Impact of generative artificial intelligence models on the performance of citizen data scientists in retail firms

Computers in Industry, Год журнала: 2024, Номер 161, С. 104128 - 104128

Опубликована: Июль 21, 2024

Язык: Английский

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Exploring the Paradoxical Use of ChatGPT in Education: Analyzing Benefits, Risks, and Coping Strategies through Integrated UTAUT and PMT Theories using a Hybrid Approach of SEM and fsQCA

Computers and Education Artificial Intelligence, Год журнала: 2024, Номер 7, С. 100329 - 100329

Опубликована: Ноя. 5, 2024

Язык: Английский

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The Critical Role of Trust in Adopting AI-Powered Educational Technology for Learning: An Instrument for Measuring Student Perceptions

Computers and Education Artificial Intelligence, Год журнала: 2025, Номер unknown, С. 100368 - 100368

Опубликована: Янв. 1, 2025

Язык: Английский

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Understanding ChatGPT adoption for data analytics learning: A UTAUT perspective among social science students in Oman

Social Sciences & Humanities Open, Год журнала: 2025, Номер 11, С. 101310 - 101310

Опубликована: Янв. 1, 2025

Язык: Английский

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Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?

Smart Learning Environments, Год журнала: 2025, Номер 12(1)

Опубликована: Фев. 6, 2025

Abstract Researchers have significantly explored language learners' attitudes toward ChatGPT through the lens of technology acceptance models, particularly with its development and integration into computer-assisted learning (CALL). However, further research in this area is necessary to apply a theoretical framework pedagogical-oriented perspective. Therefore, study, researchers utilized students' approaches environment (SAL) extended it by incorporating multilevel perspective that encompasses contextual, individual, ChatGPT-related factors. Accordingly, integrated their syllabus guided learners three universities Ardabil City use during academic year 2023–2024. In end, 214 participants answered study questionnaire. The result partial least squares modeling (PLS-SEM), Importance performance map analysis (IPMA) showed leadership, where university executive provides atmosphere for norms integration, could shape learners’ organizing approach using daily schedule. Additionally, personalization anthropomorphism were among significant factors shaped deep as source meaningful, cross-referenced CALL tool. low feedback reliability, privacy concerns, ChatGPT's perceived value contributed surface minimizing ChaGPT-related factor. On basis these findings, introduces new conceptual artificial intelligence (AILL) suggests leadership should be promoted at macro-contextual level might cover other micro-contextual, personal, factors, including price-value, personalization, motivation, which are important elements CHAGPTALL.

Язык: Английский

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The potential of AI tools in shaping digital consumers’ behavior: investigating e-commerce engagement of Chinese Generation Z

Asia Pacific Journal of Marketing and Logistics, Год журнала: 2025, Номер unknown

Опубликована: Фев. 27, 2025

Purpose The purpose of this study is to investigate the potential adoption AI-powered tools by Chinese Generation Z (Gen Z) consumers in e-commerce. It aims understand how factors, such as performance expectancy, effort social influence and facilitating conditions, affect behavioral intention user behavior towards AI-enhanced e-commerce platforms. Design/methodology/approach employed Unified Theory Acceptance Use Technology (UTAUT) framework. A survey with 24 questions across six constructs was designed distributed Gen aged 18–28. research used convenience quota sampling methods four commercial complexes a populous southwestern city, 280 valid responses collected. data analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings found that expectancy conditions positively use tools. Surprisingly, shows negative correlation intention, suggesting may not be swayed others’ opinions adopting these technologies. Facilitating both significantly behavior. Gender differences can observed, particularly impact on intention. Originality/value This extends application UTAUT model rapidly evolving sector, focusing unique characteristics consumers. By highlighting gender specific preferences generation, contributes more nuanced understanding technology acceptance e-commerce, guiding future marketing strategies platform development.

Язык: Английский

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User Intent to Use DeepSeek for Healthcare Purposes and their Trust in the Large Language Model: Multinational Survey Study (Preprint)

Опубликована: Фев. 19, 2025

Large language models (LLMs) increasingly serve as interactive healthcare resources, yet user acceptance remains underexplored. This study examines how ease of use, perceived usefulness, trust, and risk perception interact to shape intentions adopt DeepSeek, an emerging LLM-based platform, for purposes. We adapted survey items from validated technology scales assess DeepSeek’s functionality, focusing on constructs such intent use health, perception. The final 12-item questionnaire (using a four-point forced Likert scale) was pilot-tested (n=20) clarity consistency. It then distributed online users in India, United Kingdom (UK), States America (USA) who had used DeepSeek within the past two weeks. Data analysis involved descriptive frequency assessments Partial Least Squares Structural Equation Modeling (PLS-SEM) evaluate measurement structural models. equation modeling assessed direct indirect effects, including potential quadratic relationships. A total 556 complete responses were collected, with respondents almost evenly split across India (n=184), UK (n=185), (n=187). Regarding AI healthcare, when asked if they comfortable their provider using tools, 59% (n=330) fine provided doctor verified its output, 31% (n=175) enthusiastic about without conditions. In terms large model (LLM) usage over last six months, 25% (n=140) them once month, 44% (n=243) every week, 20% (n=113) daily, 11% (n=60) multiple times daily. Specifically, 33% (n=183) it monthly, 28% (n=156) weekly, (n=137) more than per 14% (n=80) day. Its primary applications included academic educational purposes (55%, n=308), functioning search engine (51%, n=282), addressing health-related queries (48%, n=265). When other LLMs like ChatGPT, 52% (n=290) likely switch, 29% (n=161) very do so. revealed that trust plays pivotal mediating role: exerts significant impact through trust. At same time, usefulness contributes development adoption. By contrast, negatively affects intent, emphasizing importance robust data governance transparency. Significant non-linear paths observed risk, indicating threshold or plateau effects. demonstrated strong reliability validity, supported by high composite reliabilities, average variance extracted, discriminant validity measures. These findings extend health informatics research illuminating multifaceted nature adoption sensitive domains. Stakeholders should invest trust-building strategies, user-centric design, mitigation measures encourage sustained safe uptake healthcare. Future work can employ longitudinal designs examine culture-specific variables clarify perceptions evolve time different regulatory environments. Such insights are critical harnessing enhance outcomes.

Язык: Английский

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Unlocking ChatGPT’s potential: a comparative study of student adoption intentions in higher education across India and Poland

Education and Information Technologies, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

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Leveraging AI in academia: university students’ adoption of ChatGPT for writing coursework (take home) assignments through the lens of UTAUT2

Cogent Education, Год журнала: 2025, Номер 12(1)

Опубликована: Апрель 2, 2025

Язык: Английский

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User Intent to Use DeepSeek for Healthcare Purposes and their Trust in the Large Language Model: Multinational Survey Study (Preprint)

JMIR Human Factors, Год журнала: 2025, Номер unknown

Опубликована: Фев. 19, 2025

Generative artificial intelligence (Gen-AI)-particularly large language models (LLMs)-has generated unprecedented interest in applications ranging from everyday Q&A to health-related inquiries. However, little is known about how users decide whether trust and adopt these technologies-particularly high-stakes contexts like personal health. This study examines ease of use, perceived usefulness, risk perception interact shape user intentions DeepSeek, an emerging LLM-based platform, for healthcare purposes. We adapted survey items validated technology acceptance scales assess focusing on constructs such as trust, intent use health, perception. A 12-item Likert scale questionnaire was developed pilot-tested (n=20) clarity consistency. It then distributed online India (IND), United Kingdom (UK), States America (USA) who had used DeepSeek within the past two weeks. Data analysis involved descriptive frequency assessments Partial Least Squares Structural Equation Modeling (PLS-SEM) evaluate measurement structural models. equation modeling assessed direct indirect effects, including potential quadratic relationships. total 556 complete responses were collected, with respondents almost evenly split across IND (n=184), UK (n=185), USA (n=187). Regarding AI healthcare, when asked if they comfortable their provider using tools, 59.3% (n=330) fine provided doctor verified its output, 31.5% (n=175) enthusiastic without conditions. primarily academic educational purposes, 50.7% (n=282) a search engine, 47.7% (n=265) queries. When over other LLMs ChatGPT, 52.1% (n=290) likely switch, 28.9% (n=161) very do so. The revealed that plays pivotal mediating role: exerts significant impact usage through trust. At same time, usefulness contributes development adoption. By contrast, negatively affects intent, emphasizing importance robust data governance transparency. Significant non-linear paths observed risk, indicating threshold or plateau effects. Users are receptive it's easy useful, trustworthy. model highlights mediator shows dynamics shaping AI-driven tool Expanding mediators privacy cultural differences could provide deeper insights. Longitudinal experimental designs establish causality track attitudes. Further investigation into phenomena refine our understanding perceptions become more familiar tools.

Язык: Английский

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