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This study adopts an individual-centered motivational-theoretic perspective and proposes four motivational factors combined with two technology characteristics to predict continuance intention. A mixed-methods design integrates a quantitative multi-wave survey of 279 participants using fsQCA and 15 semi-structured interviews. Study 1 identifies five high-intention configurations and shows ease of use and novelty are necessary; low-intention patterns are not explained by the same factors. Study 2 derives 27 use cases and additional facilitators and inhibitors, then synthesizes meta-inferences into five propositions, offering practical implications and future research directions.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/chatgpt-usage-mixed-methods-study-motivation-theoretic-analysis-of-genai-continuance-intention/565030/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/chatgpt-usage-mixed-methods-study-motivation-theoretic-analysis-of-genai-continuance-intention/565030.png","ImageObject",442,249,{"name":88,"@type":89},"dabgoudw","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-23","2026-10-12",true,{"@type":98,"interactionType":99,"userInteractionCount":79},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What research question does the study address?","Question",{"text":108,"@type":109},"Which configurations of motivational factors and technology characteristics lead to high and low continuance intention among ChatGPT users?","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does the study model continuance intention?",{"text":113,"@type":109},"It combines intrinsic and extrinsic motivational factors with two technology characteristics—perceived novelty and perceived ease of use—expecting that their configurations drive continuance intention.",{"name":115,"@type":106,"acceptedAnswer":116},"What methods are used in the mixed-methods design?",{"text":117,"@type":109},"Study 1 uses a quantitative multi-wave survey with 279 participants and analyzes configurations using fsQCA. Study 2 complements the findings with 15 semi-structured interviews and identifies private and work use cases.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},565030,1792360225,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":79,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":40,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":140},3985747860484,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","International Journal of Information Management 79 (2024) 102821  \nContents lists available at ScienceDirect  \nInternational Journal of Information Management  \njournal [homepage:](homepage: www.elsevier.com/locate/ijinfomgt)[ www.elsevier.com/locate/ijinfomgt](homepage: www.elsevier.com/locate/ijinfomgt)  \n| Research article ChatGPT usage mixed-methods | in everyday life: A motivation-theoretic study |  | |\n| --- | --- | --- | --- |\n| Vinzenz Wolf a, * , Christian Maier b\u003Cbr>a Ludwig-Maximilians-Universit¨at München; LMU Munich School of Management, Ludwigstraße 28, 80539 München, Germany b University of Bamberg; Information Systems, Health and Society in the Digital Age, Gutenbergstraße 13, 96050 Bamberg, Germany |  |  |  |\n| A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Generative artificial intelligence (GenAI) Continuance intention\u003Cbr>Motivation\u003Cbr>Fuzzy-set qualitative comparative analysis (fsQCA)\u003Cbr>Mixed-methods design |  | A B S T R A C T |  |\n|  |  | GenAI-driven technologies such as ChatGPT influence activities in all areas of life and are used in private and work contexts. This study uses an individual-centered perspective to explain what motivates users to use ChatGPT continuously. We propose that four motivational factors and two technology characteristics together lead to continuance intention among individual ChatGPT users. Therefore, we use a mixed-methods design to combine findings from a quantitative survey study and a qualitative interview study. In Study 1, we follow aconfigurational approach to analyze multi-wave data from 279 participants with fsQCA. We identify five configurations that lead to high continuance intention and show that perceived ease of use and perceived novelty are necessary for this outcome. Interestingly, the observed factors together cannot explain low continuance intention. In Study 2, we complement these findings with insights based on 15 semi-structured interviews. We illustrate the configurations by identifying 27 individual use cases in the private and work contexts as well as additional factors that facilitate and hinder individual ChatGPT continuance intention. We draw meta-inferences by combining findings of both studies to develop five propositions. Based on that, we contribute a motivational, individual perspective on GenAI continuance intention, present practical implications as well as valuable future research opportunities. |  |\n\n1. Introduction  \nGenerative artificial intelligence (GenAI) assists users in most areas of their daily lives. For example, users benefit from language translation (Kenny, 2022) and writing assistance (Dale & Viethen, 2021) in both private and work contexts. GenAI, as applied in ChatGPT, powers helpful applications such as chatbots, already half of US mobile users use voice search daily (Haan & Watts, 2023), and other application areas continue to emerge at breakneck speed (McKinsey & Company, 2022). Consumer applications rely increasingly on GenAI, and GenAI is expected to have significant and continued relevance for all industrial and life sectors, from agriculture to education, finance, and telecommunication (Webb & Chockalingam, 2023). This exceptional development raises the question of what factors motivate individual users to continue using GenAI applications such as ChatGPT in private and work contexts.  \nIn order to understand what motivates users, we follow a motivational-theoretic approach and align with research that typically differs between intrinsic and extrinsic motivational factors (Vallerand, 1997). Intrinsic factors are those that are satisfied by the mere  \nperformance of an activity, and extrinsic factors lie outside the performed activity and the performer, in the context of IS usually referring to the user’s perceived usefulness. In this study, we focus on intrinsic and extrinsic motivational factors to develop theory how these together influence continuance intention of ChatGPT, as a specific example of GenAI. We further contextual","cbCaierhqTFXj1i4","https://ap.wps.com/l/cbCaierhqTFXj1i4","pdf",1436490,"English","# Introduction\n## Motivational-theoretic framing\n## Research question and study design\n## Quantitative study and fsQCA approach\n## Qualitative interviews and use-case development","[{\"question\":\"What research question does the study address?\",\"answer\":\"Which configurations of motivational factors and technology characteristics lead to high and low continuance intention among ChatGPT users?\"},{\"question\":\"How does the study model continuance intention?\",\"answer\":\"It combines intrinsic and extrinsic motivational factors with two technology characteristics—perceived novelty and perceived ease of use—expecting that their configurations drive continuance intention.\"},{\"question\":\"What methods are used in the mixed-methods design?\",\"answer\":\"Study 1 uses a quantitative multi-wave survey with 279 participants and analyzes configurations using fsQCA. Study 2 complements the findings with 15 semi-structured interviews and identifies private and work use cases.\"}]","ChatGPT usage mixed-methods study: motivation-theoretic analysis of GenAI continuance intention | PDF",1791845963,6]