Consumer Willingness to Repair Electronics: Machine Learning-Based Analysis of Consumer Survey
Wijenayake, Ravindra Fakhredin, Farzaneh
Wijenayake, Ravindra
Fakhredin, Farzaneh
Publication Date
2025-11-13
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(c) 2025 The Authors. Full-text reproduced with authors' permission.
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Abstract
With the introduction of Right to Repair legislation, manufacturers are required to provide repair services.
However, successful implementation depends not only on product design and repair infrastructure but also
on consumer willingness to repair rather than replace. This study investigates consumer attitudes and
behaviours to repair electronics through a machine learning–based analysis of survey data from 840
respondents across 51 countries. Five consumer profiles were examined: Self Repair Oriented, Professional
Reliant, Replace Oriented, Throwaway Oriented, and Procrastinators, capturing the distinct reasons,
motivations, barriers, and interventions to repair. Results show that 73% of participants are willing to repair,
with cost savings and the enjoyment of do-it-yourself activities as major drivers, while lack of skills, tools,
knowledge, and time remain the most significant barriers. Our findings outline the steps and interventions
needed for each consumer profile to facilitate the repair of electronics.
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Citation
Wijenayake R, Fakhredin F, Mehmood I (2025) EcoDesign 2025. Tokyo, Japan. Consumer Willingness to Repair Electronics: Machine Learning-Based Analysis of Consumer Survey.
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Poster presentation