[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127459-en":3,"doc-seo-127459-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127459,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Consumer Willingness to Repair Electronics - Machine Learning-Based Analysis of Consumer Survey","Right to Repair legislation requires manufacturers to provide repair services, yet successful circular-economy impact depends on whether consumers choose repair over replacement. This study analyzes survey data from 840 respondents across 51 countries using machine learning to examine five consumer profiles: Self Repair Oriented, Professional Reliant, Replace Oriented, Throwaway Oriented, and Procrastinators. Findings indicate 73% willingness to repair, driven by cost savings and DIY enjoyment, while barriers center on skills, tools, knowledge, and time.","bradscholars  \nConsumer Willingness to Repair Electronics: Machine Learning-Based Analysis of Consumer Survey  \n\n| Item Type | Poster presentation |\n| --- | --- |\n| Authors | Wijenayake , Ravindra;Fakhredin , Farzaneh;Mehmood , Irfan |\n| Citation | Wijenayake R , Fakhredin F, Mehmood I (2025) EcoDesign 2025. Tokyo , Japan. Consumer Willingness to Repair Electronics: Machine Learning-Based Analysis of Consumer Survey. |\n| Rights | ( c) 2025 The Authors. Full-text reproduced with authors 'permission. |\n| Download date | 2026-03-18 17:29:38 |\n| Link to Item | [https://bradscholars.brad.ac. uk/handle/10454/20764](https://bradscholars.brad.ac. uk/handle/10454/20764) |\n\nConsumer Willingness to Repair Electronics: Machine Learning Based Analysis of Consumer Survey  \nRavindra Wijenayake1, Farzaneh Fakhredin2, Irfan Mehmood3  \n1 Graduate of University of Bradford, UK  \n2 Lecturer in Circular Economy and Innovation, University of Bradford., Bradford, UK  \n3 Associate Professor in Business Analytics, University of Bradford., Bradford, UK  \nAbstract:  \nWith 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.  \nKeywords: Circular economy, Repair, Right to Repair, Electronics, Consumer Behaviour, Machine Learning Analysis  \n1. INTRODUCTION  \nRecent legislative initiatives, such as the Fair Repair Act (FRA) [1] and the European Union's \"Right to Repair\"legislation [2], highlight the growing recognition ofrepair asa key component in achieving the circular economy.  \nHowever, enabling repair goes beyond designing products for easy repair or providing affordable spare parts and repair infrastructure. It also requires understanding consumer behavior and creating a culture that encourages repair andreuse over immediate replacement. [3], [4] . In this context, understanding consumer willingness to repair Electrical and Electronic Equipment (EEE) is crucial to supporting repair practices.  \nTherefore, this study aims to explore consumer attitudes and behaviours toward repair, including current practices, underlying motivations, and barriers. It seeks to identify why consumers engage in or avoid repair activities, and what interventions could effectively encourage repair and shift behaviours. This will be achieved by examining five distinct consumer profiles: Self-Repair Oriented, Professional Reliant, Replace Oriented, Throwaway Oriented, and Procrastinators  \nBy segmenting consumers in this way, the study offers amore nuanced understanding of the factors influencing repair decisions, allowing for the development of more targeted and effective strategies to promote repair.  \n2. RESEARCH METHODOLOGY  \nTo meet the research objective, the following steps were taken. First, a literature review was conducted to identify the main consumer profiles related to repair behaviour.  \nSecond, detailed data for each repair profile were collected through an online survey using a structured questionnaire. Third, data preparation was carried out, which involved handling missing values, removing outliers, simplifyi","cbCaihmH4VVaRXaT","https://ap.wps.com/l/cbCaihmH4VVaRXaT","pdf",649873,2,1,11,"English","en",105,"# Abstract\n# Introduction\n# Research Methodology\n## Literature review: defining consumer repair profiles\n## Data collection: survey design and profile-specific data gathering","[{\"question\":\"What is the main objective of the study on electronics repair?\",\"answer\":\"To investigate consumer attitudes and behaviors toward repairing electronics, including motivations, barriers, and current practices, and to identify interventions that could encourage repair.\"},{\"question\":\"How is machine learning used in the analysis?\",\"answer\":\"Machine learning-based analysis is applied to survey data from 840 respondents across 51 countries to uncover patterns and support segmentation into distinct consumer profiles.\"},{\"question\":\"What are the key drivers and barriers for consumers willing to repair?\",\"answer\":\"Cost savings and enjoyment of do-it-yourself activities drive repair willingness, while lack of skills, tools, knowledge, and time are the most significant barriers.\"}]","Consumer Willingness to Repair Electronics - Machine Learning-Based Analysis of Consumer Survey | PDF",1785938999,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"consumer-willingness-to-repair-electronics-machine-learning-based-analysis-of-consumer-survey","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/consumer-willingness-to-repair-electronics-machine-learning-based-analysis-of-consumer-survey/127459/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main objective of the study on electronics repair?","Question",{"text":76,"@type":77},"To investigate consumer attitudes and behaviors toward repairing electronics, including motivations, barriers, and current practices, and to identify interventions that could encourage repair.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is machine learning used in the analysis?",{"text":81,"@type":77},"Machine learning-based analysis is applied to survey data from 840 respondents across 51 countries to uncover patterns and support segmentation into distinct consumer profiles.",{"name":83,"@type":74,"acceptedAnswer":84},"What are the key drivers and barriers for consumers willing to repair?",{"text":85,"@type":77},"Cost savings and enjoyment of do-it-yourself activities drive repair willingness, while lack of skills, tools, knowledge, and time are the most significant barriers.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]