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This paper applies latent class analysis to students’ reported use of mobile phone features across six university campuses in four countries, and links resulting user types to behavioral intention to adopt mobile learning using structural equation modelling. Four user types are identified and differ across campus-territory groups, with significant associations to student demographics and study characteristics. Typology also relates to adoption intention, supporting its inclusion in adoption models.",{"@graph":69,"@context":114},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-effect-of-mobile-user-typology-on-mobile-learning-adoption-in-higher-education-paper/592891/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-effect-of-mobile-user-typology-on-mobile-learning-adoption-in-higher-education-paper/592891.png","ImageObject",300,407,{"name":92,"@type":93},"Marglet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108],{"name":109,"@type":110,"acceptedAnswer":111},"What variables are associated with the four identified user types?","Question",{"text":112,"@type":113},"The paper reports significant relationships between the four user types and students’ age, sex, programme level, and programme type, with different distributions across campus-territory groups.","Answer","https://schema.org",{"og:url":83,"og:type":116,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":118,"canonical":83},"index,follow",{"doc_id":120,"site_id":62},592891,1792165296,{"code":4,"msg":5,"data":123},{"doc_id":120,"user_id":124,"nickname":92,"user_avatar":125,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":126,"file_id":127,"file_url":128,"file_type":129,"file_size":130,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":131,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":132,"faqs":133,"seo_title":134,"seo_description":67,"update_tm":135,"read_time":136,"preview_page_count":4},2336477982702,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","The Effect of Mobile User Typology on Mobile Learning Adoption in Higher  \nEducation  \nLenandlar Singh, Troy D. Thomas  \nAbstract: Mobile user typology is absent from popular models for mobile learning adoption though understanding such typology can help with design of learning. This paper explores the salient mobile user typology by applying latent class analysis to reported use of mobile phone features collected from students at six university campuses in four countries (Barbados, Guyana, Jamaica, Trinidad and Tobago) . It also relates the user types to behavioural intention to adopt mobile learning within a structural equation modelling framework. The results indicate four mobile user types which are distributed differently over the campus-territory groups and to which age, sex, programme level and programme type of the students are significantly related. Furthermore, mobile user typology is related to behavioural intention to adopt mobile learning and this avails it as a candidate for inclusion in models used for mobile learning adoption. There is some extent of cross-national specificity of mobile user typology though some user categories seem to be more generalisable  \nKeywords: mobile user, typology, mobile learning adoption, technology adoption , higher education.  \nIntroduction  \nTechnology adoption in higher education offers many possibilities for extending the reach and impact of education (Kukulska-Hulme, 2007) . The mobility and portability of mobile devices, in particular, bring with them unique opportunities and mobile devices are thought to have pedagogical affordances (Daughtery & Berge, 2017) which make them potentially good for altering the way teaching and learning is done by providing new methods of instruction (Traxler, 2007) .  \nMobile learning is regarded as especially useful given the flexibility that it affords learners in ways, times, and spaces that were not previously possible (Schuck, Kearney & Burden, 2017; Lau, Chiu, Ho, Lo & See-To, 2017) and it benefits from an increasingly wide range of application programs (Kim, Ilon & Altmann, 2013) . Though mobile learning entails its share of challenges (see Bano, Zowghi, Kearney, Schuck, & Aubusson, 2018; Kukulska-Hulme, 2007; Hawi & Samaha, 2016; Pimmer, Mateescu, & Grohbiel, 2016; Kates, Wu, & Coryn, 2018; Felisoni & Godoi, 2018), its adoption has emerged as an important sub-domain and trend of technology adoption (Chee, Yahaya, Ibrahim & Hasan, 2017) and has gained much attention in relation to higher education (for example, Alrasheedi, Capretz & Raza, 2015; [Pimmer et. al](Pimmer et. al). , 2016; Al-Emran, Mezhuyev, & Kamaludin, 2018; Crompton & Burke, 2018) .  \nStudies on mobile learning adoption are often concerned with intention to adopt and they often apply established models among which the Technology Acceptance Model (TAM) (Davis, 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, G. B. Davis, F. D. Davis , 2003) are quite popular. These models focus on technology acceptance and use in general and they include several factors and individual characteristics as predictors of behavioural intention and behavioural intention as a predictor of actual use. However, these models do not include user typology and this might account, to some extent, for the paucity of segmentation studies on mobile users in relation to mobile learning. Essentially, researchers, guided by the established models, might either regard mobile user typology as unimportant to mobile learning or might not consider it at all. This is 86  \nPublished by EdTechReview (ETR) , New Delhi, India  \nISSN 1347-9008 [http://www.asianjde.org](http://www.asianjde.org)  \nThis is an open access article under the CC BY-SA license  \ntroubling since an understanding of user typology can assist with design of learning (Zawacki-Richter, Müskens, Krause, Alturki, & Aldraiweesh, 2015) . The authors argue that designing mobile learning with consideration to the ","cbCailkdDYqhE3TP","https://ap.wps.com/l/cbCailkdDYqhE3TP","pdf",478758,"English","# Introduction\n# Literature\n## Media User Typology","[{\"question\":\"What variables are associated with the four identified user types?\",\"answer\":\"The paper reports significant relationships between the four user types and students’ age, sex, programme level, and programme type, with different distributions across campus-territory groups.\"}]","The Effect of Mobile User Typology on Mobile Learning Adoption in Higher Education - Paper | PDF",1792076125,48]