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Relevant bibliographies by topics / AI adoption intention model

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Academic literature on the topic 'AI adoption intention model'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "AI adoption intention model"

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Chuang, Li-Min, and Sheng-Hsuan Huang. "AI-Supported Healthcare Technology Resistance and Behavioral Intention: A Serial Mediation Empirical Study on the JD-R Model and Employee Engagement." Systems 13, no. 4 (2025): 268. https://doi.org/10.3390/systems13040268.

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This study combines innovation resistance theory, the stimulus–organism–response (SOR) framework, and the job demands–resources model to facilitate an in-depth exploration of the barriers faced by healthcare professionals and the psychological responses they exhibit when adopting AI-supported healthcare technologies. We conducted a questionnaire survey and obtained 296 valid responses from healthcare professionals to examine the relationship between resistance to AI-supported healthcare technologies and AI adoption behavioral intentions. Using the SOR framework as a basis, this study validated

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Kim, Youngsoo, Victor Blazquez, and Taeyeon Oh. "Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model." Behavioral Sciences 14, no. 11 (2024): 1035. http://dx.doi.org/10.3390/bs14111035.

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This study addresses the academic gap in the adoption of generative AI systems by investigating the factors influencing technology acceptance and usage behavior in Korean firms. Although recent advancements in AI are accelerating digital transformation and innovation, empirical research on the adoption of these systems remains scarce. To fill this gap, this study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) model, surveying 300 employees from both large and small enterprises in South Korea. The findings reveal that effort expectancy and social influence significantly

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Fang, Wang, Meng Na, and Syed Shah Alam. "Usage Intention of AI Among Academic Librarians in China: Extension of UTAUT Model." Sustainability 17, no. 7 (2025): 2833. https://doi.org/10.3390/su17072833.

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This study explores how academic librarians adopt artificial intelligence (AI) technologies, using the Unified Theory of Acceptance and Use of Technology (UTAUT) as its main framework, expanded with elements from Personal Innovativeness in IT (PIIT) and the Technology Readiness Index (TRI). A quantitative approach was applied, gathering data from 340 academic librarians and analyzing them using PLS-SEM. The results indicate that facilitating conditions (β = 0.345, p < 0.001) and effort expectancy (β = 0.123, p = 0.034) significantly influence behavioral intention, while performance expectan

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Patel, Anil. "A Study on Factors Affecting Adoption of AI Tools on Consumers While Shopping Online." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04340.

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Abstract: This research investigates the factors influencing the adoption of AI tools, particularly chatbots, by consumers during online shopping. By integrating the Technology Acceptance Model (TAM) and Behavioral Reasoning Theory (BRT), the study explores how perceptions like ease of use, usefulness, trust, discomfort, and optimism affect consumer attitudes and intentions. Data collected from 182 respondents was analyzed using SmartPLS. The results show that attitude, perceived usefulness, and perceived ease of use significantly influence consumer intention to adopt AI chatbots. Innovation,

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Nguyen, Thi Hai Duong, Xuan Tiep Nguyen, Tran Ha Trang Le, and Quynh Anh Bui. "Determinants influencing the adoption of artificial intelligence technology in non-life insurers." Corporate Governance and Organizational Behavior Review 8, no. 1 (2024): 205–12. http://dx.doi.org/10.22495/cgobrv8i1p17.

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Although artificial intelligence (AI) technology has been widely used in the insurance industry at a global scale, studies examining the adoption of AI technology in emerging markets are few and far between. This paper fills this gap by using Cronbach’s alpha, exploratory factor analysis, confirmatory factor analysis, and structural equation model (SEM) to discover significant factors affecting their behavioral intentions to adopt AI technology in Vietnam, a developing country. Data is collected from nearly 470 employees in Vietnamese non-life insurance firms. Empirical findings show that the

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Chatterjee, Sheshadri, Bang Nguyen, Soumya Kanti Ghosh, Kalyan Kumar Bhattacharjee, and Sumana Chaudhuri. "Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations." Bottom Line 33, no. 4 (2020): 359–75. http://dx.doi.org/10.1108/bl-08-2020-0057.

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Purpose The purpose of this study is to explore the behavioral intention of the employees to adopt artificial intelligence (AI) integrated customer relationship management (CRM) system in Indian organizations. Design/methodology/approach To identify the factors impacting the behavioral intention of the employees to adopt AI integrated CRM system in Indian organizations helps of literature review and theories have been taken. Thereafter, some hypotheses have been formulated followed by the development of a theoretical model conceptually. The model has been tested statistically for validation us

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Gyesi, Kwesi, Vivian Amponsah, and Samuel Ankamah. "Forecasting Ghanaian Medical Library Users’ Artificial Intelligence (AI) Technology’s Acceptance and Use." Biblios Journal of Librarianship and Information Science, no. 88 (April 11, 2025): e004. https://doi.org/10.5195/biblios.2025.1211.

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Objective. This study investigated the behavioural intentions of medical students in an academic library regarding the use of AI-assisted technologies for research and learning. Method. Employing a survey research design and a quantitative approach, the study sampled 302 respondents using Krejcie and Morgan’s published table. Statistical analyses were conducted using the Statistical Package for Social Sciences (SPSS) version 26, with linear and multiple linear regressions utilised to establish relationships between variables. Results. The results of the study indicate that perceived usefulness

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Apostoaie, Constantin-Marius, Teodora Roman, Alexandru Maxim, and Dumitru-Tudor Jijie. "Determinants of AI adoption intention in SMEs. Romanian case study." Journal of Business Economics and Management 26, no. 1 (2025): 277–96. https://doi.org/10.3846/jbem.2025.23650.

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The paper investigates the drivers and barriers that encourage or hinder the adoption of artificial intelligence (AI) technologies within Romanian SMEs. By using the Technology-Organisation-Environment (TOE) framework, we examined the role of several factors from each TOE dimension in predicting the AI adoption behaviour. The factors were constructed through factor analysis followed by the estimation of a linear regression model. Partial least squares structural equation modelling was then used in order to further explore the relationships and to check the robustness of the linear regression m

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Lakulu, Muhammad Modi, Ayad Shihan Izkair, Mohd Fadhil Abdul Muttalib, and Nur Azlan Zainuddin. "Understanding AI and Mobile Learning Adoption in Malaysian Universities: A UTAUT-Based Model." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 11 (2025): 80–111. https://doi.org/10.3991/ijim.v19i11.52977.

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This study explores the key determinants influencing the intention to adopt artificial intelligence (AI) applications and mobile learning in Higher Education Institutions (HEIs) in Malaysia. As AI technologies and mobile learning increasingly transform the higher education landscape, it is crucial to understand the specific factors driving their adoption. The research identifies five critical determinants—social influence (SI), effort expectancy (EE), hedonic motivations (HM), performance expectancy (PE), and consumer trust (TR)—that significantly impact the intention to use AI-powered mobile

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Jang, Eun-Gyo, and Jin-Myong Lee. "Continuous Use Intention of Fashion AI Recommendation Service Applying Value-based Adoption Model." Journal of Consumer Studies 35, no. 1 (2024): 149–71. http://dx.doi.org/10.35736/jcs.35.1.7.

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Dissertations / Theses on the topic "AI adoption intention model"

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Pajany, Peroumal. "AI Transformative Influence: Extending the TRAM to Management Student's AI’s Machine Learning Adoption." Franklin University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=frank1623093426530669.

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Vikström, Fredrik. "How does Organizational Culture Impact Intention to use Customer Relationship Management Amongst Employees?" Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60033.

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Purpose: The aim of this thesis is to elaborate on if organisational culture has an impact on the intention to use a CRM system. Methodology: The data was collected by use of an online questionnaire, the questions used were created based on the literature review andmeasured according to a 5 point Likert-scale Conclusion: Organisational culture has no meaningful impact on intention touse CRM. This since each of the culture types produced results which were outside acceptable perimeters. Out of the three aspects of the technology acceptance model,attitude has the biggest impact on intention to u

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Forslund, Lia, and Mentzer Sofia von. "Sjukvårdskris och svalt mottagande av AI, hur går det ihop? : En fallstudie i vilka faktorer som har störst påverkan på införandet av artificiell intelligens." Thesis, Uppsala universitet, Institutionen för informatik och media, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414559.

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Det svenska sjukvårdssystemet är konstant under hög press och situationen benämns ofta i media som en sjukvårdskris. Radiologin är en av de medicinska discipliner som drabbats av en kontinuerligt ökande arbetsbelastning och personalbrist. Detta sätter sjukvården i en situation att konstant tvingas väga effektivitet mot kvalitet. Trots höga förväntningar på att innovationer som Artificiell Intelligens (AI) ska kunna bistå behoven, används AI idag i en mycket begränsad utsträckning. Denna studie syftar till att utreda påverkande faktorer för införandet av AI inom radiologin. För att besvara arbe

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Albadran, Norah Fahad Mrs. "Flipped Classroom Model Based Technology Acceptance and Adoption Among Faculty Members in Saudi Arabia Universities." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1587078759013376.

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Wang, Xuyang. "Factors Influence Citizen Adoption for Government E-Tax Service." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-22959.

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E-tax is an important function of e-government since it is highly related to the life of citizens (Wu & Chen, 2005). So in this paper I have discussed the factors influence the citizen adoption of government e-tax service. I have used the decomposed TPB model as my research model. This model integrated two important theories – TAM model and TPB model. The taxpayers were divided into adopters who have used the e-tax service and non-adopter who has used the conventional method to pay their tax. And the effect of these factors for adopters and non-adopters are different. Therefore, understand

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Radif, Mustafa. "A learning management system adoption framework for higher education : the case of Iraq." Thesis, Cranfield University, 2016. http://dspace.lib.cranfield.ac.uk/handle/1826/11191.

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This study focuses on the implementation of Learning Management System (LMS) in the higher education sector in Iraq. Its aim is to develop a policy adoption framework for LMS implementation by scientifically investigating LMS adoption using a model that combines the principles of the Technology Acceptance Model (TAM) and Technology-Organisation-Environment (TOE) framework. The research methodology comprises of seven stages that adopts the interpretive paradigm and a mixed-methods research design. A case study design is used to investigate LMS integration in the University of Al-Qadisiyah. A TA

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Kowalczyk, Nina K. "The Impact Of Voluntariness, Gender, And Age On Subjective Norm And Intention To Use Digital Imaging Technology In A Healthcare Environment:Testing A Theoretical Model." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1226605857.

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Zhang, Yusha, and Klavdija Zalar. "The Application of the Extended Technology Acceptance Model in Different Cultural Contexts to Understand Mobile Payment Adoption : A comparative study between China and Sweden." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-96961.

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Rapid technological progress has transformed our daily activities. One of these is how we pay for goods. Mobile payment is a payment option with great potential, but it is still struggling to become part of individuals' daily life. Previous research shows that adoption and acceptance of mobile payment vary widely between different countries. It is important to understand the determinants of mobile payment adoption, as cultural differences between countries may be one of the primary reasons for the different degrees of user mobile payment adoption. The purpose of this study is to investigate th

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Shabani, Maryam. "A Model of Crucial Factors Influencing on the Innovation Resistance for Purchasing Innovative Passenger Vehicles in Automotive Industry of Iran." Doctoral thesis, Universitat Internacional de Catalunya, 2020. http://hdl.handle.net/10803/670677.

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Purpose: The goal of this thesis is to render a model of influencing factors on Innovation resistance for purchasing innovative passenger vehicles in Auto industry of Iran. Design/Methodology/Approach: The innovative passenger vehicles that are produced by 4 car manufacturing companies of Iran are selected. Data is collected in two phases, at the first step which is qualitative phase, 13 questionnaires are distributed among panel of experts who are managers and top experts of SAIPA car manufacturing company (Appendix 1). Then in the second phase which is quantitative, the questionnaire whi

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CHEN, MENG-YANG, and 陳孟揚. "Adoption Intention in Smart Space：An Elaboration Likelihood Model." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/n2ttdz.

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碩士<br>逢甲大學<br>企業管理學系<br>105<br>Due to progression of modern medication and technology, the average life span is extending, pursuing high-quality, healthy and long life growing to an old age is more desirable than ever. Then the aged society related issues have presented serious challenges to public policy as the elderly population in Taiwan increases rapidly. Many developed countries have used "Aging in Place" as the guiding principle for devising elderly care policy. In addition, accompany with the progress of ICT(Information Communication and Technology ,ICT), the ICT integrated for aging in

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Book chapters on the topic "AI adoption intention model"

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Zhu, Yiwei, and Shiwei Sun. "Exploring Patients’ AI Adoption Intention in the Context of Healthcare." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3631-8_4.

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Gong, Zixuan, and Yanxia Cheng. "Information Adoption Intention of Tagged Online Reviews Based on Information Adoption Model." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4258-6_142.

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Sadriwala, Maruf Fatima, and Manish Dadhich. "Marketing Innovation, Subjective Norms, Behavioral Control and Intention to Adoption of Artificial Intelligence." In The AI Revolution: Driving Business Innovation and Research. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54383-8_21.

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Kumar, Nishant, Mitushi Singh, Kamal Upreti, and Divya Mohan. "Blockchain Adoption Intention in Higher Education: Role of Trust, Perceived Security and Privacy in Technology Adoption Model." In Proceedings of International Conference on Emerging Technologies and Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82616-1_27.

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Maria Boaler, A., R. Pankajakshi, Surabhi Saxena, and Purushottam Kumar. "“Consumer Adoption of AI in Online Apparel Retail Purchases Using UTAUT2 Model”." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3352-4_49.

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Denni-Fiberesima, Damiebi. "Navigating the Generative AI-Enabled Enterprise Architecture Landscape: Critical Success Factors for AI Adoption and Strategic Integration." In Navigating the Technological Tide: The Evolution and Challenges of Business Model Innovation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67434-1_20.

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Kumar, Nishant, Mitushi Singh, Kamal Upreti, and Divya Mohan. "Correction to: Blockchain Adoption Intention in Higher Education: Role of Trust, Perceived Security and Privacy in Technology Adoption Model." In Proceedings of International Conference on Emerging Technologies and Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82616-1_58.

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Petschnig, Martin, and Patrick Spieth. "How to Push Consumers’ Intention to Adopt Alternative Fuel Vehicles – An Integrative Adoption Model." In Marketing Dynamism & Sustainability: Things Change, Things Stay the Same… Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10912-1_103.

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Worek, Maija, and Päivi Aaltonen. "AI Adoption Challenges in Family-Owned Firms: A Case Study." In Technology, Work and Globalization. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74779-3_9.

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Abstract AI transformation is reshaping organizations and societies, pushing firms to embrace new technologies. In the presence of abundant technology, personnel, and personnel management becomes increasingly crucial in creating a competitive edge. Family firms, making up a significant portion of businesses worldwide, serve as an example of industrial firms with existing leveraged emphasis on softer values, yet face significant challenges in AI adoption. Various models used to understand digital transformation, such as the Technology-Organization-Environment model and the Technology Acceptance

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Srivastava, Karnika, Manoj Kumar, Rinki Verma, Shreyanshu Singh, and Pratibha Maurya. "Analyzing the Drivers of Adoption in Higher Education E-Learning." In Advances in Web Technologies and Engineering. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-2973-3.ch003.

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Learners face problems while using traditional e-learning systems but AI introduces an innovative stage of advancement in technology that gives revolution in e-learning process. Study investigates factors that influence learner behavioral intention to use AI and actual usage in higher education. The study proposed UTAUT and IS model with additional external variables like perceive risk, work engagement and self-efficacy of learner. Cross-sectional study was used to measure research models based on several theories related to AI that brings cutting-edge technologies that enhance e-learning. Dat

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Conference papers on the topic "AI adoption intention model"

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Handoko, Bambang Leo, Shaqira Aufaanashwa, Arta Moro Sundjaja, and Evelyn Hendriana. "Behavioral Intention Model for Cryptocurrency Investment Platform Adoption." In 2024 International Conference on ICT for Smart Society (ICISS). IEEE, 2024. http://dx.doi.org/10.1109/iciss62896.2024.10751599.

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Rani, Nisha, TILAK SETHI, and PARDEEP GUPTA. "Impact of Occupation of Banking Customers on Artificial Intelligence-driven Financial Assistant Adoption Factors and Behavioural Intention." In 2025 First Global Conference on AI Research and Emerging Developments. Ganitara Research Foundation, 2025. https://doi.org/10.63169/gcared2025.p26.

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Kiros, Atakilti Brhanu, Ayodeji Olalekan Salau, Satvik Vats, Keshav Kaushik, Ting Tin Tin, and Crescent Onyebuchi Omeje. "Cloud-based Machine Learning Adoption Model for Higher Education Institutions." In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit). IEEE, 2024. https://doi.org/10.1109/globalaisummit62156.2024.10947842.

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Patel, Anamika, Y. Maheshwaran, and P. Santhya. "Easing Adoption of Model Based System Engineering With Application of Generative AI." In 2024 IEEE Space, Aerospace and Defence Conference (SPACE). IEEE, 2024. http://dx.doi.org/10.1109/space63117.2024.10667868.

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Madibo, Carol Thato, Rene Van Eck, and Fhatuwani Vivian Mapande. "Readiness for Adoption Model of AI-based chatbots in Academic Institutions: A Review." In 2024 4th International Multidisciplinary Information Technology and Engineering Conference (IMITEC). IEEE, 2024. https://doi.org/10.1109/imitec60221.2024.10850987.

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Verma, Anuj, Meenakshi Verma, and Anuradha Goswami. "Adoption of AI in CRM in the Retail Sector. A perspective from Technology Acceptance Model." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10775068.

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Hernandez, Alexander A., Victor James C. Escolano, Muhammad Syukur, Ace Lagman, Roland A. Calderon, and Rosanna T. Adao. "Predicting the Factors to Artificial Intelligence in Peer-to-Peer Energy Sharing Service Adoption Intention: A Structural Equation Model Assessment." In 2024 9th International Conference on Business and Industrial Research (ICBIR). IEEE, 2024. https://doi.org/10.1109/icbir61386.2024.10875879.

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Tasić, Aleksandra, Jelena Ćulibrk, Nenad Medić, Bojana Jokanović, and Predrag Vidicki. "FACTORS THAT INFLUENCE ADOPTION OF AI IN ORGANIZATIONS." In 19th International Scientific Conference on Industrial Systems. Faculty of Technical Sciences, 2023. http://dx.doi.org/10.24867/is-2023-t1.1-8_05041.

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In the last few years, the rapid development of Artificial Intelligence (AI) has created the conditions for its increasing use in various organizations, in order to achieve the well-known goals of increasing productivity, efficiency, effectiveness and more rational use of resources. However, most companies have difficulties in implementing artificial intelligence and realizing the benefits it brings. Most of the researchers in this field, in order to examine in more detail which factors influence the adoption of new technologies in the organization and what their mutual relationship is, uses p

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Dilshan, A., J. Wijayanayake, D. Asanka, and C. Karunarathna. "Adoption success of using Generative AI apps for the ECommerce Platforms in Sri Lanka." In Proceedings of the 3rd International Conference on Sustainable & Digital Business. SLIIT Business School, 2024. https://doi.org/10.54389/burh1886.

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The digital landscape has witnessed the widespread influence of e-commerce, with the Information Technology industry embracing generative AI applications. This research aims to investigate the adoption success of existing e-commerce platforms in Sri Lanka in incorporating generative AI technologies. A systematic literature review using the PRISMA framework identified how generative AI is used in various industries, its Future Directions, Ethical Concerns, Security, and Privacy Considerations, and the most widely used and accepted models for understanding technology adoption. The Technology Acc

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Biloš, Antun, Davorin Turkalj, and Ivan Kelić. "The Role of Experience in AI Adoption: The Moderating Effect on Predictors of ChatGPT Usage Intention Among Generation Z." In 9th FEB International Scientific Conference. University of Maribor Press, 2025. https://doi.org/10.18690/um.epf.5.2025.27.

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The objective of this study was to investigate the role of experience in the adoption of artificial intelligence (AI) tools, with a specific focus on its moderating effect on the predictors of ChatGPT usage intention among Generation Z in Croatia. The research employed the extended UTAUT2 model, examining crucial predictors such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), Habit (HT), and Personal Innovativeness (PI). The moderation analysis indicated that experience significantly moderat

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Reports on the topic "AI adoption intention model"

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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,

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Hermansen, Anna, and Cailean Osborne. The Economic and Workforce Impacts of Open Source AI: Insights from Industry, Academia, and Open Source Research Publications. The Linux Foundation, 2025. https://doi.org/10.70828/itvq4899.

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In a literature review commissioned by Meta, LF Research found that open source AI (OSAI) is widely adopted, cost effective, highly performing, and leads to faster and higher-quality development of tools and models. The study included a comprehensive analysis of academic and industry literature as well as empirical data from previous LF Research surveys to determine existing evidence of the economic and workforce impacts of OSAI. The study assessed these impacts in four areas: Adoption rates: a significant majority (89%) of organizations are using some form of open source in their AI stack and

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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum

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