Breaking the barriers: multilingual user engagement to increase process engagement and technology acceptance in manufacturing

Authors: Iveta Eimontaite; Sarah Fletcher; Krystian Goławski; Tomasz Kołcon;

Breaking the barriers: multilingual user engagement to increase process engagement and technology acceptance in manufacturing

Abstract

With the start of Industry 5.0, there is greater emphasis on increased workforce sustainability. Manufacturing among other industries realised the economic importance not only of increased production efficiency, but the positive impact physical and psychological workforce wellbeing has on the company. The current paper presents a three-step approach of engaging multicultural end users for robotic technology introduction in the manufacturing where language dependent knowledge capture is challenging. The first step is video analysis of the process to determine which human factors might be key contributors to the existing processes. The second proposed step is process observation while the operators wear eye tracking glasses combined with several questions for the process clarification. This step allows to determine decision making points and visual attention sequence. Finally, a focus group conducted with small group of representative operators. The paper will introduce the use cases and protocol to achieve a two-fold aim: (i) feedback to the technology developers and engineers, the user critical aspects of the existing aspects, and (ii) to increase user acceptance and engagement with the developing technology/processes. The user acceptance and engagement with the final solution is expected to be improved due to the proposed three step engagement program delivered at the start of the project.

Keywords

user engagement, Tacit Knowledge, Language Dependent Knowledge Capture, Technology Acceptance, robot interaction, human

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    2
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Top 10%
Average
Average
Green