[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-603976-105":59,"doc-detail-603976-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","balancing-ai-transparency-trust-certainty-and-adoption","Balancing AI transparency - Trust, Certainty, and Adoption","","This study examines the non-linear relationship between transparency and AI use intention, challenging the assumption that increased transparency always enhances AI adoption. A web-based experiment with 491 participants across two interactions with AI systems—fake news detection (cognitive) and friending recommendations (social)—manipulates transparency (real, placebo, or absent). Quadratic regression and threshold analysis reveal an inverted U-shaped effect: moderate transparency builds trust and certainty, while excessive transparency causes cognitive overload and heightened scrutiny, reducing adoption. Transparency also affects intention indirectly via increased trust and reduced uncertainty, supporting adaptive transparency models.",{"@graph":69,"@context":122},[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/balancing-ai-transparency-trust-certainty-and-adoption/603976/",{"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/balancing-ai-transparency-trust-certainty-and-adoption/603976.png","ImageObject",300,407,{"name":92,"@type":93},"Maya Linwood","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What question does the study address about AI transparency?","Question",{"text":112,"@type":113},"It tests whether greater AI transparency always increases AI adoption, and instead finds a non-linear relationship between transparency and AI use intention.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is transparency manipulated in the experiment?",{"text":117,"@type":113},"Participants interact with AI systems under three transparency conditions: real transparency, placebo transparency, and absent transparency.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the main finding about how transparency affects trust and adoption?",{"text":121,"@type":113},"Moderate transparency increases trust and certainty, but excessive transparency leads to cognitive overload and stronger scrutiny, lowering AI adoption.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},603976,1792192658,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962084928432,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Original Manuscript  \nBalancing AI transparency: Trust, Certainty, and Adoption  \nInformation Development 1–23  \n© The Author(s) 2025  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/02666669251346124](DOI: 10.1177/02666669251346124)[ ](DOI: 10.1177/02666669251346124)[journals.sagepub.com/home/idv](journals.sagepub.com/home/idv)  \nVu Minh Ngo   \nSchool of Banking, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam  \nAbstract  \nThis study examines the non-linear relationship between transparency and AI use intention, challenging the assumption that increased transparency always enhances AI adoption. A web-based experiment with 491 participants across two interactions with AI systems, fake news detection (cognitive) and friending recommendations (social), are conducted to manipulate transparency (real, placebic, or absent) for this objective. Using quadratic regression analysis and threshold analysis, we find an inverted U-shaped effect, where moderate transparency fosters trust and certainty, but excessive transparency leads to cognitive overload and heightened scrutiny, reducing AI adoption. Additionally, the study identifies key causal pathways, demonstrating that transparency influences AI use intention indirectly by enhancing trust and reducing uncertainty, with certainty and trust serving as significant mediators. These findings contribute to Trust Calibration Theory and Cognitive Load Theory, advocating for adaptive transparency models that optimize AI explanations based on user expertise, task complexity, and engagement levels to maximize usability and trust.  \nKeywords  \nAI trust, certainty, transparency, AI use intention, human-technology interaction  \nSubmitted: 4 March 2025; accepted: 14 May 2025  \nIntroduction  \nArtificial Intelligence (AI) has profoundly transformed the nature of human-machine interactions by enabling autonomous decision-making, human-like reasoning, and adaptive capabilities previously reserved exclusively for humans (Lee et al., 2023; Russell and Norvig, 2021). Increasingly sophisticated AI systems, including large language models (LLMs), automated decision-making algorithms, and recommender engines, now underpin essential operations in healthcare, finance, online commerce, and various other critical domains (Bommasani et al., 2021; OpenAI, 2023). Despite the rapid diffusion and integration of these advanced technologies, persistent challenges around user trust remain a significant barrier to broader adoption (Floridi and Chiriatti, 2020; Hancock et al., 2021).  \nCentral to addressing the trust deficit in AI is the concept of transparency. Transparency fundamentally refers to the degree to which users can comprehend, evaluate, and anticipate the processes underlying AI-generated decisions and recommendations (Lipton,  \n2018) . The value of transparency lies in its capacity to promote certainty, increase predictability, and foster user confidence in AI systems by revealing their underlying logic and operational mechanisms (Hoff and Bashir, 2015; Jacovi et al., 2021) . By enhancing the interpretability of automated decisions, transparency serves as a critical factor in calibrating user trust, thus facilitating greater acceptance and reliance on AI-driven technologies.  \nHowever, the notion of transparency is often conflated or ambiguously distinguished from closely related concepts such as explainability and interpretability. Although interrelated, these terms differ significantly in both their conceptual focus and practical operationalization. Explainability typically  \nCorresponding author:  \nVu Minh Ngo, University of Economics Ho Chi Minh City, School of Banking, 59C Nguyen Dinh Chieu street, Ward 6, District 3, Ho Chi Minh City, Vietnam.  \nEmail: [vunm@ueh.edu.vn](vunm@ueh.edu.vn)  \nemphasizes providing clear, understandable, and usertailored reasons or justifications behind specific AI outputs","cbCail9ccE5n9J4Y","https://ap.wps.com/l/cbCail9ccE5n9J4Y","pdf",1107453,23,"English","# Introduction\n## Transparency, trust, and AI adoption\n## Distinguishing transparency from explainability and interpretability","[{\"question\":\"What question does the study address about AI transparency?\",\"answer\":\"It tests whether greater AI transparency always increases AI adoption, and instead finds a non-linear relationship between transparency and AI use intention.\"},{\"question\":\"How is transparency manipulated in the experiment?\",\"answer\":\"Participants interact with AI systems under three transparency conditions: real transparency, placebo transparency, and absent transparency.\"},{\"question\":\"What is the main finding about how transparency affects trust and adoption?\",\"answer\":\"Moderate transparency increases trust and certainty, but excessive transparency leads to cognitive overload and stronger scrutiny, lowering AI adoption.\"}]","Balancing AI transparency - Trust, Certainty, and Adoption | PDF",1792099769,58]