[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117454-en":3,"doc-seo-117454-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117454,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","How time fuels AI device adoption - A contextual model enriched by machine learning","AI device adoption research often emphasizes immediate influences like user needs and device functionality, while time dynamics and individual differences in temporal perspectives are less frequently examined. This study models how time perspective shapes adoption decision-making for AI smart speakers, using machine learning methods and structural equation modeling to test variations across temporal dimensions. Findings show cost-benefit driven preference for pro-adoption reasoning, with no direct time-perspective effects on intentions; effects are mediated by reasoning processes.","How time fuels AI device adoption: A contextual model enriched by machine learning  \nAuthor  \nDang , Simon , Quach , Sara , Roberts , Robin E  \nPublished 2025  \nJournal Title  \nTechnological Forecasting and Social Change  \nVersion  \nVersion of Record (VoR)  \nDOI  \n10.1016/j.techfore.2025.123975  \nRights statement  \n© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)) .  \nDownloaded from  \n[https://hdl.handle.net/10072/436965](https://hdl.handle.net/10072/436965)  \nGriffith Research Online  \n[https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au)  \nTechnological Forecasting & Social Change 212 (2025) 123975  \nContents lists available at ScienceDirect  \nTechnological Forecasting & Social Change  \njournal [homepage: www.elsevier.com/locate/techfore](homepage: www.elsevier.com/locate/techfore)  \n| How time fuels AI device machine learning |  | adoption: A contextual model enriched by |  |  |\n| --- | --- | --- | --- | --- |\n| Simon Danga,b,* , Sara Quacha, Robin E. Roberts c\u003Cbr>a Department of Marketing, Griffith University, Gold Coast, Australia\u003Cbr>b Department of Business Administration, Nong Lam University, Ho Chi Minh, Viet Nam c Department of Marketing, Griffith University, Brisbane, Australia |  |  |  |  |\n| A R T I C L E I N F O |  |  | A B S T R A C T |  |\n| Keywords: Future Present\u003Cbr>Artificial intelligence Behavioral reasoning theory Time perspective theory Machine learning |  |  | Most AI device adoption research prioritize immediate factors such as user needs and device functionality, while the complex and dynamic nature of time and individual differences in temporal perspectives are less frequently examined. This study addresses the impact of time in terms of individual differences on AI adoption behaviors, specifically highlighting how different time perspectives influence individuals' decision-making regarding AI device adoption. Machine learning techniques and structural equation modeling were employed to analyze how decision-making varies across temporal dimensions among adopters of AI smart speakers. The results show that individuals, regardless of being future- or present-oriented, show a preference for reasons supporting adoption over reasons against it, indicating a predominant cost-benefit consideration. No direct effects of time perspectiveson adoption intentions were noted; rather, the influence of time perspectives is mediated through reasoning processes. Among examined sociodemographic factors, prior experience influences attitude and intentions positively, whereas education level significantly moderates the relationship between a future time perspective and the intention to adopt AI. This paper enriches the AI adoption literature by uniquely combining Behavioral Reasoning Theory with Time Perspective Theory, offering novel insights into the mediation role of reasoning processes in the relationship between time perspectives and adoption intentions. |  |\n\n1. Introduction  \nDespite being considered an early-stage market (Park et al., 2023), smart speakers, a key subset of artificial intelligence (AI) devices, have gained traction worldwide due to their versatile utility. They function both as standalone tools for tasks such as streaming music, setting timers, and retrieving information, and as gateways to larger systems such as the broader smart home ecosystem, integrating with devices such as smart thermostats, lighting systems, and door locks (Law, 2024; Park et al., 2018). Given the sluggish growth of systems such as the smart home ecosystems (Struckell et al., 2021), understanding smart speaker adoption becomes a key factor in addressing this stagnation. This versatility uniquely positions smart speakers to facilitate the adoption and integration of a wide range of ecosystems for home, health and wellness, entertainment, educatio","cbCainvSYPkrWi0H","https://ap.wps.com/l/cbCainvSYPkrWi0H","pdf",2559684,1,17,"English","en",105,"# Introduction\n## Smart speakers as AI devices and ecosystem gateways\n## Adoption gaps and barriers (privacy, value, functionality)\n## Research motivation and study focus","[{\"question\":\"What does the study investigate about time and AI device adoption?\",\"answer\":\"It examines how individual differences in time perspectives influence AI adoption behaviors, particularly decision-making for AI smart speakers.\"},{\"question\":\"How were the data and relationships analyzed in the research?\",\"answer\":\"Machine learning techniques and structural equation modeling were used to analyze how decision-making varies across temporal dimensions among adopters.\"},{\"question\":\"What role do reasoning processes play in the effect of time perspective?\",\"answer\":\"The study finds no direct effects of time perspectives on adoption intentions; instead, the influence is mediated through reasoning processes.\"}]","How time fuels AI device adoption - A contextual model enriched by machine learning | PDF",1785675941,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"how-time-fuels-ai-device-adoption-a-contextual-model-enriched-by-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/how-time-fuels-ai-device-adoption-a-contextual-model-enriched-by-machine-learning/117454/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the study investigate about time and AI device adoption?","Question",{"text":75,"@type":76},"It examines how individual differences in time perspectives influence AI adoption behaviors, particularly decision-making for AI smart speakers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the data and relationships analyzed in the research?",{"text":80,"@type":76},"Machine learning techniques and structural equation modeling were used to analyze how decision-making varies across temporal dimensions among adopters.",{"name":82,"@type":73,"acceptedAnswer":83},"What role do reasoning processes play in the effect of time perspective?",{"text":84,"@type":76},"The study finds no direct effects of time perspectives on adoption intentions; 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