The Interaction Design of Registration Machines

The widespread adoption of Registration Self-Service Machines (RSSMs) across various sectors has enhanced convenience and streamlined processes for users. However, these systems often present substantial challenges for individuals with disabilities, necessitating additional accommodations to ensure accessibility. This study aims to bridge existing research gaps by providing actionable insights and recommendations for creating more inclusive and accessible registration machines, focusing on the needs of individuals with disabilities, such as those who are blind, visually impaired, or hearing impaired. A mixed-methods approach was employed, involving 26 participants from diverse locations. The results indicate a strong preference for self-service registration, particularly among middle-aged users (31-40 years old), although adoption rates vary across different age groups. The proposed design for a new self-registration machine integrates features to accommodate both disabled and non-disabled users, addressing the specific needs of deaf and blind individuals through visual cues, text-based instructions, audio feedback, and tactile interfaces. While the statistical analysis revealed no significant association between age groups and registration methods, the moderate effect size suggests a meaningful pattern that warrants further investigation. The findings highlight the need for continued research and development in creating more accessible and inclusive self-service technologies in healthcare settings. Future studies with larger sample sizes are recommended to validate these results and further improve the design and implementation of RSSMs to meet the diverse needs of all users.

Authors

Publication Details

Journal
AHFE international
Published
2025-01-01
DOI
https://doi.org/10.54941/ahfe1006176
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

The Interaction Design of Registration Machines

Walusimbi Peter, Hui Yi Chuan, Mengru Xue, Jecha S Jecha
AHFE international
Advanced Manufacturing and Logistics Optimization
article

The Interaction Design of Registration Machines

Walusimbi Peter, Hui Yi Chuan, Mengru Xue, Jecha S Jecha
article en

Abstract

The widespread adoption of Registration Self-Service Machines (RSSMs) across various sectors has enhanced convenience and streamlined processes for users. However, these systems often present substantial challenges for individuals with disabilities, necessitating additional accommodations to ensure accessibility. This study aims to bridge existing research gaps by providing actionable insights and recommendations for creating more inclusive and accessible registration machines, focusing on the needs of individuals with disabilities, such as those who are blind, visually impaired, or hearing impaired. A mixed-methods approach was employed, involving 26 participants from diverse locations. The results indicate a strong preference for self-service registration, particularly among middle-aged users (31-40 years old), although adoption rates vary across different age groups. The proposed design for a new self-registration machine integrates features to accommodate both disabled and non-disabled users, addressing the specific needs of deaf and blind individuals through visual cues, text-based instructions, audio feedback, and tactile interfaces. While the statistical analysis revealed no significant association between age groups and registration methods, the moderate effect size suggests a meaningful pattern that warrants further investigation. The findings highlight the need for continued research and development in creating more accessible and inclusive self-service technologies in healthcare settings. Future studies with larger sample sizes are recommended to validate these results and further improve the design and implementation of RSSMs to meet the diverse needs of all users.

AHFE internationalVol. 170
Openalex Percentile: Top 78%
Advanced Manufacturing and Logistics Optimization
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