[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-426586-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-426586-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","latent-profiles-and-predictors-of-transfer-assistive-robot-acceptance-among-korean-care-workers","LATENT PROFILES AND PREDICTORS OF TRANSFER-ASSISTIVE ROBOT ACCEPTANCE AMONG KOREAN CARE WORKERS","","South Korea’s aging population and care-worker shortages drive increasing adoption of AgeTech, with transfer-assistive robots viewed as a way to reduce workload. This study identified technology acceptance typologies for these robots among Korean care workers and examined predictors of profile membership using 2023 survey data from 421 workers engaged in transfer tasks. Latent profile analysis across self-efficacy, anxiety, attitude, ease of use, usefulness, and intention found three groups: high-acceptance, anxiety-dominant moderate-acceptance, and low-acceptance. Gender, age, education, depression, job burnout and stress, and technology enthusiasm significantly predicted membership.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/latent-profiles-and-predictors-of-transfer-assistive-robot-acceptance-among-korean-care-workers/426586/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/latent-profiles-and-predictors-of-transfer-assistive-robot-acceptance-among-korean-care-workers/426586.png","ImageObject",300,407,{"name":42,"@type":43},"Fans","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-23","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What was the purpose of the study?","Question",{"text":63,"@type":64},"To identify technology acceptance typologies for transfer-assistive robots among Korean care workers and examine factors influencing these profiles.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How many groups were identified and what characterized them?",{"text":68,"@type":64},"Three groups were found: a high-acceptance (proactive) group, an anxiety-dominant but moderate-acceptance group, and a low-acceptance (prospective) group.",{"name":70,"@type":61,"acceptedAnswer":71},"Which factors predicted group membership?",{"text":72,"@type":64},"Personal characteristics, depression, job burnout and job stress, and technology enthusiasm significantly predicted membership, with the low-acceptance group showing worse education, higher depression, and lower technology enthusiasm.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},426586,1790753570,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":22,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":30},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","1292 Innovation in Aging, 2025, Vol. 9, No. S2  \nAbstract citation ID: igaf122.4306  \nLATENT PROFILES AND PREDICTORS OF  \nTRANSFER-ASSISTIVE ROBOT ACCEPTANCE AMONG  \nKOREAN CARE WORKERS  \nHee Jeong Yoon1, and Young Sun Kim2, 1. Kyung Hee University, Yongin-si, Kyonggi-do, Republic of Korea, 2. Kyung Hee University, kyonggi-do, Kyonggi-do, Republic of Korea  \nIn South Korea, with population aging and a growing shortage of care workers, the adoption of AgeTech in care settings has been steadily increasing, and transfer-assistive robots are increasingly considered to reduce care workers’ workload. This study aimed to identify technology acceptance typologies for transfer-assistive robots among Korean care workers and to examine factors influencing these profiles. In 2023, survey data were collected from 421 care workers engaged in transfer tasks.  \nLatent profile analysis (LPA) across six domains—self-efficacy, anxiety, attitude, ease of use, usefulness, and intention—identified three distinct groups: (a) high-acceptance (proactive) group (53%),(b) anxiety-dominant but moderate-acceptance group (42%), and (c) low-acceptance (prospective) group (5%) .  \nPersonal characteristics (gender, age, education), health characteristics (depression), job-related burden factors (job burnout, job stress), and technology-related attitude (technology enthusiasm) significantly predicted group membership. The low-acceptance group (c) was more likely to be female, have lower education, higher depression, and lower technology enthusiasm than groups (a) and (b). The anxiety-dominant group (b) was older, had higher job stress, and lower technology enthusiasm compared to the high-acceptance group (a) . These findings indicate that technology acceptance patterns among care workers are heterogeneous and that personal, health, job-related burden, and technology-related factors should be considered comprehensively when examining transfer-assistive robot adoption.  \nUnderstanding these patterns and their predictors can guide tailored implementation and training strategies, supporting the effective adoption of transfer-assistive robots in care settings and informing the development of customized interventions to enhance robot acceptance among older adult care workers.","cbCaiaT29B1vMcmA","https://ap.wps.com/l/cbCaiaT29B1vMcmA","pdf",1131947,"English","# Abstract\n## Purpose and data\n## Latent profile findings\n## Predictors and implications","[{\"question\":\"What was the purpose of the study?\",\"answer\":\"To identify technology acceptance typologies for transfer-assistive robots among Korean care workers and examine factors influencing these profiles.\"},{\"question\":\"How many groups were identified and what characterized them?\",\"answer\":\"Three groups were found: a high-acceptance (proactive) group, an anxiety-dominant but moderate-acceptance group, and a low-acceptance (prospective) group.\"},{\"question\":\"Which factors predicted group membership?\",\"answer\":\"Personal characteristics, depression, job burnout and job stress, and technology enthusiasm significantly predicted membership, with the low-acceptance group showing worse education, higher depression, and lower technology enthusiasm.\"}]","LATENT PROFILES AND PREDICTORS OF TRANSFER-ASSISTIVE ROBOT ACCEPTANCE AMONG KOREAN CARE WORKERS | PDF",1790641036]